Proceedings of the FISITA 2012 World Automotive Congress: Volume 6: Vehicle Electronics [1 ed.] 978-3-642-33828-1, 978-3-642-33829-8

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Lecture Notes in Electrical Engineering Volume 194

For further volumes: http://www.springer.com/series/7818

Society of Automotive Engineers of China (SAE-China) International Federation of Automotive Engineering Societies (FISITA) •

Editors

Proceedings of the FISITA 2012 World Automotive Congress Volume 6: Vehicle Electronics

123

Editors SAE-China Beijing People’s Republic of China

ISSN 1876-1100 ISBN 978-3-642-33828-1 DOI 10.1007/978-3-642-33829-8

FISITA London UK

ISSN 1876-1119 (electronic) ISBN 978-3-642-33829-8 (eBook)

Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2012948289  Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Contents

Part I

Engine/Chassis/Body Electronic Control

Battery Monitoring Technology of Micro-Hybrid System Based on Voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-002 Feng Gao, Qiang Zhang and Chenshu Yan

3

Research on Electro Hydraulic Composite Brake System . . . . . . . . . . F2012-D01-003 Qinghe Liu, Lan Zhan and Ti He

13

Analysis and Design of Automotive Body Control Module . . . . . . . . . F2012-D01-005 Jianhui Ma, Zhixue Wang, Yanqiang Li and Liangjie Yu

25

Scalable Architecture Approach with Platform Products to Implement Advanced Car-Body E&E System in Emerging Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-006 Dayu Ding Gasoline Fuel Injector Selection and Its Effects on Engine Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-009 Shuxia Miao, Daijun Deng and Hui Zheng The Research of Starting Control Strategies for Common Rail Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-011 Hongrong Wang, Heng Zhang and Yongfu Wang

33

43

53

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Contents

Study on Twin Modes Pilot Control of Turbocharger . . . . . . . . . . . . . F2012-D01-012 Sicong Lin, Jian Wu, Anwei Zhang, Jujiang Liu and Jin Hu Braking Stability Control Algorithm for Vehicle Based on Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-013 Hongyu Zheng, Yuchao Chen and Changfu Zong

63

73

Lane Keeping System Based on Electric Power Steering System . . . . . F2012-D01-014 Hailin Zhang, Yugong Luo, Qingyun Jiang and Keqiang Li

83

Parking Brake Breaking-In Technology Based on EPB . . . . . . . . . . . . F2012-D01-015 Leon Huang, Ted Huang, Wei Xu, Dongxu Yi, Lingtao Han and Wutian Lin

99

Engineering Design of TPMS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-016 Lingtao Han, Ted Huang and Wei Xu

107

An Integrated Electric Energy Management System to ImproveFuel Economy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-018 Mingming Wang and Ted Huang Modeling on Torque Generation for Turbocharged Diesel EngineBased on Identification Method . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-021 Gang Li, Ying Huang, Fujun Zhang and Xiaoyan Dai Design of a Versatile Rapid Prototyping Engine Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-022 Bernd Eichberger, Eduard Unger and Mario Oswald Study on State Parameters Estimation for Commercial Vehicle . . . . . F2012-D01-026 Li Liu, Chaosheng Huang, Yuanfang Li and Shuming Shi

115

123

135

143

Contents

vii

The Research and Implementation of Engine-Timing-Control System Based on AUTOSAR Standard. . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-027 Weimin He, Guilin Lv, Tao Chen, Shizhen Liu and Hui Han Calibration Techniques for Modern Commercial Vehicle . . . . . . . . . . F2012-D01-028 Yong Deng, Zhongzhuang Yuan and Lai Wei Powertrain Control and System Integration Technology from OEM’S Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D01-029 Jun Li, Fengjun Li, Peng Zhang, Yongjun Li and Weimin He

Part II

157

167

177

Electrical and Electronic System

Virtual Development of Engine ECU by Modeling Technology . . . . . . F2012-D02-004 Haifeng Xu, Yukihide Niimi and Takayuki Ono The Research of a Novel High Energy Density Ultracapacitor System Applied in the Full Hybrid Vehicle. . . . . . . . . . . . . . . . . . . . . F2012-D02-008 Jianxin Zhu, Qiu Xiao, Lin Yang and Xiance Ai Starting System for Stop/Start with Change of Mind . . . . . . . . . . . . . F2012-D02-010 Koichi Osawa and Hideya Notani Distributed Diagnostic Monitoring and Fault Tolerant Control of Vehicle Electrical and Electronic Devices . . . . . . . . . . . . . . . . . . . . F2012-D02-012 Shanshan Fan, Diange Yang, Tao Zhang and Xiaomin Lian Synthesis and Nox Gas Sensing Properties of In1.82 ni0.18o3 Electrospun Nanofibers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D02-016 Jinxing Wang, Kejin Zhang, Dan Wang, Dechao Xu, Bin Zhang and Zhongling Zhao

189

199

209

221

231

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Contents

A Novel Concept of High Voltage Auxiliaries and its Feasibility Study on Blower Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D02-018 Satoshi Shiraki, Hiroyasu Kudo, Masakazu Tago, Akira Yamada, Shigeki Takahashi and Atsuyuki Hiruma Small Lights Power Distribution System Improvement of a Heavy-Duty Truck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D02-022 Leigang Ma and Fadong Yan Development Trend Analysis of Automobile Electronic System . . . . . . F2012-D02-024 Zhirong Fan, Ying Xie, Cheng Yang, Yipeng Zhang and Jian Chen Automotive ECUs Fault Diagnosis Modeling Based on the Fault Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D02-026 Yanqiang Li, Yang Li, Zhixue Wang, Ruke Zhuang and Jianxin Li Secure Microprocessor Architectures: Solutions from the Semi-Conductor Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D02-029 Klaus Scheibert and Björn Steurich Research on CAN BUS-Based Electronic and Electric Platform of Automobile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D02-031 Gouqing Tong, Lei Chen, Anzhi Yang, Fangwu Ma and Fuquan Zhao On the Application Development of 3G Technology in Automobiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D02-032 Ying Lu, Wenqiang Chen, Xingmin Wei and Fuquan Zhao

241

251

261

271

283

297

311

A Typical Application of FlexRay Bus in the Vehicle . . . . . . . . . . . . . F2012-D02-033 Yandong Dong and Wanrong Wang

327

Development of Controller Diagnostic System Based on ODX . . . . . . . F2012-D02-036 Li Li and Shanzheng Tang

341

Contents

ix

Magnetic Circuit Design for Improving Performance of In-Wheel Type IPMSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D02-041 Byeong-Hwa Lee, Jae-Woo Jung, Kyu-Seob Kim and Jung-Pyo Hong A Study on the Noise Reduction of Electrical Power Steering in Surface Permanent Magnet Synchronous Motor. . . . . . . . . . . . . . . F2012-D02-043 Do-Jin Kim, Hyeon-Jin Park and Jung-Pyo Hong Power Distribution Design of Passenger Vehicle . . . . . . . . . . . . . . . . . F2012-D02-045 Xianming Wang

Part III

351

361

369

Software and Hardware Development

Study on the Performance Modeling Approach for Automotive Embedded Control Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-004 Xiaofeng Yin, Jingxing Tan, Xiuting Wu and Qichang Yang Knowledge Based Engineering to Support Automotive Conceptual Design and Automatic Control Software Development . . . . . . . . . . . . F2012-D03-005 Fengnian Tian and Mark Voskuijl Development of an AUTOSAR Software Component Based on the V-Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-009 Dieter Nazareth and Robert Siwy MDG1: The New, Scalable, and Powerful ECU Platform from Bosch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-011 Johannes-Joerg Rueger, Alexander Wernet, Hasan-Ferit Kececi and Thomas Thiel Context-Aware Middleware for Vehicular Applications . . . . . . . . . . . F2012-D03-012 Jian Wang, Weiwen Deng and Peng Zhou

385

393

407

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x

Contents

Analysis of the Adaptation of a New Method for Four-Wheel-Hub Electric Vehicle Online-Mass Estimation . . . . . . . . . . . . . . . . . . . . . . F2012-D03-014 Jin Zhang, Zhuoping Yu, Lu Xiong and Yuan Feng Design and Implementation of Bootloader for Vehicle Control Unit Basedon Can Bus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-016 Tingqing Tan, Hanhan Tang and Yaling Zhou Automated Code Generation for Development of Electric Vehicle Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-019 Peng Geng, Minggao Ouyang, Jianqiu Li and Liangfei Xu Research on the Development Process for the ECU Control Software of Vehicle Powertrain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-020 Xiaoyan Dai, Changlu Zhao, Ying Huang, Huan Li and Gang Li MCON: Automation Tool for MATLAB Modeling Development Based on V-Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-021 Mingshi Xie and Wanrong Wang A Model-Based Design for Electronic Control Unit of Electric Motorcycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-022 Sung-Suk Jnug, Jin-Ho Kim and Jea-Wook Jeon Model Based Nonlinear Controller Design for Fuel Rail System of GDI Engine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D03-026 Pengyuan Sun, Baiyu Xin, H. Chen and J. Li Research on OBD Performance of T-GDI Engine . . . . . . . . . . . . . . . . F2012-D03-027 Song Yan, Pengyuan Sun and Tonghao Song

435

447

459

469

481

491

503

515

Contents

Part IV

xi

Electromagnetic Compatibility (EMC)

Resonance Mechanism in Power Electronic Products for Automobiles and its Relationship to EMC Performance . . . . . . . . . . . . . . . . . . . . . F2012-D04-002 Masato Izumichi Application of the MOS Tube on Power Window Switch . . . . . . . . . . F2012-D04-004 Yihai Wang, Xia Li and Rongxia Zhang Simulation Analysis of Electromagnetic Compatibility in Vehicle Ignition Control System. . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D04-008 Ya’nan Li, Wenqiang Chen, Xingmin Wei and Fuquan Zhao A Method for Improving Radiated Emission of Automotive Spark-Ignition System with Improved Micro-Genetic Algorithm . . . . . F2012-D04-009 Yanming Qin, Bin Li, Qingsong Liu, Xiangling Xu and JianPeng Zhai Simulation of Electromagnetic Characters of Vehicle Whip Antennas Based on Mom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D04-011 Liguo Zang, Youqun Zhao, Wei Wang, Jian Wang and Haiyan Sun Study on Conducted Interference and Radiated Interference of Buck-Boost Converter in Electric Automobile . . . . . . . . . . . . . . . . F2012-D04-012 Jian Wang, Youqun Zhao, Liguo Zang and Wei Wang Study on Electromagnetic Interference Restraining of Motor Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D04-013 Li Zhai, Runze Gao and Qiannan Wang Part V

525

537

543

557

565

573

585

Vehicle Sensor and Actuator

GPS Based Estimation of Vehicle Sideslip Angle Using Multi-Rate Kalman Filter with Prediction of Course Angle Measurement Residual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D05-001 B. M. Nguyen, Yafei Wang, Sehoon Oh, Hiroshi Fujimoto and Yoichi Hori

597

xii

Contents

Multi-Gas Sensor by Infrared Spectrometer . . . . . . . . . . . . . . . . . . . . F2012-D05-003 Tetsuya Enomoto, Tomoki Tanemura, Shuichi Yamashita, Hiroyuki Wado, Yukihiro Takeuchi and Yutaka Hattori Dynamic Characteristics Analysis and Experimental Study of Multilayered Piezoelectric Actuator for Automotive Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D05-004 Chuanliang Shen, Xuewei Song, Jingshi Dong and Shuming Chen Intelligent Sensor Bearing for Torque Ripple Reduction . . . . . . . . . . . F2012-D05-009 Yi Yuan, Mathieu Hubert, Stephane Moisy, Francois Auger and Luc Loron

Part VI

611

621

631

In-Vehicle Network

Study on Diagnostic Methods of Lin Slaves . . . . . . . . . . . . . . . . . . . . F2012-D06-001 Jitai Li, Ted Huang, Lifang Huang and Liguo Wang

643

The Research of Vehicle Network Control System Model . . . . . . . . . . F2012-D06-002 Kai Li, Juan Wan, Jie Bai, Jianxian Chen, Gan Chen, Fanwu Zhang and Jianguang Zhou

651

Research on Reformation Method of Vehicle Intelligent Electric/Electronic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D06-003 Weiwei Kong, Diange Yang, Tao Zhang, Bing Li and Xiaomin Lian

663

Ethernet-Based Integrated Network for Active Safety Sensors . . . . . . F2012-D06-005 Jin Ho Kim and Jae Wook Jeon

673

Architecture for Secure Tablet Integration in Automotive Network. . . F2012-D06-015 James Joy, Anurag Raghu and Jestin Joy

683

Contents

xiii

Applying Autosar Network Management in OSEK/VDX for Compatibility of Autosar and OSEK/VDX . . . . . . . . . . . . . . . . . . F2012-D06-017 Yo Han Lee, Jin Ho Kim and Jae Wook Jeon Performance Analysis of Ethernet Power link Applied to Ethernet of In-Vehicle Network . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D06-018 Hoe Young Chung, Jin Ho Kim and Jae Wook Jeon Performance Analysis of Gateway Embedded System with Function Actively Controlling CAN Messages . . . . . . . . . . . . . . . F2012-D06-019 Hoe Young Chung and Jae Wook Jeon Network Architecture Design for Reliability Based on ECU Power Supply and Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D06-020 Maoyuan Cui, Dongfeng Zhao, Libo Zhang, Youen Li, Boxiang Ma and Dongyang Ma

Part VII

705

715

725

Multi-Media/Infotainment System

Innovative Software Architecture for Next-Generation Infotainment System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D07-004 Jianming Zhou, Kerun Xu, Minjie Tian, Chendong Wang and Mingshi Xie

Part VIII

693

735

Other

The Design of Invariant Wiring Harness Network in Full Electronic Automobile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D08-003 Shicen Zheng, Wenqiang Chen, Xingmin Wei and Fuquan Zhao Inertia Compensation Based on Torque Signal in an Electric Power Steering System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F2012-D08-006 Xuewu Ji, Ning Sun, Jingguang Ge and Yahui Liu

747

761

Part I

Engine/Chassis/Body Electronic Control

Battery Monitoring Technology of Micro-Hybrid System Based on Voltage Feng Gao, Qiang Zhang and Chenshu Yan

Abstract Monitoring battery state correctly is the basis for a micro-hybrid system. In order to cut down costs, a method of monitoring battery state only by voltage is designed. It is derived by analyzing the characteristics of the micro-hybrid system and battery charging/discharging. By using this proposed method, battery sensor, which is widely used, can be cancelled. The vehicle test results show that the designed method satisfies requirements of a micro-hybrid system and the system works correctly. Comparing to the system with a battery sensor, the fuel consumption is the same in standard condition. But in real traffic the rate of fuel saving is 15 % less. Keywords Hybrid vehicle charge State of health



 Start stop system  Micro-hybrid system  State of

1 Introduction Recently, it is very hard to make electric vehicle into mass production in China because of the following reasons: 1. Some key components, such as battery, are not ready to mass production. 2. Comparing to traditional vehicle, the added costs are much.

F2012-D01-002 F. Gao (&)  Q. Zhang  C. Yan Automobile Engineering, Institute of Changan Automobile Ltd. Co, Changan, People’s Republic of China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_1,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 Micro-hybrid system

Micro-hybrid vehicle has several advantages comparing to other new energy technology. Much attention has already been paid in developed countries. A typical micro-hybrid system is shown in Fig. 1. The controller analyzes driving conditions and estimates battery state according to sensor signals. Engine will be stopped when specific conditions are satisfied. Battery state is one of the most important conditions. The widely used method is equipped a battery sensor, in which a battery model runs. It provides State of charge (SOC), State of Healthy (SOH) and State of Function (SOF). Normally voltage, current and temperature signals are necessary for a battery model [1–9]. The requirements of measurement rage and precision of current signal are strict, which lead to much more costs. In China, more than 2.5 million mini cars are produced per year. It is real important to reduce their fuel consumption and emission. Because of its cheap price, the consumers of mini car can hardly bear the added costs of micro-hybrid system. So a method of monitoring battery state by voltage only is designed to reduce the cost of micro-hybrid system. The decrease rate of voltage, combining with voltage value, is used to estimate discharge current. SOH is decided by the voltage drop at crank. By integrating the proposed logic into Engine Control Unit (ECU), the idle start/stop function can be realized without battery sensor. The effectiveness of the proposed method has been validated by vehicle tests. The results show that it has the same fuel consumption saving rate in standard test condition comparing with the system with battery sensor. But in real traffic the rate of fuel saving is 15 % less.

2 Monitoring Fundamentals by Voltage The main objective of monitoring battery state is to evaluate whether the engine can start successfully. And it is described in detail:

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Fig. 2 Battery discharging current curve. 1 50 % SOC, 2 15A, 3 25A, 4 50A, 5 75A, 6 100A

1. When the battery is full and other idle stop conditions are satisfied too, the engine will stop to reduce emission and fuel consumption. 2. When the battery is not full, the stop function will be forbidden. The controller should keep engine running to charge the battery. This can avoid the faults that the engine can not start. 3. When the battery is aged, the idle start/stop function will be disabled and the aged information should be provided to driver. The battery monitoring method greatly affects the performances of a microhybrid system. In order to achieve the above objects and cancel the battery sensor at the same time, a battery state monitoring algorithm only by voltage is designed. This new algorithm is derived by analyzing the battery working conditions of a micro-hybrid system.

2.1 Engine Stop and Discharging In real driving conditions, normally driver will not turn on/off big electrical power load frequently. When engine is stop, the battery working at the condition, which is similar with that of discharging at a constant current. Figure 2 is the voltage curves of discharge at different current. From the above curves, it can be concluded that: 1. Discharge to the same SOC at different currents, the battery voltage is different because of internal resistance. So SOC can hardly be derived by voltage directly. 2. Discharge to the same SOC at different current, the voltage change rate is different. 3. The bigger the discharge current is, the absolute value of the voltage change rate is bigger. According to the above analysis, when engine stops at idle condition, the battery works like discharge at a constant current. The discharge current may be

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estimated by voltage change rate. And combining with the voltage value, the SOC of battery may be estimated roughly.

2.2 Engine Running and Charging When engine is running, the battery is always charging. If the electrical load is smaller than the output power of the generator, the generator acts as a power source of constant voltage. Figure 3 is the battery voltage curves at different SOC and different engine speed. From Fig. 3, it can be concluded that: 1. When the SOC is almost 100 %, which means the battery is full, the voltage reaches to an almost constant value. The value is always bigger than 14.3 V. 2. When the battery is not full, its voltage is much lower and varies with SOC greatly. Form the above facts, whether the battery is full can be determined by voltage value and its stability.

2.3 Health Monitoring The battery internal resistance can be calculated by voltage and current for the system with a battery sensor. Internal resistance has a close relationship with battery aging state. But in this paper, only voltage is available, so the above normal method can not be used directly. Figure 4 is the statistical lowest voltage from lots of engine start data at different conditions, such as temperature, initial SOC, etc. From the statistical results shown in Fig. 4, it can be concluded that: 1. The lowest start voltage decreases obviously with the aging cycle when the aging cycle is bigger than 4.

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Fig. 4 Lowest voltage during start

2. When the aging cycle is less, the lowest start voltage does not decrease obviously and is above 9 V. From the above facts, the battery aging state can be derived by the lowest voltage during start. When the lowest voltage is smaller than the threshold, it can be concluded that the battery is aged and the start/stop function should be disabled. Otherwise, the battery state is monitored by voltage and the result is input to the start/stop control logic.

3 Battery Monitoring Algorithm Modeling Last section describes the fundamentals of the new battery state monitoring method. To validate its rightness, the battery monitoring algorithm is modelled by ASCET and is integrated into ECU. Figure 5 is the control logic model. A first order filter is used to flat battery voltage to avoid incorrect decision because of fluctuation of voltage. The filter is modelled in Fig. 5 module 5. Considering the digital data length and the fact that battery voltage varies very slowly with time during discharge normally, module 3 which is responsible for voltage change rate calculation, is called every 30 s. The logic of module 1 is that when all of the following conditions are satisfied, the battery is charged full: (1) Battery voltage is not smaller than 14.2 V. (2) Engine is at the state of running and the battery state flag is set. (3) Total time, when both condition (1) and (2) are satisfied, is not smaller than 60 s. The condition (1) and (3) are to ensure that the battery voltage is stable and its value is high enough. This logic only need to be called when engine is running and the battery is not full, so condition (2) is added to avoid disturbances from other conditions.

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Fig. 5 ASCET model of monitoring algorithm. 1 Full charge decision logic, 2 over discharge decision logic, 3 calculation of voltage change rate, 4 lowest voltage during start detection logic, 5 voltage signal filter algorithm

The logic of module 2 in Fig. 5 is that when both the following two condition are satisfied, the battery is over discharged: (1) Battery voltage is smaller than the threshold. (2) Battery state flag is reset. The voltage limit in condition (1) is a function of battery temperature and voltage change rate. The data is stored in ECU and as shown in Fig. 6.

4 Vehicle Experiments To verify the validity and correctness of the proposed battery state monitoring method, the following three kinds of tests have been done: functional test, standard test and vehicle test in real traffic. The functional test is to verify that the start/stop function can be realized. The standard test is to verify the fuel saving rate of the proposed method. The vehicle test is to validate its reliability and real fuel saving rate. Some functional test results are shown in Fig. 7 and it can be concluded that: (1) When the idle stop condition is satisfied, the engine is stopped immediately. (2) When battery voltage is below the threshold at 175 s, the battery state flag is set and the engine starts automatically.

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To validate the effects on fuel consumption, a comparing test at standard NEDC condition has been done between the micro-hybrid system with battery sensor and that using the proposed battery monitoring technology. The test results are the same and the fuel saving rate are 4.3 %. Furthermore, to verify the reliability of the designed system, vehicle tests in real traffic have been done too. Nine vehicles are used, among which three uses the micro-hybrid system with a battery sensor, three are fixed with the new microhybrid system, other three vehicles without micro-hybrid system. Driving routine is the same as the 609 bus of Chongqing City, which includes city road and highway. Driving condition is almost the same as most vehicles of families. The test lasts one month and the driving distance is more than ten thousand kilometers. During the whole test, the functionality of the micro-hybrid system is correct and there is no starting failure because of battery over discharge. According to the statistical data of fuel consumption, the fuel saving rate of the micro-hybrid system using the proposed battery monitoring technology is 7.5 %. It is less than the fuel saving rate of the system with battery sensor, which is 8.6 %. The reason is that the proposed method monitors battery state only by voltage and can not estimate SOC precisely. This will lead to starting engine earlier. And in some condition that the engine may be stopped, the controller does not do it.

5 Conclusions To reduce the cost of micro-hybrid system and make it acceptable by the market especially for mini cars, a battery state monitoring algorithm by voltage is designed in this paper. The algorithm is modeled by ASCET and integrated in ECU. The proposed algorithm is validated by function test, NEDC test and real traffic test. The test results show that: 1. The proposed battery monitoring algorithm by voltage can satisfy the requirements of micro-hybrid system and the start/stop function works properly. 2. By applying the proposed algorithm, the battery sensor can be canceled and the cost of the micro-hybrid system can be reduced by about 10*15 %. 3. The fuel consumption saving rate of the proposed algorithm, which is 4.3 % less comparing with the vehicle without start/stop system, is the same as the micro-hybrid system with a battery sensor at the NEDC running condition. 4. In real traffic condition, the fuel consumption saving rate of the proposed algorithm is 15 % less than the system with a battery sensor.

References 1. Kallfelz A (2006) Battery monitoring considerations for hybrid vehicles and other battery systems with dynamic duty loads. Battery Power Prod Technol 10(3):1–3

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2. Pop V, Bergveld HJ, Regtien PPL et al (2007) Battery aging and its influence on the electromotive force. J Electrochem Soc 154(8):744–750 3. Picciano N (2007) Battery aging and characterization of nickel metal hydride and lead acid batteries. The Ohio State University, Mechanical Engineering 4. Ross Michael MD (2001) A simple but comprehensive lead acid battery model for hybrid system simulation. In: Proceedings of PV horizon: workshop on photovoltaic hybrid systems, Montreal 5. Jackey RA (2007) A simple effective lead acid battery modeling process for electrical system component selection. In: SAE world congress and exhibition, Detroit 6. Medora NK, Kusko A (2007) Analysis of battery cable faults using dynamic battery model. In: International battery conference, Tampa 7. Suozzo C (2008) Lead acid battery aging and state of health diagnosis. The Ohio State University, Electrical Engineering 8. Li G-H (2005) Study on the hybrid drive system and the power battery control strategy. Department of Automation, TianJin University 9. Jiang X-H (2007) Study on the management system of lithium ion battery. Graduate University of Chinese Academy of Sciences

Research on Electro Hydraulic Composite Brake System Qinghe Liu, Lan Zhan and Ti He

Abstract This paper describes a proposal of electro hydraulic composite brake system, which is based on the high-speed switching valve, and sets up a algorithm of the composite braking resistance distribution, according to the law of ECE and motor’s external characteristic. By using the simulator ADVISOR, the algorithm is analyzed. The result shows that this algorithm can realize the braking safety primly, at the same time, recuperates energy of 220 kJ, leading to a 0.75 % increase of battery status.

 

Keywords Composite brake system Brake resistance distribution energy regeneration Braking safety ECE law





Braking

1 Technical Paper: Introduction Since the 21st century, faced with the much more serious problems of energy exhaustion and environmental pollution, many car-producing countries have been controlling the polluting emissions and fuel economy of their vehicles by setting F2012-D01-003 Q. Liu  L. Zhan (&) Harbin Institute of Technology, Weihai, China e-mail: [email protected] Q. Liu e-mail: [email protected] T. He Weihai Xili Electronics LTD, Weihai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_2,  Springer-Verlag Berlin Heidelberg 2013

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more strict standards. Therefore, it brings the new energy vehicle, which can regenerate energy while braking, to public focus. Regenerative braking system reserves part of the braking energy for driving the vehicles, which can increase vehicle economy effectively. But the effect of regenerating energy is decided by the certain algorithm of the composite braking force distribution. Because the traditional braking system cannot realize independent and variable control of the hydraulic braking force, many complex algorithms, which have strict requirements for the control of the hydraulic braking force, can not be applied to real cars. This traditional system limits the regenerative efficiency of braking energy and the improvement of braking safety. However, the electro hydraulic composite brake system provides a practical way to solve this problem. In this study, we aim to research on the electro hydraulic composite brake system. Based on the high-speed switching valve, a proposal of this system, which provides a foundation for the following study, is raised. According to the law of ECE and motor’s external characteristic, we set up an algorithm of the composite braking force distribution. Finally, by using the simulator ADVISOR, this algorithm is analyzed, from the perspectives of energy regeneration and braking safety. The ultimate result shows that this algorithm of the composite braking force distribution is superior in both of the aspects.

2 Structural Concept of the Composite Electro Hydraulic Brake System During the composite braking process, in order to realize active control of the hydraulic braking force, the electro hydraulic brake system should contain the following basic functions. 1. The electro hydraulic composite brake system should have similar pedal feeling with the traditional ones. 2. According to the master controller’s requirements of the composite brake system, this system should have the active ability to increase and decrease the pressure of the hydraulic brake system. Hydraulic brake should be separated with pedal operation. After the functional analyses of the composite brake system and the electro hydraulic brake system, we raise a proposal of the electro hydraulic composite brake system (Fig. 1), which is based on the high-speed switching valve. The most obvious attribute of this proposal is that it can satisfy the requirements of varies algorithms of the composite braking force distribution toward exact control of the hydraulic braking force [1]. Attributes of the proposal:

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Fig. 1 The structure of the electro hydraulic composite brake system. 1 braking pedal, 2 braking pedal position sensor; 3a, 3b, 3c, 3d high-speed switching valve for oil circulation; 4a, 4b, 4c, 4d high-speed switching valve for feeding, 5 braking oilcan, 6 wheel pump, 7a, 7b, 7c, 7d pressure sensor, 8a, 8b, 8c, 8d wheel braking cylinder; 9a, 9b, 9c, 9d wheel speed sensor; 10 accumulator; 11 motor; 12a, 12b spare high-speed switching valve for brake; 13a, 13b equilibrium valve; 14 brake master cylinder

1. Electro hydraulic brake system can independently control the hydraulic braking force of every wheel, providing the foundation for those much more complex and efficient algorithms of the composite braking force distribution. 2. In this proposal, the electro hydraulic system cut down the mechanical connection between braking pedal and braking pipe. To ensure the driver’s pedal feeling while braking, this system adds a pedal feeling simulator, which is used to simulate the braking force reaction to the pedal. 3. In the braking process, once the electro hydraulic brake system suddenly broke down or the braking control system failed, the spare braking system would begin to work [2]. Figure 2 shows the control logic of the electro hydraulic composite brake system. According to the signals sent by the position sensor of braking pedal, the electro hydraulic braking control unit gets the driver’s braking intention and sends the intention to the electro hydraulic composite braking control unit via a bus. At the same time, via the bus network, the composite braking control unit receives the signals of car speed and battery’s state of charge from VMS (Vehicle Manage

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Fig. 2 Control logic of the electro hydraulic composite brake system

System). In accordance with the algorithm of composite braking control, it calculates the regenerative braking force, which should be provided by the regeneration brake system, and the hydraulic braking force supplied by the electro hydraulic brake system. Based on the results, signals are sent to the hydraulic control unit, by controlling the hydraulic actuator, the pressure of every wheel cylinder is controlled, thus the hydraulic braking forces are controlled. Simultaneously, signals are sent to the motor controller, controlling the braking force generated by it to satisfy the total requirement of the severity of braking [3].

3 Performance Test of Electro Hydraulic Brake System As the electronic control of the hydraulic brake system is the premise of realizing electro hydraulic brake, performance test of the new proposal’s electro hydraulic is conducted. The performance test is divided to two work conditions: the step-input and sinusoidal-input. 1. The wheel baking cylinder’s pressure response under step pressure Set the objective pressure of increasing pressure to 2.5, 5, 7, 8.5 MPa respectively in the controller, the control effect is showed in Fig. 3. The Initial pressure is 9 MPa, set the objective pressure of decreasing pressure to 6, 4, 2, 1 MPa respectively, the control effect is showed in Fig. 4. As the figures show, the control

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Fig. 3 Step increasing of wheel

Fig. 4 Step decreasing of wheel

system can effectively follow the step-input pressure, the steady-state error is minor, the time of reaction is within 100 ms, and can satisfy the brake system’s requirement towards the pressure reaction speed. 2. The wheel baking cylinder’s pressure response under sinusoidal pressure The (a) in Fig. 5 shows the performance curve of the wheel baking cylinder’s pressure response under sinusoidal pressure of 3 sinð3tÞ þ 4, when the switching valve is in the zone of linearity. (b) shows the performance curve of the wheel baking cylinder’s pressure response under sinusoidal pressure of 3 sinð3tÞ þ 4, when permitted to work in the nonlinear region. (c) shows the performance curve of the wheel baking cylinder’s pressure response under sinusoidal pressure of 3 sinð4tÞ þ 4. (d) shows the performance curve of the wheel baking cylinder’s pressure response under sinusoidal pressure of 3 sinð4tÞ þ 4, when permitted to work in the nonlinear region.

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Fig. 5 The performance curve of the wheel baking cylinder’s pressure

The experiment result shows that under inputting sinusoidal required pressure, the real pressure can effectively follow the objective pressure. When keeping the switching valves working in the zone of linearity, the pressure fluctuates markedly. This is largely due to the high dutyfactor (minimum value is 0.05) of the PWM signal inputted by the controller. Therefore, markedly fluctuation of pressure would happen once the switching valves are regulated. When the switching valves are allowed to work in the nonlinear region, the pressure of the wheel cylinder can follow the objective pressure much more exactly. This is because when the difference between the required pressure and objective pressure is small, the pressure can be fine-tuned by using the space of dutyfactor between 0.03 and 0.05.

4 The Composite Braking Control Algorithm, Based on the Law of ECE and Motor’s External Characteristic According to the law of ECE and motor’s external characteristic, we set up a composite braking control algorithm. On the premise of satisfying braking safety and braking severity, we set our primary goal as recovering the maximum energy.

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Fig. 6 Composite braking force distribution curve

Therefore, the recovered energy should be used as more as possible while braking, to increase the efficiency of recovering. When braking severity is low, only the motor provides regenerated braking force for the front wheels. When the braking severity is medium or high, the hydraulic system and regenerated braking system provide the force corporately. Before reaching the ideal distributive severity of braking force, the braking force of front wheels is only provided by the motor. The electro hydraulic brake system does not provide any braking force. The braking force of the rear wheels is provided by the hydraulic system. When the ideal distributive severity is reached, every wheel’s braking force is distributed according to the ideal braking force distribution curve. The motor provides its maximum regenerative braking force for the front wheels, if it is not enough, hydraulic braking system would be added, while the rear’s is solely provided by the hydraulic brake system [4]. Before braking force is distributed, the electro hydraulic composite control unit, according to the received signal from the braking pedal, calculates the car’s objective braking severity z and total required braking force Fr , at the same time, according to the motor’s rev and the transmission agent’s transmission ratio, calculates the largest regenerative braking force Fmax that can be provided by the motor. First, calculating the front wheel’s braking force on the curve of ECE law, while the braking force of the rear wheel is zero. 8 < F ¼ z þ 0:07 ðb þ zh ÞG=L xb1 g 0:85 ð1Þ : 0 ¼ Gz  Fxb1 Fxb1 ¼ Fxb1d

ð2Þ

In the equation, Fxb1d —abscissa of the point that ECE curve cross with the horizontal axe Judging from the magnitude comparison between Fxb1d and the maximum braking force Fmax provided by the motor, there are two kinds of braking force distribution methods:

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Fig. 7 Composite braking force distribution curve

When Fmax [ Fxb1d , the braking force distribution is the ODEAB curve (Fig. 6, [5]) (1) z  zD , total regenerative braking section. Fm ¼ Fr ; Fhf ¼ 0; Fhr ¼ 0

Fm Fhf Fhr

In the equation, motor’s regenerative braking force (N); front wheels’ hydraulic braking force (N); rear wheels’ hydraulic braking force (N).

(2) zD \z  zE , braking force distribution changes along the curve of ECE law

Fm ¼

z þ 0:07 ðb þ zhg ÞG=L; Fhf ¼ 0; Fhr ¼ Fr  Fm 0:85

(3) zE \z  zA , motor provides the maximum braking force

Fm ¼ Fmax ; Fhf ¼ 0; Fhr ¼ Fr  Fmax (4) z [ zA , braking force distribution changes along curve I

Fm ¼ Fmax ; Fhf ¼ Fr

b þ hg Fr =G a  hg Fr =G  Fm ; Fhr ¼ Fr L L

When Fmax  Fxb1d , the braking force distribution changes along curve OCAB (Fig. 7).

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Fig. 8 The model of braking force distribution strategy

(1) z  zC , total regenerative braking section, braking force only provided by the motor.

Fm ¼ Fr ; Fhf ¼ 0; Fhr ¼ 0 (2) zC \z  zA , motor provides the maximum braking force

Fm ¼ Fmax ; Fhf ¼ o; Fhr ¼ Fr  Fmax (3) z [ zA , braking force distribution changes along curve I

Fm ¼ Fmax ; Fhf ¼ Fr

b þ hg Fr =G a  hg Fr =G  Fm ; Fhr ¼ Fr L L

5 Simulation of the Electro Hydraulic Composite Braking Strategy 5.1 Building of the Simulative Model By using the ADVISOR simulator, we conduct simulative analysis toward the algorithm of the composite braking force distribution. Primarily, we inset the law of ECE and motor’s external characteristic into the policy module of ADVISOR (Fig. 8), the module’s input signal is the car’s total required braking force, and the output signals are the front wheels’ total required braking force and their frictional braking force [6].

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Fig. 9 Braking force distribution curve

Using the models of every assembled vehicle element in ADVISOR, some assembled m documents are modified necessarily. For example, it happens when selecting transmission ratio of driving chain, we try to make the motor work in the zone of high efficiency and high torquemoment, however, we also have to make sure that transmission is fixed in the certain gear when braking.

5.2 Analysis of the Simulation Result In a process of slow braking, to test the rationality and validity of the braking force distribution algorithm, we simulate the distribution of the car’s braking force, the situation of the wheel’s motion and energy regeneration, to test the rationality and validity of the braking force distribution algorithm. From the analysis of Figs. 9 and 10, we can know that in the beginning period of braking, the car is in the total regenerative braking situation, in which the hydraulic braking system doesn’t provide braking force. As the total braking force reaches 3000 N, the hydraulic braking system begins to provide braking force for the rear wheel. At this time, the braking force provided by the motor hasn’t reached the maximum, to satisfy the braking severity requirement, and avoid the front wheels, using the attachment coefficient, breakthrough the limitation of ECE law, hydraulic braking force is added to the rear wheels, sharing some of the braking force. After the braking force reaches 5000 N, hydraulic braking force is also provided to the front wheels, and the motor works in the zone of constanttorsion, which is under the basic speed, providing the largest braking force under this rev. At this time, all the wheels’ braking force is distributed according to the ideal braking force distribution. The slip ratios of front and rear wheels are equal, and equal to the braking severity [7]. As the Figs. 11 and 12 shows, in the beginning period of braking, the battery situation increases slightly, which is due to the reasons that the regenerative braking force is comparatively small, the recovered braking energy is less and some of the equipments in the car consumes certain amount of electricity.

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Fig. 10 Car speed and slip ratio curve

Fig. 11 Motor’s regenerative power and charging electric current

Fig. 12 The battery’s regenerative energy and SOC

However, as the braking severity increases, the regenerative braking force increases and the motor’s generated output increases and reaches the maximum. Subsequently, the car’s speed decreases, the motor’s rev decreases, the motor begins to work in the zone of constant-torsion, and the motor’s regenerative power decreases to 0. In the whole process of braking, the motor’s maximum regenerative power is 42 kW, the maximum charging electric current is 110 A, the battery situation increases 0.75 %, from 70 to 70.75 %, total regenerative energy is 220 kJ. From all these data, we can know that the algorithm of braking force distribution mentioned in this paper can realize the braking safety and regenerate comparatively large amount of braking energy.

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6 Conclusion 1. We raise a proposal of electro hydraulic composite brake system, which is based on the high-speed switching valve, and verify the feasibility of the electro hydraulic braking system through tests. 2. An algorithm of the composite braking force distribution is set up, according to the law of ECE and motor’s external characteristic. 3. Based on the ADVISOR simulator, the electro hydraulic composite brake system’s braking strategy is simulated, and according to the simulation result, the braking safety and the amount of regenerative braking energy are analyzed.

Acknowledgments The authors would like to thank Weihai Science and Technology Development Plan 2010-3-96 and Harbin Institute of Technology Innovation Fund HIT.NSRIF.201017, for financial support.

References 1. Bo Y, Wanda Q, Yaohua H (2007) Automobile electro-hydraulic brake system. Bus Technol Res 1:25–28 2. Sakai A (2008) Toyota braking system for hybrid vehicle with regenerative. In: 14th international electric vehicle symposium (EVS14), 41–748 3. Reuter DF, Lloyd EW, Zehnder JW, Elliott JA (2003) Hydraulic design considerations for EHB systems. SAE Trans 112(6):304–314 4. Masayasu Ohkubo KN (2005) Apparatus for increasing brake cylinder pressure by controlling pump motor and reducing the pressure by controlling electric energy applied to control valve. USPTO, Toyota, pp 1–20 5. Qinghe L, Zechang S, Pengwei W, Xidong L (2008) Research on Electro-hydraulic Parallel Brake System for Electric Vehicle. Chin J Automot Eng 30(6):528–530 6. Kun X (2009) A study on the braking force distribution algorithm of composite brake system. Beijing Automot Engg 6:42–45 7. Zhuoping Y, Lu X, Lijun Z (2009) A Study on the matching of electro-hydraulic brake system. Automot Eng 27(4):456–462

Analysis and Design of Automotive Body Control Module Jianhui Ma, Zhixue Wang, Yanqiang Li and Liangjie Yu

Abstract In the BCM’s industrialization process, we need design appropriate BCM for different car models. In order to reduce the complexity of the design while avoiding duplication of design work, This paper summarize the experience in the recent years design of the BCM, analyze the system structure, working mechanism, and basic design principles based on one particular BCM section, analyze its external interfaces attributes and complex control logic. Design generic, common embedded software structures for body control and basic modules that can be configured and assembled, These software components is flexible and configurable, based on the software structure and basic module library, we can quickly start the development of appropriate body controller software for BCM of different car models. Keywords BCM

 Software architecture  Portability

1 Introduction Along with the development of automotive electronics and networking technology widely used in automobiles, Body Control Module (BCM) integrated body network gateway with lighting control, wiper control, window control, RKE key access control and door lock control function, is becoming a mainstream. F2012-D01-005 J. Ma (&)  Z. Wang  Y. Li  L. Yu Shan Dong Key Laboratory of Automotive Electronics, Automatic Institute of Shan Dong Academy, Jinan 250014, Shan Dong, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_3,  Springer-Verlag Berlin Heidelberg 2013

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Although the BCM have many input and output interfaces, and complex control logic, but the BCM of different car models is basically same in its works and system structure, only have certain differences in module combination or some specific module’s design, so it is necessary to analyze the system structure and basic design principles of BCM based on one particular section, design generic, common embedded software structures for body control and basic modules that can be configured and assembled. Based on the software structure and basic module library, we can quickly start the development of appropriate body controller software for BCM of different car models, reduces development complexity and improves the development efficiency. This software must have following features. 1. Using scalable reactive software architecture, adding new features without breaking existing software structure, and won’t influence the behavior of existing systems; 2. Establish an effective relationship between reuse and assembly, in the development of new BCM module, avoid duplication of development of basic software modules, and avoid increased costs and extended development cycle; 3. The software interface standards of the universal basic module have uniform agreement, ensuring the independence of modules and portability at the level of applications; Combined with the development of BCM for a car model, the author analyzed the design principle of BCM and the specific implementation from BCM system structure, software architecture and the realization of part of the module.

2 System Structure BCM is a typical body central controller combined centralized control and distributed control, its input interface includes a series of switch signals and driving pulse signal, output interface is a series of control objects that includes locks, lights, wipers, windows and alarm. At the same time, BCM communicates with remote control keys by RF signal, and exchange information of control command and status with sensor nodes and windows node via LIN bus. its external interface shown in Fig. 1. BCM is a typical control system, through the detection of the switching signal and pulse signal and a series of combinational logic, achieve the load drive control, also achieved RKE Keyless entry and anti-theft alarm function. At the same time, as a Body Control LIN network master node, BCM scheduling the entire LIN network communication and network management. Its system structure show in Fig. 2, the output control is the core module of BCM, other function module also include input signal detection, LIN communication, the RKE communication, anti-theft alarm state management.

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Car speed sensor Switch signal traffic signal Right front window ECU

Central lock

LIN

BCM Body controller

rain/sun sensor

Remote keyless entry

Left rear window ECU

High speed motor light

Right rear window ECU

Low speed motor External light

Turn light

Wiper/wash er

License plate light

Internal light

Position light

Keyhole light

Indoor light

Fig. 1 BCM external interface diagram

3 Software Design According to BCM design features and design resource requirements, while taking into account the cost factor, the author chose the Freescale 16-bit automotivegrade MCU to achieve its software design. As a basic software and design solutions of a series of BCM, this paper analyzes the BCM software architecture design and the programming of some modules, explain how to ensure software scalability and module reusability from the system level and the micro level. In the following, first analyzed BCM software architecture design, and then describe timer management and switch signal detection these basic module’s realization.

3.1 Software Structure In order to save the limited resources of MCU, the BCM software design does not use the operating system, and because different car model BCM’s input detection, output control, communication and control logic is or less the same, it is necessary and feasible to design a common body control module software architecture. Based on the software architecture solutions, develop appropriate body controller

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Lock control Anti-theft alarm state management Light control

Input signal detection

Output control

Wiper control

Alarm control communication Window contrl=ol

LIN bus

RKE

Fig. 2 BCM system structure

software for different models, improve ECU software reliability and development efficiency, reduce development complexity. The software structure is in the form of interrupt ? the main loop body. The system enters main loop after power-on initialization, the body of the loop including the following module: timer management, input signal detection and statistic, signal reception and the extraction of LIN application layer, RKE communication, anti-theft alarm status management, output control (including the window control, wiper control, door lock control, alarm control, light control), fill LIN send signals, clear event. The order of these modules in the loop is very important, reflecting the working principle of the BCM, the main body of the loop as follows: for(;;) { TimerTick(); InputDetect(); l_SignalDetect(); Rke_Decrypte(); AlarmStateManage(); WindowControl(); WiperControl(); LockControl(); AlarmControl();

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LightControl(); l_app(); l_Com(); ClearEvent(); } First, timing information is the input signal of all other modules, so put the timer management on the top of the loop body, and then, because switch control is BCM’s main control logic, so followed by is the switch signal detection in the main loop. Input signal of other control logic is pulse, LIN signal and RKE signal. LIN data link layer is achieved in the UART receive interrupt service routine, and application layer signal receiving part in l_SignalDetect. Rke_Decrypte achieve remote control key’s learning, the RKE key signal detection and statistics. The input signal of Alarm state management is switch signal and RKE signals, and also it is the input signal of the output control logic, so placed Alarm State Manage after Input Detect and Rke_Decrypte, followed by the output control and the the filling of the LIN send signal. All the control modules are event-driven, if an event occurs, then perform the appropriate control logic. Since many events are shared, in order to ensure the event to digest more than one module, put clear event operation—Clear Event on the end of the main loop. Due to certain events is set in the interrupt, then it is need to introduced the concept of synchronization in logic circuit design to software architecture design, treat each entry of the main loop as a synchronous clock, the event set in the interrupt is synchronized in the main loop, thus avoiding instability that the event is cleared before been digested (Fig. 3).

3.2 Basic Module Design 3.2.1 Timer Management BCM timing applications include statistics of input signal time characteristics, the output logic timing and timeout handling, LIN master node schedules the rotation of the time slice and IDLE time detection, which is characterized by the timing accuracy is not required, but the timing number is more, based on these features, designed timer management module. Due to limited hardware timer and range of timing application, can’t assign hardware timer for each timing applications, so use software timer simulate hardware timer. According to the timing characteristics and classification of the application, design software timer data structure in the form of structure, organized these software timer in the form of a structure array, the array member is software timer node. As all software timer reference clock source, the hardware timer is set to 1 ms cycle timing, manage the hardware timer in the interrupt service routine-cumulative global clock tick Jiffs, set clock synchronization flag TimerTicked to 1. In the main loop, function

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Fig. 3 BCM main loop software flow chart

Timer management

Input signal detection

LIN signal extraction

RKE

Anti-theft alarm state management Load control window,wiper,lock,alarm,li ght

LIN signal filling

Clear event

TimerTick performed all the software timer management according to TimerTicked and Jiffs. So achieved simulate multiple software timer by a single hardware timer. Software timer data structure is designed as follows: typedef struct { TimerState timer_state; ulong timeout; ulong duration; unsigned cycle:1; unsigned cnt_times:8; unsigned overflow_flag:1; TimerId timer_id; }Timer;

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‘‘Timer_state’’ means if a software timer is in running condition, ‘‘timeout’’ is overtime application’s timeout threshold, ‘‘duration’’ is the timing of the software timer since its launch, ‘‘cycle’’ indicates whether the periodic timing, ‘‘cnt_times’’ indicates times of multiple timing when not a periodic timing, ‘‘overflow_flag’’ indicate whether application timeout occurs, ‘‘timer_id’’ is used to identify a software timer in the software timer array. Thus, the member variable describes all the ‘‘timing features’’ and provides a good read-write interface. In function TimerTick of main loop, manage multiple software timers in order. The software timer only runs when tick occurs under the circumstances of their own status as RUNNING, its ‘‘duration’’ accumulate with the tick, when ‘‘duration’’ matches its timeout value, set overflow_flag, and then determine whether it is a cycle timer. If it is a cycle timer, restart the timer and clear the ‘‘duration’’, if not, determine whether multiple timing, to determine whether to restart the timer or stop the timer.

3.2.2 Switch Signal Detection The switching signal detection is relatively simple in the hardware design, just current limiting ? filter ? voltage divider, then detect with MCU IO pin. In program design, need to determine switch current state and its changes. Because BCM needs to collect so much switch signal, that in order to program simple with clear logic, define a structure to unify each switch signal, structure is defined as follows: typedef struct{ unsigned switch_state:1; unsigned swon_event:1; unsigned swoff_event:1; unsigned cursw:1; uchar detect_cnt:3; e_SwId switch_id; }s_Switch; In the above structure, ‘‘switch_state’’ defined the current state of the switching signal, ‘‘swon_event’’ said switch changes from disconnected to connected, and ‘‘swoff_event’’ said switch changes from connected to disconnected, ‘‘cursw’’ and ‘‘detect_cnt’’ used in switch signal software debounce function. In specific application, define a s_Switch structure array Sw[MAX_SWITCH], each switch corresponding to a structure variable, addressing with the member variable ‘‘switch_id’’ in above structure, the ‘‘switch_id’’ is defined as follows with enumerate type: typedef enum{

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IGNITIONKEY_SWITCH, IGNITION_SWITCH, COLLISION_IO_SWITCH, SPEED_IO_SWITCH, FRONTDOORKEY_LOCK_SWITCH, FRONTDOORKEY_UNLOCK_SWITCH, LEFTFRONT_DOOR_SWITCH, RIGHTFRONT_DOOR_SWITCH, …. …. }e_SwId; So that if the status of the left front door switch is used, directly use Sw[LEFTFRONT_DOOR_SWITCH].switch_state, and if you want change of its status, directly use Sw[LEFTFRONT_DOOR_SWITCH].swon_event and Sw[LEFTFRONT_DOOR_SWITCH]. swoff_event. Specific procedures are not discussed here.

4 Conclusion According to characteristics and working principle of BCM, analyzed the external interface and system architecture, designed a common software structure and basic module that has been applied successfully in the software design of a BCM for one car model, It has steady performance in the real vehicle test, with practical value and significance.

Scalable Architecture Approach with Platform Products to Implement Advanced Car-Body E&E System in Emerging Markets Dayu Ding

Abstract With the dramatic development of the passenger vehicle industry in emerging countries—(‘‘BRIC’’ as it is also known) indicating the fast developing passenger vehicle market in Brazil, Russia, India and China, simple vehicle E&E system architectures are no longer sufficient to fulfill the demands from the market for fancy vehicle features such as Passive Start and Entry System (PASE). That in effect requires support from advanced car body E&E systems. However, traditional implementation of advanced car body E&E systems in developed markets, e.g. EU countries, requires high R&D efforts due to the complexity of the system and diversity of the architectures and requirements. Furthermore, take rates of advanced car body features in the low cost car segment (Affordable CAR) are also an undetermined factor, which makes the situation more complicated for advanced car body E&E architecture designs for this market. Thus the conflict of high system implementation, cost for advanced car body features as well as the low system cost demand from low cost car segments, and the uncertain take rates of advanced car body features in low cost car segments are becoming the major topics to be resolved by every E&E system supplier and vehicle manufacturer in BRIC markets. This paper describes a system implementation concept to resolve the above issue using a scalable architecture approach with platform products from the Affordable CAR segment. By using a scalable architecture approach, the advanced car body E&E system implementation cost could be invested in a step-wise way, which reduces the risk of uncertainty of take rate of advanced car body feature deployment in the low cost car

F2012-D01-006 D. Ding (&) Body and Security, Continental Automotive Changchun Co., Ltd., Shanghai Branch, 523Dalian Road, Shanghai, China e-mail: [email protected] URL: www.continental-corporation.com SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_4,  Springer-Verlag Berlin Heidelberg 2013

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segment. By using a platform products the total system development costs can be controlled in an acceptable level, which can make advanced car body feature deployment in the low cost car segment become reality. An example of a scalable system approach to implement different levels of car body E&E system by using products from Continental low cost platform family is also illustrated in this paper. A scalable stepwise system approach is described for Remote Keyless Entry and Immobilizer system as 1st step, Engine Start Stop and Push Button Start system as 2nd step and finally complete PASE system as the last step is illustrated here. The concept of system function/feature integration, its scalability, and key factors to be considered for optimum level of functional integration are described. The evolution of platform products, benefits for passenger vehicle manufactures and its scalability for implementations of E&E system for Affordable CAR vehicle segments are explained.

1 Introduction 1.1 Background As per strategy analytics, in the emerging market, the growth of Ultra Low Cost Car (ULCC) and Low Cost Car (LCC) is phenomenal. The analytics expect the growth of ULCC production to reach above 6 million units by 2015 [1].

A very interesting discovery in the low cost car (LCC) segment in emerging markets is that ‘‘low cost’’ does not really mean low content of the car body features, instead, integrating more advanced car body features have been regarded

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as unique selling point by local OEM to increase quality-to-price ratio for their vehicle in front of the competition brought by similar models produced by global car makers in the same region.

2 PASE System and its Challenge in Emerging Markets PASE is an abbreviation term for Passive Start and Entry System and it is a part of Continental’s wireless product portfolio. Modern PASE systems work on worldwide released ISM bands (Industrial, Scientific, and Medical Band) for radio communication and use LF and RF transmission technology. Continental was the first electronics supplier company launching PASE systems to the market in 1999 by introducing the first PASE system for S-Class car series of Mercedes-Benz as a major automotive innovation. In 2002, Continental and RENAULT rolled out the PASE system cross platform for the Megane2 as a scalable PASE system innovation. In 2007, Continental (plus former Siemens VDO) had the market share of 58 % in Europe and 50 % in US. Benefits of a PASE system had been soon accepted by end users and especially in Asian regions. This system feature has been regarded as one of the most fancy car body features that attract users by following items: • • • •

Easy transmitter handling for locking and unlocking Convenient and quick access to the vehicle Easy engine start and stop by one touch button Highest comfort level for the driver without inconvenient search of the key/ID by entry.

However, since the development of modern PASE systems needs to consider LF and RF wireless communication, terminal control for KL15 and KL50, and ESCL control (which ranks in functional safety level ASIL D in ISO-26262 standard), the system development cost is very high. This makes it extremely

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difficult to amortize or justify the development cost of such a system on a car model with a rather low or unknown sales volume. In most cases precise information about the production volume or feature take rates is not available or not possible. The dilemma for the local car makers is that they need advanced and fancy car body features in order to attract their customers on one hand, but on the other hand see a bottle neck in financing the development of those features or systems. These 2 factors make the deployment of PASE systems on low cost cars impossible for most of the local car makers. As conclusion, below 3 major challenges are faced in emerging markets for implementing advanced car body systems in low cost car segments: Special Market Characteristic: Non-stable, Immature, Fragmented Extreme Cost Targets: Fierce competition with global and local suppliers even for low volume projects Demanding Scalability: Easy adaptation for different customers, markets and functions

3 Platform Products In mature markets, automotive engineers define product specifications focusing on technical innovations and ask electronic suppliers to implement the product for global markets. High development cost is amortized on a big global production volume. The same development model is not going to work for local car makers of emerging markets because those OEMs cannot afford the high development cost with their uncertain volumes in the local markets. Furthermore, their engineers cannot offer many technical innovations due to lack of experience and know-how. However, local car makers still aim to have good quality products with good feature content to grow in low cost vehicle segments.

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Fig. 1 Continental platform BCM family

Fig. 2 Continental easy PASE platform

Platform products are the perfect solution for them, where they need electronic products with a stable and high quality at acceptable costs. Platform products are developed not like those electronics developed in mature markets. Instead it is the electronics system supplier who defines the technical architecture using mature technology to define a scalable product that can be

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flexibly configured on a system level to implement the same features as those in mature market vehicles but in a slightly different way. The development cost has been effectively controlled by the platform approach: • Sharing same PCB and master electrical design • Sharing same housing/cover/connector and tooling • Sharing same software platform and application functional modules Benefits of platform approach to car makers are: • Short time to market thanks to development only on top of existing platform products with necessary adaptations • Acceptable development costs thanks to sharing R&D costs so that only application efforts will be accounted instead of a whole new product development. • Reduce the risk of uncertain take-rates thanks to product tooling and manufacturing costs will only apply proportionally via actual volume. Example of platform products can be illustrated by Continental ‘‘platform BCM’’ family and ‘‘Easy PASE’’ system (Figs. 1 and 2).

4 Scalable Approach Using Platform Products as Total Solution To resolve the challenges of local car makers in emerging markets, helping them to setup advanced car body features in low cost vehicle architectures, using a scalable approach with platform product is the most perfect engineering solution. Here we will take an example of how to build up a PASE system in a car body architecture in the low cost vehicle segment [2]. A scalable PASE system composes dynamically 3 steps of approach offered to car makers. Each step is not overlapping with the other one and all development efforts and costs spent on a previous step can be reused in the next step as a baseline. The step-wise scalable system approach for PASE system development makes development costs feasible and dramatically reduces the development risk for local car makers for advanced car body electronic system implementation in low cost vehicles. Step-1: Base Development starts with RKE (Remote Keyless Entry ) platform BCM with integrated RF receiver ? transmitter) and an Immobilizer (either stand-alone or as integrated function in the platform BCM). The immobilizer and RKE when integrated into a platform BCM enhance the vehicle security and comfort to the user.

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Step-2: Upgrade to ISS System and Passive Start with SSB The base variant can be upgraded with Idle Stop Start (ISS) function associated with an intelligent battery sensor. The ISS function when integrated into a platform BCM leads to improving fuel economy and reduction of carbon footprint. Usually the ISS function is associated with the Engine Management System (EMS). The integration of the ISS function into a platform BCM is feasible when a vehicle comes with a standard EMS that cannot provide this function without major modifications.

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Adding a Start Stop Button (SSB) enhances the comfort level to the end user. The immobilizer antenna associated with a key fob slot and the SSB enable the engine start/stop with a simple push button operation.

Step-3: Upgrade to Full PASE System with Passive Entry With the PASE ECU and the new generation of remote control keys on top of an ISS/SSB system makes it possible for the driver to open the door without holding the key in the hand. In step-3 the PASE module together with up to six antennas mounted at specific locations in the vehicle interior and an easy-tooperate start-stop button in the dashboard allows passive entry and passive go. Furthermore, advanced features for the high end cars like the anti-pinch for the window lifter systems and Tire Pressure Monitoring System (TPMS) can be integrated and/or supported by the platform BCM module.

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5 Scalable Approach: Success Story Using the platform products in a scalable system approach for advanced car body systems has gained high momentum in the emerging markets and are running successfully. The following OEMs adapted the platform products for their carlines. Example Proton, the Malaysian car maker, had successfully launched the sedan ‘‘Prevé’’ in March 2012. A Passive Start system using Continental platform products and a scalable system architecture illustrated as STEP-2 approach based on a traditional RKE system is offered here.

This sedan design based on a Continental platform BCM solution (the Advanced Function Controller) using a conventional RKE system is upgraded with

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a terminal control function, a Start Stop Button (SSB) and a Gear Shift Lock controller (GSL), which results in a Passive Start System with low engineering costs and risk. Most important is that the upgrade to a full PASE system is now just an add on with a PASE controller and LF antennas. This is a good example for how a local car maker can integrate advanced car body features at low cost

6 Conclusion The platform products are standard, available off the shelf. The product customization and system integration can be realized with low R&D costs. Furthermore, the ‘Time to Market’ is greatly optimized with the scalable architecture approach. The uncompromisingly cost-optimized total architecture was of top priority, wherein flexibility, scalability, functionality, reliability and safety are not disregarded. With this strategy, it offers the best electronic concept at the best price and of the best quality in the emerging markets Acknowledgments I sincerely thank my management for their constant encouragement and support extended. The technical support rendered by Markus Gentzsch on scalable PASE system approach is immense, without that this paper is not possible. Finally I thank my review team, for their extended support in refining this document.

References 1. Scalable System Architecture Using Continental BFC, by Harikrishna Khandavalli 2. Scalable PASE 09, 2011, by Mr. Markus Gentzsch

Gasoline Fuel Injector Selection and Its Effects on Engine Performance Shuxia Miao, Daijun Deng and Hui Zheng

Abstract Fuel injector is very important for engine performance. Normally it is supplied by system suppliers as a package and OEMs do not have much choice in China. This study offered an experimental method offuel injector selection. Based on CAE simulation, two types of fuel injectors were selected and their key parameters and the engine dynamometer performances are tested in lab. An optimized fuel injector is selected based on the comprehensive test results. Keywords Engine

 Fuel injector  Calibration  CAE

1 Introduction Gasoline fuel injector selection is very important for engine performance including fuel economic and emission performance [1, 2], properly selected fuel injector could improve engine dynamical performance; reduce the fuel consumption and emission as well. Very often the fuel injector is supplied by Engine Management System (EMS) suppliers as a package with fuel rail, engine control unit and even oxygen sensors together, China Original Equipment Manufacturers (OEMs) do not have much choice. This study offered a way of fuel injector selection by analyzing the key injector characteristics together with the dynamical performance on engine dynamometer. F2012-D01-009 S. Miao (&)  D. Deng  H. Zheng Changan Auto Global R&D Center, Changan Automobile Co LTD, Chongqing, People’s Republic of China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_5,  Springer-Verlag Berlin Heidelberg 2013

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Two different types of fuel injectors for a manifold-injected spark ignition engine were selected based on supplier’s recommendation and Computer Aided Engineering (CAE) simulation, then the fuel injectors are tested in laboratory on the critical characteristics; such characteristic parameters measured in lab include injection cone angle, stroke penetration distance, fuel mist particulate distribution, etc. under different combined conditions of fuel pressure, back pressure and inject fuel mass. Fuel injector model is also set-up and verified with the testing results. The injectors are further dynamically calibrated within the system on engine dynamometer. Engine performance map of power, torque and emission are measured with each injector. Based on the comprehensive performance of component test and engine/vehicle results, an optimized fuel injector is selected finally.

2 Test Method of Fuel Injector Selection The test method of fuel injector selection includes CAE analysis of oil beam, component test and engine dynamometer test as discussed later on. Considering the available resources and future commercial application, injector selection is limited in a reservoir of regular 4-hole injectors with a hole diameter of 0.2 mm.

2.1 CAE Analysis of Oil Beam The injector spray model is built up based on the injector 3D data. The characteristic effects of wet wall, spray distribution, atomizing and mixing are simulated. Based on the simulation results, the basic parameters of stroke penetration distance, installation angle, injection cone angle are determined. Errors may result from equipment precision, experiences of operator, etc. therefore, CAE method is also necessary to validate the correctness of experiment results.

2.2 Component Test In China, injectors are always selected by supplier according to their experiences and CAE. Since the supplier does not have the detail information about engine, this method results in numerous wastes of time and resources, yet without optimized performance. In this paper, a series of test conditions are designed and customized according to Society of Automotive Engineering (SAE) standard [3, 4], and experiences. The detailed test conditions are listed in Table 1.

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Table 1 Component test conditions (room temperature: 25 C) Index Injection pressure (bar) Injection amount (mg/stroke)

Ambient pressure (bar)

1 2 3 4 5 6 7 8 9 10

0.3 0.7 1.0 0.3 0.7 0.7 1.0 0.3 0.7 1.0

3.5 3.5 3.5 3.8 3.8 3.8 3.8 4.0 4.0 4.0

5 15 35 5 15 25 35 5 15 35

2.2.1 Injector Flow Rate Test Averaging method is used in flow rate test. The injection pulse width is selected between 1.5 and 20 ms based on normal engine performance. Under each condition, the total amount of fuel in 100 strokes is summarized and the tests were repeated 3 times. Average value is the final flow rate. 2.2.2 Stroke Penetration Distance and Injection Cone Angle Test A synchronized high speed camera was used to test and then the data was analyzed automatically in order to obtain data of stroke penetration, injection corn angles, etc. The experimental setting is similar to that in Ref. [5] which was used to study gasoline direct injectors (GDI). 95 % statistical probability is setup as measuring limitation, the stroke penetration distance is the projection of the line, which connects the farthest point and the injector orifice centre, along the injector axis. The max angle relative to the injector axis at the section, which is 30 mm below the injector orifice, is injection cone angle. 2.2.3 Fuel Mass Distribution and Fuel Mist Particulate Distribution Test The fuel mist particulate distribution is derived according to the spray distribution at the cross section, which is 30 mm below the injector orifice. The fuel mist particle distribution tests are done based on standard SAE J2715 [6].

2.3 Engine Dynamometer Test Engine dynamometer tests with the 2 different injectors were run to further confirm the component differences and applicable performance. The test was run on AVL

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Table 2 Test engine parameters Parameter

Unit

Value

Displacement Rated power Speed at rated power Max torque Speed at max torque Lowest fuel consumption rate Idle speed

L kW r/m Nm r/m g/KWh r/m

1.5 80 5,500*6,500 140 3,300*4,300 250 720 ± 50

DynoRoad 202/12SL 220KW dynamometer with AVL 735S fuel meter and Horiba 7,200 emission analyzer. The main parameters of the test engine are shown in Table 2. To make better comparison between the 2 injectors, engine was equipped with same hardware except injectors, and is controlled by the same EMS system with specific calibration catering to different injectors. The universal characteristic is tested using different injectors. The injector is selected considering both the component and dynamometer test results.

3 Test Results and Analysis Two injectors are selected. One is labelled injector #1 and the other is injector #2.

3.1 CAE Analysis of Oil Beam The objective is to determine the primary parameters. Based on the manifold model analysis, the best injector installation angle is 135, with the spray cone angle is 30*45 and the stroke penetration distance is 110 mm which can avoid severe wet-wall phenomena.

3.2 Component Characteristics Test The injector flow rate test results are shown in the Fig. 1. Averagely, injector #1 has an 11 % higher flow rate than that of injector #2. There is no significant difference of the stroke penetration distances between the two injectors in most cases as shown in Fig. 2a. However, it is noted that such penetration distance is affected by many factors including spray pressure; stroke fuel amount (i.e. injection pulse width) and back pressure, when the spray pressure or stoke fuel

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Fig. 1 Injector flow rate test results

Fig. 2 The injector penetration distance under condition of injection pressure/fuel mass/back pressure as: a 3.5 bar/15 mg/70 kPa, and b 4.0 bar/35 mg/100 kPa

mass increase, the differences of the 2 injectors become visible as injector #2 leads as much as 9 mm longer penetration distance as shown in Fig. 2b. Under different testing conditions, the injection cone angle of injector #1 is always larger than that of injector #2. The test results are shown in the Fig. 3. As it can be seen, the higher spray pressure, the larger the differences are. When spray pressure reaches 4.0 bar, the differences of cone angle of these two injectors can reach as much as 30 %. Larger injection cone angle may results better mixture, but it could also lead to worse wet-wall in SPI engine as well. One of the typical result photos of fuel mass distribution tests are shown in the following Fig. 4. Injector #1 shows better distribution pattern than injector #2 in same testing conditions.

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Fig. 3 Comparison of injection cone angles

Fig. 4 Typical injection fuel mass distribution a 3.5 bar, 30 kPa, 5 mg, and b 3.5 bar, 100 kPa, 35 mg

3.2.1 Fuel Mist Particulate Distribution Test The test results of statistics are shown in the Fig. 5. Injector #1 is much better than injector #2. The fuel particulates Sauter Mean Diameter (SMD) of injector #1 with a peak distribution concentrated around 20 um is about 20–30 % smaller that that of injector #2, theoretically, it should help better fuel–air mixture and better complete combustion in the chamber.

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Fig. 5 SMD distribution of fuel particulates of 2 injectors. a Injector #1. b Injector #2

Fig. 6 CAE simulation and verification of component testing results. a Penetration distance. b SMD distribution

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Fig. 7 Engine dynamometer test results with 2 different injectors. a Engine dynamic performance. b Emission results

3.2.2 CAE Analysis Simulations have been done under different conditions by using AVL-FIRE software. The deviation is smaller than 3 %. Some typical comparison results are

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shown in the Fig. 6. It validates that the experimental results are generally accurate in spite of complicated testing conditions, and the CAE results are also verified through experiments in contrast. In sum, the component test results show that there are some differences between the 2 selected injectors. Injector #1 has larger fuel rate and larger cone angles, but relatively minor penetration distance, much smaller fuel particulate SMD and better mass distribution. The test results and CAE analysis verified the correctness of each other.

3.3 Engine Dynamometer Test The engine dynamometer test results are shown in the Fig. 7. When the engine is running in wide open throttle (WOT) condition, the fuel economy performance of injector #1 is worse than injector #2 under 3,000 rpm, and then the differences become meaningless. The dynamic performances of the two injectors are almost the same means both injectors have sufficient flow rate without surprise. It also reminds that injectors can not be selected simply based on engine characteristic power and torque performances. Yet, the emission results show complicated patterns. It seems that the combustion with injector #2 is less completed with less CO2 but more CO/THC in tailpipe especially when engine rpm goes beyond 3,000 rpm. It is still under investigation whether such phenomenal is related to the worse fuel distribution and relatively longer stroke pulse width of inject #2 to supply same amount fuel under WOT condition. Each engine cycle time will be less than 40 ms, e.g. less than 10 ms for the combustion phase when rpm goes beyond 3,000 rpm, so that longer stroke pulse width may leads to insufficient mixture and combustion time in cylinder chamber when cam timing is fixed. While under 3,000 engine rpm, fueling injection time is relatively short so that injector #2 can still warrant enough mixture and combustion time in chamber. In the case of combustion with injector #1, it may result in lower temperature or lower explosion pressure due to much better fuel distribution and much small fuel particulates SMD, as a result of that, the NOx concentration in tailpipe is lower. It has to be pointed out that the original EMS system used in dynamometer test is specially designed and calibrated based on only one of these injectors, the air–fuel (AF) ratio in the tests still exists minor differences even though deliberated calibration and adjustment have been carried out. As it is well known, minor air– fuel ratio differences could still cause noticeable changes in THC/CO/NOx emissions. Considering the critical impact of AF ratio, even though injector #1 consumes more fuel in low engine rpm regime, it is still picked for the engine development based on its comprehensive performance as shown above.

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4 Conclusions An experimental method is setup from component characteristics identification to engine dynamometer test and CAE verification, in order to optimize and select a best performance injector. Through the tests, it can be concluded that component test results of injector #1 are better in combustion atomizing and emission output, while injector #2 are worse in the SMD of mist particles which is bad for mixing of fuel and air. Injector #1 is chosen for the 1.5 L engine. Acknowledgments Many thanks go to Professor Yuying Zhang and Mr. Hui Lin in the Institute of Automotive Engineering, Shanghai Jiao Tong University for their great support during the components test and CAE analysis.

References 1. J Ni (1996) Automobile internal combustion engine principle. Publishing of Tongji University, Shanghai 2. Ding N, Gao W, Ping Y (2010) CFD and visualization research on spray characteristics of direct injection engine. Tongji University 3. Chen H, Xu M, Zeng W (2009) An improved monisize droplet generator for SMD calibration. Shaihai Jiaotong University 4. SAE J1832 (2000) Low pressure gasoline fuel injectors. SAE International, Warrendal 5. Das S, Chang S-I, Kirwan J (2009) Pattern recognition for multi-hole gasoline direct injectors using CFD modeling. SAE 2009-01-1488 6. SAE J2715 (2007) Gasoline fuel injector spray measurements and characterizations. SAE International, Warrendale

The Research of Starting Control Strategies for Common Rail Diesel Engine Hongrong Wang, Heng Zhang and Yongfu Wang

Abstract The excellent torque and performance behavior combined with low fuel consumption and exhaust emissions of modern diesel engines which equipped with high pressure common rail system has increased their market day by day. But the combustion instability and white smoke emissions are serious problems during cold starting and the transient emissions during the engine start-up process were still high for ultra low emission control. In this investigation, start control strategies and experiments were designed to improve the start performance. The research was carried out on a 4-cylinder 2.5 L turbocharged diesel engine equipped with BOSCH common rail system, which the Electronic Control Unit was self-developed based on the high performance microcontroller SPC563M64, so the control strategies and parameters could be verified expediently. The initiation of diesel fuel combustion is dependent on the compression temperature, compression pressure, fuel properties and fuel injection characteristic, so the start-up process was divided into four modules, and the main and pilot injection quality, injection phase, rail pressure control style and the target pressure control value were optimized respectively according to the characteristics of the four stages, so the controlling could be more accurate. The injection quality and phase is corrected according to the engine temperature and intake air temperature. It was found that the injection parameters of the injection phase and quality of the start accelerate module have large effects on the start-up time and smoke emission, with proper pilot injection quality and main injection phase, the F2012-D01-011 H. Wang (&)  H. Zhang  Y. Wang China Automotive Engineering Research Institute Co., Ltd, Chong Qing 400039, China e-mail: [email protected] Y. Wang e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_6,  Springer-Verlag Berlin Heidelberg 2013

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starting time and smoke were reduced, and the starting performance was improved dramatically on the test engine. Keywords Diesel engine Optimized parameters



Common rail



Starting control



Four module



1 Introduction The excellent torque and performance behavior combined with low fuel consumption and exhaust emissions of modern diesel engines witch equipped with high pressure common rail system have get the favor of more and more users. Electronic control technology has been a prerequisite mean for diesel engine to meet future emission legislation combined with high power density and low fuel consumption. However, there are some challenging problems to be solved such as the high transient emissions during the engine start-up process for the ultra low emission control. The initiation of diesel fuel combustion is dependent on the compression temperature, compression pressure, fuel properties and fuel injection characteristic. During cranking, air temperature in cylinder is lower than those of during any other modes of engine operation. The low compression temperatures and pressures are caused partly by the low ambient temperature, and more importantly by the excessive heat losses and blow-by losses at low cranking speeds. Consequently, low compression temperatures and pressures result in poor start ability of diesel engine [1]. The effect of cranking speed on compression pressure and temperature is very important especially at low cranking speeds (below 200 rpm). An increase from 100 to 200 rpm cause an increase of about 70 C in the compression temperature which confirms the importance of maintaining high cranking speeds under cold starting conditions as it is the most effective means of obtaining high compression temperatures [2]. According to Phatak and Nakamura [3], at higher cranking speeds, the loss of time for auto ignition reactions far overweighs any marginal gain in peak pressure and temperature because of reduced blow-by and heat transfer. Higher compression temperatures and pressures lead to shorter ignition delay and remarkable reduction in fuel adhering to the walls [4]. In this paper, in order to improve the starting performance of the common rail diesel engine, an electrical control unit was developed based on the high performance microcontroller SPC563M64 and the starting control strategies were designed, the start-up process was divided into four stages by cranking speeds and rail pressure, and the control parameters were optimized respectively according to the characteristics of the four stages.

The Research of Starting Control Strategies VSS VDD Power on

Position

ADC ADC ADC

Temperature

12V ± 5V UBat 3.3V

Power Management Lvel Level Level

Pressure

55 Boost-Supply

75~110 V Injector

Analog circuit

SPC563M54 Driver Microcontroller

Key 1

Key n

Digital circuit

Valve MIL Relays Display

Communication

K-Line CAN-Line FlexRay

Fig. 1 The layout of the schematic of the ECU

2 Control Unit Design 2.1 Overall Development Scheme of the ECU In order to improve the starting performance of the engine, an Electronic Control Unit (ECU) was developed based on the high performance microcontroller unit (MCU) SPC563M64. This automotive MCU is a family of system-on-chip (SoC) device that is built on Power Architecture technology, and contain many new features coupled with high performance 90 nm CMOS technology to provide substantial reduction of cost per feature and significant performance improvement. Figure 1 shows the schematic diagram of the control unit for the diesel high pressure common rail system. The control unit including the microcontroller module, the power management module, the digital signals conditioning module, the analog signals conditioning module such as the pressure signals, the temperature signals and the position signals, the driver module, the communication module and so on.

2.2 The Dirver Module of the ECU Fuel injectors are high-speed electromagnetic solenoid valve and controlled by the FPGA (Field Programmable Gate Array) with peak module and hold module to form the complex drive waves witch are used to produce peak current and hold current with minimum peak time. It could avoid the processor from wasting time for basic procedures by shifting software parts to the Hardware (FPGA). The peak module is powered by boost voltage to reach the peak current as quickly as possible, and the hold module is power by battery voltage to reduce energy consumption. Other electromagnetic solenoid valves and relays are drived by intelligent driver IC, and there are protection and diagnostic circuits to detect and

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Fig. 2 The hardware of the control unit

monitor the fault of the actuators. Figure 2 is the hardware of the control unit for the diesel high pressure common rail system.

3 Start Control Strategies 3.1 The Design of Four Modules Start Strategies According to the characteristics of start process of the diesel engine, it is divided into four modules including start initial module, start accelerate module, start transient module and start close-loop control module as Fig. 3. Each module are controlled and switched by the state of the start module, and the injected quality, rail pressure and the starting of energizing (SOE) of the injections were optimized respectively according to the characteristics of the four stages, so the controlling could be more accurate and effective. The control logic is shown in Fig. 4.

3.2 Start Initial Module Control In the early start, ECU powers on, the starter drag the engine rotate, the rail pressure control valve will open after ECU initialization, and the rail pressure is established in the common rail, if the rail pressure above the threshold value, the fuel injection valve will ready to open to complete the fuel injection, and this initial module is end. If the rail pressure is still lower than the threshold value after the set-point time, the fault indicator is on and the rail pressure control valve will be shut off. At this module, the engine speed is less than n0, in order to build the rail pressure up as quickly as possible, the pressure control valve is open loop control

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Fig. 3 Start control logic

Fig. 4 Four module design of start strategies for diesel engine

with a constant value, which could be changed according to the voltage of the battery. There would be no fuel injection because the rail pressure is too low and the fuel could not be atomized well.

3.3 Start Accelerate Module Control Figure 5 shows the control strategies of the start accelerate module. In this module, there are two injections per working cycle (pilot injection and main injection), ECU calculates the basic pilot injection quantity (InjCtl_StartqPI_Base), basic

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Fig. 5 The control stragies of start accelerate module

main injection quantity (InjCtl_StartqMI_Base) and the basic pilot injection phase(StartPhiPI_Base), basic main injection phase (StartPhiMI_Base) according to the engine speed and the position of the accelerate pedal. All these base values are corrected by engine coolant temperature (t_coolant), intake air temperature (t_air) and intake air pressure (p_air). In Start Accelerate Control module, the rail pressure is closed loop control with the feedback value of the pressure sensor. The basic set point value is calculated from the base map, and the base value is corrected and limited based on different environmental conditions.

3.4 Start Transient Module Control The engine speed increases rapidly as a large injection quantity is injected in Start Accelerate Control module, which is far greater than the quantity needs of the idle condition. According to PID closed loop control theory, the injection quantity would increases based on the large injection quantity that is controlled in Start Accelerate Control module when the engine speed is up to the target idle speed, but engine doesn’t need such a large injection quantity so as to be able to smoothly transit to the speed closed loop control stage, this will result in increasing

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Table 1 The main specifications of engine Number of cylinders/bore/stroke Total displacement/L Compression ratio Maximum torque/speed/(N.m r/min) Maximum power/speed/(kW r/min) Fuel injection system Injection pressure/MPa

4-92-94 2.498 18.5 260/2,200 75/3,600 Common rail Variable up to 145

the overshoot of speed. So, the start transient module is designed to decrease the injection quantity that is related to engine speed. In the start transient module, the base injection quantity is got from the base injection MAP according to the engine speed and the engine temperature, Then the correction quantity is determined from the correction CUR according to induct air temperature and pressure, the working status of the appurtenance such as air condition and so on.

3.5 Start Colsed-Loop Control Module The fourth control module of the start process is closed loop control. There are three key functions in this module, the first function is used to calculate the target engine speed, which is determined by the engine temperature and the working status of the appurtenance. The second function is used to calculate the logic status of module according to the engine speed and other working conditions. The third function is used to calculate the control parameters of the module.

4 Experiments and Results The tests were carried out to validate the control strategies on an in-line, four cylinders, direct injection diesel engine that equipped with a common rail fuel system, a turbocharger, an intercooler and so on. Table 1 report the main specifications of the engine and of the fuel injection equipment. Ambient conditions were checked and fixed during the experiments Different environments and engine temperature have different influence on the process engine start. In start control strategy, injection quantity and injection phase are calculated by maps which are related to environmental parameters, and these parameters in maps and curves should calibrated by experiments. The pilot and main injection quality in start control strategy is difference in the four individual modules. The pilot and main injection quality in Start Accelerate Control module are the most important parameters for the performance of the

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Fig. 6 Main injection quality optimize

Time/(s)

somke/(m-1)

Fig. 7 Smoke emission optimize

2 1.5 1 0.5 0 15 20 25 30 35 40 45 50 55

Injection quality/(mg)

Fig. 8 Pilot injection quality optimize Engine speed/(rpm)

900 750 600

2.0mg

450

4.5mg

300

1.0mg

2.5mg 3.0mg

150 0

0

2

4

6

8

10

Time/(s)

engine starting. Experiments were done to optimize the pilot and main injection quality in Start Accelerate Control module, and the ambient temperature, coolant temperature and oil temperature was 25 C. Figure 6 shows the results of engine speed of start process with various main injection qualities in Start Accelerate Control module. It is obvious that injection quality of 35 mg is considered as the optimal value of the test engine. Figure 7 shows the maximal smoke emission with various main injection qualities at engine starting process. It is obvious that injection quality of 35 mg is considered as the optimal value of the test engine. Figure 8 shows the results of engine speed of start process for various pilot injection qualities in Start Accelerate Control module. It is obvious that injection quality of 2 mg is considered as the optimal value of the test engine. Injection phase is related to the engine speed and coolant temperature, and the main injection phase has great influence on the start performance of the engine. Figure 9 shows the results of engine speed of start process with various main injection phases in Start Accelerate module. It is apparently shown that injection

The Research of Starting Control Strategies Fig. 9 Main injection phase optimize

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Engine speed/(rpm)

700 600 500

9º CA

400

12º CA

300

7º CA

200 100 0

0

1

2

3

4

5

6

7

8

30

35

40

45

Fig. 10 Smoke emission contrast with various rail pressure

somke/(m -1)

Time/(s)

1 0.8 0.6 0.4 0.2 0 20

25

Target rail pressure (Mpa)

phase of BTDC 9 C. A is considered as the optimal injection phase of the test engine. The rail pressure is one of the most important parameters for engine starting control. Open loop control strategy was used in the start Initial module to build up the rail pressure as soon as possible, and closed loop control strategy was used in other control module to control the inject pressure and quality accurately. The target value of the closed loop module is very important on the start performance of the engine. Figure 10 shows the results of the maximal smoke emission of start process for various target rail pressure, and according to the results, 40 MPa is the optimal value of the test engine.

5 Conclusion In this study, An Electronic Control Unit was developed based on the high performance microcontroller SPC563M64 and the engine starting control strategy was established for improving the starting performance of the high pressure common rail diesel engine. The starting process was divided into four modules in the new starting control strategy, and the control parameters were optimized respectively according to the characteristics of the respective module, which could achieve more control accurate. The experimental results showed that the control strategies and optimized parameters could obtain quite a good control effects on testing engine.

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Acknowledgments This paper has been supported by the ‘‘Electronic Control Unit Design and Application for Diesel Engine’’ and ‘‘The Development of the Electronically Controlled high pressure common rail diesel engine for Light vehicle’’ project which were organized by the P.R.C. Ministry of Science and Technology and the P.R.C. Ministry of Industry and Information Technology. The authors are grateful to the support of the projects.

References 1. Liu H, Henein NA, Bryzik W (2003) Simulation of diesel engines cold-start. SAE Paper 200301-0080 2. Austen W, Lyn WT (1959–1960) Some investigations on cold starting phenomena in diesel engines. SAE Paper no. 5 3. Phatak RE, Nakamura T (1983) Cold startability of open-chamber direct-injection diesel engines—part I measurement technique and effects of compression ratio. SAE Paper 831335 4. Tsunemoto H, Yamada T, Ishitani H (1986) Behavior of adhering fuel on cold combustion chamber wall in direct injection diesel engines. SAE Paper 861235

Study on Twin Modes Pilot Control of Turbocharger Sicong Lin, Jian Wu, Anwei Zhang, Jujiang Liu and Jin Hu

Abstract As the operating status of the turbocharger is complex, nonlinear and requires high transient response, this paper studies a new control strategy which add self-adaption pilot control to the traditional PID control method, and adopts the static PI control or the dynamic PID control according to the control error, finally, carries out nonlinear transform on the output signal. The control strategy can both increase the respond speed of the turbocharger and avoid the over boost phenomenon. The experimental study is carried out under a 1.8 L turbocharged engine on the engine test bench and the automobile hub test bench, validates the feasibility and practicability of the control strategy. Keywords Twin modes

 Pilot  Nonlinear  Self-adaption  Control strategy

1 Foreword Current focus on the reduction of tailpipe CO2 emissions and fuel consumption of road vehicles, people is increasing the interest on downsizing and turbocharging. As the operating status of the turbocharger is complex, nonlinear and hysteresis, thus increases the control difficulty of the control system. The traditional PID control method is provided with the excellence of structure simpleness, high stability, high reliability and easy to realize and therefore it is widely applied in industry control, and acquires good control effect, but the traditional PID control method just can be used on the control system whose control F2012-D01-012 S. Lin (&)  J. Wu  A. Zhang  J. Liu  J. Hu GAC Engineering, Shanghai, China SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_7,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 Trubocharger control system. 1-three way electromagnetic valve, p2 — boost pressure, pD —pressure chamber’s pressure, PWMcontrol signal, VT —volume flow through turbocharger, VWG —volume flow through waste gate

object can be described with precise mathematical model better, it is not suit for the turbocharger control system. Contraposes the complex operation status of the turbocharger and requires high transient response, the control strategy discussed in this paper designs static and dynamic control modes with self-adaption and pilot control. In order to avoid the influence of the nonlinear between the control signal and the control result, carries out nonlinear transform specially before the signal outputs, and reaches satisfying control effect at last.

2 The Characteristics of Gasoline Engine Turbocharging System and its Control Requirements The turbocharger depends on a three ways electromagnetic valve to control the opening angle of the waste gate valve, the turbocharger control system is illustrated in Fig. 1, the three ways electromagnetic valve connects to upstream of the compressor, downstream of the compressor and the pressure chamber of the valve, the control system controls the pressure of the pressure chamber to change the opening angle of the waste gate with the control signal PWM, then realizes the control of the boost pressure [1]. In order to reach both good driveability and excellent power from a car which equipped with turbocharged engine, the control system need to control the turbocharger accurately and rapidly, and reduce the hysteresis of the power as more as possible.

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3 Twin Modes Pilot PID Control Strategy The PID control method is applied in the industry control widely, both practical application and theoretical analysis indicate that the PID control for most industry control object can reach satisfying control effect, but for gasline engine turbocharging system which with characteristic complex working condition, nonlinear and hysteresis, it can’t reach good control effect with a group preset PID parameters. This paper designs twin modes PID control strategy which will execute static PI control or dynamic PID control according to the control error, when the control error is smaller than a certain value, carries out the static PI control to realize good and stably control effect, when the control error is bigger enough, carries out the dynamic PID control for the purpose of increasing the respond speed and resisting environment influence. The proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd of the dynamic PID control are not a group of fix data, they will change along with the control error. To increase respond speed and reduce the control error quickly the greater proportional coefficient Kp and integral coefficient Ki will be used when large control error occurs, they are an increasing function of the control error [2, 3]. To ensure the stability of the control the D part of the PID control will not work unless the engine speed is higher than 3,000 r/min. The dynamic PID parameter need to be set through dynamic test on the engine test bench, and should be checked carefully on car [4, 5]. In order to avoid overshooting and increase control precision, set the preset value and the upper limit, lower limit of the I part to define the range of the I part. When the control error is less than a certain value for some time, the range of the I part will move down, when the control error is bigger than a certain value, the range of the I part will move up, therefore it will adjust the range of the I part automatically, then the I part parameter will keep in reasonable scope all the time, this is self-adaption pilot control of the PID control [6, 7].

4 Nonlinear Transform of the Control Output Signal As the turbocharging system is nonlinear, the controller outputs the PID control signal after nonlinear transform to make the control signal and the control result present a linear relationship, and the control precision will increases. The nonlinear transform make the traditional PID control method which is suit to linear system can apply to nonlinear system. The controller computes the PID control duty cycle base on the control error first, and then computes the preset boost pressure base on the PID control duty cycle via the I part precontrol line, finally, computes the output duty cycle base on the preset boost pressure via the curve of the boost pressure and the duty cycle, the process is illustrated in Fig. 2.

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pressure/kPa

205 195

5000

I part precontrol line

185 175 165

curve of duty cycle and boost pressure

155 145 135 125 0

5

10 15

20 25 30

35 40

45 50 55

60 65

70 75 80

85 90

95 100

duty cycle/% PID control output

after nolinear transform output

Fig. 2 Nonlinear transform of the output signal

Table 1 Parameters of test engine Engine type Displacement Bore 9 stroke Compress ratio Intake system Number of intake valve Max torque Max power

T483, DOHC, Multi-injection 1.75 L 83 9 91 mm 9.5 Turbocharge 16 230 Nm/1,700*5,000 r/min 130 kW/5,500 r/min

5 Test and Analysis Carries out the tests of an 1.8 L turbocharged engine on the engine test bench and on the automobile hub test bench that simulates the real road condition, validates the feasibility and practicability of the control strategy.

5.1 Parameters of Test Engine See Table 1.

5,000

4,000

3,000

2,000

14.06 15.26 16.19 16.56 13.94 14.97 16.02 16.72 14.01 14.93 15.9 16.83 13.97 14.99 16.08 16.58

1,557 1,623 1,706 1,752 1,483 1,550 1,593 1,653 1,457 1,496 1,541 1,651 1,532 1,619 1,734 1,820

1,561 1,621 1,706 1,751 1,480 1,549 1,593 1,652 1,457 1,492 1,542 1,651 1,531 1,617 1,733 1,820

Table 2 Test result of steady working condition on engine test bench Engine speed/r/min BMEP/bar Target boost pressure/mbar Actual boost pressure/mbar 31.18 35.52 42.88 46.1 18.37 26.14 30.66 35.88 20.25 25.81 32.75 41.5 38.86 47.74 56.56 62.21

Duty cycle/ % -4 2 0 1 3 1 0 1 0 4 -1 0 1 2 1 0

Control error/mbar

-0.256904 0.1232286 0 0.0570776 0.2022927 0.0645161 0 0.0604961 0 0.2673797 -0.064893 0 0.0652742 0.123533 0.0576701 0

Error rate/ %

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Fig. 3 Test result of with and without twin modes PID control of dynamic condition on 2,500 r/ min P2_T(p2_t)—target boost pressure, P2(p2)—actual boost pressure, E(e)—control error, PWM(pwm)—control signal

5.2 Tests of Steady Working Condition on Engine Test Bench and Results 5.2.1 Test Conditions Carries out the tests on the conditions below: Engine speed (r/min): 2000, 3000, 4000, 5000 BMEP(bar): 14, 15, 16, 17, BMEPmax

5.2.2 Test Results The twin modes pilot PID control strategy discussed in this paper carries out nonlinear transform of the control signal, make the traditional PID control method can apply to the nonlinear turbocharging system. It shows very high control precision on all the steady working conditions of turbocharger PID close loop control, and the control error is less than 5 mbar, see Table 2.

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Fig. 4 Test result of with and without pilot control of dynamic condition on 2,500 r/min PWM_IMN(pwm_imn)—I part lower limit, PWM_IMX(pwm_imx)—I part upper limit, PWM_I(pwm_i)—I part value

5.3 Tests of Dynamic Working Condition on Engine Test Bench and Result 5.3.1 Test Condition Keep the engine speed on 2,500 r/min, increase the load of the engine from low load to full load in 0.1 s, carries out the tests of PID control with and without twin modes, and then carries out the tests of PID control with and without pilot control.

5.3.2 The Test Tesults of PID Control With and Without Twin Modes On 2,500 r/min, the increase of the boost pressure is 632 mbar for the test with twin modes PID control in 1.5 s, and 503 mbar for the test without twin modes PID control. The former increase 25.6 % more than the latter, illustrated in Fig. 3. When use twin modes PID control strategy, it will adopt static or dynamic control mode according to the control error automatically. The dynamic mode can output larger duty cycle quickly and increase the dynamic respond of the turbocharger especially when rapid accelerate acceleration condition take place.

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Fig. 5 Test result of with and without pilot PID control on automobile hub test bench N(n)—engine speed, PEDLE(pedle)—opening angle of fuel pedle

5.3.3 The Test Results of With and Without Pilot Control It can be found that it can avoid the boost pressure overshoot with pilot control from Fig. 4, the overshoot take place in different level when without pilot control. Adds pilot control to the PID control method to regulate the upper limit and lower limit of the control signal, and reach the purpose of avoiding overshooting.

5.4 Tests of Dynamic Working Condition on Automobile Hub Test Bench Carries out the tests with and without twin modes pilot PID control on the automobile hub test bench. The test results is illustrated in Fig. 5, when with twin modes pilot PID control, the boost pressure reaches the target value quickly without any overshoot, and then follows the target value very well; when without, the boost pressure increases slowly, and can’t follows the target value well. In 1.5 s, the former make the boost pressure increase 679 mbar, and the later just 483 mbar, the former increase 40.6 % more than the latter.

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6 Summary The turbocharging system has the characteristics of nonlinear, hysteresis and model uncertain, the traditional PID control method is not competent for the control of the gasoline engine turbocharger. The twin modes PID control strategy executes static PI control or dynamic PID control according to the control error, to increase respond speed of the turbocharger and reduce the control error quickly the control parameters of the dynamic PID control are the increasing function of the control error. Make the self-adaption pilot control of the I part can increase the control precision and avoid overshooting. The controller outputs the PID control signal after nonlinear transform to cover the disadvantage that the traditional PID control method is not fit to the nonlinear system. Finally, realizes the stably and effectively control of the turbocharger. The test results indicates that the twin modes pilot PID control discussed in this paper can reach good control effect.

References 1. Moody JF. Variable Geometry Turbocharging with Electronic Control. SAE860107 2. Zhong Q, Xie J, Li H (1999) PID controller with variable arguments. Inf Control 28(4): 273–277 3. Chen Y, Zhu C, Li J, Wan Y (2005) The research of the variable parameters PID governor. Control Autom 21(10–1):47–48 4. Gong Z, Zhou M, Li X, Quyang M (2001) A research on the variable geometry turbocharger control system for diesel engine. Autom Eng 23(4):279–282 5. Xu B, Liu Z (2007) The algorithm research on an electronic control system of variable nozzle turbocharger. Internal Combust Engines 5:28–31 6. Hoopes HS, Xu FL (1983) Fuzzy self-tuning of PID controllers. ISA Trans 22:49–58 7. Li W, Fan Y (2008) Application of fuzzy self-adaptive PID to adjustable nozzle turbocharger. Passenger Car Tech Res 6:4–6

Braking Stability Control Algorithm for Vehicle Based on Fuzzy Logic Hongyu Zheng, Yuchao Chen and Changfu Zong

Abstract A stability control strategy for vehicle electronic hydraulic brake system (EHB) related to active safety was developed for vehicle. A nonlinear electronic-hydraulic brake system mathematical model was built through analyzing the effects of the composing modules and linking pipelines of the EHB system on the performance. The vehicle stability control algorithm was simulated using software CarSim and MATLAB/Simulink under typical conditions. The results showed that control strategy can effectively control vehicle motion and meet the requirements of design. Keywords Vehicle engineering safety Fuzzy control Stability







Electronic hydraulic brake system



Active

1 Introduction In recent years, with the development of electronic technologies and control theories, vehicle handling and stability has become more and more important for drivers. EHB (Electronic Hydraulic Braking) system is a new type of vehicle active control braking systems, which replaced the mechanical connection between brake pedal and brake wheel cylinder by wire and the driver’s braking F2012-D01-013 H. Zheng (&)  C. Zong State Key Laboratory of Automotive Simulation and Control in Jilin University, Jilin, China e-mail: [email protected] Y. Chen FAW Group Corporation R&D Center, Changchun, China SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_8,  Springer-Verlag Berlin Heidelberg 2013

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behaviour is transmitted to the ECU (Electronic Control Unit) in the form of electronic signal, then ECU identify the driver’s braking intention by collecting others vehicle sensors signals, making out the optimal cylinder brake pressure by controlling the solenoid valve to regulate cylinder pressure [1]. EHB can significantly improve the braking performance and vehicle handling and stability, while providing a variety of support functions, sharing information with other vehicle control systems to provide the development of the vehicle chassis integrating and realize the ultimate intelligent vehicle control system [2]. Based on the EHB system basic structure and working principle, this paper built mathematical model by EHB system and studied the stability control algorithm to improve vehicle stability. A nonlinear electronic hydraulic brake system mathematical model was built by software Matlab/Simulink through analyzing the effects of the composing modules and linking pipelines of the EHB system on the performance. Based on fuzzy logic, the vehicle stability control algorithm was simulated using software CarSim and MATLAB/Simulink under typical conditions.

2 Basic Structure of EHB System EHB system can be broken with an electrical ‘‘joystick’’ completely independent of the traditional brake pedal. The signals of brake pedal sensor and wheel velocity sensor were transmitted to the ECU and integrated steering angle sensor, yaw rate sensor and lateral acceleration sensor etc. by CAN bus. According to signals, ECU can know about brake intention of driver and vehicle states to control the switching of the solenoid valve to regulate the wheel brake cylinder pressure which improved vehicle stability [3]. EHB system divided into some categories: brake pedal system, wheel, brake actuators, sensors, ECU, power source and assist systems [4] (Fig. 1). Brake pedal system include brake pedal, pedal stroke sensor, pedal speed sensor. Pedal stroke and velocity sensors can measure signals transmit it to ECU. According to signals, ECU can know about brake intention of driver through the arithmetic in the ECU. Wheel and brake actuators are controlled by the signals of ECU to provide brake pressure with vehicle. Compared to traditional brake system, EHB has added to four brake pedal stroke sensors, brake velocity sensors and so on. The sensors consist of two kinds: the first sensor is used to know about intention of driver, for example brake pedal stroke sensors. The second is used to know about the drive state of vehicle, for example wheel sensors and yaw rate sensor. The sensors and ECU are integrative with CAN bus to communicate with other ECUs [5]. ECU is the most important in the EHB. It can get all signals come from sensors and actuators and estimate to how to control brake actuators.

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Power Source

actuator Controler

actuator Controler

ECU Controler

Controler

actuator

actuator

Brake pedal sensor

Fig. 1 Base structure of EHB system

3 Hydraulic System Mathematical Model of EHB Hydraulic brake system model is consists of hydraulic unit, high speed on–off valve, hydraulic pipelines, wheel cylinders and so on [6].

3.1 Hydraulic Unit From deal gas state equations can be seen pA VAn ¼ p1 V1n ¼ p2 V2n ¼ constant

ð1Þ

where V1, V2 are max and min pressure volume of gas. Where system pressure need be kept or leakage compensation by accumulator, n = 1, where need compensate a great deal of oil liquid, n = 1.4.

3.2 High Speed On-Off Valve Model The flow equation of hydraulic valve port sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2jp2  p1 j Q ¼ Cd A q

ð2Þ

where Q is liquid flow rate, Cd is flow coefficients, A is flow area, P1, P2 are fluid unsteady flow. High speed on–off valve is controlled by PWM signal with traffic have an approximately linear relationship as

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A ¼ A0 IPWM

ð3Þ

where A0 is the biggest opening state of valve port, IPWM is the distribute coefficient of PWM signal and its range between 0 and 1.

3.3 Brake Wheel Cylinders System Model The flux continuum equation of wheel cylinders as Qw ¼

Vw dPw Kw dt

ð4Þ

where Vw is volume of wheel cylinders, Pw is pressure of wheel cylinders, Kw is representative volume modulus of elastic of wheel cylinders. 1 1 A2w ¼ þ Kw Kl kbrake Vw

ð5Þ

where is Kw stiffness of brake, Kl is modulus of elasticity of brake fluid, kbrake is synthetically stiffness that include distortion of wheel cylinders and compressive stress.

3.4 Brake Model According to mechanism of friction, brake torque of wheel can be described to Tb as Tb ¼ GNb

ð6Þ

N b ¼ P w Aw

ð7Þ

where G is a constant factor about material, structure and temperature of brake, can be determined by experiment, Nb is piston thrust of wheel cylinders, Pw is pressure of wheel cylinders, Aw is piston area of wheel cylinders.

4 Stability Control Algorithm Based on EHB Based on vehicle stability control theory, a stability control algorithm was established combined with structural characteristics of EHB hydraulic system. Reference to vehicle linear model with two degree of freedom (2DOF), an additional vehicle yaw torque was designed to comparing vehicle actual states with ideal states which were computed by two degree of freedom [7].

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4.1 Computing Ideal States by Two Degree of Freedom Model Equations of 2 DOFs linear model for vehicle as 8   bKr  aKf > >  r þ Kf d < mVx ðb_ þ rÞ ¼  Kf þ Kr b þ Vx > a2 Kf þ b2 Kr > : Iz r_ ¼ ðbKr  aKf Þb   r þ aKf d Vx

ð8Þ

where m is vehicle mass, Vx is vehicle speed, b is sideslip angle, r is yaw rate, kf is cornering stiffness of front axis, kr is cornering stiffness of back axis, a is distance from center of mass to front axis, b is distance from center of mass to back axis, d is front wheel steering angle, Iz is moment of inertia to Z axis. Vx  d l  ð1 þ KVx2 Þ

ð9Þ

mðbKr  aKf Þ Kf Kr l2

ð10Þ

rN ¼ K¼

where rN is ideal yaw rate, l is the distance of between front and back axis, K is stability factor. If actual yaw rate great than rN to a threshold value, it must be controlled by brake torque for wheel. This may use fuzzy logic control algorithm to control EHB system.        l  g Vx      sgnðdÞ d ; ð11Þ rN ¼ min  lð1 þ KVx2 Þ   Vx  Dr ¼ r  rN

ð12Þ

where l is road friction coefficient, Dr is the difference of actual yaw rate and ideal yaw rate. In order to avoid error too small to adjust frequently, so this need set a threshold value as jDr j  jcrN j

ð13Þ

where c is a threshold value and positive number. According to driver’ handling and vehicle movement state, ECU can get vehicle movement state and compute an ideal vehicle movement state. Compare actual vehicle movement state with ideal vehicle movement state to compute an error and introduction to ECU, ECU calculates a yaw moment to renew vehicle stability. The yaw moment is distributed to four wheels to transform wheel cylinder pressure. According to wheel cylinder pressure of sensor measured and vehicle needed, ECU generates PWM signals to control solenoid valves to adjust wheel cylinder pressure that control vehicle stability.

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Table 1 Inference table of the fuzzy controller Dr Very low Low Medium Mr D_r

High

Very high

Very low Low Medium High Very high

Low High High Very high Very high

Medium High Very high Very high Very high

Very Very Very Low Low

Very Very Low Low Low

Very low Low Medium Medium High

4.2 Stability Control Algorithm This paper chose fuzzy control method to control brake torque for wheel. Firstly, the process to be controlled is difficult to mathematical model and the driving environment is complex and strongly nonlinear. The fuzzy logic methods deal with these difficulties by allowing the fuzzy rules. The input variable of fuzzy control algorithm is Dr and Dr’s derivative and output variable is Mr to additional yaw torque of vehicle. The fuzzy controller is defined by 25 rules Table 1.

4.3 Brake Force Distribution of Yaw Moment EHB system can adjust wheel cylinder pressure all four wheel but different brake distribution can attain different yaw moment. As usual, adjust wheel cylinder pressure of one of diagonal wheels can control vehicle stability and the other doesn’t have obvious effect. Brake Force Distribution Table 2. The relation of wheel braking force with yaw rate can be described as 8  d > > < Ff cos d þ sin d  a ¼ Mr 2 ð14Þ > d > : Fr ¼ M r 2

where Ff is front wheel braking force, Fr is rear wheel braking force, d is distance from front axis to back axis.

5 Simulation Result It use software Matlab/Simulink to built mathematical model of the EHB system and vehicle dynamic model was simulated using software CarSim.

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Table 2 Brake force distribution methods Frong wheel steering angle Difference of yaw rate Left turn

Right turn

No turn

Dr [ cDrN Dr \ -cDrN -cDr N B Dr B cDrN Dr [ cDrN Dr \ -cDrN -cDrN B Dr B cDrN Dr [ cDrN Dr \ -cDrN -cDrN B Dr B cDrN

Brake force distribution Right front wheel braking Left rear wheel braking No braking Left front wheel braking Right rear wheel braking No braking Right front wheel braking Left rear wheel braking No braking

Fig. 2 Input of front wheel steering angle

5.1 l-Low Condition Simulation experiments include that vehicle speed is 80 km/s, road friction coefficient is 0.3, front wheel steering angle is a sinusoid. Figure 2 is front steering angle, period is 4 s and amplitude is 0.03 rad, Fig. 3 is yaw rate and Fig. 4 is vehicle running path. From the simulation show that yaw rate can’t track ideal yaw rate of non vehicle stability control. Vehicle stability control algorithm can make yaw rate track ideal yaw rate based on fuzzy control logic and the path was more precisely than the vehicle with no control.

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Fig. 3 Yaw rate

Fig. 4 Vehicle running path

5.2 Double Change Condition Double change condition refers to the ISO/TR 3888-1 [8]. The simulation experiments include that vehicle speed is 100 km/h and road friction coefficient is 0.4. Figure 5 is front steering angle, Fig. 6 is yaw rate and Fig. 7 is vehicle running path. In the simulation result that yaw rate can’t track ideal yaw rate of non vehicle stability control and vehicle lost stability. Based on fuzzy control logic, vehicle stability control can make yaw rate track ideal yaw rate and the path was more precisely than the vehicle with no control.

Braking Stability Control Algorithm Fig. 5 Front wheel steering angle

Fig. 6 Yaw rate

Fig. 7 Vehicle running path

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6 Conclusion The mathematical models were built of EHB system by Matlab/Simulink and vehicle dynamic models by CarSim, it provides platform for theoretical study and product development of vehicle system, and besides, the research on the stability control algorithm of EHB system based on fuzzy control logic. The computer simulation test results show that vehicle stability can’t be kept if there is no stability control and the control effect of fuzzy control logic improved the vehicle stability.

References 1. Petruccelli L, Velardocchia M, Sorniotti A (2003) Electro-hydraulic braking system modelling and simulation. SAE Paper, No.2003-01-3336 2. Joachim L, Bryan K (2003) Virtual design of a 42 V brake-by-wire system. SAE Paper, No. 2003-01-0305 3. Hac A, Bodie MO (2002) Improvements in vehicle handling through integrated control of chassis systems. Int J Veh Auton Syst 1:83–110 4. Fortina A, Velardocchia M, Sorniotii A (2003) Braking system components modelling. SAE Paper, No.2003-01-3335 5. Velardocchia M (2006) A methodology to investigate the dynamic characteristics of ESP and EHB Hydraulic Units. SAE Paper No.2006-01-1281 6. Reuter DF, Lloyd EW, Zehnder JW (2003) Hydraulic design considerations for EHB systems. SAE Paper, No.2003-01-0324 7. Ghoneim YA, Lin WC, Sidlosky DM, Sidlosky DM et al (2000) Integrated chassis control system to enhance vehicle stability. Int J Veh Des 23:124–144 8. ISO/TR 3888-1 (1999) Passenger cars—test track of a severe lane change manoeuvre—part 1: double change

Lane Keeping System Based on Electric Power Steering System Hailin Zhang, Yugong Luo, Qingyun Jiang and Keqiang Li

Abstract For integration issues between Electric Power Steering System and Lane Keeping System, a Lane Keeping coordinated control method combining time to lane cross and judgment of driver’s operating behavior has been proposed. Lane Keeping System model, Magic Formula Tire model, 7-DOF vehicle model and EPS model were built based on theoretical analysis. Then Hardware-in-theLoop experiment was done on EPS bench. Simulation and Hardware-in-the-Loop experiment results show that Lane Keeping coordinated control method can solve coordinated problems between conventional power steering component and lane keeping executive component, and can keep the vehicle in the lane, thus ensure the safety of the vehicle while driving.







Keywords Lane keeping Coordinated control Hardware-in-the-loop Vehicle model EPS



1 Introduction According to statistics, nearly 40 % of fatal accidents are caused by lane departure [1]. The function of Lane Keeping System is to maintain the vehicle in the lane, then ensure the drivers’ safety.

F2012-D01-014 H. Zhang (&)  Y. Luo  Q. Jiang  K. Li State Key Lab of Automotive Safety and Energy, Tsinghua University, Beijing, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_9,  Springer-Verlag Berlin Heidelberg 2013

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EPS system has a series of advantages such as energy saving and simple structure. Lane Keeping System based on EPS can achieve shared using of EPS structure and reduce design costs of Lane Keeping System. In Lane Keeping System based on EPS, EPS is conventional power steering component and also lane keeping executive component. Coordinated control between these two functions is the key of the research. Literature [2, 3] studied Lane Keeping System based on EPS, but were not related to coordinated control problems between conventional power steering component and lane keeping executive component. Literature [4] designed individual steering mechanism for Lane Keeping System. The mechanism didn’t have conventional power steering function, so there weren’t coordinated control problems. For coordinated control problems mentioned above, a Lane Keeping coordinated control method combining time to lane cross [1] and judgment of driver’s operating behavior has been proposed. This method combined calculation of time to lane cross and judgment of driver’s operating behavior. Lane Keeping System model, Magic Formula Tire model, 7-DOF vehicle model and EPS model were built based on theoretical analysis. Then Hardware-in-the-Loop experiment was done on EPS bench. Simulation and Hardware-in-the-Loop experiment results show that coordinated control method of Lane Keeping System can decide whether EPS is to achieve conventional power steering or is to respond commands from lane keeping controller through judgment of driver’s behavior and vehicle states, achieve coordinated control of these two functions and keep the vehicle in the lane in order to ensure safety while driving.

2 Overall Structure of Lane Keeping System Based on EPS The driver has the highest priority while driving, so driver’s operating behavior must be considered when designing control strategies. The controller can decide whether EPS is to achieve conventional power steering or is to respond commands from lane keeping controller through integrated judgment of time to lane cross and driver’s operating behavior, thus achieve coordinated control. Based on ideas mentioned above, this paper has established overall structure of Lane Keeping System based on EPS, which is shown in Fig. 1. Lane Keeping System is inside the dashed box. Road environmental information and driver’s information are inputs of vehicle model. Vehicle’s state parameters can be obtained through calculation of vehicle model. We can judge whether the vehicle has the danger of lane departure through driver’s behavior and time to lane cross. If there is danger of lane departure, steering angle command is calculated by lateral driver model. Then PWM signals are generated by PID controller, input of which is error of steering angle. The motor executes PWM signals, generates corresponding front wheel steering angle, then keeps the vehicle in the lane. If there isn’t danger of lane departure, EPS works under conventional power steering mode.

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3 Lane Keeping Coordinated Control Method During lane keeping process, we first need to determine whether there is danger of lane departure through information on all aspects. In this paper, we determine whether there is danger of lane departure through judgment of driver’s behavior and alarm algorithm based on time to lane cross (TLC). Alarm algorithm based on TLC is to calculate time before the wheel crosses the edge of the lane through establishing vehicle motion model and predicting the trajectory of the vehicle. When the calculated time is less than certain threshold, the system will warn the driver of danger of lane departure. Lane keeping coordinated control algorithm is shown in Fig. 2. Figure 2 shows when the driver is operating the steering wheel, the driver has the highest priority, then EPS works under conventional power steering mode. When the driver is not operating the steering wheel, if TLC is more than certain threshold, the vehicle doesn’t deviate from the lane and there is no need to implement aid; if TLC is less than certain threshold, there is danger of lane departure. We calculate target angle from lateral driver model. Then PWM signals are generated by PID controller, input of which is the difference between target angle and actual angle. Then motor performs PWM signals, drives steering mechanism and keeps the vehicle in the lane. As is shown in Fig. 2, judgment of driver’s operating status is very important in lane keeping coordinated control process. In this paper, torque signal is used to judge driver’s operating status. Specific judgment process is shown in Fig. 3. In Fig. 3, the system first gets torque signal from torque sensor. When the torque is more than the set threshold, the driver is operating the vehicle. When the torque is less than the set threshold, if the time when the torque is less than the set threshold is more than certain threshold, the driver is not operating the vehicle, otherwise the driver is operating the vehicle.

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4 Lane Keeping System Simulation Model Simulation model of Lane Keeping System based on EPS are built in Matlab/ Simulink. The block diagram of simulation model is shown if Fig. 4. In Fig. 4, 7-DOF vehicle model gets front-wheel angle from EPS model and sends aligning torque and velocity to EPS model. Auxiliary mode judgment model determines whether EPS works under conventional power steering mode or lane keeping mode through integrating the driver’s operating behavior and time to lane cross information. If the system works under lane keeping mode, lateral driver model calculates target steering wheel angle and sends it to EPS model, then the whole control process is completed.

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4.1 Vehicle Model This chapter selects seven degrees of freedom including longitudinal, lateral, yaw and four wheels’ rotating, then establishes 7-DOF vehicle model. Vehicle model is shown in Fig. 5 and the parameters are listed in Table 1.

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Table 1 Meaning of parameters of 7-DOF vehicle model Parameter Meaning Parameter m JZ d a b JF JR r

Vehicle mass Yaw inertia Tread Centroid to front axle distance Centroid to rear axle distance Front wheel rotation inertia Rear wheel rotation inertia Wheel radius

vX vY xZ x11,x12,x21,x22 d FX11,FX12,FX21,FX22 FY11,FY12,FY21,FY22 TD11,TD12 TB11,TB12,TB21,TB22

Meaning x-velocity y-velocity Yaw velocity Wheel angular velocity Front wheel angle Longitudinal force of tire Lateral force of tire Wheel drive torque Wheel brake torque

7-DOF vehicle model equations are as follows. P 8 mð_vX  v_ Y xZ Þ¼ P FX ¼ðFX11 þFX12 ÞcosdðFY11 þFY12 ÞsindþFX21 þFX22 > > > > mð_vY þ v_ X xZ Þ¼ FY ¼ðFX11 þFX12 ÞsindþðFY11 þFY12 ÞcosdþFY21 þFY22 > > > X > d d > > JZ x_ Z ¼ MZ ¼½ðFX12 FX11 ÞcosdþðFY11 FY12 Þsind þðFX22 FX21 Þ > > 2 2 < þ½ðF þF ÞcosdþðF þF ÞsindaðF þF Þb Y11 Y12 X11 X12 Y21 Y22 P > > _ J ¼ M ¼T T F r x > F 11 Y11 D11 B11 X11 > P > > > JF x_ 12 ¼ PMY12 ¼TD12 TB12 FX12 r > > > > J x_ ¼ M ¼TB21 FX21 r > : R 21 P Y21 JR x_ 22 ¼ MY22 ¼TB22 FX22 r

ð1Þ

4.2 Tire Model The tire plays a role of media between vehicle and road. Tire model is the basis of vehicle dynamics simulation. Magic tire model proposed by Pacejka is used in this paper [5]. The general form of magic tire model is as follows. yðxÞ ¼ D sinfC atan½Bðx þ Sh Þð1  EÞ þ E atanðBðx þ Sh ÞÞg þ Sv

ð2Þ

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Fig. 6 Schematic diagram of lateral driver model

4.3 Lateral Driver Model Single point preview driver model proposed by Guo Konghui is used in this paper. Schematic diagram is shown in Fig. 6 [6, 7]. In Fig. 6, XOY is a fixed coordinate system, f(t) is the center line equation of expected trajectory, T is preview time, y(t) is the current vehicle coordinates. To track f(t), the optimal steering wheel angle is: d ¼

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ð3Þ

Of which: d is the optimal steering wheel angle, i is the gear ratio of steering system, L is wheelbase, d is preview distance. During lane keeping control process, if lane departure happens, we can calculate target steering wheel angle from lateral driver model mentioned above if lane center line and related parameters are given. The difference between target steering wheel angle and actual steering wheel angle is sent to PID controller, from which PWM signals are generated. Motor performs PWM signals and then the whole lane keeping control process is completed.

4.4 EPS System Model Schematic diagram of column-type power steering system is shown in Fig. 7. Parameters of EPS model are all shown in Fig. 7. The meanings of related parameters are listed in Table 2 [8, 9]. Dynamic equations of steering system are as follows: 8 < Th  Kðhs  hd Þ ¼ Bs h_ s þ Js €hs ð4Þ Kðhs  hd Þ þ Ta  Tr ¼ Bd h_ d þ Jd €hd : hm ¼ G m hd Voltage equation of DC motor is:

U ¼ LI_ þ IR þ KE h_ m

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Fig. 7 EPS structure

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ð6Þ

Steering system dynamics can be solved through Eqs. (4), (5), (6). Relevant parameters are calculated for further solution.

5 Lane Keeping Hardware-in-the-Loop Experimental Platform Hardware-in-the-loop (HIL) experimental platform is built based on simulation. HIL experimental platform is shown in Fig. 8. HIL experimental platform consists of dSPACE system, EPS bench and motor drive module. Simulation model of Lane Keeping System based on EPS is running in dSPACE system. dSPACE collects torque and angle signals from EPS bench, current signal from motor drive module. These signals are sent to simulation model running in dSPACE system. After a series of calculation, PWM signals are

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Table 2 Meanings of parameters of EPS model Parameter Meaning Js Jd Bm Bd hd Tr L KE I Ta Gm Jm Bs Th hs hm U R K Ka Tm

Inertia of steering wheel and upper steering shaft Inertia of front wheel and steering mechanism equivalent to steering shaft Motor damping coefficient Damping coefficient of front wheel and steering mechanism equivalent to steering shaft Angle of front wheel equivalent to steering shaft Aligning torque Motor inductance Motor back EMF coefficient Motor current Assistant torque Worm gear ratio Motor rotation inertia Steering shaft damping coefficient Steering wheel input torque Steering wheel angle Motor angle Motor voltage Armature resistance Torsion bar stiffness Motor torque coefficient Motor torque

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6 Analysis of Simulation and HIL Experimental Results In EPS system, the effect of EPS will be weakened with the increase of velocity. In order to take obvious power steering effect and lane keeping effect into account, the choice of velocity is 20 km/h.

6.1 Analysis of Simulation Results Simulation case 1: straight line driving, velocity 20 km/h, ground friction coefficient 0.9, lane width 3.5 m, TLC threshold 3.5 s. Steering wheel angle, TLC and motor voltage are shown in Fig. 9. From 3 to 4.3 s (Inside the dashed oval frame), there is driver input and motor works under conventional power steering mode. Motor voltage from 3 to 4.3 s in Fig. 9 presents conventional power steering state. After 4.3 s, there is no driver input. If TLC is less than 3.5 s, motor begins to work to adjust vehicle position. If TLC is more than 3.5 s, motor doesn’t work and doesn’t provide auxiliary. In summary, the control method can achieve coordinated control effect. Control results of trajectory of vehicle centroid in case 1 are shown in Fig. 10. When there is danger of lane departure, Lane Keeping System takes over control of the vehicle and controls vehicle centroid near the centerline of the lane in order to ensure the safety of the vehicle.

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presents conventional power steering state. After 1.5 s, there is no driver input. If TLC is less than 3.5 s, motor begins to work to adjust vehicle trajectory. If TLC is more than 3.5 s, motor doesn’t work and doesn’t provide auxiliary. In summary, the control method can achieve coordinated control effect. Control results of trajectory of vehicle centroid in case 2 are shown in Fig. 12. When lane departure happens, the system can adjust the vehicle’s position in a very short time, thus ensure traffic safety. Simulation results verify that Lane keeping control method designed in this paper is effective.

6.2 Analysis of HIL Experimental Results During HIL experiment, when the driver is not operating, hands are completely off the steering wheel. When the driver is operating, hands are on the steering wheel. HIL case 1: straight line driving, velocity 20 km/h, ground friction coefficient 0.9, lane width 3.5 m, TLC threshold 3.5 s. Control results of trajectory of vehicle centroid in HIL case 1 are shown in Fig. 13. The results show that when lane departure happens, the system can adjust the vehicle to the center line of the lane, which is in line with expected result. Parameters’ changing in HIL case 1 is shown in Fig. 14. From 3.5 to 4.5 s (Inside the dashed oval frame), the torque exceeds the set threshold. According to Fig. 3, the driver is operating. After 4.5 s, the torque is less than the set threshold and persists for some time. According to Fig. 3, the driver is not operating. From 3.5 to 4.5 s, the driver is operating when EPS works under conventional power steering mode and there is assist current in motor. After 4.5 s, the driver is not operating. The system changes to lane keeping process. When TLC is less than the set threshold, motor begins to work to adjust vehicle position through changing front wheel angle. When TLC is more than the set threshold, there is almost no current in the motor and the system works without assistance of the motor. The results show that control method designed in this chapter can achieve coordinated control effects.

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keeping mode. If TLC is less than the set threshold, motor works to change front wheel angle and to adjust vehicle trajectory. If TLC is more than the set threshold, there is no assistance of the motor in the system. Results show that control method designed in this paper has coordinated control effects.

7 Conclusions For coordinated control problems in Lane Keeping System based on EPS, a lane keeping coordinated control method considering TLC and judgment of driver’s operating behavior has been proposed. Lane keeping simulation model was built based on this. Simulation study of control method and HIL experiments under relevant cases were done. Conclusions are as follows: 1. Lane keeping coordinated control method designed in this paper considers both driver’s operating status and lane departure information and can solve coordinated control problems between conventional power steering component and lane keeping executive component when the driver has the highest priority, which is in line with reality. 2. Complete lane keeping simulation model based on EPS has been established by integrating lateral driver model, 7-DOF vehicle model, magic tire model and EPS model. The integrated model can simulate vehicle dynamics well while lane keeping process. 3. Simulation and HIL experimental results show that lane keeping coordinated control method designed in this paper can keep the vehicle in the lane and ensure traffic safety with good control effect.

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References 1. Wang R, Yu T, Guo L (2005) A survey on the research of vision-based lane departure warning system. Automot Eng 27(4):463–466 2. Jing-Fu L, Jui-Hung W, Yi-Feng S (2007) Development of an interactive lane keeping control system for vehicle. IEEE vehicle power and propulsion conference, pp 702–706 3. Meister T, Fleck R, Fischer M (2006) Enabling technologies for lateral dynamic assistant systems. SAE Paper 2006-01-1172 4. Ma Y, Wang J, Xu Y et al (2004) Lane keeping system of autonomous vehicle. Intell Transp Syst Z1:7–12 5. Pacejka HB (2006) Tire and vehicle dynamics. Oxford, Elsevier, pp 172–196 6. Guo K (1991) Dynamics of controllability for automobile. Jilin Science and Technology Publishing House, Changchun, pp 500–503 7. Guo K (1984) Drivers-vehicle close-loop simulation of handling by ‘‘preselect optimal curvature method’’. Automot Eng 3:1–16 8. Shimizu Y, Kawai T (1991) Development of electric power steering. SAE Paper 910014 9. Hu J, Li T, Qin D (2008) Modeling and simulation of electric power steering system based on vehicle whole dynamics. J Syst Simul 20(6):1577–1581

Parking Brake Breaking-In Technology Based on EPB Leon Huang, Ted Huang, Wei Xu, Dongxu Yi, Lingtao Han and Wutian Lin

Abstract When the parking brake shoes are installed, it must be initialized with Breaking-in process, in order to meet the parking performance requirements, especially the independent parking brake. This article describes a vehicle dynamic Breaking-in technology for independent parking brake, based on electronic parking brake system. Keywords EPB

 Break-in  After service  DIH  EOL

1 The Necessity of Breaking-In Process for Independent Parking Brake The new installation of the brake friction plate cannot be fully fit and brake surface, because of its irregular surface and mounting structure reasons. Initial friction plate efficiency will be low; resulting in vehicle brake performance does not reach the goal, when the friction joint area is too small. As shown in Fig. 1: To solve this problem, the Breaking-in process will increase friction plate joint area in braking. The Breaking-in, or Running-in, is the process of sliding friction under pressure between friction materials and brake disc or drum. The joints area will increase while the irregularities of the friction materials are polishing. Braking efficiency will continue improving in Breaking-in. F2012-D01-015 L. Huang (&)  T. Huang (&)  W. Xu  D. Yi  L. Han  W. Lin Guangzhou Automobile Group CO., LTD Automotive Engineering Institute, Guangdong, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_10,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 The indication of friction plate joint surface

For service brake, Breaking-in process can occur in vehicles moving. Depressing the brake pedal, the friction lining will be abraded in wheels rolling and it will improve the braking efficiency continuously [1, 2]. However, for the parking brake, it only works in static mode generally, and the working frequency is far less than the service brake, so it cannot be Break-in in application of conventional. If the initial braking efficiency is unqualified, the parking brake may not provide enough parking force in slope. It will cause the risk. Therefore, the friction plate, shared by parking brake and service brake, can be used without special Breaking-in. But the independent friction plate for parking brake, such as Drum in Hat (DIH), is very necessary to be Break-in. (See Fig. 2)

2 The Conventional Mechanical Handbrake Breaking-In There are two methods to Break-in the conventional mechanical handbrake, which connecting to DIH.

2.1 Parts Breaking-In The first method is approach named ‘‘parts Break-in’’, after parking brake is assembled with shoes and drum together. There is a Breaking-in equipment to ensure the parking brake is acceptable. Then the parking brake assembly can be sent to the vehicle assembly process. In this way, the consistency of the vehicle parking performance can be ensured at equipment, but the costs will be higher from the equipment construction and operating. In addition, this method isn’t applicable for lining replacing in after service. It is not worth to replace entire ‘‘well-done’’ parking brake, if it only because abrasion of the friction plate.

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Fig. 2 Independent parking brake and service brake

2.2 Vehicle Dynamic Breaking-In The second Break-in method is ‘‘vehicle dynamic Breaking-in’’. New car off the assembly line can be Break-in when moving in a low parking force, similar to service brake process. When mechanical handbrake to be pulled up the appropriate latch number, can make the parking brake force not only maintaining the friction plate to joint, but also to ensure that force not big enough to locking wheel completely. So choosing the appropriate parking force is important conditions fit the dynamic Breaking-in. When you select the appropriate Breaking-in parameters, you can also ensure the consistency of braking efficiency in batch after Breaking-in. The dynamic Breaking-in process can be finished in wheel revolving test equipment to fit vehicle factory. It can also be done on the testing road, so it can meet the demands of factory and after service at the same time.

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Fig. 3 Electronic parking breaking-in system hardware architecture

3 The Electronic Parking Brake Breaking-In Electronic parking brake system (EPB), which is different from the mechanical handbrake, users can not choose the parking force free. When the vehicle static parked, the biggish force will completely lock the wheels, and the vehicle will be difficult to accelerate. So it’s unable to complete the dynamic Breaking-in. EPB started brake when vehicle moving, will enter an emergency mode called ‘‘dynamic braking’’. After rapid deceleration, the vehicle will be stopped eventually. The wheel does not roll, so amount of Breaking-in will not meet the requirements. In order to achieve Break-in effect, it must be repeated using the ‘‘dynamic braking’’ mode to Breaking-in by repeating acceleration and deceleration cycles. But by this method, each Breaking-in amount is limited, and cannot be precise cumulative. Process is difficult to control and count manually, with the huge workload and low efficiency. Therefore, the EPB system function, similar to the mechanical hand brake, can select the appropriate parking force and perform the vehicle dynamic Breaking-in. An efficient Breaking-in system need to precisely control the amount of Breakingin. Amount of Breaking-in is defined in the following:

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Fig. 4 Detailed description following

A¼kFL A F L

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The following hardware structure can be obtained running the force and distance (Figs. 3, 4): In this figure, the equipment can get the parking force from wheels. Breaking-in equipment is any diagnostics equipment adding Breaking-in software.

4 The Breaking-In Process The function of different part in Breaking-in system Breaking-in equipment sends a target to EPB ECU, and monitors and controls the Breaking-in process. At the same time, equipment can also be receiving commands from operator. EPB ECU is responsible for controlling the actuators, precise control of the force of the parking and calculation of the running distance.

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Exit Breaking-in processes (a) Breaking-in distance The running distance calculated by the EPB ECU, integrating the wheel speed to distance value by signals from CAN network. EPB will automatically quit Breaking-in and release parking brake when distance reach or exceed the distance target. (b) Through the brake efficiency: Running equipment receives real-time cable force from EPB ECU and continues testing parking force to calculate the real-time parking brake efficiency. When the parking efficiency monitored by equipment is greater than target, the equipment will send command to the EPB to exit Breaking-in. c) The Breaking-in process can be manual intervention to exit to face special unknown situation. Confirmation of Breaking-In Effect Exit (a) is an open-loop control, according to the parameters set, it can be ensured that all vehicle match Breaking-in parameters. But all vehicles should pass the parking force testing process, as same as handbrake; Exit (b) is a closed-loop control, Breaking-in at the same time to detect the efficiency of parking. Therefore, after the end of the Breaking-in process, all vehicles can meet the objectives. Due to individual differences of brakes, every brake need different Breaking-in distance to fit to parking efficiency. The target distance of Breaking-in is the longest one, in order to meet all vehicles. So Exit (b) can save time. Development of new car model For different vehicles or brake, we only need to add a new Breaking-in parameters. There is no need to change the ECU or equipment software logic.

5 Breaking-In Parameter Settings 1. Breaking-in force F As refer earlier, the right Breaking-in force, which maintaining friction plate to joint and not lock the wheels, is important. We can get a range from vehicle test. Sometimes we choose a big one, to reduce the distance of Breaking-in. 2. Parking Brake efficiency g We need calculate the target of qualified parking brake efficiency. The definition of the parking braking efficiency:

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g ¼ K  ðFP  FC Þ FP FC

Parking force; Cable Force

We set efficiency (gF ) the perfect fully Breaking-in shoes as 100 %, and get the ratio of ‘‘FP’’ and ‘‘FC’’ from vehicles which are perfect fully Break-in. then we get constant K. Then we can get target efficiency (gT ) from the vehicle performance target. For example, we need park at slope of 20 % when cable force below 800 N. Any vehicle meet or over target efficiency can be set passing. 3. Breaking-in distance L This technology is based on distance accumulating and force controlling. Based on Breaking-in force has been selected and qualified efficiency goals, vehicle calibration test by vehicles of a quantity can get the range of Breaking-in distance. The maximum one in all samples should be choose.

References 1. Han Byul C, Youngsup S, Paljoo Y, Choong Woo L, Doo Ho L, Young Ok L, Chung Choo C (2007) A fault detection method for Electric Parking Brake (EPB) Systems with sensor less estimation using current ripples. In: 14th Asia pacific automotive engineering conference, 2007 2. Raajha MP, Lakshmi Narayanan V (2003) High-performance drum brake assembly for automotive braking applications. In: 21st Annual brake colloquium and exhibition, p 384

Engineering Design of TPMS Lingtao Han, Ted Huang and Wei Xu

Abstract Research and/or Engineering Questions/Objective: Tire Pressure Monitoring System (TPMS) mainly focus on the tire pressure, temperature monitoring. Or over-inflated tire under inflated, will affect the car’s safety. According to the traffic control department of statistics, 46 % of accidents on the highway due to tire failure caused the puncture accounted for 70 %. TPMS can monitor real-time tire pressure and temperature, the driver informed in advance, improve vehicle safety. And tire pressure is too low will result in increased resistance and improve fuel consumption. Improved by monitoring the tire pressure can improve fuel economy. Methodology: This article describes the principle of TPMS systems, the system components, and solve the TPMS R & D among the key issues to be resolved, providing a human interface and information communications for the overall design performance, and ultimately go through test tools with CAN, the spectrum analyzer, network analyzer test validation. Results: This design of TPMS systems, real-time monitoring of tire pressure, temperature, acceleration, and the man–machine interface can be displayed to the driver. For the tire failures and system failures can itself provide timely text, graphics, alarm. Optimized layout of the entire system through the vehicle to improve the reception rate and reception stability. Communication with other systems using the CAN approach, stable and reliable signal transmission speed. Based on the above system design, in engineering has been widely used to improve the protection of tire safety and fuel economy has made some achievements. Deficiencies and limitations Limitations of this study: This paper focuses on the tire pressure monitoring, and providing alarm. But the puncture cannot be predicted, and the puncture of no implementation action. What does the paper offer that is new in the field in comparison to other works of the author: F2012-D01-016 L. Han (&)  T. Huang  W. Xu GAC Engineering, Shanghai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_11,  Springer-Verlag Berlin Heidelberg 2013

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The system uses sensors directly, in real time and accurate monitoring of tire pressure, temperature, acceleration, battery voltage sensor signal. Relative to the indirect tire pressure monitoring, with a measurement accuracy, system stability, the advantages of wide range of applications. RF signal using FSK modulation mode, better than the adjustment means ASK. Conclusion: This tire pressure monitoring system designed to achieve an accurate tire pressure and tire temperature monitoring alarm display can be applied in the real car. Keywords TPMS protocol

 Hardware design  Software design  Test on vehicle  RF

1 Proposal Design TPMS is a wireless transmission system for a vehicle, apply to short-range radio signal transmission. The main transmission signal including a tire pressure signal, the temperature signal, and sensor battery voltage status signal and the centripetal acceleration signal when wheel rotating. Figure 1 is a schematic diagram of the TPMS system. The signal such as pressure or temperature will be detected by the signal source then encoded for the radio frequency signal transmitted out by the transmitter. Receiver in working condition will receive the signal from the transmitter, then decode, and analyze the data, sent the data to the LCD for display. RF signal, because of reflection, diffraction, etc., will be degraded, When system design is finished, it should be checked on vehicle. The Fig. 1 shown is for a low-line TPMS, TPMS mentioned in this article refers to the low-line system.

2 System Components The TPMS system is composed of 4 sensors (transmitter) and a receiver, the spare tire does not install the sensor. Sensor working principle is shown in Fig. 2. The sensor uses a 3 V lithium battery as power supply, the sensor contains a pressure sensor, temperature sensor, voltage sensor, the centripetal acceleration sensor, an MCU, the RF transmitter circuit, the antenna. Each sensor will input the signal detected to the MCU, MCU processes the signal then gives the data to the RF converter circuit to convert to the RF signal, the RF signal will be send out through the transmitting antenna. Choose 433.92 MHz FSK modulation mode according to China radio environment. Such as Fig. 3, the receiver working principle, the receiver is powered by 12 V battery, IG signal as an excitation signal wake-up receiver circuit such as the CAN Transceiver. Receiver antenna receives the RF signal, then decode the signal, and then sent to the MCU for processing to restore the data and send the data to CAN

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109 Signal coding

Sender

RF

LCD Display

Signal decoding

Receiver

Fig. 1 Diagram of TPMS

Pressure Sensor Temperature Sensor 3V

Voltage Sensor

MCU

RF Transmitter Circuit

Antenna Match Network

Acceleration Sensor

Fig. 2 Diagram of sensor

Transceiver, CAN Transceiver transmit the data to data frame for CAN bus. LCD read from the CAN bus, and give data display to the driver for safety. LCD display interface as shown Figs. 4 and 5, contains the pressure display, temperature display, warning lamp. Normal driving circumstances, LCD display real-time temperature of each tire, pressure value of each tire, alarm lamp does not shine on this condition; when a tire pressure or temperature failure, alarm lamp lights, the display interface displays the current pressure and temperature, and tire failure temperature, pressure value flashes to alert the driver. If the system fails, warning lamp lights, the interface prompts the system failure.

3 Software Design 3.1 Signal Coding TPMS using the Manchester encoding, in Manchester encoding, every middle there is a transition from high to low transition of said ‘‘1’’, ‘‘0’’ from low to high transition, such as in Fig. 6 [1]. Manchester encoding in the source coding main reasons are the following: 1. Manchester encoded sequence there is no DC component. To avoid miscarriage of justice because the receiver amplifier zero drift and back-end points of the

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Antenna Match Network

FSK Receiver Circuit

MCU

CAN Transceiver

LDC

IGN 12 V

Fig. 3 Diagram of receiver

Fig. 4 Display

Fig. 5 Warning lamp

comparator signal DC review an error of judgment caused by the decoding circuit. 2. The Manchester coding, synchronization signal for the receiver circuit is a typical self-synchronizing encoding. The so-called self-synchronization refers to the direct method to extract the sync signal from the data signal waveform.

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0

0

111

1

1

0

1

Fig. 6 Manchester coding Table 1 RF Frame structure Preamble Sensor ID Battery voltage Pressure Temperature Acceleration Check sum EOM

3. The Manchester encoded codeword is limited, can only be a hexadecimal number 5, A, 6, 9 of the various combinations, so long as the data frame synchronization code take a sequence of these combinations, it can completely avoid the misjudgement of the header. 4. The Manchester coding is relatively simple, suitable for the weak system of processing capacity. Visible, the Manchester coding is not only way to achieve simple, but also has good anti-interference performance and self-synchronization capabilities.

3.2 RF Protocol TPMS Sensor RF Frame have the following structure as Table 1.‘ 3.2.1 Preamble Each message must start with 15 bits of zeros and followed with one bit of zero.

3.2.2 Sensor ID Sensor ID follow the Pre-amble. Sensor ID has 32 bits programmed at factory.

3.2.3 Pressure Byte The pressure is transmitted using an 8-bit representation, 1 bit representing 1.37 kPa. The transmitted pressure data should be linear between incremental

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Table 2 Pressure data

Table 3 Temperature data

No.

Real pressure

Pressure data transmitted

1 2 3 254 255

100 101.37 102.74 450 Over range

0x00 0x01 0x02 0xFE

Invalid

Temperature data transmitted

Real temperature (C)

0–0x0F 0x10 0x11 …… 0xB5 0xB6– 0xFF

Invalid data -40 -39 …… 125 Invalid data

pressure measurement. The Sensing minimum absolute Pressure is 100 kPa, and maximum absolute Pressure is 450 kPa (Table 2).

3.2.4 Temperature Byte The temperature is transmitted using an 8-bit digital representation, 1 bit representing 1 C (Table 3).

4 Test on Vehicle The sensor sends RF signals as a data packet, each data packet contains three RF Frames. The position of receiver on vehicle will cause the RF signal diffraction, reflection, etc., directly affect the reception effect, you need do a reception test on vehicle. Sensor for a wheel, the reception rate is calculated by two ways: 1. Nrd Nsd 2.

Data Packet reception rate ¼

Nrd : Nsd

Number of Data Packet received by receiver; Number of Data Packet send by the sensor; Frame reception rate ¼

Nrf ; Nsf

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Lab Top Sensor1

CANoe

Sensor2

Receiver

Sensor3

Sensor4

Fig. 7 Theory diagram of test

Nrf Nsf

Number of Frame received by receiver; Number of Frame send by the sensor.

Figure 7 shown, theory diagram of test. The test software is installed in the lab top. For a wheel Sensor such as Sensor 1, when the receiver gets a Data Packet from Sensor 1, receiver will give the information to Lab Top via CANoe. The data packet counter in the testing software of the Lab Top will plus 1. In the end of the trial, the counter shows Data Packet reception number Nrd for sensor 1. Similarly frame counter gives the number of frame send by sensor 1 Nrf. Sensor launch RF is cyclical, and the cycle is T is fixed. Launched for a wheel of Sensor Data Packet number, such as Sensor 1, when the receiver is first received Data Packet, the receiver gives information to the Lab Top and the data packet launch counter in the software gets activation, and every T time counter plus 1 until the end of the trial, you can record the launch number of the data packet Nsd. Similarly record number Nsf of the launch Frame. According to (1–2) it can be calculated out of a wheel Sensor Data Packet reception rate and frame reception rate. Similarly the other three tires. To judge the reception effect of the entire system.

Reference 1. Ningning W (2006) Key technology research and engineering application of TPMS, Wanfang Data

An Integrated Electric Energy Management System to Improve Fuel Economy Mingming Wang and Ted Huang

Abstract Fuel consumption and greenhouse gas emissions pose serious challenges to automotive industry. Today’s vehicles require much more electric energy due to the much wider array of electrical and electronic on-board comfort and safety systems. The balance of power delivery to different systems is becoming more and more difficult. In response to the growing need for more electric power, an integrated electric energy management system is introduced. An energy management unit (EMU) is the brain of whole system; it integrates charging management, auto start stop function, battery monitoring and electric load management. Based on the battery state of charge (SOC), EMU determines the strategy for energy management. The EMU controls charging voltage by a LIN connected alternator regulator to maintain SOC. When the battery soc is low, EMU increases the charging voltage to stimulate battery charging. But when soc is in a normal range, charging voltage is adjusted according to vehicle motion to improve fuel economy. The auto start stop function turns off and restarts engine automatically, when the battery SOC is in an appropriate status. EMU also determines which electric load the power should be preferentially supplied to when the battery SOC is low or when the alternator malfunctions. In these situations, the EMU will reduce electric power delivered to such components as a seat heater, for example, in order to ensure enough power for safety systems such as the x-by-wire systems. A closed-loop control of the battery SOC improves stability of electric power net. EMU increases the charging voltage when vehicle is decelerating, and decreases the charging voltage when vehicle is accelerating. Regeneration increases fuel efficiency while simultaneously enhancing driving dynamics.

F2012-D01-018 M. Wang  T. Huang Guangzhou Automobile Group CO.,LTD Automotive Engineering Institute, Guangdong, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_12,  Springer-Verlag Berlin Heidelberg 2013

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A continuous charging voltage adjustment way is introduced. This avoids the abrupt torque output, and improves NVH performance. Auto start stop function combined with charging management delivers more reductions in fuel consumption and CO2 emissions. Keywords Energy management agement Regeneration



 Fuel economy  Start stop  Charging man-

1 Introduction 1.1 Trends of Growing Need for Electric Power Today’s vehicles require much more electric energy than older ones, due to the much wider array of electrical and electronic on-board comfort and safety systems. Electric loads in a conventional ICE vehicle are increased up to 2–3 kW. In general, in the case of power 1 kW, driving 100 km each needs to consume 0.7–1.2 L of petrol. Fuel consumption and exhaust emissions pose serious challenges due to the increase of electric power. Also the reliability of power supply is strongly required, when electric security systems such as x-by-wire systems (e.g. steering-by-wire, brake-by-wire) are introduced in the vehicles [1, 2]. The balance of power delivery to different systems is becoming more and more difficult.

1.2 Solutions to Improve Fuel Economy For a conventional internal combustion engine (ICE) vehicle, there are several ways to improve fuel economy by improving the electric energy system. Charging management is one technique. Regeneration is converting a vehicle’s kinetic energy to electric energy for battery recharging during deceleration. Regeneration improves fuel efficiency by up to two percent, though this result is influenced by the engine capacity and electric load. But overcharge or insufficient battery SOC can still be problems if there is no battery SOC monitoring. The second way is the auto start stop system which turns off the engine each time the vehicle comes to a complete halt—such as at traffic lights—and restarts it automatically. This is an effective way for drivers living in urban areas to reduce fuel consumption by an estimated 5 % [3]. But inadequate battery SOC level can be caused by frequent engine stop, which limits fuel reduction effect.

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Fig. 1 Power flow in a conventional ICE vehicle

1.3 Introduction of Electric Energy Management System This paper presents an electric energy management system which integrates charging management, auto start stop function, battery monitoring and electrical load management. This system shows the following features: • Strategy for energy management is based on the battery state of charge (SOC). • Charging voltage is increased to stimulate battery charging when the battery soc is low, but when soc is in a normal range, charging voltage is adjusted according to vehicle motion to improve fuel economy. • Start stop function turns off and restarts engine automatically, when the battery SOC is in an appropriate status. • Power supply priority is applied to preferentially delivery electricity to the important loads, when battery SOC level is low.

2 Analysis of Electric Energy Management 2.1 Electric Power Flow in a Conventional Vehicle In a conventional ICE vehicle, usually the electric energy system consists of an alternator that generates electric power, an electric storage device, such as a leadacid battery, and various electric loads [4]. The alternator tries to maintain a fixed voltage level on the power net. A traditional lead-acid battery is present for supplying IG-off loads and for making the power net more robust against peakpower demands. The power flow is shown in Fig. 1. The power flow starts with fuel which is injected into the engine. The output power of engine splits up into several directions: one part goes to the transmission for vehicle propulsion, while other part goes to the alternator. The alternator generates electric power for various electric loads, and also charges the battery. Contrary to the electric loads, the power flow of the battery can be positive as well as negative. In the end, all power, except for losses, is used for vehicle propulsion and for electric devices connected to the power net.

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Fig. 2 Model of electric energy management controller

2.2 Problems of Common Electric Energy System Electric energy system in a conventional ICE vehicle has several problems as follows: • Bad battery maintenance: There is no monitoring function for battery SOC, both charging side and consuming side are out of control from the electric energy system. Insufficient SOC and overcharge both can be caused by the imbalance of generated power and load power. • Lack of protection for important loads: There is no priority distinction for different electric loads. Important loads can also be shut down when the SOC is low. • Fuel economy degradation: The alternator is connected to the engine by a belt with a fixed gear ratio, and output voltage is fixed. This means that electric power is going to be generated even though efficiency of the engine is worse. In such a case, more fuel is required to generate electricity and fuel economy deteriorates.

2.3 Model of Electric Energy Controller The defects of the electric energy system in a conventional ICE vehicle are caused by the fact that there is no vehicle level control of electric power flow. Therefore, an electric energy controller is needed. The electric energy system should be restructured as Fig. 2. The electric energy management system consists of three subsystems: generation, storage and distribution. In such a system, generation can be adjusted according to the vehicle motion status, battery sensor is present for monitoring the SOC to keep good battery maintenance, and power supply priority is introduced to

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Fig. 3 Implementation of electric energy management system

guarantee the safety of important loads. Similar like the idea of ‘‘torque based control’’, SOC of battery is the core parameter to coordinate three subsystems. Based on the battery state of charge (SOC), system determines the strategy for energy management.

3 Design of an Integrated Electric Energy Management System 3.1 Structure of an Integrated Electric Energy Management System This chapter presents the implementation of an integrated electric energy management system, which is shown in Fig. 3. An energy management unit (EMU) is the brain of whole system. Based on the battery state of charge (SOC), EMU determines the strategy for energy management. Charging management, auto start stop function, battery monitoring and electric load management are all controlled by EMU. A battery sensor computes the SOC of battery from voltage current and temperature signals, and sends the info to EMU by LIN bus. EMU controls charging voltage by a LIN connected alternator regulator to maintain SOC. When the battery soc is low, EMU increases the charging voltage to stimulate battery charging. But when soc is in a normal range, charging voltage is adjusted according to vehicle motion to improve fuel economy. Producing electricity in this highly efficient way delivers an additional advantage: when accelerating, the alternator is restrained; more power of the engine can be directed to the drive wheels. Regeneration increases fuel efficiency while simultaneously enhancing driving dynamics.

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The status of power train can be derived from clutch sensor and neutral sensor (for manual transmission) or TCU (Transmission Control Unit for automatic transmission). Also engine status signals from EMS (Engine management system) are also necessary for the auto start stop function. The auto start stop function turns off and restarts engine automatically, when the battery SOC is in an appropriate status. If necessary for comfort or safety reasons, EMU will restart the engine: for example, if the vehicle begins to roll or refrigeration is needed for a comfortable temperature. Because of good battery maintenance, battery SOC never falls too low, the auto start stop function is available in most cases, makes more fuel reduction. EMU determines which electric load the power should be preferentially supplied. When the battery SOC is low or when the electric system malfunctions, EMU shut down the electric loads with low supply priority such as a seat heater by a CAN connected digital relay controller. Fuel reduction is obtained from regenerative braking and engine shut-off. For regeneration, the alternator absorbs energy from the drive train and stores it into the battery in terms of electric energy. This is the most economical way to charge the battery, since it requires no additional fuel. When used consistently, auto start stop function can deliver significant reductions in fuel consumption and emissions.

3.2 Torque Control Improvement for Regeneration For most regeneration cases, charge voltage is stetted in only three fixed steps [3]. As showed in Fig. 4, system increases the alternator’s adjustment voltage during accelerating phase while decreases the voltage when vehicle is decelerating. Adjustment voltage is set to be a default value if cruise driving is recognized or in a malfunction mode. By controlling the alternator, the engine torque output can be influenced. Discontinuous torque output to wheels can be felt due to discrete voltage setting steps. A better way for charging voltage adjustment is to set the voltage in continuous steps. Setting voltage is dependent on vehicle’s acceleration which is the first derivative of vehicle speed. The relationship between setting voltage and acceleration can be expressed as a linear function which is shown in Fig. 5. A nonlinear function can also be adopted for accelerating the response. The setting voltage V can be represented as follow: V ¼ V default þ k  a Where V default is the default voltage, a is acceleration of vehicle and k is the coefficient. The value of acceleration becomes positive during accelerating while minus if vehicle is decelerating. This brings the same effect as above fixed voltage adjustment but avoids the abrupt torque output, and improves NVH performance.

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Fig. 4 Charge voltage adjustment by estimating vehicle motion

Fig. 5 Relationship between setting voltage and acceleration

4 Conclusion Improving fuel economy and restricting emissions has always been a major challenge to the automotive industry. Historically, the research was focused on improving the mechanical side of the vehicle. Because of the growing need for more electric power, electric energy management becomes a promising resolution. This paper presents a design of electric energy management system which integrates charging management, auto start stop function, battery monitoring and electrical load management. A closed-loop control of the battery SOC improves stability of electric power net. Auto start stop function combined with charging management delivers more reductions in fuel consumption and CO2 emissions. A continuous charging voltage adjustment way is introduced. This avoids the abrupt torque output, and improves NVH performance.

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References 1. Shen J, Masrur A, Garg VK, Monroe J (2003) Automotive electric power and energy management: a system approach. In Business Briefing: Global Automotive Manufacturing and Technology. Touch Briefings, April 2003 2. Kazuyoshi O, Keisuke T (2004) Concept of Vehicle Electric Power Flow Management System (VEF). SAE 2004 World Congress & Exhibition, Detroit, Michigan. SAE Paper 2004–01-0361 3. Tadatoshi A, Syunichi M (2008) A Stand-Alone Charging Management System to Improve Fuel Economy, Based on an Algorithm of Estimating Vehicle Motion. SAE 2008 World Congress & Exhibition, Detroit, Michigan,. SAE Paper 2008-21-0045 4. Emadi A, Ehsani M, Miller JM (2003) Vehicular electric power systems: Land, Sea, Air, and Space Vehicles [M]. Marcel Dekker, New York

Modeling on Torque Generation for Turbocharged Diesel Engine Based on Identification Method Gang Li, Ying Huang, Fujun Zhang and Xiaoyan Dai

Abstract Torque-based engine control system has been developed for complexity of powertrain control. Compared with open-loop torque control, close-loop control can improve control performance radically and reduce calibration workload greatly. For turbocharged diesel engine, dynamic torque generation model of Fuel Delivery-Mean Indicated Torque should be provided. The relationship between fuel delivery and mean indicated torque is nonlinear. In different operating points, engine demonstrates different dynamic torque output characteristic. Through piecewise linearization and identification, dynamic torque generation model has been obtained in different operating points. Based on this model, close-loop torque and engine speed control algorithm can be investigated further.





Keywords Turbocharged diesel engine Dynamic characteristic analysis Torque generation model Piecewise linearization Identification





1 Introduction Indicated torque is an important variable for powertrain control and torque-based engine control system. Torque can be obtained through measurement or reconstruction of engine speed [1, 2], but the sensor is expensive and reconstruction is

F2012-D01-021 G. Li (&)  Y. Huang  F. Zhang  X. Dai Beijing Institute of Technology, Beijing 100081, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_13,  Springer-Verlag Berlin Heidelberg 2013

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difficult to access. The development of torque model with certain accuracy became a research focus. Hong Munan proposed online torque estimation algorithm based on mean value engine model, and identified the parameters of the estimation algorithm [3]. Y. Chamaillard obtained the transfer function between the throttle and indicated torque by system identification, and designed a robust control algorithm [4]. In addition, neural network approach based on the engine test data is also applied to establish the torque model [5, 6]. Compared with gasoline engine, the relationship between fuel delivery and mean indicated torque of turbocharged diesel engine shows stronger nonlinearity. In different operating points, turbocharged diesel engine demonstrates different torque output characteristics. In addition, torque response of turbocharged engine is lagged to fuel response, especially under the fast torque changing case and in the various operating points, lag time is not consistent [7, 8]. Establishing a uniform torque dynamic model in all operation conditions is difficult. In this paper, considering the steady-state and dynamic torque characteristic, piecewise linearization and identification have been implemented; and dynamic torque generation model in all operating points has been obtained.

2 A Detailed Engine Model for Identification 2.1 Analysis of Torque Output Characteristics Firstly a detailed model of turbocharged diesel engine will be established. The detailed model should predict state and dynamic characteristics of the average indicated torque in different operating points, and neglect the high frequency torque fluctuations. For turbocharged diesel engine: Mi ¼ mf  g  Qlhv =120000

ð1Þ

where Mi is the mean indicated torque, the unit is Nm; Mf is the fuel delivery, the unit is mg; g is the thermal efficiency; Qlhv is the low heat value, the unit is J/mg, about 43.2. In different operating points, indicated thermal efficiency of turbocharged diesel engine displays non-linearity. Combustion timing and velocity have changed with different operating points, thus affecting indicated thermal efficiency of engine. Some research indicates that the indicated efficiency mainly depends on the engine speed, load and fuel injection timing [8, 9]. The calibration of injection timing is carried based on the principle of economy and emissions. Therefore, steady-state indicates efficiency is directly related to the engine operating points. In dynamic working conditions, the response of indicated torque lags behind the change of fuel injection quantity for turbocharged diesel engine. Under transient

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Table 1 Sub models and modelling method adopted Sub-model Modeling method Combustion and torque production Engine friction model Crankshaft rotation model Intake manifold model Intercooler model Exhaust manifold Compressor model Turbine model Turbocharger rotor model

Experimental data MAP Experimental data MAP First order differential equation Experimental data MAP and first order differential equation Experimental data MAP and first order differential equation Experimental data MAP and first order differential equation Character MAP Character MAP First order differential equation

operating conditions, the efficiency decreased significantly. The specific reasons include that the inertia of turbocharged system, volume effect of intake and exhaust manifold, delay of heat transfer and energy conversion [10, 12]. These reasons lead to air–fuel ratio deviation from the steady-state value and the loss of heat transfer balance in the dynamic process [11].

2.2 A Detailed Model for Identification Deutz V6 turbocharged diesel engine is chosen as the research object in this paper.A dynamic engine model for identification has been established. According to Sect. 2.1 analyses, the modeling should focus on some important links to indicate torque characters. It is known that indicated efficiency is function of engine speed and A/F ratio. An indicated efficiency MAP was obtained by fitting the experimental data, as shown: git ¼ MAPðA=F; ne Þ

ð2Þ

where A/F is the air–fuel ratio, ne is the engine speed. Quasi-steady empirical along with filling and emptying method were used for the airpath modeling. Filling and emptying method was used to gain better transient prediction of pressures and temperatures in manifolds [12]. Therefore, turbocharged diesel engine model was divided into a series of control volumes. Table 1 gives some sub models and methods for the modeling. More detailed descriptions of these sub-models are given in literature 13. Moreover, considering the crank angle lag of the intake flow, fuel injection, indicated torque and exhaust gas flow, time delay has been added in all working conditions. In addition, verification for heat transfer coefficient, temperature rise model, and heat transfer model of the exhaust pipe have been carried out based on experimental data.

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Table 2 Operating points chosen for piecewise linearization 70 mg 100 mg 1,000 rpm 468.6 Nm 873.1 Nm 1,400 rpm 510.6 Nm 906.2 Nm 1,800 rpm 490.4 Nm 920.3 Nm

130 mg 1246.5 Nm 1331.4 Nm 1395.8 Nm

160 mg 1556.3 Nm 1756.4 Nm 1741.7 Nm

3 Piecewise Linearization and Analysis of Torque Generation Model Structure The relationship between injection quantity and mean indicated torque is nonlinear. In different working conditions, engine demonstrates different dynamic torque output characteristic. Through piecewise linearization and theory analysis, the structure and order of dynamic torque generation model will be determined in the selected engine working conditions.

3.1 Piecewise Linearization and Normalization Using piecewise linearization method, a nonlinear system can be divided into a number of linear intervals. Considering that engine torque generation model demonstrate linear near the selected working condition, we can use a number of linear models for different working conditions to describe engine torque generation process. Step size in the division of the operating point is usually determined according to the severity of nonlinearity of the system. According to analysis of the global nonlinear behavior of the turbocharged diesel in Sect. 2.1, the operating points are chosen as shown in Table 2, covering small load, medium load and heavy load region in the low-speed, medium-speed as well as high-speed segments. In general, controller design is easier to perform using linear models and normalized variables. In the case of non-normalized variables, it is hard to analysis characters of the system and compares controllers to each other. In this paper, based on the idea of normalization, fuel delivery quantity per cycle and torque are transformed into normalized quantity normalized torque in different operating points. In the N0 operating point, normalization process is performed as follows: steady-state fuel delivery value is Mf0 and indicated torque value is Ti0, the neighborhood variable is defined as: Mf ðtÞ ¼ Mf 0 ðtÞ  uðtÞ

ð3Þ

Ti ðtÞ ¼ Ti0 ðtÞ  yðtÞ

ð4Þ

The new variables u(t) and y(t) are dimensionless and around 1 in magnitude:

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uðtÞ ¼ 1 þ duðtÞ

ð5Þ

yðtÞ ¼ 1 þ dyðtÞ

ð6Þ

dM

i where duðtÞ ¼ Mf f ; dyðtÞ ¼ dT Ti After the above processing, a model GðsÞ ¼ dyðsÞ=duðsÞ can be identified in each defined operating point.

3.2 Analysis of Torque Generation Model Structure Mean indicated torque is product of the amount of fuel injected, the lower heating value of the fuel, and indicated efficiency: Ti ¼ K  M f  g

ð7Þ

dTi ¼ K  Mf  dg þ K  g  dMf

ð8Þ

dy 1 dg ¼  þ1 du du g

ð9Þ

Formula 9 shows that, dy=du related to diesel engine efficiency g of the current operating point, efficiency change dg and excitation signal du. Based on analysis of efficiency characteristics in Sect. 2.1, both steady-state efficiency and change of dynamic efficiency are not the same in different operation points. With the same excitation signal du, the torque model parameters are corresponding to with the indicated efficiency g and indicated efficiency change dg. Using the built detailed turbocharged engine model, give step-fuel excitation in the selected operating point. According to the simulation results, shown in Fig. 1, dynamic conditions of the turbocharged diesel engine indicated efficiency with the increase of fuel supply showing decreases rapidly and then gradually increase the torque step increases and then gradually approaches a steady state value. Dynamic process will be divided into the following three stages. The first stage: The increasing of fuel delivery leads to decline of air–fuel ratio, resulting in the rapid decline of the indicated efficiency. But the step increase of fuel delivery will cause that the indicated torque still increased in the next cycle. The first stage can be approximate to a proportional link: G1 ðsÞ ¼ K1

ð10Þ

The second stage: After several operating cycles, the turbine drives compressor to a high speed with the increasing of exhaust gas energy. Due to the large power difference between turbine and compressor, the speed of turbocharger rapidly increases. That will cause the indicating torque of engine increases gradually. The second stage can be approximate to a proportion and inertia link:

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Indicated Torque Nm

Indicated Efficiency %

Fuel Delivery mg/cc

160 140 120 100 0.48 0.46 0.44 0.42 0.40 0.38 1400 1300 1200 1100 1000

29

30

31

32

33

time (s) Fig. 1 PRBS for identification

G2 ðsÞ ¼

K2 T2 S þ 1

ð11Þ

The third stage: Unbalanced of turbine power and compressor’s power leads to the turbocharger speed continues to increase, and the heat transfer of the cylinder and the exhaust pipe slowly approached equilibrium. The torque reached gradually a steady state value. The third stage can also be approximate to a proportion and inertia link, with a longer duration of inertia link: G3 ðsÞ ¼

K3 T3 S þ 1

ð12Þ

According to Fig. 2, the duration of third stage is longer, but indicated torque change is small. Therefore, the third stage can be ignored in most conditions. And transfer function of Fuel Delivery-Mean Indicated Torque can be simplified: G ðsÞ ¼ K1 þ G ðsÞ ¼

K2 T2 S þ 1

K  ðT2 S=K  þ 1Þ T2 S þ 1

ð13Þ ð14Þ

Modeling on Torque Generation for Turbocharged Diesel Engine Fig. 2 Torque generation dynamic process for turbocharged diesel engine

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0.4

PRBS

0.2

0.0

-0.2

-0.4 10

15

20

25

30

35

40

time/(s)

Where K  ¼ K1 þ K2 : As shown in formula (14) torque generation model can be approximated to a first-order system, including a proportional link, a one-order differential link and a one-order inertial link. In the same time, pure delay link also should be considered.

4 Identification for Torque Generation Model In different operating points, diesel engine demonstrates different dynamic torque output characteristic. According to the previous analysis and piecewise linearization, the structure of torque generation model was approximately obtained. In this section, parameters identification of torque model is developed in each defined operating point.

4.1 Design of identification experiment signal The general transfer function is defined as GðsÞ ¼

dYðsÞ b0 þ b1 s þ    þ bnb snb ¼ eLs 1 þ a1 s þ    þ ana sna dYðsÞ

ð15Þ

Firstly time delay should be determined in the operating points. Y. Chamaillard thinks time delay of torque model is in function of the engine speed and load, but more sensitive to the engine speed [5]. Time delay values (Table 3) have been chosen: A Pseudo-Random Binary Sequence (PRBS) is adopted for identification. Based on analysis of Sect. 3.2, designing of the PRBS is proposed, including the clock cycle Dt, signal amplitude and sequence period length Ts. Simulation shows that the

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Table 3 Time delay choice for each speed ne (r/min) 1,000 Time delay(s) 0.02

1,400 0.014

1,800 0.011

Engine Speed (rpm)

1420 1410 1400 1390

mass flow (kg/h)

1380 1000 900 800 700 600

Intake Pressure (/bar)

2.4 2.2 2.0 1.8 1.6

10

15

20

25

30

35

40

time (s) Fig. 3 Changes of boost pressure, mass flow and engine speed

Fig. 4 Comparison of the identification results

identification Output

0.3

Magnitude

0.2 0.1 0.0 -0.1 -0.2 -0.3 15

20

25

30

time (s)

35

40

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Table 4 Identification results in different conditions 1,000 r/min 1,400 r/min 1:21ð0:96s þ1Þ 1:2ð1:03s þ1Þ 70 mg 1:16s þ1 1:24s þ1 1:05ð1:15s þ1Þ 1:10ð0:96s þ1Þ 100 mg 1:21s þ1 1:06s þ1 0:93ð0:54s þ1Þ 0:87ð1:32s þ1Þ 130 mg 0:49sþ1 1:14s þ1 1:01ð0:25s þ1Þ 0:80ð0:47s þ1Þ 160 mg 0:26s þ1 0:38s þ1

Fig. 5 Proportion valueof torque model

1,800 r/min

1:17ð1:13s þ1Þ 1:32s þ1 1:14ð1:26s þ1Þ 0:91s þ1 0:91ð0:89s þ1Þ 0:81s þ1 0:80ð0:77s þ1Þ 0:62s þ1

1.25 1400rpm 1600rpm 1800rpm

1.20

proportion value

1.15 1.10 1.05 1.00 0.95 0.90 0.85 0.80 0.75 60

80

100

120

140

160

fuel delivery (mg)

Fig. 6 Derivative time constant of torque model Derivative time constant

1.4 1.2 1.0 0.8 0.6 0.4

1400rpm 1600rpm 1800rpm

0.2 60

80

100

120

140

160

fuel delivery (mg)

time of turbocharger transition process is about 0.6*1.0 s. Thus the clock cycle Dt is taken as 0.5 s. Reference to the empirical formulaðNPRBS  1Þ  Dt [ Ts , in order to ensure the transition process time greater than the system, NPRBS is chosen as 25

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and the PRBS sequence period is 12 s. In a certain defined operating point, with the PRBS as the input signal, changes of boost pressure, mass flow and instantaneous speed are present in Fig. 3. The input signal meets the needs of the system identification.

4.2 Identification and Results Analysis According to Sect. 3, time delay and the transfer function structure has been defined. Ignoring the third stage of the dynamic process, the transfer function can be approximate to a first-order system. Select the engine speed 1400 r/min, fueldelivery 100 mg/cycle as an identification point. Identification is computed by a classical recursive least-squares (RLS) method. Figure 4 compares the identification results with the output of the detailed model. Identification results show that a first order system can meet the accuracy need of identification. Similarly, identification has been carried out successfully in the other operation points, as shown in Table 4. The system identification results show that the transfer function parameters are not the same in each operation points, and the diesel engine is a nonlinear time-varying system. Proportion coefficient, inertia time constant and derivative time constant in different conditions are present in Figs. 5, 6 and 7. The proportion links is similar to the trend indicated thermal efficiency, in the range of 0.77–1.2. According to Sect. 3 analyses, the dg change of the dynamic process is smaller than difference of g with the small exciting signal, so the proportion value and indicated thermal efficiency trends are similar in each defined operating points. Derivative time constant increases at first then decreases with the increasing fuel injection quantity, in the range of 0.2–1.3. Inertia time constant showing the trend of decrease with increasing fuel injection quantity, in the range of 0.3–1.2. One reason is that turbocharger speed is rising faster in the dynamic process when engine runs in high-load operation point.

5 Conclusions Considering key transient phenomenon of turbocharged diesel engine, including turbocharger inertia, volume effect, thermal transient and energy transport delay, a detailed engine model was constructed for identification. Dynamic torque generation model can be approximated to a first-order system, including proportional link, one-order differential link, one-order inertial link and pure lagging link. Parameters of dynamic torque generation model are associated with operating points. Time delay decreases with the speed increase. The trend of proportional

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inertia time constant

1.4 1.2 1.0 0.8 0.6

1400rpm 1600rpm 1800rpm

0.4 0.2 60

80

100

120

140

160

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factor is equivalent to the trend of thermal efficiency. The time delay is a function of engine load and speed, and it decreases with increasing of fuel injection quantity.

References 1. Marciszko F (2004) Torque sensor based powertrain control. Daimler Chrysler AG 2004: 24–30 2. Rizzoni G (1989) Estimate of indicated torque from crankshaft speed fluctuations: a model for the dynamics of the IC engine. IEEE Trans Veh Techno 38(3):168–179 3. Hong MN, Ouyang M (2009) On-board torque estimation base on mean value SI engine models. J Mech Eng 45(4):290–294 4. Chamaillard Y, Higelin P, Charlet A (2004) A simple method for robust control design, application on a non-linear and delayed system: engine torque control. Control Eng Pract 12(4):417–429 5. Du C, Yan F, Yan Y, Yang P (2008) Methods of engine torque estimation for control algorithms. Trans CSICE 26(5):446–451 6. Müller R, Schneider B (2000) Approximation and control of the engine torque using neural networks. SAE Paper 2000-01-0929 7. Wang Z, WU N, XU Y, LIU Z (2007) Study on combustion parameter from a turbocharged DI diesel engine under transient operating conditions. Trans CSICE 25(5):385–389 8. Serrano JR, Arnau FJ, Dolz V, Piqueras P (2009) Methodology for characterisation and simulation of turbocharged diesel engines combustion during transient operation. Part 1: Data acquisition and post-processing. Appl Therm Eng 29(1):142–149 9. Oh S, Kim D, Kim J et al. (2009) Real-time IMEP estimation for torque-based engine control using an in-cylinder pressure sensor. SAE Paper 2009-01-0244 10. Galindo J, Bermu´ V, dez Serrano J, Lo´pez J (2001) Cycle-to-cycle diesel combustion characterization during engine transient operation. SAE Paper 2001-01-3262

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11. Horlock J, Winterbone D (1986) The thermodynamics and gas dynamics of internalcombustion engines. Oxford University Press, Oxford 12. Kao M, Moskwa J (1993) Turbocharged diesel engine modeling for nonlinear engine control and state estimation. Proceedings of the 1993 ASME Winter Annual Meeting. ASME, New Orleans, p 135–146

Design of a Versatile Rapid Prototyping Engine Management System Bernd Eichberger, Eduard Unger and Mario Oswald

Abstract The performance and capabilities of an electronic control unit for internal combustion engines are key issues for engine research and development. The objective of this hardware and software co- development was the design of a versatile and rugged rapid prototyping engine management system (RPEMS). This work has been carried out in cooperation between AVL List GmbH Graz and University of Technology, Institute of Electronics, Graz. The advantages and drawbacks of available RPEMSs have been analysed and compared to current and future requirements of modern internal combustion engine control. Hardware and software functionality and the mechanical design have been optimized and focused on upgradeability and scalability. The main version of the hardware is for direct injection gasoline engine control, with subsequent variants for diesel engine and for vehicle control. The new RPEMS and its derivatives turned out as reliable and powerful tools. They are equally well suited for laboratory use and small volume field tests. Two hardware versions control up to eight cylinders of a direct or intake-manifold gasoline or of a diesel engine, two high-pressure pumps, four knock detectors, several bus interfaces and feature additional I/O lines with dedicated sensor/actuator interfaces for special development purposes. For safety–critical applications, two units can be linked together in a master–slave or redundant configuration. A dedicated version for vehicle control supports the development of hybrid drives. While former works of the authors were related to specific detailed aspects of engine control, the design of this new RPEMS is a practical example of prototype engine control and F2012-D01-022 B. Eichberger (&) Graz University of Technology, Graz, Austria e-mail: [email protected] E. Unger  M. Oswald AVL List GmbH, Graz, Austria SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_14,  Springer-Verlag Berlin Heidelberg 2013

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application-oriented engineering. The paper outlines the specific placement of the product for and within the engine development process. Therefore it presents the requirements on the electronic control unit from a functional point of view and shows how they have been fulfilled by means of joint hardware/software research and development. A hardware development platform as well as software toolboxes have successfully been developed. The outcome—AVL RPEMS—serves as a prototype engine control unit for initial prototype and extra-low volume production vehicles. It proved its fitness at test bench and demo vehicle applications and equally well under tough fleet testing operation. Keywords Engine management Co-design

 Rapid prototyping  Hardware  Software 

1 Introduction The quest for an on-going reduction of CO2 emissions poses a key challenge for the global car industry. The European Parliament agreed to limit the CO2 emissions from new passenger cars sold within the European Union to an average of 130 g/km by 2015. Further proposals schedule to limit them to 95 g/km by the year 2020. Drivers request lower fuel consumption without compromise in engine performance. For that purpose, modern internal combustion engines (ICE) heavily rely on powerful and feature-rich electronic control units. Complex software algorithms control and monitor the engine and its additional systems under all operating conditions. It is this combination of mechanical, electrical and software engineering that cares for an optimum performance of an ICE. The objective of this hardware and software co-development was the design of a versatile and rugged rapid prototyping engine management system (RPEMS). Such prototype engine management systems are available in a variety of designs, with pros and cons. AVL List GmbH decided to develop both the hardware and software of this RPEMS in house. The main reason for this decision was the full access and control of the software functionality at all development stages. A basis of decision-making whether buying or developing a PEMS is, of course, also costs versus performance criteria.

2 Requirements and Scopes of Applications The main application of the RPEMS is to support the development of new internal combustion engines. Moreover, it is equally well suited for engine upgrades and small volume field tests of preproduction cars. Prototype engine control units are available on the market in an ample variety and shape. They are restricted with respect to modifications of software algorithms and/or

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internal parameters, variables or engine operating maps. In series applications, this access is supported by the ECU supplier using CCP, XCP or similar protocols, such that OEMs can calibrate the engine controller for series production. These options are inadequate in case of a non-standard engine or engine development including new components as the control program itself cannot be altered. A modification of the engine which involves different types or numbers of sensors or actuators cannot be handled without modification of the software itself, quite often including hardware parts too. The RPEMS should be used both engine test bench conditions and automotive mechanical, thermal and electrical environment conditions. This narrows the range of available products. A continuous workflow would require using the same RPEMS from the early development stage up to low volume series tests, making costs a basic decision criterion. Such a versatile RPEMS has been developed, meeting most of the demands to a great extent. It supports a super-set of hardware and software services, whereupon unnecessary hardware parts can be omitted as a mounting option [1–3]. Combining gasoline and diesel engine control into a single unit turned out to be impractical since they differ significantly in the design of the actuator driver stages. Three main hardware types have been derived from the basic development, namely • RPEMS DI for modern direct injection turbo charged gasoline engines. • RPEMS CR for common rail turbo charged diesel engines. • RPEMS VCU for vehicle control (safety functions, e-cars). RPEMS DI is the lead variant, which the others have been derived from. All of them share the same main hardware functions while they differ in the type and number of dedicated sensor and actuator interfaces [4–9]. Functional safety can be quite an important demand, especially for test runs in the field and use in prototype cars. Full compliance with regulations and engineering standards may not be adequate at such a design stage because of the organizational overhead that they implicate. For safety–critical applications a special variant is foreseen. It uses the peripheral control processor (PCP) of the TriCore microprocessor with special safety software (PROSIL) [10]. The PCP communicates via a serial peripheral interface (SPI) with a signature watchdog device, which is part of the main voltage regulator, in a question—answer mode (Level 3 of 3-stage EGAS monitoring concept) [11–13].

3 Hardware Design In the first instance, the properties of the attached sensors and actuators and the way they are evaluated/controlled determine the requirements on the hardware. Next are bus interfaces, computing capacity and power supply. Standard components or dedicated integrated circuits for automotive operation can be used. The latter usually offer more adjustment options, especially for driving actuators and

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DEBUG

SD-Card

USB EMI / ESD Protection

Microprocessor Board

Power Supply

Power

Bus

Supervisory

Conditioning

Drivers

Transceivers

Functions

EMI ESD Protection

EMI ESD Protection

EMI ESD Protection

EMI OVP Filter

INPUTS Sensors

OUTPUTS Actuators

Bus Interfaces

Power KL30, 31, 15

Signal

Fig. 1 RPEMS hardware block diagram

evaluating special sensor signals (UEGO). Figure 1 illustrates the functional block diagram of the RPEMS unit. It is desirable to have a hardware architecture which widely complies with large-scale production engine management units. The circuitry-wise realization strongly interacts with the low-level hardware drivers of the firmware. Lower level drivers and the hardware abstraction layer (HAL) would keep the real hardware functions away from the application program. This allows for the re-use of software parts, regardless of changes in hardware. However, differences may exist in response times, general timing performance, error handling, to mention just a few. The RPEMS is equipped, wherever applicable, with dedicated automotive application specific integrated circuits (ASICs). They represent proven solutions and can be configured by software according to the particular sensors and actuators. Moreover, they relieve the microprocessor from low-level real-time tasks by preconditioning signals or stand-alone handling of control loops. All variants of the RPEMS use an Infineon TriCore microprocessor. They differ in computing power (clock speed), ROM/RAM size, number of I/O lines and onchip peripherals. The key hardware features of the RPEMS and its derivatives are: • • • • • • • • •

Prepared for Gasoline Engines up to 8 Cylinders Prepared for PFI and DI Injection (Hybrid Injection) Integrated Ignition Power Stages (IGBT) and Smart Coil Drivers 2x Fuel High Pressure Pump Drive. 2x H-Bridge. 3x (optional 4x) CAN Interfaces. 25 Low-Side Drivers (Digital, PWM). 2x Wide Band Oxygen Sensor (LSU), 2x HEGO Sensors. Various analog, digital, speed and frequency inputs.

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Fig. 2 RPEMS printed circuit board, partially populated

Figure 2 shows the top side of the partially populated printed circuit board. The connectors and electronic components are mounted on a four-layer printed circuit board, except for the microprocessor and its support parts. These are mounted on a small pluggable sub-print, which is accessible by removing the top cover of the RPEMS housing. This main board and sub board concept enables a scalable performance, ranging from a ‘‘low-end’’ TC1796 up to a ‘‘high-end’’ TC1793, TC1798 microprocessor. Figure 3 shows an exterior view of the RPEMS. ‘‘Future’’ is an addendum for marketing.

4 Hardware Variants A ubiquitous hardware is the preferred choice, but this would implicate a significant complexity and increase in costs. The solution was a modular hardware design at the circuit/functional and at the layout level. Such an approach reduces time and effort for an upgrade or design change. The main part of the development efforts is spent on the first variant, which undergoes several re-designs and improvements of EMC/ESD compliance. This was the RPEMS DI for gasoline direct injection engines. Its injector driver stages are totally incompatible for diesel injectors, so this part of the hardware had to be modified. Bus interfaces or sensor inputs can be adapted in a similar way. Test expenditures for such a derived hardware variant are remarkably lower than for a new design. Alternative hardware components undergo tests and optimisations on dedicated prototype boards before being implemented on the RPEMS printed circuit board. The benefit of this straightforward replace ability is a minimal number of re-designs and a short lead time. Figure 4 shows the change of selected hardware components in order to transform the DI variant into the CR variant.

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Fig. 3 RPEMS exterior view

RPEMS - DI

RPEMS - CR

Gasoline direct injection variant

Diesel common rail variant

(Modified actuator drivers)

Fig. 4 RPEMS hardware layout: further development from gasoline (DI) to diesel (CR) variant

5 Software Design Application Software (ASW) libraries have been developed to run applications on 3 different hardware variants (DI, CR, VCU). Approved functional blocks are ready for re-use and help to reduce the setup time of new configurations. The algorithms mainly focus on engine/vehicle control strategies—diagnosis is of minor relevance. All control strategies are developed by means of a graphical programming user interface (ASCET, SIMULINK).

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Fig. 5 RPEMS software concept

Main features of all variants of RPEMS are: • • • • •

Complete SW development @ AVL with ASCET-MD/SE 5.2, 6.1 [14] Integration of Simulink/Targetlink via C-Source Code Standard calibration interface: CCP (Can Calibration Protocol) [15] Standard calibration interface compatible to INCA-PC [16] High reusability of software components.

The software is characterized by its hierarchical structure and the underlying real-time operating system. Access to the hardware is given by a hardware abstraction layer (HAL). This HAL requires only a small extra amount of processing time and substantially eases the development of the application program. All real-time and periodic functions run at this level. Adding new functions or applying changes to this HAL is easily done because all these routines are available as source code. Separating the application program from hardware-dependent routines makes it much easier to transfer the software from the prototyping stage to the final application. The hardware abstraction layer (HAL) consists of two parts: layer 1 handles peripheral hardware using dedicated on-chip interfaces, layer 2 is a SW driver layer (LLD) which converts raw input/output data into physical units and vice versa. Most of this functionality is performed by the Peripheral Control Processor (PCP) of the TriCore Microprocessor, thereby unloading the main CPU from time-critical tasks. Figure 5 shows an overview of the RPEMS software concept. The libraries use a data base structure. To build a project the necessary modules can easily be linked together using a graphical project editor. Additionally the scheduling of the whole project can be done in a graphical way. After this configuration process the software development environment generates the executable code for the microprocessor. The resulting *.hex and *.a2l files are ready for downloading to the engine controller.

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6 Conclusions A hardware development platform as well as software toolboxes have successfully been developed. The outcome—AVL RPEMS—serves as a prototype engine control unit for initial prototype and extra-low volume production vehicles. It proved its fitness at test bench and demo vehicle applications and equally well under tough fleet testing operation.

References 1. Unger E (2002) HCCI engine control. Graz University of Technology, Doctoral thesis 2. Oswald M (2010) Development of an engine control unit based on Infineons TC1797 microcontroller. Graz University of Technology, Diploma thesis 3. ISO 7637-2 (2011) Road vehicles: electrical disturbances from conduction and coupling, Part 2: electrical transient conduction along supply lines only 4. ISO/CD 7637-3 Road vehicles: electrical disturbances from conduction and coupling–Part 3: electrical transient transmission by capacitive and inductive coupling via lines other than supply lines 5. ISO 10483-1(2004) Road vehicles, intelligent power switches, Part 1: high-side intelligent power switch 6. ISO 10483-2 (1996) Road vehicles, intelligent power switches, Part 2: Low-side intelligent power switch 7. ISO 10605 (2008) ISO 10605 (2008)/CD Amd 1 ISO 10605 (2008)/Cor 1 (2010) Road vehicles, test methods for electrical disturbances from electrostatic discharge 8. ISO 11452-5 (2002) Road vehicles, component test methods for electrical disturbances from narrowband radiated electromagnetic energy, Part 5: Strip line 9. HITEX Development Tools GmbH, Germany, Internet home page: www.hitex.com 10. ISO 26262-4 (2011) Road vehicles, functional safety, Part 4: Product development at the system level 11. ISO 26262-5 (2011) Road vehicles, functional safety, Part 5: Product development at the hardware level 12. ISO 26262-6 (2011) Road vehicles, functional safety. Part 6: Product development at the software level 13. ETAS GmbH, Germany, Internet home page: www.etas.com 14. ASAM MCD-1 CCP V2.1.0 (1999) CAN Calibration Protocol V2.1.0, ASAM e.V. 18 February 1999 15. ASAM MCD-1 XCP (2003) The universal measurement and calibration protocol family V1.0.0, ASAM e.V. 28 April 2003 16. Schaffer Horst (2008) Development of an HAL for the 32-bit microcontroller TC1775 from Infineon. Graz University of Technology, Diploma thesis

Study on State Parameters Estimation for Commercial Vehicle Li Liu, Chaosheng Huang, Yuanfang Li and Shuming Shi

Abstract Vehicle mass and road gradient are the important parameters for engine torque control, transmission shift scheduling and vehicle longitudinal control. It will add manufacturing cost to use more sensors to obtain these values. Therefore, there is increasing concern on the estimation methods of vehicle mass and road gradient based on the vehicle model. In this paper, on the premise of no additional sensors, the engine torque, engine speed, velocity, acceleration/brake/ clutch pedal signals and gear from the CAN bus are used as the original data. The estimation methods of vehicle mass and road gradient are studied by applying vehicle dynamic, Luenberger state observer and Recursive Least Square with varying forgetting factors. Furthermore, the real time estimation arithmetic is validated through dSPACE/MicroAutoBox system on FAW J5 commercial vehicle.





Keywords Vehicle mass estimation Road gradient estimation State parameter CAN BUS Commercial vehicle





Nomenclature Vehicle traction force (N) Ft Road gradient resistance (N) Fi Rolling resistance (N) Ff Fw Air resistance (N)

F2012-D01-026 L. Liu  C. Huang China FAW R and D Center, Changchun, China Y. Li  S. Shi (&) College of Traffic, Jilin University, Changchun, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_15,  Springer-Verlag Berlin Heidelberg 2013

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Acceleration resistance (N) Vehicle velocity (m/s) Road gradient (rad) Acceleration (m/s2) Vehicle mass mfull ¼ 31; 240; mempty ¼ 11; 940 (kg) Acceleration of gravity (g = 9.8) (m/s2) Coefficient of the revolving mass changes to linear mass Rolling resistance coefficient (f = 0.0059)

1 Introduction In modern vehicle control systems, more accurate signals and parameters are required for engine, transmission and vehicle control systems. It will add manufacturing cost to use more sensors, therefore, there is increasing concern on the estimation methods based on the vehicle model. Vehicle mass and road gradient are the important parameters for engine torque control, transmission shift scheduling and vehicle longitudinal control. From the view of accuracy and manufacturing cost, it is a great project to study how to obtain accurate and real-time vehicle mass and road gradient without additional sensors. There are basically three methods on road gradient parameter research. The first method is to obtain the road gradient by calculating vertical velocity and horizontal velocity based on GPS data or by longitudinal motion equation and filter based on GPS data and Toque sensors [1–3]. The second is to estimate the road gradient and vehicle pitch angle by filter based on vertical/longitudinal acceleration sensor and wheel speed sensor [4, 5]. The last is state parameters estimation method based on the CAN bus data and vehicle equation of motion [6–8]. Although the sensors can obtain the data easily and directly, the accuracy could not meet the requirement and the manufacturing cost will increase greatly. The arithmetic studies on road gradient and vehicle mass estimation mainly focus on Least Square method [9], Kalman Filter [10, 11] and state observer [12, 13]. Least Square method belongs to system identification, and the other two methods belong to state estimation. Kalman Filter solves the optimal solution by estimating the minimum value of mean square deviation, and state observer eliminates deviations by deviation feedback. The computation of state observer is simpler than that of Kalman Filter. On vehicle mass parameter research, since mass is a slow-changing variable, it is more rational and simpler to treat vehicle mass as system parameter and use Least Square method. For road gradient estimation, road gradient is a time-varying state variable, and it is suitable to use Luenberger state observer.

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Fig. 1 Sketch of longitudinal forces of an ascending car

In this paper, on the premise of no additional sensors, the engine torque, engine speed, velocity, acceleration/brake/clutch pedal signals and gear from the CAN bus are used as the original data. The estimation methods of vehicle mass and road gradient are studied by applying vehicle dynamic, Luenberger state observer and Recursive Least Square with varying forgetting factors. Furthermore, the real-time estimation arithmetic is validated through dSPACE/MicroAutoBox system on FAW J5 commercial vehicle.

2 State Parameters Estimate Method 2.1 Road Gradient Estimation Method The effect of the lateral dynamics on road gradient estimation is neglected. The vehicle body side slip angle is assumed to be zero. This assumption is true for straight on driving situations. Setting up the force balance of the forces is displayed in Fig. 1. The forces balance equation is shown in Eq. (1). Ft ¼ Fw þ Ff þ Fi þ Fj

ð1Þ

The equation is changed into the following format: dm m  g  sin i |fflffl{zfflffl}v_ ¼ Ft  Ff  Fw |fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl} Fj

ð2Þ

Fi

To reduce the computational complexity, a linear observer is employed here. Therefore, Eq. (1) has to be linearized. For the linearization, the following assumptions are made [14]: • The road gradient angle of public roads is limited to approx. ±12. This yields tan a ¼ sin a  i • The forces Ft ; Ff and Fw are merged into a resultant force Ftfw ¼ Ft  Ff  Fw : This is advantageous because the nonlinear term Fw becomes part of the input. The remaining state space model therefore is linear. As a consequence of these assumptions, Eq. (2) is simplified

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dm  v_ ¼ Ftfw  m  g  i

ð3Þ

Equation (3) is now transformed to a state space model. The state vector x for the linear model contains the velocity v and the road gradient angle i: The input u is the resultant force Ftfw : The road gradient model can then be written as         1= 0 g=d v v_ ðdmÞ 0 Ftfw þ ¼ ð4Þ i 0 0 i_ 0 0 0 y ¼ ½1

  v 0 i

ð5Þ

State vector : x ¼ ½v; iT

ð6Þ

 T Input vector : u ¼ Ftfw ; 0

ð7Þ

Output vector : y ¼ v   0 g=d State matrix : A ¼ 0 0 Input matrix : B ¼



1= ðmdÞ 0

Output matrix : C ¼ ½ 1

0 0 0



ð8Þ ð9Þ ð10Þ ð11Þ

In this model, matrix A; B; C; uand y could be measured or calculated from the CAN bus data. The observer design is carried out by means of pole placement. This requires an analysis of the system observability. The observability matrix QB must have maximum rand. The matrix QB is calculated by means of Eq. (12), when n demotes the system order.     1 0 C ð12Þ ¼ QB ¼ 0 g=d CA As QB is square, the maximum rank can be checked by means of the determination of QB :   1 0 ð13Þ ¼ g=d 6¼ 0 detðQB Þ ¼ det 0 g=d According to Eq. (13) the determinant of QB is g=d: Therefore, the rows of matrix QB are linearly independent and the linear road gradient model (4) is observable. Thus, the observer design by means of pole placement is feasible. Since the system order is n ¼ 2; the observer gain matrix L consists of two elements l1 and l2 : In order to calculate these elements, the poles of the observed

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system must be placed appropriately. Its characteristic polynomial of the closed loop system is       l1 0 0 g=d s 0 þ  detðsI  A þ LC Þ ¼ det 0 0 l2 0 0 s ð14Þ 2 gl 2 ¼s þ sl1  =d The eigenvalues are denoted as k1 and k2 ; and are chosen according to the following equation. s2 þ sl1  gl2=d ¼ ðs  k1 Þðs  k2 Þ

¼ s 2  s ð k1 þ k 2 Þ þ k1 k 2

ð15Þ

For pole-placement, the coefficients of Eq. (15) are compared. This yields the elements of the observer gain matrix L: l1 ¼ k1  k2 ;

l2 ¼

k1  k2 g= d

ð16Þ

Considering the constraints of L and the engineer problem, after calibration the eigenvalues are fixed k1 ¼ 0:9;

k2 ¼ 1

ð17Þ

2.2 Mass Estimate Method For a SISO system, the process model [14] is



A z1 yðkÞ ¼ B z1 uðkÞ þ eðkÞ

ð18Þ

where, yðkÞ is output vector, uðkÞ is input vector, eðkÞ is white noise. Change the process model into Least Squares, the model structure is ym ðkÞ ¼ hs ðkÞh þ eðkÞ

ð19Þ

ym ðkÞ is assumed to be known, and the inputs uðkÞ are known or can be measured without errors. The task of the parameter estimation is to determine the process parameter h. The modeled outputs ym ðkÞ must correspond as exactly as possible with the measured output yp ðkÞ of the system. The quality of the correspondence is defined in that the sum of the square of the observed errors at a particular time instant k is minimized:

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Fig. 5 Engine speed and output torque

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Fig. 8 Road gradient

Equation (21) could be written as following: m  ðgf þ gi þ daÞ þe Ft  Fw ¼ |{z} |fflfflfflffl{zfflfflfflffl} |fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl} Ftw

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Fig. 9 Vehicle mass

Fig. 10 Velocity and acceleration

^ ð k  1Þ  ^ ðk Þ ¼ m ^ ðk  1Þ þ cðkÞ½Ftw ðkÞ  ae ðkÞm m cðkÞ ¼ Pðk  1Þae ðkÞ½ae ðkÞPðk  1Þae ðkÞ þ lðkÞ1 PðkÞ ¼ lð1kÞ ½I  cðkÞae ðkÞPðk  1Þ

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Via the introduction of the forgetting factor l, which increases the elements in the covariance matrix PðkÞ for each iteration and the weights of the new data higher than the older data, one is able to slowly forget past values. This overcomes the consistency property of least squares estimators for time variant parameters. The forgetting factor is set as follows [15].

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Fig. 11 Engine speed and output torque

Fig. 12 Accelerator pedal, brake pedal and clutch pedal

lðtÞ ¼ 1  0:05  0:98t

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3 Vehicle Experiment 3.1 Simulation Model and Vehicle Online Test The application object is FAW J5 automatic commercial vehicle, and the original data, such as engine output torque, engine speed, vehicle speed, accelerator pedal position, and gear are all from CAN bus.

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Fig. 13 Gear

The simulation model to estimate the road gradient and vehicle mass is build based on Matlab/Simulink, shown in Fig. 2. This model has four parts: CAN bus data read model, data preprocess model, parameter estimation model and data post-process model. After building the simulation model and parameter calibration, the real-time estimation arithmetic is validated through dSPACE/MicroAutoBox system, shown in Fig. 2. In order to fully validate the estimation result, experiments with different vehicle mass (empty load and full load) are carried out in mountain areas.

3.2 Estimation Results Analysis There are many real-time vehicle tests that we have done on different types of road, containing flat road, constant gradient road and montanic road with empty load and full load. In this part, only two kinds of experiments are shown. Figures 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, and 15 respectively show the vehicle state information and estimate results of vehicle mass and road gradient when the vehicle is driving in mountain areas with empty load and full load. There are complicated multi-working conditions, such as vehicle starting stage condition and continuous shift/brake condition, which could validate the estimate results during normal vehicle driving operations. It is shown from the empty load experiments that the real value and estimated value of vehicle mass is 11.94 and 12.6 t respectively. The trend of estimated road gradient matched with that of the measured value, and the maximum absolute error of road gradient is 1 %. The conclusion of full loaded experiments is similar with that of empty loaded experiments. The real vehicle mass is 31.24 t and the estimated value is 29.4 t. There is a little difference in the estimated road gradient, which is caused by

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Fig. 14 Road gradient

Fig. 15 Vehicle mass

shifting at the beginning, and the maximum absolute error between measured value and estimated value is about 1.5 %. It is concluded from the above experiment results that the Luenberger state observer and Recursive Least Square is effective for the estimation of road gradient and vehicle mass. According to the statistics of all vehicle experiments, the estimated range of vehicle mass changes from 10 to 32 t, and the absolute error of vehicle mass is less than 5 t. Also the range of road gradient changes from -6 to 10 % and the absolute error of road gradient is less than 2 %. The accuracy and range fully satisfy the engineering requirement. Furthermore, these estimated values could rapidly get constringency within a short time. Therefore, the arithmetic proposed in this paper is a practical method for engineer problem, satisfying both the accuracy and real time performance, and the estimation method could be considered to be applied on the commercial vehicle product.

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4 Conclusion Vehicle mass and road gradient real-time estimation are realized through the Luenberger state observer and Recursive Least Square method for commercial vehicle. The estimation arithmetic of vehicle mass and road gradient directly uses the CAN BUS signals as the original data, which need no additional sensors and do not increase the manufacturing cost. Furthermore, by this estimation arithmetic, the vehicle mass and road gradient are real-time estimated, and the estimation precision could also satisfy the engineering requirement. The real-time estimate value of vehicle mass and road gradient will provide great references for engine torque control and economical transmission shift scheduling. There is significant research value for fuel-saving of future commercial vehicle.

References 1. Hahn J-O, Rajamani R, Alexander L (2004) Real-time identification of road-bank angle using differential GPS. IEEE Trans Control Syst Technol 12(4):589–599 2. Johansson K (2005) Road slope estimation with standard truck sensors. KTH, Sweden 3. Bae HS, Ryu J, Gerdes JC (2001) Road grade and vehicle parameter estimation for longitudinal control using GPS. IEEE intelligent transportation systems conference (ITSC 2001) 4. Ohnishi H, Ishii J, Kayano M, Katayama H (2000) Study on road slope estimation for automatic transmission control. JSAE Rev 21(2):235–240 5. Shi J, Lu T, Li X, Zhang J (2011) Self-adaptive slope gearshift strategy for automatic transmission vehicles. Trans Chin Soc Agric 42(4):1–7 6. Mangan S, Wang J (2007) Development of a novel sensorless longitudinal road gradient estimation method based on vehicle CAN bus data. IEEE/ASME Trans Mechatron 12(3):375–386 7. Mangan S, Wang J, Wu Q (2003) Longitudinal road gradient estimation using vehicle CAN bus data. IEEE 8. Wang Y, Dong R, Wang S, Zheng X (2010) Real-time road slope recognition algorithm for heavy truck based on SAE J1939 protocol. Automot Eng (7):640–642+647 9. Fathy HK, Kang D, Stein JL (2008) Online vehicle mass estimation using recursive least squares and supervisory data extraction. American control conference. Westin Seattle Hotel, Washington 10. Eriksson A (2009) Implementation and evaluation of a mass estimation algorithm. KTH, Sweden 11. Winstead V, Kolmanovsky IV (2005) Estimation of road grade and vehicle mass via model predictive control. The 2005 IEEE conference on control applications, Toronto 12. Vahidi A, Druzhinina M, Stefanopoulou A, Peng K (2003) Simultaneous mass and timevarying grade estimation for heavy-duty vehicles. IEEE 13. Vahidi A, Stefanopoulou A, Peng K Recursive least squares with forgetting for online estimation of vehicle mass and road grade-theory and experiments 14. Kiencke U, Nielsen L (2005) Automotive control systems for engine, driveline, and vehicle, 2nd edn. Springer 15. Ljung L (2009) System identification theory for the user, 2nd edn. Prentice hall, New Jersey

The Research and Implementation of Engine-Timing-Control System Based on AUTOSAR Standard Weimin He, Guilin Lv, Tao Chen, Shizhen Liu and Hui Han

Abstract The main task of the engine-timing-control system (timing system for abbreviation) is to build up the engine phase according to the crankshaft and camshaft signals, and to control the actuators accurately according to the engine phase. The timing system is mainly constructed with the following modules: engine phase management module, injection control module, ignition control module and pump control module. FAW designed and implemented the timing system based on AUTOSAR standard. The timing system can support gasoline engine, diesel engine and alternative fuel engine through parameter configuration via AUTOSAR standard tools. The timing system has good portability, scalability and reliability, and it can reduce the cost and shorten the cycle of the system development. Keywords AUTOSAR

 Engine-timing-control system  Platform

1 Introduction of the Timing System Timing system is one part of the core of the engine-control-system, and it has a significant impact on engine smoothness, robustness, safety and emissions. The inputs and outputs of the timing system are shown in Fig. 1.

F2012-D01-027 W. He  G. Lv (&)  T. Chen  S. Liu  H. Han China FAW Co. Ltd R&D Center, Changchun, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_16,  Springer-Verlag Berlin Heidelberg 2013

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Inj Drive signals in GDI Project

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Fig. 2 The combinations of inputs and outputs of the timing system

The timing system receives signals from the crankshaft and camshaft and builds up the engine phase to control the actuators. At the same time, the timing system receives the inputs from application software, such as injection control command, ignition control command and pump control command. Then it activates the actuators when the specific events happened. Simultaneously, it provides the critical information of the engine to application, such as engine speed, engine acceleration etc. The control of the timing system is complicated. In order to illustrate the complexity of the timing system, the combinations of the crankshaft and camshaft signals and the injection signals are shown in Fig. 2. As shown in Fig. 2, the inputs are complex. For many different types of engines, there are different combinations of crankshaft and camshaft signals. For example, a GDI engine has a 60-2 teeth crankshaft and a two-long-two-short camshaft. A natural gas engine has a 60-2 teeth crankshaft and an N ? 1 teeth camshaft (N is the engine cylinder number). A port fuel injection engine has a 36-6 teeth crankshaft and an N - 1 teeth camshaft (N is the engine cylinder number).

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As shown in Fig. 2, the outputs are quite complex too. First of all, there are large quantities and many categories of actuators. A specific drive form is required for each category of actuators. Secondly, even for the same category, the drive form may be different because the types of the actuators are different. At last, even for the same type of actuators, the output drive signals may be different due to the differences of the hardware circuit. Figure 3 shows the fuel injection characteristics of different injectors. The development of the timing system has relatively strong pertinence. A specific design of the system may be carried out according to a specific type of engine or a specified engine [1]. When there are new requirements or there comes a new engine, the timing system will carry out a large number of modifications, even need to be re-designed and re-developed. In order to reduce the duplication of work and to improve the efficiency of development, a timing system platform is urgently needed. The existence of AUTOSAR provides guidance to the design and development of the timing system. AUTOSAR defines a set of criteria to be used in different platforms. It can improve the capability of software reuse and reduce the cost of the software development [1]. FAW developed the timing system platform which followed the AUTOSAR standard by using the standard AUTOSAR tools.

2 The Design of the Timing System in AUTOSAR To meet the requirements mentioned above, FAW carried out fully requirements analysis on many types of engines. And the design of the timing system consists of modular design, hierarchical design and standardized design.

2.1 The Modular Design of Timing System in AUTOSAR As shown in Fig. 4, the timing system can be divided into the following modules [2]: engine phase management module (the left two columns), injection control

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module (the third column), ignition control module (the fourth column) and pump control module (the last column). The core module of the timing system is the engine phase management module. This module was divided into sub-modules to fulfil detailed functions [3, 4]. The CrkDrv and CamDrv module mainly recognize the crank and cam signals to provide the basic information to the CrkIf and CamIf. Through the CrkIf and CamIf module, the upper modules can get and set information of the crank and cam. The EngPosMnmt module manages the crank and cam information and generates the engine position information for other modules. When there is something wrong with the crankshaft or camshaft, the LimpCrk and LimpCam module can catch the faults and switch normal mode to limp home mode. In limp home mode, the EngPosMnmt module generates the engine angle by using the crankshaft or camshaft. At the same time, it manages the state machine of the timing system, ensure the stability and robust during the mode switching. The engine speed information which should be used in other modules is generated by the EndSpd module. The injection control module was divided into some sub-modules too. The InjDrv module mainly drives the injectors and provides basic injection information to InjIf. The InjIf is the interface module between the upper modules and InjDrv. The upper modules mainly completer the injection data management, multiinjection and injection correction functions. These modules get and set information of the injectors through the InjIf module.

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The ignition control module and the pump control module are similar with the injection control module.

2.2 The Hierarchical Design of Timing System in AUTOSAR The principle of the hierarchical software design is that the boundary between software levels must clear enough to help the users to understand, and each level of the hierarchy can achieve their specific function well [5]. The hierarchical graph of the timing system is shown in Fig. 4. The bottom of the hierarchies is the timing control driver layer, its role is to make the timing control abstract layer and the actual hardware environment completely isolated. The main task of the timing control drive layer is to complete the driven task of the sensors and the actuators, and to provide base information for the application software. For different engine control systems, the realization of the hardware platform maybe different, so there are maybe different implementations of the timing control driver layer according to different hardware platforms. By selecting suitable hardware platform driven library, the modules can be integrated into the engine control system quickly. For example, if the single-chip of MPC series is used, the developers only need to prepare the timing driver library for eTPU2; and if the single-chip of TriCore series is used, the developers only need to prepare the timing driver library for GPTA. The role of the timing control abstract layer is to make the upper service layer and the lower driver layer completely isolated. When the upper level of the system wants to get and set the information of the sensors and actuators, there is no need for the upper layer to pay attention to the actual type of the sensors and actuators. All you need to do is to control the sensors and actuators at logic aspect. The role of the timing control service layer is to provide services through the standard interfaces and to make the upper application and the timing system completely isolated. When the upper application layer needs to access the timing system, there is no need to pay attentions to the actual implementation of the timing system. The application layer only needs to access the timing system through RTE.

2.3 The Standardized Design of Timing System in AUTOSAR In order to achieve the rapid transplantation, a series of system standardization work must be carried out [6]. Between the adjacent layers of the software, FAW standardizes the interfaces for each module. The detailed work includes the name of the interfaces, the parameter types of the interfaces, the parameters value range, the call methods, etc.

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Fig. 5 The interface of the timing system and interrupt system

The interfaces of the timing control abstraction layer are also standardized. Through the standardized interfaces, the development of the timing control abstract layer can be relatively independent. No matter what the realization methods are used, the modules can be integrated into the timing system. The upper level of the software can access the lower level services through the interfaces to get the needed information. At the same time, the timing control abstract layer can provide critical information to the upper layer through the interfaces. The interface of the timing control service layer is between the whole timing system and the application [7]. As shown in Fig. 5, the timing system can provide the appropriate information, such as the state of the engine, the diagnosis information of the system, the engine speed information as well as the interrupts, to the upper layer through the standard interfaces. And the control commands coming from the application can be passed to the timing system through the interfaces. FAW carried out standardization for every function module and unified the description and explanation of the modules and unified the definition of the interfaces. FAW standardized the interrupt mechanism of the timing system. FAW defined every trigger event for each module, and managed the events in the interrupt service routine. The interrupt mechanism is shown in Fig. 5. Every module in the timing system can generate specific events to trigger the interrupt service. In the interrupt service, the system can determine whether to process the interrupt directly or not. For some events that need the upper layer to process or need the OS services, the system will trigger tasks for the events. Some emergency events can be directly handled in the interrupt service routine. AUTOSAR defines methodology for the development of the system. The methodology not only includes the system development process but also includes the corresponding development tools and the exchange format between the various steps of the development. When developing the system, the developers must be strict with the standardized process and must have direct responsibility on their own software modules. The developers need to write the module description files

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and the code generation rule files used to integrate the modules into the system which followed the AUTOSAR standard.

3 The Application of Timing System in GDI Project When the engine timing system is used in different types of engines, all you need to do is to select the corresponding function modules according to your requirements and configure the modules of the system. The main single-chip used in the gasoline engine project is MPC56xx. In order to reduce the work load of the single-chip, a specifically designed eTPU2 module is used to handle the timing events [8]. The relationship between the framework of the timing system and the gasoline direct injection project is shown in Fig. 6. The timing control driver layer of the timing system is implemented by using eTPU2. The eTPU2 can exchange information with CPU through the SPRAM. This will complete the information exchange between the timing control driver layer and the timing control abstract layer. Referring to the AUTOSAR methodology, FAW followed the following steps to configure the timing system [9]. First of all, it has to configure the timing system at the engine aspect, such as the type of the engine, the cylinder numbers of the engine, the number of the crankshaft and camshaft. Secondly, the detailed information of the timing system should be configured, such as the type of the crankshaft and camshaft and the type of the sensors and the actuators.

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Fig. 7 The configuration diagram of timing system

Fig. 8 The curve of the start phase of the engine

As shown in Fig. 7, the configuration information is stored in the ECU configuration description file. The system developers had designed the code templates, the module description files and the code generation rule files. Through the configuration of the timing system using standard tools, the source code of the timing system can be generated quickly.

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4 The Validation of the Timing System in GDI Project After the configuration, integration and test, the timing system now is running well in the ECU. FAW monitored the states of the engine while the engine was running, and FAW found that the timing system has outstanding on functions and performance. The curve of the start phase of the engine is shown in Fig. 8. The engine can be synchronized in a short period of time, and the engine can achieve the respective engine speed in a relative short period of time. Through many engine bench tests, it shows that the gasoline direct injection engine has outstanding stability, good performance. The timing system can satisfy the requirements of the projects. When applying the timing system, FAW only made a few modifications to the corresponding modules. The development period of the timing system is reduced greatly and the development efficiency is improved a lot.

5 Conclusions In order to improve the reliability, portability and efficiency of the timing system, FAW followed the core idea of AUTOSAR standard and carried out the modular design, hierarchical design and standardized design of the system. In order to verify the design, FAW has applied the timing system on a gasoline direct injection engine. Through the configuration, integration and test of the modules, the timing system runs well in the ECU. It shows that the timing system has good characteristics of platform, and it can be used in an engine project through simple configuration.

References 1. Gao H (2010) Embedded Software Development for Vehicle Electronic Control System Based on AUTOSAR. Automot Electr Appl 5:11–14 2. AUTOSAR_BasicSoftwareModules Rel. 3.1 [S] (2008) www.autosar.org 3. Hu Q (2011) Design and implementation of ECU software for electronic gasoline engine based on AUTOSAR. J Zhejiang University (Engineering Science) 45(6):1120–1121 4. Zhou Z (2008) Research of modulation of diesel engine control software based on AUTOSAR. Comput Sci Technol Zhejiang University 6(12):35 5. Wu R (2011) Analyze and design of automotive electronics software development platform based on AUTOSAR standard. Comput Sci Technol Zhejiang University 8:33–38 6. Zhang P (2010) Gasoline engine ECU software design according to AUTOSAR. Comput Sci Technol Zhejiang University 4:21–25 7. AUTOSAR_SWS_RTE Rel. 3.1 [S] (2008) www.autosar.org 8. eTPU reference manual Rel [S] www.freescale.com 9. AUTOSAR_Methodology Rel. 3.1 [S] (2008) www.autosar.org

Calibration Techniques for Modern Commercial Vehicle Yong Deng, Zhong Zhuang Yuan and Lai Wei

Abstract In recent year, electronic control system was increasingly applied in modern commercial vehicle, and the calibration work is the key technology of using electronic control system on the commercial vehicle. To satisfy national emission legislation and commercial vehicle products updating, there is a great challenge that how to master this technology. This paper introduce calibration function, calibration workflow and calibration method of modern commercial vehicle. Meanwhile, the challenges and trends of modern commercial vehicle calibration techniques were pointed out in this paper.



Keywords Modern commercial vehicle Electronic control system calibration Technology trends Model based calibration





 Vehicle

1 Introduction With the rapid development of automotive electronic technology, various kinds of electronic control system are widely applied in the modern commercial vehicle. Compared with traditional commercial vehicle with mechanical control system, the performance, fuel economy, safety, comfort and exhaust emission of the modern commercial vehicle are considerably improved through the electronic control system.

F2012-D01-028 Y. Deng (&)  Zhong ZhuangYuan  L. Wei China FAW Group Corporation R&D Center, Changchum, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_17,  Springer-Verlag Berlin Heidelberg 2013

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For the automotive electronic control system, the control parameters are calculated via the maps. At first the control unit determines the working point according to external sensor signals, then the control unit calculate the control parameters by looking for predefined maps. At last the control unit sends the command to actuators. The optimal value of control parameter is very important for control results and is optimized by calibration work. Vehicle calibration is an important step to apply the automotive electronic technology to modern commercial vehicle. With severe market competition, modern vehicle control system become more complicated and updating cycle become stringent, products life cycle is accelerated, all of these provide challenges for the vehicle calibration technology. To meet these challenges, some new calibration technologies such as Model Based Calibration have been introduced into modern commercial vehicle calibration work. At the same time, the scope of application of modern commercial vehicle calibration has expanded from engine control to gearbox control, traction control, brake system control and so on. Vehicle calibration technology has been one of the commercial enterprise’s core technologies to enhance the ability of independent innovation and the competitiveness of products [1].

2 Calibration Function of Modern Commercial Vehicle 2.1 Basic Function of Electronic Control System Realization via Calibration For a typical diesel engine control system, not only the mechanical control system but also electronic control system, realization of fuel mass and injection timing control is the basic function. For mechanical control system, the control function is realised by mechanism. Correspondingly, for electronic control system, the control function is realised by optimized control parameters in ECU, and the value of the control parameters is optimized by calibration work.

2.2 Advantages of Electronic Control System is Realized Via Calibration Compared with the mechanical control system, the electronic control system is with more advantages, such as more powerful and more control functions. As for the performance, the mechanical control system is limited by inherent characteristics of mechanism, it can only realise optimal control in part of the operation range. But the electronic control system is very flexible and can realise optimal control almost in whole operation range.

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Fig. 1 Application of common rail injection control system for diesel engine

As for the function, the mechanical control system can only realise the basic control function, such as fuel mass, rail pressure and injection timing. But the electronic control system can also realise cruise, smoke limitation, engine protection, diagnosis, and so on. Especially, the diagnosis function can detect and locate the fault for repairing the vehicle. The vehicle with failure can run in limp home mode through diagnosis function for vehicle’s safety. Vehicle calibration work supplies correct control function parameters to assure optimal control results. The vehicle control function will be badly affected with incorrectly calibrated control parameters.

2.3 Flexibility of Electronic Control System Realization Via Calibration Generally, the hardware and software of one type electronic control system are fixed, but thousands of control parameters are adjustable by calibration, thus the electronic control system can realise flexible application easily. As for a typical high pressure common rail injection control system, it can be applied in many different diesel engines and commercial vehicles for different performance and function requirement. Different calibration data can fit for different engine and vehicle configuration via calibration work (Fig. 1).

170 Fig. 2 A typical vehicle calibration workflow

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3 Calibration Workflow for Modern Commercial Vehicle According to work principle, function features of the electronic control system, the goal of the vehicle development, the calibration workflow is not fixed. There are some differences in calibration workflow for different companies and vehicle development project. The overall thought of calibration workflow is basically the same. The typical calibration workflow is shown in the following figure (Fig. 2).

4 Calibration Approach for Modern Commercial Vehicle 4.1 Manual Calibration Approach For all the operating points in the map, calibration engineer adjust the control parameters of the electronic control system, measure and record the test results, then do a lot of analyses based on engineer’s experience to get the better value of control parameters. In this way the calibration results are limited by the engineer’s technological level and experience. This calibration approach is easy to learn, but be inefficient, time-consuming, laborious and inaccurate.

4.2 Calibration Approach Optimization The calibration approach optimization combines the mathematical optimization theory with the calibration technology. This approach is designed for limited operating points in the map. With the help of conventional equipment and calibration system, calibration engineer can monitor the test results real time and adjust the control parameters on-line. The mathematical model of the vehicle control parameters was built based on statistical method. With the help of the mathematical model and optimization algorithm, calibration engineer can optimize the control parameters on-line or off-line to get the optimal data. The advantage of this approach is with short calibration cycle, high calibration precision and low calibration cost [2].

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Fig. 3 The typical automatic calibration process with the help of CAMEO

4.3 Automatic Calibration Approach With the help of automatic calibration system, calibration engineer can predefine a calibration test program. According to the predefined program, the host computer of automatic calibration system is in charge of whole calibration test process. The test bench computer is in charge of all of the test equipments, control equipments, and change the control parameters of electronic control system as required. The host computer of calibration system, the test bench computer and calibration console can make communication with each other through control interface. The setting of operating points, the measurement of test results and the optimization of control parameters and be done simultaneously. The advantage of this approach is high calibration efficiency and good calibration results. But he disadvantage of this approach is expensive equipment and complicated technology. The typical automatic calibration system is CAMEO of AVL company(Austria) and VEGA of SCHENCK company(Germany) (Fig. 3).

5 Calibration Challenge of Modern Commercial Vehicle 5.1 Calibration Requirements Challenges To reduce the air pollution by commercial vehicle, the emission legislation become more and more strict. For customer they want a commercial vehicle with high performance, fuel economy, safety and comfort. For manufacturer they want reduce development time and cost. All of these requirements challenge the modern commercial vehicle calibration.

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5.2 New Technology Application Challenges Today more and more new technology was applied in modern commercial vehicle. For example, turbo charging, EGR, advanced exhaust treatment system, AMT, hybrid, etc. All kinds of electronic control system become more and more complicated, at the same time, it means more difficulty for modern commercial vehicle calibration.

5.3 Workload Challenges Firstly, the control unit becomes more and more complicated, the labels that need to be calibrated increase manifold, for example, there are over 8,000 labels in the EDC17 system from BOSCH, these include maps (3D), curves (2D) and constant values (1D). Secondly, with the specialized division of labour was encouraged, more and more vehicle variants such as van, dump truck, truck, semi-tractor, were developed in a major series of vehicle. Finally, more and more electronic control technology are applied in modern commercial vehicle, the vehicle are getting more intelligentized not only because of electronic engine control, but also because of gearbox control, ABS, ESP, ACC, etc. All of above will increase the vehicle calibration workload manifold.

6 Calibration Technology Trends for Modern Commercial Vehicle 6.1 Refined Calibration With the high requirement to detail quality of commercial vehicle form customer, the control system become more and more complicated and can do fine control to most of the vehicle operation range. In order to achieve that, the refined calibration work is needed. For example, as for engine start calibration, the goal is not only focused on successful start, but also focused on start duration, start noise, start smoke and engine speed transition from start to idle. All of the related control parameters should be refinedly calibrated.

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Cruise Control Data

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Vehicle Dataset Pump Data

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Fig. 4 The example of calibration data modularization

6.2 Advanced Calibration Data Management 6.2.1 Calibration Data Modularization An important feature of modern commercial vehicle calibration is an ever expanding workload because of a number of vehicle variants. When a basic vehicle calibration project is completed, a number of vehicle variants calibration should be done next. Therefore the calibration workload is expanded and the data management becomes more and more difficult. The calibration data modularization is a practical and effective solution. The calibration dataset is split into many modules, each module is related to a certain control function or vehicle part. As for the basic vehicle and the variant vehicle, part of the data modules is same, and the others are different because of variant. Therefore the main work of the vehicle variant calibration is to research and find out the differences between basic vehicle and variant vehicle, then do necessary calibration work about them. Finally the correct modules will be integrated into dataset for vehicle variant. In this way the calibration workload and the cost of data management is reduced (Fig. 4).

6.2.2 Calibration Data Management Tool Application In recent yeas, the professional calibration data management tool is applied in calibration work. A typical example is eASEE.cdm software tool from Vector Company. The calibration data management tool can split calibration work into data modules and tasks that can be easily distributed and managed over a large team, and ensure that all calibration engineers across all projects, groups and locations consistently use the same processes and data for uniform results. In this way, the data quality and consistency is increased, at the same time, the work efficiency through the overall calibration process is increased because of collaboration of different calibration engineers (Fig. 5).

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Fig. 5 The example of calibration data management tool

6.3 Model-Based Calibration Technology To meet stricter demand of commercial vehicle performance and emission legislation, more kinds of electronic control system are developed. The system function becomes more and more powerful, and the structure becomes more and more complicated. At the same time, the labels that need to be calibrated and calibration workload increase manifold. In such cases, the traditional calibration technology can not meet the demand of commercial vehicle calibration, but the model-based calibration (MBC) technology can do it efficiently. The model-based calibration technology can get optimal control parameter values by building and utilizing the physical model or mathematical model of the relations between control parameters and calibration results. This calibration technology has a significant advantage in reducing calibration workload, shortening calibration cycle and cutting down the calibration cost [3]. The typical model based calibration workflow is shown below: DOE design ? Data collection ? Data modelling ? Calibration ? Implementation (Fig. 6). The model-based calibration technology has been applied maturely and wildly in modern commercial vehicle development. Most of the application is focused on steady state. In future, the model based calibration technology in transient state, model based control techniques and the calibration techniques based on coupling models will guide the trend of model-based calibration for modern commercial vehicle [4].

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Fig. 6 Model based calibration workflow

Fig. 7 Multi-system coordination control calibration

6.4 Multi-System Coordination Calibration With the development of electronic control technology, various kinds of electronic control system such as diesel engine control system, gearbox control system, ABS, ASR, are applied widely in the modern commercial vehicle. The control system has come out of the isolated work mode. With the help of CAN bus, all kinds of working information are received and sent among different control systems, in this way, different control systems realize torque and speed coordination control.

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Correspondingly, the calibration work is done not only in a certain control system but also in several control system that coordinate with each other. Multi-system control calibration can accomplish optimal coordination control and have important effect on electronic control system. For example, in a truck with diesel engine and AMT gearbox, engine ECU and AMT TCU communicate operating information and control command through CAN bus. The TCU send torque/peed control command to ECU, and the ECU response rapidly and accurately. Based on the accurate calibration to the torque/peed coordination control, the diesel engine and the AMT gearbox can work well with each other according to driver demand, road condition and vehicle load. Moreover, the vehicle performance, fuel economy, safety, comfort and emission are improved (Fig. 7).

7 Conclusion With the rapid increase of commercial vehicle market, the automotive electronic technology was greatly improved because of severe competition. Correspondingly, automobile enterprises pay more attention to commercial vehicle calibration techniques, and commercial vehicle calibration techniques was greatly improved and widely applied. In future, it will has a great potential.

References 1. Jun L, Qu WD, Wei G (2009) The automobile powertrain core technology and FAW’s strategy for environmental protection and energy conservation. Automobile Technol 5:1–7 2. Maloney P (2009) Objective determination of minimum engine mapping requirements for optimal SI DIVCP engine calibration. SAE Paper 2009-01-0246 3. Liu FS, Qiu T, Liu XH, Cheng CQ, Bai XD (2005) Model based calibration method for electronic controlled diesel engine. Vehicle Engine 6:1–4 4. Art O, Keith B (2003) A practical implementation of ASAM-GDI on an automated model based calibration system. SAE 2003-01-1030

Powertrain Control and System Integration Technology from OEM’S Perspective Jun Li, Fengjun Li, Peng Zhang, Yongjun Li and Weimin He

Abstract Powertrain control is significant for commercial vehicles to meet the requirements of energy-saving and emission regulations; also it is critical to improve the power performance of vehicles. Under the pressure of cost, the diversities of user-requirement, frequently changing market, FAW makes a deep study of powertrain control, while some non OEM-specific functions, for example some components in basic software, are developed by suppliers and integrated by FAW. The product development platform for powertrain is built following this way, based on which some OEM-specific features can be implemented rapidly and efficiently. These features will make the products more competitive in the market. This paper gives an overview to the powertrain control development situation of FAW, including the system integration strategy and the coordination control between the networked ECUs on vehicle. Keywords Powertrain control

 Software integration  AUTOSAR

1 Introduction Powertrain system integration is one of FAW’s development strategies, which consists of three levels: the integration of suppliers’ resources; coordinated control among powertrain control modules; FAW-specific functions development in single control module. It is achieved with consideration of FAW developing capability and commercial requirements, a compromise between energy-saving demands, F2012-D01-029 J. Li (&)  F. Li  P. Zhang  Y. Li  W. He CHINA FAW Co., Ltd R&D CENTER, Changchum, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_18,  Springer-Verlag Berlin Heidelberg 2013

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emission regulations, costs, performance and user-spec features. It improves competitiveness of products in the market.

2 Integration Method of Suppliers’ Resources During Electrical and Electronic (E/E) design stage, the functions of general and OEM-specific will be defined and distinguished by FAW, then the specifications of general functions will be distributed to different suppliers to be developed. At vehicle E/E level, OEM is facing great challenges. The interaction among various electronic systems, caused by automotive network environment, has to be taken into account during the development processes. All the interfaces between OEM and suppliers need to be clearly defined. In the traditional way, a large volume of design documents needs to be written by hands. If there is no proper methodology and standardized software architecture, the E/E level development and integration work will be very difficult. AUTOSAR provides a solution for this problem. The objective of the AUTOSAR partnership is the establishment of an open standard for automotive E/E architecture. The AUTOSAR Software Components have standardised description formats and well-defined interfaces, which are also described and standardised. After FAW has defined interfaces of components for the suppliers according to AUTOSAR, the Virtual Functional Bus (VFB) will allow a virtual integration of them in an early development phase [1]. FAW has already made a basic software platform based on AUTOSAR, and the standardization of development methodology in FAW is now being towards the right direction. In FAW’s AUTOSAR basic software, some powertrain specific drivers in Complex Device Driver (CDD), for example engine timing driver, are developed and integrated.

3 Powertrain Coordinate Control The powertrain coordinate control involves many electronic systems such as transmission, engine, ABS and so on. In order to achieve better vehicle performance, it’s necessary to coordinate the actions of the electronics control systems. Several key points will be presented below.

3.1 Vehicle Torque Control In order to meet the torque coordinate requirements from other electronic systems, such as Transmission control System, ABS and so on, FAW has implemented

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Fig. 1 Torque control structure diagram

vehicle torque control, which is evolved from fuel-mass control. The control architecture of vehicle torque and engine torque is designed from OEM’s perspective at the beginning of the development. According to the demands from regulations, market and customers, many sub-functions of torque control, including engine torque physical model, torque coordinate control, fault monitoring, engine protection, etc., are implemented by FAW, which satisfy expectation of power, economy and emission performance. Vehicle torque model is in charge of the collection of the driver’s intention and the demands from other electronic systems, and then a proper control mode is decided on how to satisfy the control requirements of vehicle speed, vehicle-load, thermal management and storage battery. The destination values of engine speed and demand torque are output to engine torque model. Engine torque model will monitor and analyse such dynamic data as engine speed, torque, intake and exhaust, oil, fuel, emission and other important data, necessary protection measures will be taken if needed. Finally, the demand engine torque is set according to idle speed demand in different conditions by engine toque model. The whole torque control structure is shown in the following figure (Fig. 1).

3.2 Multiple Parameters Shifting Strategy Based on Traction Force FAW’s shifting strategy is based on traction force. The traction force before shifting, the driving resistance and the traction force needed after shifting will be computed from the parameters of the engine output torque, friction torque and

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Fig. 2 Primary gear selection

engine performance curve [2]. Many techniques are used to avoid frequent and unexpected shifting, including the vehicle-speed loss compensation, engine speed limit, gear limit, basic gear calibration, accelerator pedal signal filter and other ways to do some dynamic adaption to the shifting rules. This strategy is more superior and has better performance compared to the two-parameter shifting strategy under complex road environment and vehicle load (Fig. 2, 3).

3.3 Automatic Driveaway Controls During the automatic driveaway control, the engine and clutch are involved. Clutch engagement control follows the engine torque dynamic change, at the same time the engine speed are limited. By this way, the life of the clutch and related accessories is lengthened, with the driving comfort also enhanced (Fig. 4).

3.4 Shifting Coordinate Control Based on Networked ECUs During the process of gear shifting, the coordinate control of engine torque and engine speed is implemented by exchange information between AMT ECU and Engine ECU on CAN bus, such information of Engine ECU as the engine speed, engine load and friction torque should be available for AMT ECU. Engine torque limit during shifting will make the switch process from the power interruption to

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Fig. 3 Determination of gear limited by traction force

Fig. 4 Engine control and clutch control during driveaway

resumption smoother. The vehicle speed limit can make the shifting process more comfortable, shorter, and helps to improve the energy efficiency and shorten drivepower lost time. The shifting strategy of AMT can be optimized according to the wheel speeds signals on CAN bus from ABS. By analyzing and computing the difference of the wheel speeds, the direction and the angle of the vehicle is turning can be identified. According to this, the shifting strategy of AMT can be optimized to avoid circle shifting during going around the curve and improve the comfort of driving. In vehicle brake direction, especially in the complex hill-road conditions, a proper brake mode needs to decide on from engine brake, exhaust brake, the retarder brake and etc., and a proper gear needs to be selected. The decision should be made according to the vehicle current load, road slope and road friction coefficient. In this way, the danger caused by brake ability loss is reduced in complex hill-road conditions (Fig. 5).

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Fig. 5 Communication between networked ECUs

3.5 Powertrain Vehicle Coordinate Control Based on VCU Some of the electronic functions will be developed by FAW, but most of them will be provided by suppliers. It’s crucial for OEMs to have a good way to manage all the functions, therefore brings up some topics such as the E/E architecture design, the distributed network control (normally this function is realized in VCU) and so on. Vehicle Control Unit (VCU) can be used to implement the powertrain coordinate control. VCU will at least has two CAN bus channels. One is connected to the powertrain CAN bus; the other will support other functions such as diagnosis, calibration. VCU is set up in the cab and act as the brain of the vehicle and the interfaces between the driver and vehicle functions. It provides a better platform for future commercial vehicle coordinate control. In vehicle driving direction, VCU will identify the current control demands according to the information from the drivers and CAN bus. Then control demands, for example request engine running mode switch or gear shifting, will be sent to the powertrain electronic system. The required engine running modes, economy running mode or max torque output mode, are determined according to related information such as accelerator pedal action, the longitudinal acceleration and so on. Normally powertrain control parameters need to be adapted according to the different performance requirement of vehicle variants. In order to improve the performance of the coordinate, more accurate torque resolution and faster response of engine torque are needed.

4 Faw-Specific Functions Development Electronic control system (such as engine control system) consists of sensors, actuators, ECU hardware, basic software and application software. The application software is the embodiment of user requirements and OEM-specific features, and therefore it is developed by FAW. Through a balance among quality, cost and longterm strategy, other parts from the suppliers will be integrated. See also Fig. 6.

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Fig. 6 System integration strategy

4.1 Fuelling System Control Control of fuelling system is critical for diesel engine control, which will directly affect the overall performance of the diesel engine. FAW has developed the software compatible with FAW Electronic Unit Pump (FEUP) and FAW Common Rail Integration (FCRI), and the fuelling systems of main suppliers are also supported. This will greatly improve the flexibility for OEM to choose fuelling systems from the suppliers. Generally different fuelling systems have different demands for driving mode and control method. FAW has a platform solution to the variable demands from different diesel fuelling systems, in which many sub-functions are implemented, such as rail pressure control, multiple-injection, cylinder trim, misfire identification, fault disposal and so on. Software calibration switches are used to enable or disable the corresponding software functions, and therefore the platform can be conveniently configured for specific use cases (Fig. 7).

4.2 Vehicle Load Computing The load of vehicle is computed based on vehicle longitudinal dynamics equation. During the process of gear shifting, transmitting mechanism is disconnected, and the road slope aroadslope is estimated according to the equation below. In the equation fR is the value of rolling resistance coefficient, ax is the vehicle acceleration on running direction, g is the gravity acceleration.   ax aroadSlope ¼ arcsin fR  g After the process of shifting is finished, aroadslope is also available. Suppose that aroadslope has no changes, and then the load will be computed according to the equation below.

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Fig. 7 Fuelling system control

m¼

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In the equation, m means vehicle mass, Fx means the driving force on the wheels, the result of cwAqv2/2 is air resistance, R (JRj/(rjRj)) means inertia of rotational mass. By applying the vehicle load parameter in the control strategy, the vehicle traction calculation accuracy and the shifting strategy can be improved further.

5 Conclusions By now FAW has already developed her own E/E architecture and powertrain control and integration technique. Powertrain control system based on these concepts has already been put into mass-production on multiple vehicle variants. Heavy commercial vehicle will be more and more energy-efficient, intelligent, safety and reliable. In order to satisfy the requirements and demands from the customers and market, FAW will make deep research on the powertrain coordination control, by which to contribute to the realization of the vehicle differentiation competition with other OEMS.

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In the future more and more electronic control functions, which are configurable and selectable by customers, will be applied on FAW commercial vehicles, such as Electronically Controlled Air Suspension (ECAS), Traction Control System/Electronic Stability Program (TCS/ESP), Adaptive Cruise Control (ACC), Central Tire Inflation System (CTIS) and so on.

References 1. The Technology Roadmap Report Published by Supplier Business Ltd 2. Yu Z (2009) Vehicle theories. China Machine Press, Beijing, p 3

Part II

Electrical and Electronic System

Virtual Development of Engine ECU by Modeling Technology Haifeng Xu, Yukihide Niimi and Takayuki Ono

Abstract Along with the evolution of vehicle electronic systems from domainspecific control to the integrated control of the entire vehicle system, ECU systems have become increasingly complicated and large-scale. This has made it difficult to set out an optimal architecture of the ECU system efficiently at the early planning stages. As well, the conventional ECU development methodology is also becoming difficult to achieve the increasingly strict requirements for safety design based on multi-ECU systems. Conventionally, optimizing electronic systems requires fabricating many prototypes and evaluating them repeatedly, but with their increasing scale, this method has become impractical. We therefore believe virtual development is a required step. Although functional-level simulators and implementation-level simulators are being used currently, these are both separate and independent. Because of this, it is necessary to introduce virtual development as a new physical-level development environment to connect functional-level and implementation-level. In terms of not only function but also safety design, virtual development has the ability to inject failures that are difficult to recreate in an actual device. Therefore we have started introducing the virtual development of ECU systems by using system level modeling and simulation technology with SystemC language which provides the concept of time. In the phase of physicallevel design, because a virtual ECU system is developed by designing each functional model of system such as ADC and drive circuit and connecting these models as a system, the behavior of the whole ECU system can be verified easily without having actual devices. Therefore the optimized structure of ECU, such as microcomputer, software and peripheral LSI, can be determined efficiently at the F2012-D02-004 H. Xu (&)  Y. Niimi  T. Ono DENSO Corporation, Kariya, Japan e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_19,  Springer-Verlag Berlin Heidelberg 2013

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early stages of ECU development. Safety design can also be achieved efficiently because the data transferred in the system can be changed to failure data forcibly by covering functional models with failure models. We believe that maximizing the performance of ECUs in electronic systems, and ensuring that these systems meet safety design requirements will require methods to visualize things that are difficult to visualize, and that this visualization is needed both before and after manufacturing. Virtual development of ECU systems by using system level modeling and simulation technology with SystemC language provides a useful method to achieve these requirements. Keywords Modeling

 Simulation  SystemC  ECU  Safety

1 Introduction As part of the push towards a lower-carbon society, electronic control systems for automobiles are developing and evolving from domain-specific control in the vehicle (power train, body, safety, etc.) to the integrated control of the entire vehicle. The ECU, which forms the backbone of such control systems are thus growing in scale and complexity. The development of ECUs in this changing environment requires having an overview of the entire electronic system at the planning stages; this overview would set out an optimized ECU structure in which even the structure of the chipsets are defined; without such an overview, it will be difficult to keep up with vehicle requirements and specifications. As well, the more stringent design requirements for safety that straddle multiple systems are becoming difficult to achieve using the conventional ECU development approach [1]. In response to these issues, DENSO has started introducing the virtual development of electronic systems in order to further leverage our experience developing the logical architecture and physical architecture that support our vehicle product planning, as well as our experience implementing this architecture over the entire vehicle. This paper discusses the virtual development of engine ECUs based on the perspectives described above.

2 Issues in ECU Development Currently, when developing automotive electronic systems, an overview of the entire vehicle is created, and the architecture is developed using logical models with a high level of abstraction to make the structure of the entire vehicle easy to understand, and the functions to distribute to each ECU are decided. In the next stage, the ECU development phase, the software and hardware allocation is reviewed along with the microcomputer and ASIC configuration, and

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the ECU implementation specifications are decided. The phase after that is the actual implementation design phase, where implementation-level simulators are used to perform detailed design. However, at this detailed design stage, the various constraints interact in complex ways, and specifications must be repeatedly verified with the constraints in the ECU system specifications and the constraints in the implementation. This is because as the development process moves downstream, the amount of information required increases; however large amounts of information becomes apparent for the first time downstream. In order to keep such coordination to the minimum, it is necessary to determine as much information at the upstream stages and to create a large-scale, detailed verification environment.

3 Aims of Virtual Development Conventionally, implementing the processes mentioned above requires fabricating large numbers of prototypes and evaluating them repeatedly, but with their increasing scale, this method of optimization has become impractical. Because of this, we have introduced virtual development as a new physical-level development environment to connect the logical and implementation domains. In terms of not only function but also safety design, in which failsafe specifications are considered to ensure the completeness of failsafes for different malfunctions, one of the elements that is required of virtual development is the ability to inject failures that are difficult to recreate in an actual device. Thus, it was decided to use modeling technology based on SystemC, a language that can be used to rapidly run system level simulations while having the notion of time, in order to create the virtual development environment. This ECU modeling technology is described in more detail below.

4 ECU Modeling Technology 4.1 ECU Modeling Concepts Based on the issues described above, the virtual development should be applied to meet the following requirements. (1) Optimizing the allocation of hardware and software. (2) Reviewing the configuration of microcomputers and ASICs. (3) Performing failure simulations. Though the models that would be used to meet requirements 1–3 may need different levels of abstraction [2], we believe that it is possible to come close to connecting these models with differing levels of abstraction in what is practically a

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Fig. 1 Schematics of system model

single virtual environment. As shown in Fig. 1, building a virtual ECU environment by creating and connecting all the necessary models of LAN, various sensors, functional blocks in control units (ECUs), and actuators being controlled will allow us to verify the feasibility of the operation of the entire system and to review the suitability of software and hardware structure and microcomputer performance.

4.2 Component Modeling Based on the existing system structure, we modeled each functional block including the AD converters in the ICs as well as the microcomputer peripherals such as the drive circuits and digital filters. This allows not only the overall activity to be observed but also the detailed behavior of each block. The microcomputer manufacturer provided the model of the microcomputer core which is a cycle-accurate ISS model, and this model was connected to the other models. By doing so, Requirement 1 (layout and review of hardware and software) and Requirement 2 (verifying microcomputer performance by calculating the CPU processing load and RAM/ ROM usage) are satisfied. However, because having everything at a detailed level of abstraction results in the disadvantage of increased simulation time, the behavior in the models is investigated making strategic use of transaction level and pin level interfaces between models to adjust the abstraction based on whether or not a block is under detailed review [3]. This allows the total number of runtime events in the

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Fig. 2 Model interface Initiator

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simulation to be decreased in order to create an environment in which large-scale systems can be run at high speeds. Figure 2 shows two kinds of model interfaces. Figure 3 shows use of transaction level interface and pin level interface. When it considers how to model 32-bit communication line, transaction level interface is for verifying overall operation, and pin level interface is for verifying communication method. These are different abstractions. The degree of abstraction is frame-based for the first type and bit-based for the second type, and the simulation process has a single event for the first type and 64 events for the second type. The disadvantage for the first type is that bit errors during transmission cannot be simulated, and for the second is that the simulation takes too long. It was for these reasons that we developed a modeling method that maintained the advantages of both and resolved their disadvantages. The method is to add a switching event between transaction level and pin level modeling so as to enable dynamic switching. This can reduce the overall simulation time while still allowing performing detailed verifications.

4.3 Failure Modeling To inject failures, the failure modes were first analyzed. The results of this analysis reveals that failures can occur in various locations including physical connections and gates inside ICs, but all of these failures can be classified into a few modes such as disconnection, locking, corruption, drift, and oscillation. Another issue is where and how to inject these failures. Because the locations where failures can be observed in an actual device are at its various terminals, a failure model is laid over the functional model as shown in Fig. 4, and the failures are defined in the output (a GND short failure is shown), which forces the system to treat the data transferred as abnormal values; this simplifies the failure model and makes failure injection easier, all without making any changes to the functional model. The final issue is the timing of the failure. The failure model added above is given a failure changeover signal as an input with the value and time of occurrence

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Fig. 3 Use of different interface Fig. 4 Failure model

Failure model

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set in the initial settings; because such failures can be analyzed in the same way as a regular simulation, it is easy to express not only steady-state failures but also transient failures, and we are able to use this method to verify safety design. However, when performing a bit corruption failure in the communication data during transmission between models, in order for the failure model to inject a malfunction with respect to particular bits, the functional model transmits data one bit at a time and this results in the disadvantage of increased simulation time [1]. Therefore, an effective modeling method that can be used to freely inject bit corruption malfunctions during transmission even with frame-based communications is explained below. Specifically, the framed-based data that the functional model tries to transmit is outputted at the transmission start time, and the failure model saves the outputted data until the transmission end time. If a bit corruption failure occurs at the given time during saving, the abnormal data is calculated based on the time that the failure occurs, and the saved transmission data is substituted with the abnormal data, and at the transmission end time, the saved abnormal data is transmitted. In this way, it is now possible to shorten the simulation time and perform detailed verifications at the bit level.

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5 Application Examples Two examples are shown below to explain the application of virtual development. Example 1 covers Requirement 1 (optimizing the allocation of hardware and software) and Requirement 3 (failure simulations). Example 2 covers Requirement 2 (reviewing the configuration of microcomputers and ASICs).

5.1 Example 1 During the early stages of ECU development with conventional development methods, which have no actual devices, CPU processing loads cannot be verified, so it is difficult to verify the suitability of allocation of hardware and software. The introduction of virtual development technology is an efficient way to solve this problem. This example is the development of the ECU for a 4-cylinder engine. At the ECU implementation specification review stage, the virtual ECU is built based on the CPU model. At this point, the allocation of hardware and software has not been decided, so a temporary allocation based on design experience may be devised. If an allocation cannot be devised, a software implementation may be used. Next, the operation of the virtual ECU system is verified. In terms of the control software, the statistical data for each task and function call can be obtained, which allows the CPU processing load across the range of engine speeds to be analyzed as shown in Fig. 5. If there is some extra CPU processing capacity, some of the hardware-based processing can be transferred to the software, and if there are any highload tasks, the corresponding processing can be transferred from the software to the hardware; all of this information is useful when reviewing the system. When doing so, as shown in Fig. 6, the feasibility of the operation of the entire system across the range of engine speeds and the timing of operations can be observed, and the performance of the portions that have been made hardware can be verified as well. Next, Fig. 7 shows the injection of a failure into the actuator drive circuit as well as the results of the system failsafe analysis. Here, after an over current failure was injected into the drive circuit, the power to the actuator was cut off. The specification called for 600 ms max., and the power was cut in 500 ms, so the effectiveness of the system failsafe was confirmed.

5.2 Example 2 At the early stages of ECU development, all of the possible ECU implementation possibilities need to be laid out, so that they can be optimized and to review their suitability in terms of mass production and cost. Because of this, the different

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Fig. 5 CPU processing load

Fig. 6 Analysis of engine ECU Fig. 7 Analysis of failure model

Max delay: 600ms Over current

Power supply cut off 500ms

configurations of microcomputers and ASICs need to be reviewed. For example, when reviewing the configuration of microcomputers and ASICs, one option is to use a custom microcomputer with a wide range of functions and to use a generalpurpose IC. Another option is to use a general-purpose microcomputer and build custom functions into an IC. By using virtual development technology, it is possible to verify whether each configuration option satisfies the performance requirements.

Virtual Development of Engine ECU by Modeling Technology Fig. 8 Possible configuration of microcomputer and ASIC

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Explained below is an example of using a general-purpose microcomputer with few compare channels to build into the ASIC the custom microcomputer’s compare function for driving the injectors. When a compare function is built into the ASIC, as shown in Fig. 8, specifications for communication between the CPU and compare function, implementation specifications for the timer required for the compare function to operate, and software specifications required for communication must be considered. Also, the communication performance and CPU processing load must be verified to satisfy the ECU performance requirements. The review procedure with virtual development technology is outlined below. In Step 1, the implementation specification options are reviewed and the models are built. A specific example of a specification is this: The CPU uses the existing CSI communication channel in a general-purpose microcomputer to read the timer value stored in the ASIC, calculates the expected injector operation time and injector number, and sends the result to the ASIC; the compare function determines the injector operation timing based on the received data. Such detailed specification options are reviewed in this step to build the required models and create the virtual ECU. In Step 2, the operation is verified. Examples of what is verified are the feasibility of the operation, accuracy of the operation (injection start timing and duration), CPU processing load, and RAM/ROM usage. In terms of methods to verify operation, the operating frequency for CSI communication is adjusted to verify operation across the range of engine speeds. For example, Fig. 9 shows the delay in injection start time when the CSI communication operation frequency is varied from 4, 2, to 1 MHz in accordance with the specifications of the generalpurpose microcomputer. At frequencies of 2 MHz and higher, the delay time is within the allowable range defined in the ECU performance requirements. At 1 MHz, the communication delay is too long, and the injection operations at high engine speeds are abnormal. The injection duration and the CPU processing load and RAM/ROM usage can be verified in the same way, so the suitability of microcomputer performance can be evaluated. In Step 3, the results are fed back into the implementation specification options. Based on the results of the operation verification, it is judged if the ECU performance requirements are satisfied. If necessary, the implementation specification options are improved through feedback, and the operation is verified again.

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Delay (us)

Engine Speed

Normal range

CSI operation frequency (MHz)

Fig. 9 Analysis of injection delay

In the ways described above, the introduction of virtual development technology allows verifying various implementation specification options that are difficult to verify in actual devices and deriving the optimal solution efficiently.

6 Outlook The role of electronic systems in the move toward a lower-carbon society is expected to grow increasingly important. We believe that maximizing the performance of ECUs in electronic systems, which continue to grow in scale and complexity, and ensuring that these systems meet safety design requirements will require methods to visualize things that are difficult to visualize, and that this visualization is needed both before and after manufacturing. We would like to use the modeling technology described in this paper as a base for creating a virtual development environment and to carry out the development of vehicle electronic systems and products that contribute to society.

References 1. Niimi Y, Ono T, Tsuchiya N (2012) Virtual development of engine ECU by modeling technology. [J] SAE technical paper, 2012-01-0007:1–5 2. Bailey B, Martin G, Piziali A (2007) ESL design and verification [M]. Morgan Kaufmann, USA 3. STARC (2008) TL modeling guide, 2nd edn [M] Semiconductor technology academic research center, Japan

The Research of a Novel High Energy Density Ultracapacitor System Applied in the Full Hybrid Vehicle Jianxin Zhu, Qiu Xiao, Lin Yang and Xiance Ai

Abstract This paper studied a novel high energy density ultracapacitor and develops the applicable ultracapacitor management system. The ultracapacitor system is successively tested on the charge/discharge test bench and the hybrid energy test bench. The result indicates that the ultracapacitor system has a advantage of low resistance, high efficiency, and long life span, its energy density and power density can fulfill the requirement of the full hybrid vehicle.



Keywords Ultracapacitor system High energy Density Performance study Emulation of road test



 Full hybrid vehicle 

1 Introduction The ultracapacitor has a property of high power density, high efficiency, low resistance and long lifespan, so it is suitable for the application in the hybrid vehicle. ‘‘Ortuzar M’’ has studied the hybrid vehicle which uses the lead-acid battery as the main energy source and the ultracapacitor as the second energy source [1]. ‘‘Gopalakrishnan S’’ has studied the application of the ultracapacitor as the energy recover device in a conventional vehicle [2]. ‘‘Farzad A’’ has designed a hybrid energy storage device which consists of the battery and the ultracapacitor [3]. These applications have improved the economy performance of the vehicle and the lifespan of the battery. F2012-D02-008 J. Zhu (&)  Q. Xiao  L. Yang  X. Ai Shanghai Jiaotong University, Shanghai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_20,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 The charge/discharge principle of lithium-ion hybrid ultracapacitor

The previous application of the ultracapacitor is either on a mild vehicle or used as a part of the hybrid energy storage device with the battery. In this paper, a novel high energy density lithium-ion hybrid ultracapacitor is researched and the appropriate ultracapacitor management is designed. The ultracapacitor system is to be applied in the full hybrid vehicle as the single energy storage device and its characterization is studied in this paper. The ultracapacitor system is firstly tested in the charge/discharge test bench to study the basic characterization of the ultracapacitor. Then in order to simulate the actual vehicle operating conditions, the emulation experiment of road test is successively done on the charge/discharge test bench and then on the hybridenergy test bench. This paper first introduces the ultracapacitor system and the test equipment. Then the experiment results are presented and analyzed. Finally the conclusion is got.

2 Ultracapacitor System 2.1 The Principle of the High Energy Density Ultracapacitor The conventional ultracapacitor stores energy through physical electrostatic adherence, which possesses high power density and low energy density. The proposed novel ultracapacitor in this paper is lithium-ion hybrid ultracapacitor which possesses the advantages of conventional ultracapacitor and lithium-ion battery. The principle of the novel ultracapacitor is illustrated in the Fig. 1. It adopts the oxidation reduction material lithium electrode as the positive electrode, the activated carbon as the negative electrode and the organic electrolyte as the electrolyte. The new ultracapacitor stores the energy through the oxidation reduction between the electrode and the electrolyte [4]. The rated operating voltage of the cell is 2.7–4.0 V and the rated capacitance of the cell achieves to about 9000 F, which enables the ultracapacitor to possess high energy density. The proposed ultracapacitor system consists of 90 cells and weighs

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Fig. 2 The structure of the ultracapacitor management system

Thermotank DC/DC Charge/Discharge Device

Current Ultracapacitor Package Control Signal

CAN Signal

Ultracapacitor Management System

Sampling Signal Computer

CAN Bus

Fig. 3 The structure of the discharge/charge test bench

61 kg. The rated capacitance of the ultracapacitor system is 100 F and the maximum continuous operating current is 200 A.

2.2 The Structure of the Ultracapacitor Management System The structure of the ultracapacitor management system is illustrated in the Fig. 2. The management system introduces a distributed structure and the cells are divided into six groups. The submodule is responsible for the sampling of cell voltage and temperature. The main module is responsible for the sampling of the current and the total voltage. Besides the basic function, the management system also need to complete the work of heat management, high voltage management, etc.

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Motor 1

Motor 2 Engine

Gear-box

Ultracapacitor + system -

DC/AC

Fig. 4 The structure of the hybrid energy test bench

3 The Test Equipment 3.1 The Charge/Discharge Test Bench The structure of the test bench is illustrated in the Fig. 3, which includes the ultracapacitor system (ultracapacitor package and the ultracapacitor management system), the computer and the DC/DC charge/discharge device. This Test bench is responsible for the basic characterization test and the road simulation cycle test.

3.2 The Hybrid Energy Test Bench In order to simulate the actual operating condition of the full hybrid vehicle, the ultracapacitor system is tested on a hybrid energy test bench together with the gasoline engine and the electric motor system .The structure of the hybrid energy test bench is illustrated in the Fig. 4. The parameters of the key components in the hybrid energy test bench are illustrated in the Table 1.

4 The Result and the Analysis of the Basic Characterization Test 4.1 The Constant Current Charge/Discharge Test The Fig. 5 is the constant current discharge curve under different currents. The maximum available removed charge is near to 3.0 A.h when the current is 20 A. The reason of the reduce of the available discharge electric quantity at large current condition is because there is larger voltage drop in the resistance and the available discharge voltage interval decreases.

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Table 1 The parameters of the key component in the hybrid energy test bench Component name Parameter Engine Motor 2 Motor 1

Maximum power /kW

Maximum speed/rpm

Maximum Torque/N.m

69 36 56

6,000 10,500 8,500

128 80 200

Fig. 5 Total voltage as a function of charge removed for a series of constantcurrent tests

Figure 6 illustrates the relationship between the test current and the basic characterization of the ultracapacitor system, such as efficiency, resistance and capacitance. It is known from the Fig. 6 that the resistance of the ultracapacitor is about 0.3 X,which is much smaller than the battery. The resistance will increase to 0.35 X when the current decrease to 10 A, which has little effect on the energy loss. The energy efficiency of the the charge/discharge cycle process is usually higher than 90 %, which can help to improve the energy recovery rate of the hybrid vehicle. The capacitance of the ultracapacitor is about 100 F, considering that the operating voltage interval of the ultracapacitor system is 243–360 V, the maximum available energy can achieve to 980 W.h. The resistance is caculated at 350 OCV point at the beginning of discharge, the capacitance is calculated at the 330–290 V OCV interval and the energy efficiency is calculated at the 350–250 V interval.

4.2 The Minimum Hybrid Plus Power Characterization Test The Fig. 7 illustrates the relationship between the open circuit voltage (OCV) and the state of charge (SOC) from the minimum Hybrid Plus Power Characterization (HPPC) test [5]. It can be seen from the figure that there are nearly linear relationship between the OCV and SOC, which will benefit the SOC calculation.

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Fig. 6 Discharge ESR, capacitance and energy efficiency for a series of constant-current tests

The zero SOC point is set at the point when the OCV is 243 V, and the 100 % SOC point is the 360 V OCV point. During the HPPC Test the discharge plus current is 50 A and the charge plus current is 37.5 A. Figure 8 illustrates the relationship between the plus power and the depth of the discharge (DOD).It is seen from the figure that the plus regen power achieves to 30 kW in the 20 % DOD point, and the plus discharge power is over 20 kW at the 80 % DOD point, which means the ultracapacitor system has a high power density. Considering the long lifespan of the ultracapacitor under deep charge/discharge cycle performance, the operating DOD interval of the ultracapacitor system in the control strategy can set in a wide interval.

4.3 The Lifespan Test For the lithium-ion hybrid ultracapacitor, the long time charge/discharge performance can cause the electrode material changed, dissepiment damaged and side functions happening, which can increase the equivalent series resistance and reduce the capacitance. The raw of the capacitance deterioration can be illustrated in the Fig. 9. During the lifespan test the limited voltage interval is 260–350 V(20–80 % SOC) and the test current is 25 A. From the figure it can be known that the 20 % reduction in rated capacitance may occur after 1,500 h(6,000 times). Considering the fact that the influence of temperature has a doubling effect for every 10 C and the temperature of the ultracapacitor system in the normal ambient temperature environment is about 35 C, so the lifespan of the ultracapacitor system could achieve to 6,000 h

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Fig. 7 Open circuit voltage versus SOC in the HPPC Test

Fig. 8 Plus power capability results from HPPC test

(24,000 times). It should be noted that the lifespan test data is got from the manufacturer and can be only used to estimate the rough life. In our experiment the capacitance of the ultracapacitor decreases to less than 1 F after 300 h of the basic characterization test and the road condition simulation test. According to the 20 % deterioration criterion, considering the experiment condition is tougher than the real operating condition, the ultracapacitor can fulfill the requirement of 10 year service life [6].

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Fig. 9 The capacitance deterioration curve of the ultracapacitor at 55C

Fig. 10 The power requirement of the energy storage device in the hybrid vehicle during the on-road test

5 The Emulation of Road Test on the Test Bench 5.1 The Emulation of Road Test on the Charge/Discharge Test Bench According to the 3,000 h on-road test in the previous hybrid vehicle research experience, the power requirement of the storage device is analyzed and then broken down into 15,000 small cycles .The small cycle operating mode is illustrated in the Fig. 10. According to small cycle operating mode, the road simulation test criterion is formed. The ultracapacitor system is tested on the charge/discharge test bench according to the road simulation test criterion and the experiment result is illustrated in the Fig. 11. From the Fig. 11 it is known that the maximum cell voltage difference is not more than 0.2 V and the cell voltage difference is less than 0.05 V during the open

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Fig. 11 The SOC and maximum cell voltage difference during the road simulation cycle test

Fig. 12 The current requirement and SOC under the ECE_EUDC road condition

circuit condition, which is more precise because it is independent of the sampling error. So the ultracapacitor system possesses a good ability of equalization. In addition, the SOC is always between the interval of 0.4–0.7, which means that the energy density of the ultracapacitor can fulfill the requirement of the full hybrid vehicle well.

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5.2 The Emulation of Road Test on the Hybrid Energy Test Bench In order to study the performance of the ultracapacitor system in the full hybrid vehicle. The ultracapacitor system is tested on the hybrid energy test bench. In the control strategy the SOC operating interval is set between 0.2 and 0.8, the objective SOC operating point is 0.5 and the engine starts when the SOC is below the point 0.4. The current requirement and SOC under the ECE_EUDC road condition is illustrated in the Fig. 12. From the figure it is known that the SOC is always between the interval of 0.4–0.7. The experiment result indicates that the fuel consumption is 5.2 L/100 km, and the emission index of HC, CO and NOX is 0.693, 0.708, 0.105 g/km, which means that the hybrid vehicle with the ultracapacitor system would have a good performance of economy and emission.

6 The Conclusion The proposed ultracapacitor system has low resistance, high efficiency, and long lifespan. It can can deliver high regen and discharge power in a large SOC interval. Moreover, because of the large capacitance and high operating voltage, the maximum available energy of the ultracapacitor achieves to nearly 1000 W.h. From the experiment result it is known that the ultracapacitor can meet the demands of full hybrid vehicle. More experiments are needed to study the deterioration detail of the characters such as capacitance, resistance and efficiency. Additionally, further tests need to be done in the hybrid vehicle and the control strategy needs to be optimized.

References 1. Ortuzar M, Moreno J, Dixon J (2007) Ultracapacitor-based auxiliary energy system for an electric vehicle: implementation and evaluation. J Ind Electron 54(4):2147–2156 2. Gopalakrishnan S, Namuduri C, Reynolds M (2011) Ultracapacitor based active energy recovery scheme for fuel economy improvement in conventional vehicles [J] SAE technical paper 2011-01-0345, doi:10.4271/2011-01-0345 3. Farzad A, Abas G (2011) Hybrid lithium-ion/ultracap energy storage systems for plug-in hybrid electric vehicles [C]. Vehicle power and propulsion conference, IEEE 4. Bockenfeld N, Kuhnel Composite liFePO4/AC high rate performance electrodes for Li-ion capacitors [J]. Piwer siyrces, 2012, 1969(8) 4136–4142 5. Idaho national engineering and environmental laboratory (2004) FreedomCAR ultracapacitor test manual [M]. USA:DOE/NE ID, 2004-09 6. Tongzhen Wei, Sibo Wang (2009) Deterioration diagnosis of ultracapacitor for power electronics applications [C]. Sustainable power generation and supply, IEEE

Starting System for Stop/Start with Change of Mind Koichi Osawa and Hideya Notani

Abstract Further fuel consumption improvement of stop/start vehicles can be achieved by expanding the fuel cut period. In order to maintain vehicle response, ability to restart the engine during an engine stop event, known as change of mind (CoM), is necessary. Two current technologies to enable CoM are belt driven starter generator and permanently engagement starter. Both these technologies, however, require modification to additional vehicle components. The conventional pinion-shift-type starter has constant timing between pinion movement and motor rotation. If applied to CoM, the conventional starter risks making a loud noise or suffering serious mechanical damage during pinion engagement into the rotating ring gear of the engine and so is considered as unacceptable. Theses issues can be solved by using a new pinion-shifttype starter, which has independently controllable pinion movement and motor, and starter operation control, which can change the starter operation depending on engine speed. Furthermore, engine speed prediction based on engine rotational energy gives more accurate starter operation control which improves noise and restart response. The new pinion-shift-type starter and starter operation control with engine speed prediction has been fitted to vehicles and the performance compared to conventional starter systems. It is confirmed that engagement noise for CoM restart is equivalent to normal start engagement and that restart response is faster than current starting system with conventional starters. In this paper, details of the newly developed starter and starter control are described, together with performance of the new starting system. Keywords Stop/start speed prediction

 Fuel consumption  Change of mind  Starter  Engine

F2012-D02-010 K. Osawa (&)  H. Notani DENSO Corporation, Kariya, Japan e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_21,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction Recently, many low fuel consumption technologies, such as hybrid and stop/start, have been introduced in the market to reduce CO2 emissions. Stop/start is a particularly attractive technology for fuel consumption improvement because it can be easily installed on the existing vehicles. On stop/start vehicles, further fuel consumption improvement can be achieved by expanding the fuel cut period. Current stop/start vehicles cut the fuel after the vehicle has completely stopped. Therefore, cutting fuel before the vehicle stops will save more fuel than the current system. In the case of fuel cut timing at 20 km/h, the simulation result is shown in Table 1. An additional 3 % improvement in fuel consumption is expected due to expanding the fuel cut on a 2.0 L gasoline, automatic transmission vehicle over the NEDC (New European Driving Cycle). However, restart response requirements during engine run down (i.e. engine stopping), which is called change of mind (CoM), will increase as shown on Fig. 1, because the vehicle is still decelerating. In the case of CoM, it is important to maintain driveability by restating the engine as soon as possible. Therefore, a starting system is required to restart an engine when it is sopping. Currently, belt driven starter generator and permanently engagement starter can restart engine at CoM event. Both these systems, however, require vehicle components modifications and such modifications make these technologies less attractive to vehicle manufacturers. This paper describes the details and performance of a new starting system, consisting of a newly developed starter, based on conventional pinion-shift-type starter, and added starter control.

2 Subject of CoM Restart by Pinion-Shift-Type Starter A conventional pinion-shift-type starter has one solenoid which moves the starter pinion gear forward and also closes the contacts to energize the motor. Therefore, the delay time from pinion movement to motor rotation is fixed, and the motor is rotated just before the pinion engages into the vehicle ring gear completely. When the ring gear is rotating, this fixed behaviour can prevent smooth engagement. This is more notable if the motor is energizing at high engine speed when the relative speed between pinion and ring gear is large. The impact of the pinion and ring gear engaging with a large relative speed can cause a loud noise during engagement. In addition, engine oscillation can cause the engine to go in reverse direction just before stopping. If the motor is energized during engine reverse rotation, a large impact will occur during engagement, and this can cause serious damage to the ring gear and starter. To prevent these undesirable phenomena described the above, it is important to control the pinion engagement with the ring gear when the relative speed is in the desired range as shown in Fig. 2. The desired operation at different condition is described below:

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Table 1 Simulation results of fuel consumption improvement ratio Conventional New System function Fuel consumption improvement by simulation (2 L Gasoline)

Engine stop during vehicle stop NEDC Automatic transmission 4.5 %

Engine stop during deceleration vehicle speed: 20 km/h NEDC Automatic transmission 7.5 %

Fig. 1 Necessity of change of mind (CoM)

a. At high engine speed: Motor rotation before pinion touch At first, energize the motor to increase pinion speed, then move the pinion when the speed difference between the pinion and the ring gear is within the desired range. When the pinion contacts the ring gear, a relative speed difference of 0– 200 rpm will ensure smooth and stable engagement. b. At low engine speed: Motor rotation after pinion touch At first, move the pinion, then energize the motor after complete engagement. c. At engine reverse rotation: Pinion pre-engagement Even though engine restart may not be required, move the pinion just before engine rotation direction changes to reverse, then engage completely. Then if a CoM restart is required during engine reverse rotation, the pinion is already engaged, and so the starter can crank the engine by just energizing its motor. To realize the above operation, the following two are required.

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High speed Low speed

Oscillation

at engine deceleration (normal rotation) Action

Engine speed

a) High speed Restart signal Pinion shift

Motor ON

time

at engine oscillation (reverse rotation)

b) Low speed Restart signal Pinion shift

Motor ON

time

Decrease relative speed between pinion and ring gear Restart signal Motor ON Pinion shift

Starting time

Restart signal Pinion shift Motor ON

c) With pre-engagement Restart signal Pinion shift

Motor ON

time

before reaching engine speed=0 Engage by ring gear rotation (completion of engagement before reverse rotation)

Pinion shift Restart signal Motor ON

No delay

Fig. 2 Operation of CoM

• a pinion-shift-type starter which can move the pinion and energize the motor independently • a starter operation control which can operates the starter with accurate timing To operate the starter at accurate timing, it is necessary to accurately determine the engine speed when the pinion engages into the ring gear. However, the pinionshift-type starter always has some clearance between the pinion and the ring gear to avoid interference during engine running. Therefore, there is a time delay from when the pinion starts moving to contact with the ring gear. In addition, the engine speed determined by crank angle sensor has a greater error at lower speed. This error may increase during the time delay between start of pinion movement and pinion contact with the ring gear. Therefore, the starter operation control needs to have the following function: • Predict the future engine speed accurately. • Start moving the pinion at the correct timing to ensure that the relative speed at contact of the pinion and the ring gear is within the desired range.

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Advanced Starter

Conventional Starter One Solenoid for pinion engagement and motor rotation

Solenoid for motor control (on/off) Solenoid for Pinion engagement

Core

Replace Solenoid

Same Motor as Conventional

Motor

Pinion Gear

Starter can not operate whilst engine decelerates

Separate solenoid functions for pinion engagement and motor control

Advancement

Starter can operate whilst engine run down

Fig. 3 Advanced starter corresponding to CoM

3 New Starter for CoM 3.1 Structure The newly developed starter is shown in Fig. 3. To realize the operation shown in Fig. 2, this starter has two independent solenoids: one to move the pinion gear, and the other to energize the motor. These two solenoids are located along the same axis, and share the same core since both plungers operate in opposite directions. This arrangement helps to reduce the whole solenoid length. The rest of the structure and components are the same as a conventional starter. As a result, the change in starter packaging on the vehicle compared to a conventional starter is minimized.

3.2 Engagement Mechanism into Rotating Ring Gear The engagement mechanism of the pinion into the rotating ring gear (positive direction) is shown in Fig. 4. The preconditions are that the relative speed between the pinion and the ring gear is 0–200 rpm, and that the starter motor stars to apply its torque only after complete engagement. After the pinion moves and contacts with the ring gear, the pinion chamfer and the ring gear chamfer are in contact. Even though the pinion contacts with the ring gear at each edge surfaces first, the contact surface moves to the chamfer because the ring gear is rotating and the pinion is pushed by the starter solenoid force.

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Pinion synchronizes with ring gear synchronized

Pinion engages into ring gear Complete engagement

Accelerated

Ring gear (Rotating)

Fig. 4 Mechanism of starter pinion engagement with rotating ring gear

The ring gear rotating force is transferred to the pinion through the chamfer, then the pinion starts rotating and accelerating. The starter contains an overruning clutch, therefore, it allows the pinion to rotate with little friction. Since the ring gear speed is also decelerating, its speed tends towards the pinion speed. When the pinion speed matches with the ring gear speed, the pinion will engage completely into ring gear.

4 Starter Operation Control 4.1 Key Phenomenon for Engine Run Down Speed Prediction After fuel cut, the engine speed runs down with oscillations due to the repeated intake and compression of the engine cylinders. This engine run down behaviour is affected by many factors such as coolant temperature, engine oil viscosity, engine friction change due to aging and mechanical loads of auxiliaries, and so on. Therefore, the engine speed prediction based on the actual engine condition is necessary to accurately determine the engine speed. The engine run down behaviour depends on mechanical losses, therefore the kinetic energy change of the engine is considered. After fuel cut, the engine speed decreases due to losses in rotational energy by pumping losses, friction losses, and mechanical losses of auxiliaries as shown in Fig. 5. Each loss is assumed as almost constant below idle speed [1]. Based on these phenomena, the future engine speed can be predicted from rotational energy change from previous stroke. The details of prediction method are described below.

4.2 Engine Run Down Speed Prediction 4.2.1 Mechanism of Engine Run Down The energy change of crank angle change ðhi ! hiþ1 Þ at engine stroke number (j) is calculated by the following equation.

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Change in energy for one Δθ CA

Ei +j 1 = Ei j − Eloss (i →i +1)

Pumping Loss E p

Calculation Timing

Eloss (i →i +1) = E p (i →i +1) + E f (i →i +1) + Ea (i →i +1)

Previous Stroke

2

Neij+1 = Neij −

Ne i j Engine speed

2

Auxiliary Loss Ea

Eloss (i →i +1) J ⋅ 2π 2

Estimation for energy loss within the last stroke Kinetic energy E Angular velocity ω Inertia J Engine Speed Ne

−

Eloss (i →i +1) J ⋅ 2π 2

2

= Neij+1 − Neij 2

Ne i j+−11 Δθ

2

= Neij+−11 − Neij −1

E loss ( i → i +1)

Ne i j −1

1 2 Jω 2 = J ⋅ 2π 2 Ne 2

E=

Friction Loss E f

Next Stroke ( j )

j− 1

Kinetic energy

2

θi θi +1

Ne i j+1 Δθ

θi θi +1 Time

Fig. 5 Mechanism of engine deceleration j Eiþ1 ¼ Eij  Elossði!iþ1Þ

ð1Þ

where, Eloss is the combined losses of pumping losses, friction losses, and mechanical losses of auxiliaries. The rotational energy of engine E is defined by the following equations: 1 E ¼ Jx2 ¼ J  2p2 Ne2 2

ð2Þ

where, J is engine inertia, x is angular speed, Ne is engine speed. Equation (1) can be rearranged in terms engine speed Ne as below: 2

2

j ¼ Neji  Neiþ1

Elossði!iþ1Þ J  2p2

ð3Þ

Assuming each loss is constant against engine speed, the relationship between the engine speed at engine stroke (j - 1) and the engine speed at the same crank angle after one stroke (j) is given by the flowing equation: 

Elossði!iþ1Þ 2 2 2 j12 ¼ Nejiþ1  Neji ¼ Nej1 iþ1  Nei J  2p2

ð4Þ

Engine Run Down Speed Prediction From Eq. (4), the difference of the squares of the engine speed at the last stroke j ^ iþ1 (j - 1), and the engine speed at the calculation timing, the engine speed Ne and time D^tiþ1 at next crank angle (after Dh) are given by the following equations:

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Engine Speed

Ne i j −1 Ne j −1 i +1

1)

Next Stroke (

Engine Speed based on Crank sensor signal (Resolution : 30degCA)

Ne i j+−21

2 2 2 Nˆ eij+1 = Neij + Neij+−11 − Neij −1

θi +1

θi

Ne i j

θi+2

θi +1

θi+4

θ i +5

2 2 Nˆ eij+ 2 = Nˆ eij+1 + Neij+−21 − Neij+−11

θi+2

i

θ i +3 100 rpm

Δθ

j )

Δtˆi +1

Real Engine Speed

Predicted θi +3 Engine Speed

Δtˆi + 2

Δθ

0.01 sec

θi+4 Delay Correction

Time Fig. 6 Engine speed prediction

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 j12 j ^ iþ1 ¼ Neji þ Nej1 Ne iþ1  Nei D^tiþ1 ¼ a 13 ðNe j þDhNe ^ j Þ i

ð5Þ

iþ1

where, a is the correction coefficient for time delay. Typically, the engine speed is calculated from pulse output of the crank angle sensor. Therefore, the time delay will be longer due to slower response at low engine speed. It is possible to predict the engine speed more accurately by correcting this delay. Repeating the calculation of the engine speed at next crank angle based on Eq. (5) will give the predicted engine run down behaviour (Fig. 6). Confirmation Results on Actual Vehicles The relationship between predicted engine speed and measured engine speed is shown in Fig. 7. The predicted engine speed is calculated from the engine speed measured by crank angle signal with 30 degCA resolution (in order to avoid the missing teeth). The measured engine speed is determined from an angle encoder signal with 0.5 degCA resolution. One of the test vehicles is a 3 cylinder petrol engine which has large oscillations during run down, and the other vehicle is the diesel engine which has a high compression ratio. The predicted engine run down

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Measured Speed [rpm]

600 500

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400 300 200 100 0 0

100

200

300

400

500

600

Predicted Speed [rpm]

speed is selected 50 ms later from calculation timing as time delay correction in this case. As shown in Fig. 7, the predicted speed agrees closely with the measured speed. This prediction method improves the robustness of the predicted speed by using the latest measured speed, and updating the predicted speed at each 30 degCA, so preventing an accumulation of errors and adopting to changing conditions.

5 Merit of Developed Starting System 5.1 Engagement Noise Figure 8 shows the relationship between the engine speed at restart and the engagement noise measured at 1 m from the vehicle front. The test vehicle has a three cylinder petrol engine which results in large oscillations during run down. In the case of no speed prediction, the starter control logic has to include various factors which affect engine run down behaviour variation. The starter trigger timing is sometimes earlier than proper timing due to such variation. In addition, the relative speed between the pinion and the ring gear are larger than expected. The engagement noise depends on the relative speed between the pinion and the ring gear. Therefore, the engagement noise is larger than normal engagement noise. On the other hand, the engine speed prediction improves the relative speed between the pinion and the ring gear as expected. As a result, the engagement noise variation is equivalent to the noise level of normal starter engagement into a stationary ring gear.

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Fig. 9 Improvement at startup time ratio

5.2 Starting Response The starting response improvement using the new advanced starter for different engine speeds at CoM requirement is shown in Fig. 9. In the high engine speed area, the start-up time is longer at higher CoM requirement speed because of the acceleration time of the pinion speed by the motor. In the low engine speed area, the start-up time is a minimum at the point that the engine speed is equivalent to

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the starter cranking speed. This is because the starter does not need to crank the engine against its rotational inertia. At lower speed, the starter needs to crank the engine against its inertia, therefore, start-up time is longer. Without speed prediction, the starter trigger timing cannot be optimised for all situations and so sometimes enters a waiting mode until changing the starter operation mode to ‘‘motor rotation after pinion touch’’ which impacts on system response time. As a result of the speed prediction, the ratio of start-up time improvement against conventional starter is approximately 65 %.

6 Conclusion A new starting system for CoM has been developed with the following features: a new pinion-shift-type starter with independently controllable pinion movement and motor; starter operation control with engine speed prediction. With this starting system, it is confirmed that the engagement noise at CoM is equivalent to normal cranking noise with a conventional starter. In addition, the starting response is better than current starting systems using a conventional starter on a vehicle.

Reference 1. Hiroyuki H, Yukio H, Yoshishige O (1986) Internal combustion engine (Japanese). CORONA Publishing Co., Ltd, pp 72–76

Distributed Diagnostic Monitoring and Fault Tolerant Control of Vehicle Electrical and Electronic Devices Shanshan Fan, Diange Yang, Tao Zhang and Xiaomin Lian

Abstract To ensure the normal operation of the vehicle electrical and electronic device system (VEEDS), this paper presented a distributed on-line fault diagnostic method for the VEEDS, and also designed the fault tolerant control (FTC) system for it. All the vehicle electrical and electronic devices (VEEDs) are divided into two classes: backbone device and subnet device, which are connected by the CAN/ LIN bus, forming a distributed network. The electrical devices have the function of self-diagnosis, and can feedback the diagnostic information through the bus in real time. There is a central coordinator to receive the state information of all the devices and carry out the fault tolerant control of VEEDS. The distributed fault diagnostic and tolerant control method was implemented on an automotive electrical system, and the test results show that this method is available for the monitoring of the vehicle electrical system in real time, and can obviously improve the performance of the VEEDS whenever faults occur. Keywords Diagnostic monitoring tronic devices

 Fault tolerant control  Electrical and elec-

F2012-D02-012 S. Fan  D. Yang (&)  T. Zhang  X. Lian State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_22,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction Early fault detection and diagnostic (FDD) methods mainly dependent on artificial observation, reasoning analysis and logistic estimation. Later on some measuring instruments were introduced in fault diagnosis, leading it to transfer from qualitative analysis to quantitative analysis, but the diagnostic methods still remain in a passive position [1]. Now the FDD methods involve three major paradigms: model-based, signal-based and knowledge-based approaches [2]. In [3] an ABS model was developed for fault diagnosis of automotive antilock braking system, which can isolate faults accurately and make fault-severity estimation. However, the model-based diagnosis demands an accurate mathematic model of the system. Usually it is difficult to achieve in practical and it is more difficult for vehicles which are in a changeable exterior environment and have complex dynamics. Several researchers have applied signal-based approaches to automotive diagnosis [4, 5]: analyses the amplitude, phase and frequency of the object signal as well as their relationship with the fault symptom to extract the fault reason. This method avoids the difficulty of establishing mathematic model and has strong practicability. But its application is limited to certain mechanical components for single signal source. Knowledge-based diagnosis makes comprehensive analysis and reasoning of the object based on a range of knowledge of the object (known as system structure and function, fault reason and symptom, etc.), to detect and diagnose the system fault. Fault tree analysis (FTA) is one kind of effective knowledge-based diagnosis [6]. But for intricate systems like the vehicle, the complexity of the structure and function, and the coupling relationship between the internal and external parts make the system fault knowledge still relatively limited at present. With the rapid development and wide application of electronic, intelligentized and networked technology, vehicle fault diagnosis is gradually transformed from off-line to on-line diagnosis. On one hand, modern vehicle has equipped microprocessor or microcomputer to detect all subsystem data for control and diagnosis [7]. On the other hand, with the use of bus technology in-vehicle network system has come forth, aimed at solving the problem of multi-node information collection; also made it possible for controllers to share information resources and realized the distributed control of the system [8]. This paper presents a distributed fault diagnosis and tolerant control mechanism suitable for vehicle electrical and electronic system. All the VEEDs are classified as backbone node and subnet node, which have unified connection interfaces with the network, forming the completely distributed electric and electronic system architecture. The basic functionality and diagnosis of all the in-vehicle electric devices are implemented as digitalized and networked nodes, which can share information in the network. Each VEED has a certain degree of coupling with one another when working. Based on the coordinated knowledge of VEEDS, the central coordinator establishes FTC algorithm when it receives all the state

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information of the nodes. The research will reduce potential and hidden risks in VEEDS to a level that is deemed to be safe, and increase the accuracy and efficiency of fault diagnosis, meanwhile, lay the foundation of vehicle safe design.

2 Distributed Fault Diagnosis Network In this section, we briefly describe the distributed fault diagnosis network of VEEDS. It is composed of two classes: backbone network and sub-network. According to the importance and the position of electric apparatus in automotives, all the VEEDs are divided into backbone device and subnet device, separately networked by the backbone network and sub-network. A fully distributed and hierarchical automotive fault diagnosis network is shown in Fig. 1. As shown in the figure, the backbone devices are essential components of a vehicle, which play an indispensable role for the car running system, such as the engine, starter, ABS, etc. They are connected by the CAN bus and digitalized as CAN nodes. While the rest devices are subnet devices. Taking their different spatial position and power supply into consideration, they are divided into several sub-networks. Devices are connected by the LIN bus and digitalized as LIN nodes in the subnets. In the architecture, the gateways need to be configured to link the sub-networks to the backbone network. In the distributed fault diagnosis network of VEEDS, each node is intelligentized by adding an electronic control unit, where the independent logical function and self diagnosis are performed. Because of the self governed network node, VEEDS can realize distributed Networked Control. There is also an in-vehicle

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Fig. 2 Circuit diagram of actuator driver module

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computer equipped with the touch screen interface to control the VEEDs and display the diagnostic information of them. In addition, the central coordinator, a backbone node in the architecture, can receive all the state data of devices and realize the FTC of the system.

3 Self-Diagnosis of Veed 3.1 Self Diagnosis of Devices A large variety of electrical devices are involved in an automobile. For the fault diagnostic method, we need to make a classification design for each type of device to increase the reusability of the method, meanwhile decrease the design and commissioning work. There are three types of devices in the VEEDS: sensor, switch and actuator. Each type of device has a special fault diagnostic method.

3.1.1 Actuator Actuators are such kind of electrical devices, whose main function is to open and close the devices through controllers, and transmit the diagnostic information (such as normal, short circuit and open circuit) to the network. They adopt the load feedback diagnostic method in this thesis. The figure below is the circuit diagram of an actuator: (Fig. 2) The chip, BTS6143, in the figure above is used to drive the actuator. And the load feedback current (Iout) has a mathematics ratio relation with the load current (Iad): Iad ¼ Iout =K

ð1Þ

In the equation above, K is a constant, which is determined by the resistance in the circuit diagram, while Iad is obtained by AD sample. So Iout can be calculated by the equation. When Iout is greater than 1.5 times of the normal value of itself, we regard it as short circuit; when it is close to 0, regard as open circuit.

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3.1.2 Sensor Sensors adopt the limit value method for fault diagnosis. The upper limit and lower limit are determined by the characteristics of the sensors. When the value collected by sensor exceeds the threshold, the sensor is considered broken-down. smin  s  smax

ð2Þ

In (2), smin is the lower limit of the sensor, smax is the upper limit, and s is the sample result of the sensor. When s is in normal state, it meets the formula requirements. Once it is off upper or lower limit, it is out of order.

3.1.3 Switch Switches need to use the human–machine interface for fault diagnosis. For every electrical device has a state display in the in-vehicle computer. For example, when driver open the ignition switch, the screen will show ‘‘open’’. So in this method, once the real state of a switch does not match with the state displayed in screen, the switch is considered as fault.

3.2 Fusion of Diagnostic Information In the VEEDS network, all the state information especially the diagnostic information, transfers and shares across devices via the CAN and LIN bus. The backbone network adopts the Controller Area Network (CAN) protocol, while the sub-network adopts the Local Interconnect Network (LIN) protocol. The backbone devices directly transfer their information via the CAN bus, but the information of subnet devices is transmitted by the gateways. When the central coordinator receives all the state information of the VEEDs, it will make a judgment about whether the state-jump needs a response or not. After the control logical operation in coordinator, it sends out the control command to each VEED. It is in this way that all VEEDs exchange information data and work commands, and realize the FTC of the system. In addition, the diagnostic information of the devices will be displayed in the screen of the in-vehicle computer. In Fig. 3, C refers to the backbone network, Lj refers to the sub-network, cj is the gateway which combines the C and Lj. di(i = 1,2,…N) is backbone node,djk(k = 1,2,…,m(j)) is the subnet node. From the graph, we can perceive where the information is going, just as the arrow points to.

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4 Fault Tolerant Control of Central Coordinator In the fully distributed VEEDS network, when a certain device has malfunction, the devices associated with it will take action to respond to the fault. We adopt the central coordinator to process the diagnostic data and carry out FTC for the VEEDS. Devices, which have association, are sub-divided in a coordinated sub-system. The central coordinator makes state response respectively for every sub-system. A coordinated subsystem can be expressed as A, including the initiative devices DT, constraint devices DC and target devices DY. A ¼ ðDT ; DC ; DY Þ

ð3Þ

The initiative devices are generally switches, those who provoke the coordinated event. The state set for DT is ST: ST ¼ ðSTi ji ¼ 1; 2;    ; NÞ

ð4Þ

The state set for constraint devices DC is SC: SC ¼ ðSCj jj ¼ 1; 2;    ; NÞ

ð5Þ

The state set for target devices DY is SY: SY ¼ ðSYk jk ¼ 1; 2;    ; NÞ

ð6Þ

All the state of devices in a coordinated subsystem can be described as S: S = fST ; SC ; SY g

ð7Þ

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Once there are abrupt changes of parameters in S, the target devices will take action to prevent dangers. So the central coordinator is like a computer brain, which has stored a state response mechanism for the target devices. The following is the state machine of a target device, which is determined by the state change in S (Fig. 4). In the figure, ci(i = 1,2,…,m) is the logical calculation of S. ci ¼ fi ðS)

ði ¼ 1; 2; . . .mÞ

ð8Þ

After the control logical operation in coordinator, it sends out the control command. The command of target device DY is defined as ZY. ZY ¼ ðZYi ji ¼ 1; 2;    ; NÞ

ð9Þ

ZY corresponds to the state set of target device SY, and determined by the equation below. Zi ¼ gi ðCi Þ

ð10Þ

The formula(3–10)is known as the state response equation of target device. From(4–8) and (3–10),we can see that the FTC is actually the state response to the fault of devices in the subsystem. The central coordinator receives diagnostic information and sends out control commands, and the VEEDs execute commands. The advantages of the central coordinator are as following: (1) devices have no direct message relationship; (2) algorithm of each electronic controller has become simpler; (3) the reliability of system has been improved.

5 Results of Experiment 5.1 Test for the Distributed Diagnosis of VEEDS To illustrate the distributed diagnostic method, we established a distributed vehicular network via the CAN/LIN bus. This test platform features the following settings: (1) All the electrical devices are connected to the network through standard interface. (2) 13 backbone devices, including engine, ABS, air-condition, dashboard, etc.

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Fig. 5 Fault monitoring in screen of the in-vehicle computer a All devices’ self-diagnosis. b Online fault monitoring

(3) 52 local devices and 6 gateways, forming 6 sub-networks. (4) All the devices can realize the function of self-diagnosis. (5) The collected diagnostic information can be displayed by the in-vehicle computer. As for fault monitoring, the in-vehicle computer has two ways to show the diagnostic result, as follows. The automobile once power on, all devices in it are being self-diagnosed. Then the diagnostic information is fed back automatically to the in-vehicle computer. Figure 5a is the display screen of the in-vehicle computer, it shows the total number of devices to be diagnosed and the number of fault devices. Meanwhile, it creates a display list directly showing the fault devices. Furthermore, the in-vehicle computer can monitor the state of devices in real time. Every device matches with a button in the screen. Once the button is pressed, it will send a diagnostic request to the device. As Fig. 5b shown, if the device has malfunction, the button will become red; if it is normal, the button remains green. Experiments show that in the distributed VEEDS network, devices can realize the function of self-detection, and feed back the diagnostic data to the network. The in-vehicle computer can monitor the state of the system devices in real time and make early fault warning for them.

5.2 Test for FTC As to the FTC, we take the turn lamp for example. When ignition switch is ‘‘ON’’ and turn light switch is shifted to left, left turning lights flash at about 85 cyc./min; when turn light switch is shifted to right, right turning lights flash at about 85 cyc./ min. In this section, we create a coordinated subsystem for the devices mentioned above. That is to say, the devices in the subsystem are categorized into three types.

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Fig. 6 Right turn lamps flash at double frequency

(1) Initiative device: turn light switch; (2) Constraint device: ignition switch; (3) Target device: turn lamps. During flashing mode, if a turn lamp is failed, the central coordinator will figure out the FTC command, which is to let the other turn lamps in the same side flash at about double frequency of normal operation mode. As shown in Fig. 6, when one of the right side turn lights is failed, the others flash at double frequency. We carried out a road test for the vehicle. In the process of test, when devices’ failure occurs, the system can make fault tolerant response timely.

6 Conclusion In this thesis, a distributed fault diagnostic method is implemented. It can realize self-diagnosis of VEEDs, and can share diagnostic information based on CAN and LIN bus. Furthermore, attribute to the in-vehicle computer, it realized the on-line fault monitoring and early warning for devices. The central coordinator, which can take all the state information of associated devices into account and make accurate response to the state jump, solved the problem of fault tolerant control of the system. It increases the level of safety, and has important realistic meaning. However, the degree of fault tolerance and problem of reliability in the fault tolerant control system remained to be analyzed in the future.

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References 1. Minglei J (2010) The development of automotive fault diagnosis.China new technologies and products, No. 9 2. Chigusa S, Prokhorov D, Liu Qiao, Kihoon Choi, Pattipati K (2007) Application of an effective data-driven approach to real-time fault diagnosis in automotive engines. aerospace conference IEEE 3. Jianhui Luo, Namburu M, Pattipati KR, Liu Qiao, Chigusa S (2010) Integrated model-based and data-driven diagnosis of automotive antilock braking systems. Systems, man and cybernetics, Part A: systems and humans, IEEE Transactions on March 2010 4. Shahram A, Abbas S (2009) Fault detection of vehicle suspension system using wavelet analysis. Veh Syst Dyn 47(4):403–418 5. Kong L-l, Xiao Y-k (2006) Analysis and comparison of engine steady and unsteady vibration signal. J Trans CSICE 24(1):72–77 6. Narayanan N, Viswanadham N (1987) Methodology for knowledge acquisition and reasoning in failure analysis of systems. IEEE Trans on syst man cybern 17(2):274–288 7. Li C-C, Yang S-H, Lin S-H, Lin C-Y (2005) Development of on-line diagnostics and real time early warning system for vehicles. Sensors for industry conference, 8-10 Feb. 2005 8. Zhang B (2009) Research on automobile fault diagnosis and forecast system based on CANbus information [D]. Harbin Institute of Technology, Heilongjiang

Synthesis and Nox Gas Sensing Properties of In1.82 ni0.18o3 Electrospun Nanofibers Jinxing Wang, Kejin Zhang, Dan Wang, Dechao Xu, Bin Zhang and Zhongling Zhao

Abstract The detection and control of nitrogen oxides (NOX) in exhaust gases emitted by combustion engines has been an important subject in the last decades. Regulations of vehicle emissions focus on the minimization of NOX in automotive exhaust gases, particularly in lean combustion exhaust gases. Fast response times and high sensitivity of NOX sensor in lean combustion environments are necessary to meet those regulations. In this paper a new sensing material (In1.82Ni0.18O3 nanofibers) was synthesized via a simple and effective electrospinning method. The morphology and crystal structure of the as-prepared samples were characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM) and X-ray photoelectron spectra (XPS). Potentiometric-type NOX sensor based on yttria-stabilized zirconia (YSZ) with In1.82Ni0.18O3 nanofibers sensing electrode was prepared and its gas sensing properties were also tested. The results show that large-scale In1.82Ni0.18O3 nanofibers with diameters ranging from 40 to 80 nm and lengths of several tens of micrometers were successfully synthesized by this technique. A loose reticular porous non-woven lap structure was formed by many fibers. The results of sensing tests show that the sensitivity DEMF of sensor prepared can reach 85 mV for 500 ppm NO, and the DEMF is stable. Moreover, the sensor also exhibited fast response time and good selectivity. Keywords NOX Sensitivity

 Gas sensor  Electrospinning  In1.82Ni0.18O3 nanofibers 

F2012-D02-016 J. Wang (&)  K. Zhang  D. Wang  D. Xu  B. Zhang  Z. Zhao FAW Group Corporation R&D Center, Changchun, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_23,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction Lean-burn gasoline and direct-injection diesel engines offer the possibility of significant improvements in automotive fuel efficiency. Their developments, however, also induce high NOX emissions [1, 2]. NOX gas has caused serious damage to human health and the surrounding environment. In order to reduce the emission of nitrogen oxide and implementation of stringent regulations of NOX emission require the development of new technologies for NOX gas sensor which can work steady in cruel combustion gas environment. Several publications have described a method of NOX sensing based on the electrochemical oxygen pump cell using yttria-stabilized zirconia (YSZ). According to the introduction of Patent EP1942338A1, the NOX sensor consists of two internal cavities and three oxygen pumping cells [3]. Its measuring concept consists of lowing an oxygen concentration of a measuring gas to a predetermined level in the first internal cavity, in which NOX does not decompose, and further lowing the oxygen concentration of the measuring gas to a predetermined level in the second internal cavity. The second cavity also contains a NOX detection cell (the third oxygen pumping cells) with a rhodium catalytic electrode which has NOX reduction catalytic activity. Therefore, NOX decomposes on the measuring electrode and the oxygen generated is detected as an oxygen pumping current which is in proportion to NOX concentrations. Patent 97117135.1[4] has also reported a new method by measuring the electromotive force (EMF) between measuring electrode and reference electrode. This new method works as potentiometric sensor. It should be noted that, regardless of the pump current or electromotive force (EMF), the catalytic electrode materials was very critical, because it direct effects the sensitivity and response time of the NOX sensor. At present, the catalytic electrode usually using noble metals, such as Pt, Rh et al., as Patent US2010/0243447 A1, EP 2107366A2, US 2008/0156644 A1 reported[5–7]. However, the noble metal will lose its catalytic activity after undergoing long-term high temperature aging and exposure to toxic gases (such as SO2 and Pb). The failure of noble metal seriously affects the life and sensitivity of gas sensor. Therefore, it is urgent to develop non noble metal catalyst electrode materials. In2O3 with wide band-gap, good catalysis and high electric conductance has aroused significant interests in recent years. It has been proven to be an excellent sensing material for detection of many toxic and combustible gases after doped with metal ions in its crystal lattice. Because the dopant metal ions could be introduced into the structure of host material to change its lattice parameters, leading to a larger lattice distortion. The larger lattice distortion is beneficial for interaction between gas and material surface. In addition, as a result of indium oxide gas-sensitive mechanism is based on the tested gas adsorption and surface reaction, therefore, higher specific surface area of gas sensitive material is beneficial for gas sensing performance improvement.

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Fig. 1 Schematic image of the sensor

Recently, interest in one-dimensional (1D) has been greatly stimulated because of its increased surface-to-volume ratio and high density of surface sites. Considerable efforts have been made to fabricate 1D sensing nanomaterials via thermal oxidation, thermal evaporation, self-catalytic growth, molten salt synthesis, and electrospinning [8]. Electrosping, as a simple and versatile method, has gained great interest because it can produce 1D nanofibers with high long-diameter ratio, which makes the electron transport more effective and improves the performance of gas sensors [9]. In this paper, a new type gas sensitive material (In1.82Ni0.18O3 nanofiber material) was prepared by a relatively simple electrospinning technique at the first time. Potentiometric-type NOx sensor based on yttria-stabilized zirconia (YSZ) with In1.82Ni0.18O3 nanofibers sensing electrode was prepared and its gas sensing properties were also tested.

2 Experimental To prepare In1.82Ni0.18O3 solid solution nanofibers, 0.38 g In(NO3)34.5H2O and 0.025 g Ni(CH3COO)24H2O powders were added to 8.8 g mixed solvent contained DMF/EtOH with the weight ratio of 1:1 and stirred for 2 h. Then 0.8 g PVP was added to the above solution with stirring for 6 h. The obtained solution was then loaded into a plastic syringe and connected to a high-voltage power supply. 20 kV was provided between the cathode (a flat aluminum foil) and the anode (syringe) at a distance of 25 cm. In order to remove PVP completely, the composite nanofibers were calcined in air at 600 C for 4 h. Then, 10 at. % In1.82Ni0.18O3 nanofibers were obtained. Details of the design and fabrication of the potentiometric gas sensors are given in [10]. Figure 1 shows a schematic image of the as-fabricated sensor. Figure 2 shows an image of a real sensor. Gas sensing properties were measured using a dynamic test system. The sensors were tested in a Micro reactor which was connected to several gas reservoirs. Gas mixtures were regulated by mass flow controllers and computer control. Available gases were NO, NO2, CO2, CO. A temperature sensor was installed near the gas sensor. Temperature in the gas chamber was stabilized at 600 C. The sensitivity

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Fig. 2 An image of a real sensor

of the sensor is defined as the DEMF. The time taken by the sensor to achieve 90 % of the total EMF change was defined as the response time or the recovery time. The samples were characterized by X-ray diffractometer (XRD) (Shimadzu XD-3AX), field emission scanning electron microscopy (FE-SEM) (JEOL JSM6700F at 3 kV), transmission electron microscopy (TEM) (HITACHI H-8100 using an acceleration voltage of 200 keV), high–resolution transmission electron microscopy (HRTEM JEM–3010), X-ray photoelectron spectra (XPS) (ESCALAB Mark II spectrometer with Al Ka radiation).

3 Results and Discussion 3.1 Materials Characterizatics In order to confirm that the Ni ions were incorporated into the lattice structure, XRD was performed. The crystalline structures of In2O3 nanofibers and In1.82Ni0.18O3 solid solution nanofibers were characterized by X-ray diffraction (XRD) patterns, as shown in Fig. 3. It can be seen that a slight shift of XRD peak to higher angle for the In1.82Ni0.18O3 samples (Fig. 3b) compared with that of pure In2O3 (Fig. 3a). For each samples, all the observed diffraction peaks can be indexed to cubic indium oxide (JCPDS file NO. 06-0416), and no additional peaks for other phases have been found. The lattice parameters of In1.82Ni0.18O3 samples (a = 10.091 Å) is slightly less than that of pure In2O3 (a = 10.118 Å), which is consistent with the formation of a substitution solid solution (In1.82Ni0.18O3) [11]. It should be noted that although the ionic radius of Ni2+ (r = 0.78 Å) is bigger than that of In3+ (r = 0.72 Å), the lattice parameters decrease after substitution. This phenomenon can be explained by considering the change of Ni ions state as follow. In order to maintain the charge neutrality, an electron exchange process takes place in In1.82Ni0.18O3 solid solution structure. It leads to a partial transition of Ni2+ ions into Ni3+ which has smaller ionic radius (0.56 Å). Ni2þ þ In3þ $ Ni3þ þ In2þ

The presence of Ni3+ was confirmed by the next XPS.

ð1Þ

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Fig. 3 XRD patterns of a pure In2O3 and b In1.82Ni0.18O3 nanofibers

Fig. 4 XPS spectrum of Ni 2p3/2 peak of In1.82Ni0.18O3 nanofibers

Determination of the state of the Ni ions was carried out by measuring Ni 2p3/2 binging energy (BE) with XPS and shown in Fig. 4. The BE 855.6 eV is assigned to Ni3+ [12] which comes from the process of an electron exchange (formula 1), and in an octahedral oxygen neighbourhood in the In2O3 crystal lattice [12]. The appearance of Ni3+ ions is further confirmed the formation of In1.82Ni0.18O3 substitution solid solution. The BE 854.1 eV is assigned to Ni2+ which also in the In2O3 crystal lattice, and created more vacant oxygen which leading more oxygen species absorbed on the surface of In1.82Ni0.18O3 solid solution nanofibers. The general morphologies of the In1.82Ni0.18O3 nanofibers were studied with field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), and high–resolution transmission electron microscopy (HRTEM). Large-scale nanofibers with diameters ranging from 40 to 80 nm and lengths of several tens of micrometers can be found in the FE-SEM images

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Fig. 5 FE-SEM images of In1.82Ni0.18O3 solid solution nanofibers at different magnifications

(Fig. 5) at different magnifications. A loose reticular porous non-woven lap structure was formed by many fibers and the average diameter of these nanofibers is about 50 nm. From the TEM image (Fig. 6a), it can be seen that each nanofiber consists of many ultrafine particles with an average diameter of 20 nm. Lattice images are clearly observed from the HRTEM image (Fig. 6b), indicating the In1.82Ni0.18O3 nanofibers are highly crystalline. The interplaner spacing of 0.41 nm are corresponds to the (211) planes of cubic In2O3.

4 Sensing Characteristics Figure 7 shows the correlation between the sensitivity DEMF and the NO concentration for the sensor using In1.82Ni0.18O3 nanofibers-measuring electrode. It is seen that the sensitivity DEMF can reach 85 mV for 500 ppm NO. The sensitivity of the sensor as a function of stepwise increasing the NO concentration from 0 to 500 ppm was shown in Fig. 8. At an NO concentration of 40 ppm, the response time was very fast (about 1 s). After purging of NH3 from the gas phase, the sensitivity was quickly recovered to the initial level. In addition, at each NO concentration, a stable DEMF value was observed. The quick response and recovery characteristics of our sample are based on its structures. The large surface of the nanofibers makes the absorption of target gas molecules on the surface of the sensor easily. Simultaneously, the high long-diameter ratio of the nanofibers makes the electron transport more effective. To further understand the practicability of our fibers, the sensor was exposed to various 400 ppm gases at 600 C. Most of the tested gas mixtures were similar to typical exhaust gases from lean burn engines. The selectivity shown in Fig. 9 indicates that the In1.82Ni0.18O3 nanofibers are less sensitive to NO2, totally insensitive to CO2, and negative sensitivity to CO. Thus the obtained nanofibers exhibit prominent and good selectivity.

Synthesis and Nox Gas Sensing Properties

Fig. 6 a TEM and b HRTEM images of In1.82Ni0.18O3 solid solution nanofibers Fig. 7 Dependence of DEMF on the NO concentrations for the sensor

Fig. 8 The response and recovery characteristics of the sensor at different NO concentrations

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Fig. 9 The selectivity of the sensor at different gases

5 Conclusion In summary, large-scale In1.82Ni0.18O3 nanofibers with diameters ranging from 40 to 80 nm and lengths of several tens of micrometers were successfully synthesized through an electrospinning method. A loose reticular porous non-woven lap structure was formed by many fibers. Potentiometric-type NOx sensor based on yttria-stabilized zirconia (YSZ) with In1.82Ni0.18O3 nanofibers sensing electrode was prepared. The results of sensing tests show that the sensor exhibited high and stable sensitivity DEMF, fast response time and good selectivity. The results demonstrate that In1.82Ni0.18O3 nanofibers have excellent potential applications for fabrication high performance NOX sensors.

References 1. Brogan M, Brisley R, Walker A, Webster D et al. (1995) Evaluation of NOx storage catalysts as an effective system for NOx removal from the exhaust gas of lean burn gasoline engines SAE 1995, 1995/952490 2. Mello J, Mellor A (1999) NOx emissions from direct injection diesel engines with water/ steam dilution SAE 1999, 1999/1999-01-0836 3. Suzuki Y, Nakagaki K, Suzuki H NOX-decomposing electrode and method for producing NOX sensor EP 1942338A1 4. Nobuhide K NOX sensor and method of measuring NOX, US 005948964A 5. Shinji F, Aya S, Yukimasa M, Sumiko H Pumping electrode of gas sensor, method of manufacturing conductive paste, and gas sensor US 2010/0243447 A1 6. Horisaka S, Lee SJ NOX sensor EP 2107366A2 7. Suzuki Y, Suzuki H NOX-decomposing electrode and method for producing NOX sensor US 2008/0156644 A1 8. Xia Y, Yang P, Sun Y et al (2003) One-dimensional nanostructures: synthesis, characterization, and applications. Adv Mater 15(5):353–389 9. Ding B, Wang M, Yu JY et al (2009) Gas sensor based on electrospun nanofibers. Sensors 9(3):1609–1624

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10. Carlos LG, Ramos FM, Albert C et al (2011) Role of nanostructured WO3 in ion-conducting sensors for the detection of NOx in exhaust gases from lean combustion engines. Solid State Ionics 184(1):83–87 11. Bogdanov P, Ivanovskaya M, Comini E et al (1999) Effect of nickel ions on sensitivity of In2O3 thin film sensors to NO2. Sens Actuators, B 57:153–158 12. Ivanovskaya M, Bogdanov P (1998) Effect of NiIII ions on the properties of In2O3-based ceramic sensors. Sens Actuators, B 53:44–53

A Novel Concept of High Voltage Auxiliaries and its Feasibility Study on Blower Motors Satoshi Shiraki, Hiroyasu Kudo, Masakazu Tago, Akira Yamada, Shigeki Takahashi and Atsuyuki Hiruma

Abstract Hybrid/Electric Vehicles are expected to be one of the solutions for energy and environmental problems. Up to Now, low power automotive electronics have operated under a battery voltage of 12 V and a large current of more than 10 A. Because of this high current, the power electronic circuits cause substantial losses of power through wire harnesses, a DC/DC converter, semiconductors, and so on. In this paper, we have proposed a novel concept of high voltage auxiliaries, which replaces the 12 V loads with the high voltage loads driven directly by the high voltage battery. It is assured that the power efficiency of the high voltage test system is as high as 94 %, which is at least 10 % higher than that of conventional 12 V blower motor systems.



Keywords Blower motor Fuel Economy Efficiency Micro-Inverter IC



 High Voltage Auxiliary  Inverter

1 Introduction Hybrid/Electric Vehicles are expected to be one of the solutions for energy and environmental problems [1]. The power supply system equipped on Hybrid/ Electric Vehicle has a dual voltage battery, which consists of a high voltage battery (100–430 V) [2] and a 12 V battery connected through the step down DC/DC converter charged from the high voltage battery. Up to Now, low power automotive electronics have operated under a battery voltage of 12 V and a large F2012-D02-018 S. Shiraki (&)  H. Kudo  M. Tago  A. Yamada  S. Takahashi  A. Hiruma Denso Corporation, Kariya, Japan e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_24,  Springer-Verlag Berlin Heidelberg 2013

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current of more than 10 A. Because of this high current, the power electronic circuits cause substantial losses of power through wire harnesses, a DC/DC converter, semiconductors, and so on. In this paper, we have proposed a novel concept of high voltage auxiliaries, which replaces the 12 V loads with the high voltage loads driven directly by the high voltage battery. The purpose of replacing the 12 V loads with the high voltage loads is to achieve higher efficiency using fewer electric parts, for better fuel economy. Therefore the most important points of the concept are as follows. 1. High power automotive electronics are generally controlled with a sinusoidal waveform current. Typically, they are controlled by vector controlled sinusoidal drives, which require a high performance Central Processing Unit (CPU). A sensor less sinusoidal wave drive technology has been developed for low power electronics, which maximizes the power by controlling the phase difference between the motor voltage and the motor current without using a high performance CPU. 2. Low power automotive electronics, blower motors for example, operate under a battery voltage of 12 V and a large current of 30 A. By elevating the battery voltage, a drastic reduction in motor current (0.5 * 2A) becomes possible and permits a shift from six high current discrete power Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) to single chip micro-inverter ICs. We have successfully developed the record high blocking voltage of 750 V and the largest current capability of 4.5 A micro-inverter IC [3]. Firstly, the concept of high voltage auxiliaries is shown. Then, it is shown the most important technologies in the concept, which are the sensorless sinusoidal wave drive technology and the single chip micro-inverter IC technology. Finally, a test system of high voltage blower motor is driven by using these technologies. It is experimentally assured that the power efficiency of the high voltage test system is higher than that of conventional 12 V system.

2 Concept of High Voltage Auxiliaries Figure 1 shows a circuit diagram of voltage system. A step down DC/DC converter is used to charge supply currents from the high voltage battery. Up to now, automotive low power electronics operate under a battery voltage of 12 V. By means of elevating supply voltage using high voltage battery, we can achieve better efficiency and reduction of electronic parts. Figure 2 shows a block diagram of high voltage blower motors. For example, blower fan motors are driven by a large current of 30 A under a 12 V battery. And the electronic circuits are assembled from many electronic parts. On the other hand, under a high voltage of 300 V battery, a drastic reduction using motor current * 2 A becomes possible and permits a one-chip high voltage microinverter IC.

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3 Sensorless Sinusoidal Wave Drive Technology High power automotive electronics are generally controlled with a sinusoidal waveform current. Typically, they are controlled by vector controlled sinusoidal drives, which require a high performance CPU. In this system, it is not necessary to detect or estimate the rotor position. Figure 3 shows a sensorless sinusoidal wave drive technology. The technology does not require current sensors but can be used to estimate the phase difference between the motor voltage and the motor current by sampling the voltage of shunt resistors. It can maximize the power by controlling the phase difference. The features of this technology are the simplified control algorithm and the novel detection method of the induced voltage. Figure 4 shows a detection method of induced voltage and motor current at zero vector.

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4 Micro Inverter IC In the consumer electronics field, micro-inverter ICs up to 500 V 3 A ratings have been developed and widely used [4, 5]. On the other hand in the automotive electronics of HEV/EV, micro-inverter ICs have not yet been used because of the severe requirements related to blocking voltage (BV), current capability, efficiency, temperature, and so on. Even if it takes a BV for example, more than 700 V in all temperature range is required for a HEV application. Yasuhara et al. reported that his SOI lateral diode achieved the high BV of 650 V by means of introducing interface-N-layer (INL) with 1.3 9 1012 cm-2 arsenic ion dose on a 3 lm-thick buried oxide (BOX) [6]. Akiyama et al. reported that the higher BV of 1050 V was achieved in his lateral diode fabricated in silicon-on-double-insulator (SODI) structure, which was formed by backside silicon etching of SOI wafer followed by dielectric layer deposition and metal electrode formation [7]. Endo et al. reported that his lateral insulated gate bipolar transistor (LIGBT) with SRFP fabricated in SOI with 2 lm-thick BOX achieved good characteristics not only in the high BV of 580 V, but also in the fast switching of 280 ns [8]. However, there are no reports that a SOI micro-inverter IC

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4.1 High Voltage and High Reliability Technology 4.1.1 Thinner BOX SOI with Lightly Doped INL Figure 5 shows schematic cross-section of SOI micro-inverter IC consisting of LIGBT, lateral diode (LDiode), lateral-diffused metal–oxide–semiconductor (LDMOS), and complementary metal–oxide–semiconductor (CMOS). By introducing a lightly doped INL on the BOX film, BV of lateral power devices is considerably improved. Figure 6 shows simulated and measured blocking characteristics of SOI lateral devices. Thickness of the BOX is selected as 5 lm which is the practical limit of bonded SOI wafer. Applying the INL on the BOX, the highest BV is experimentally obtained as 780 V (INL dose: 1.7 9 1012 cm-2) which is comparable to 800 V of 8 lm BOX SOI without INL. The optimized INL dose of 1.7 9 1012 cm-2 is somewhat larger than 1.3 9 1012 cm-2 which was previously reported.

4.1.2 High reliability SOI Technology Over 750 V Figure 7 shows schematic electric field reduction structure using poly silicon based SRFP. The BV of LIGBTs is influenced by the high voltage metal wiring

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line over the SRFP. It was found that the BV greatly depends on the spacing of the poly-silicon of SRFP. Figure 8 shows the simulated and measured BV of SOI LIGBT with the poly-Si spacing as a parameter. The highest BV of 760 V is experimentally obtained when the narrow 1.2 lm spacing is adopted. The BV of 760 V is increased by 180 V, compared with the previously reported data. The optimized spacing of 1.2 lm shows that a turbulence of electric field at the silicon surface caused by overlaid high voltage metal wiring can be effectively relaxed by narrowing the spacing of SRFP. The critical importance in the automotive application is the reliability issue. It has been found, for the first time, that the stable and reliable high blocking voltage of 760 V is assured by controlling the sheet-resistance of the poly-Si layer of the SRFP. Figure 9 shows the long-term endurance test of blocking voltage under bias (600 V) and temperature (175 C) stress. Two cases of SRFP poly-silicon sheet resistances, RS,—92.8 kX/square (impurity dose: 2.5 9 1013 cm-2) and 2.1 MX/ square (1.2 9 1013 cm-2)—were evaluated. 92.8 kX/square of RS is low enough to ensure 1000 h endurance. It has been found that the stability of BV strongly

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depends on RS. The relationship between RS of poly-Si and long-term stability of BV is seemed to be related to interface trapped charge density and current through the SRFP.

4.2 High-Speed and Low-Dissipation E2LIGBT We have developed and re-optimized the high speed E2LIGBT for the high current 4.5 A micro-inverter application [8]. Figure 10 shows measured electrical characteristics of the high current E2LIGBT. In Fig. 10a and b, VON is considerably as low as 3.1 V at 4.5 A (170 A/cm2) and the fall time, tF, is remarkably as short as 52 ns.

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4.2.1 Fabricated IC and Control Circuits Figure 11 shows photo of the fabricated 750 V 4.5 A SOI micro-inverter IC, which is composed of E2LIGBTs, free-wheeling LDiodes, and the control circuits. The control circuits consist of pulse width modulation (PWM) controller, gate drivers, voltage regulators, bootstrap diodes, and so on. The chip size is 6.2 9 9.3 mm2. In contrast to conventional micro-inverters, which use 30 V 2 lm CMOS, we employed 5 V 0.6 lm CMOS circuits, which make it possible to integrate high level intelligent functions

5 Demonstration of Inverter Operation The test system of a blower motor (250 W 290 V ratings) is successfully driven by the sensorless sinusoidal wave drive technology and the micro-inverter IC as shown in Fig. 12. The experimental conditions are motor speed = 3000 rpm (maximum rating), VBATTERY = 290 V, IBATTERY = 0.90 A, PWM frequency = 20 kHz. Measured input power and power loss in the micro-inverter IC are 261 W and 16 W, respectively. The power efficiency has achieved as high as 94 %, which is at least 10 % higher than that of conventional 12 V blower motor systems. The printed circuit board size reduction has been realized by 40 % by elevating supply voltage from the 12 V voltage battery to the high voltage battery (290 V).

6 Conclusions We have proposed a novel concept of high voltage auxiliaries, which replaces the current 12 V loads with the high voltage loads driven directly by the high voltage battery. And it is assured that the power efficiency of the new test system is as high as 94 %, which is at least 10 % higher than that of conventional 12 V blower motor system.

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Acknowledgments The authors would like to thank Akio Nakagawa, Norihito Tokura, and Takashi Suzuki for useful discussion, Takeshi Sakai, Shinya Sakurai, and Shogo Ikeura for sample preparation, Yoshitomo Takeuchi and Kazutoshi Shiomi for inverter evaluation. The authors also wish to thank Yasushi Tanaka, Noriyuki Iwamori, and Masahiro Sou for their support throughout this study.

References 1. Kawahashi A (2004) In: Proceedings of International Symposium. Power Semiconductor Devices and ICs, p 23 2. Rahman K, Anwar M, Schulz S, Kaiser E, Turnbull P, Gleason S, Given B, Grimmer M (2011) SAE Tech Pap Ser, 2011-01-0355 3. Shiraki S, Takahashi S, Yamada A, Yamamoto M, Senda K, Ashida Y, Hiruma A, Tokura N (2012). Jpn J Appl Phys 51(04DP03):1–4 4. Nakagawa A, Funaki H, Yamaguchi Y, Suzuki F (1991) In: Proceedings of international symposium. Power semiconductor devices and ICs, p 321 5. Sakurai K, Maeda D, Hasegawa H (2008) In: Proceedings of international symposium. Power semiconductor devices and ICs, p 323 6. Yasuhara N, Nakagawa A, Furukawa K (1991) IEDM Tech Dig, p 141 7. Akiyama H, Yasuda N, Moritani J, Takanashi K, Majumdar G (2004). In: Proceedings of the international symposium. Power semiconductor devices and ICs, p 375 8. Endo K, Baba Y, Udo Y, Yasui M, Sano Y (1994).In: Proceedings of the international symposium. Power semiconductor devices and ICs, p 379 9. Ashida Y, Takahashi S, Shiraki S, Tokura N, Nakagawa A (2012) Jpn. J Appl Phys 51(04DP02):1–5

Small Lights Power Distribution System Improvement of a Heavy-Duty Truck Leigang Ma and Fadong Yan

Abstract This paper mainly introduces a harness automobile design case, about a heavy truck small lights power system and the distribution about the improvement of the specific case. Detailed description of the small lights system load power, loop wire diameter, the status quo power distribution structure survey, data calculation and analysis to identify the problems and then propose a variety of improvement programs, through the production process costs, resource requirements, the process of using the logic implementation, security, reliability, design concerns in five trade-off for the program to determine, by experimental verification to ensure that the program improved results. Elaborate on their understanding of the automotive wiring harness design, harness design aim for other vehicles to provide references. Specific theoretical calculations about each loop of wire did not begin due to the relationship of the papers focus. Keywords Power distribution

Æ Wiring harness design Æ Small lights system

1 Introduction Automation, intelligence is a tendency of the development of the car, in this tendency, the us car appliances also more and more. If the car compared to the human body, so the line is the “blood vessels + nerve”, carrying energy feeding, F2012-D02-022 L. Ma (&) Æ F. Yan Anhui Jianghuai Automobile Co. Ltd, R & D Centre, Hefei, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_25, Ó Springer-Verlag Berlin Heidelberg 2013

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the signal transmission and the realization of the function of the electrical. And how well each module and reduce energy distribution of each blood vessels carrying pressure becomes very important. Can say power distribution is the most important car circuit design, also is in the design of the core part of the vehicle [1, 2]. We in the power supply distribution design work mainly follow the following several principles: (1) Must provide sufficient capacity to fuse to ensure that all electrical appliances to correct functionality; (2) The capacity of the fuse should be small enough so that a short circuit to fuse to protect conductors and connected systems; (3) Should pay attention to electrical box fuse decorate, in order to ensure that the heat dissipation good; (4) don’t over design (using a large cross section of the wire), over-design will bring a lot of pressure to the cost and layout of the vehicle; (5) Electrical equipment must be a single set the fuse, if it affects vehicle safety performance or interfere with other electrical appliances (6) Resistance type load and inductance type load, should avoid sharing a fuse. Small light system is a huge system, lighting system and the external light is poor, open the system to provide backlighting for the instrument, cassette players, the key switch, but also for the external contour of the vehicle to identify indicators. Not only that, it’s open or fog lights system to open a prerequisite. It can be said that the small light system is an important part in the vehicle electrical system and do an important work of the small lamp power distribution system is our vehicle harness design. This paper mainly introduces a heavy trucks small lights power system and the distribution of the optimization of the structure improvement process, through the analysis of the specific case, to elaborate on my understanding of the automotive wiring harness design.

2 A Heavy Card Light System Present Situation and Analysis 2.1 Small Lights System Present Situation A heavy truck (24 V power supply system) of small lights system include two door-side lamps, four front-side lamps, eight rear-side lamps, two up-side lamps, three speed-lamps, six other side-lamps, eight switch lamps, instrument backlighting, air conditioning panel backlighting, radio-lamp, ashtray-lamp, cigar lighter-lamp, clock-lamp, the total load power is 182 W, most loop using a wire line sectional area of 0.5 or 0.85 mm2. Fog lamps system include two front fog lamps loop wire line sectional area of 0.5 mm2 and one rear fog lamp loop wire line sectional area of 0.85 mm2, the total load power is 131 W (Table 1 and 2.)

Small Lights Power Distribution System Table 1 Small lights system load list Code number Equipment name Power (W) 1 Door-side lamb 2 Front-side lamb 3 Rear-side lamb 4 Up-side lamb 5 Speed-lamb 6 Other-side lamb 7 Switch-lamb 8 Instrument-lamb 9 AC panel-lamb 10 Ashtray-lamb 11 Cigar lighter-lamb 12 Radio-lamb 13 Clock-lamb Total power (W)

5 5 5 5 5 10 1 9 2 2 2 2 2

Table 2 Fog lamps system load list Code number Equipment name Power (W) 1 Front fog lamb 2 Rear fog lamb Total power (W)

55 21

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The original state power distribution and control principle diagram shown as shown in Fig. 1.

2.2 Current Calculation Analysis After the start of the output voltage generator can be up to 28 V, we first to the system current calculation: The current of small lights system with Voltage of 28 V: ð182 W=24 VÞ  ð28 V=24 VÞ ¼ 8:85 A The current of fog lamps system with Voltage of 28 V: ð131 W=24 VÞ  ð28 V=24 VÞ ¼ 6:37 A Second, the fuse chooses calculation: The small lights system, fog lamps system total fuse calculated

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Fig. 1 The original state power distribution and control principle diagram

(1) Capacity of the fuse required on normal temperature: ð8:85 þ 6:37Þ A=70 % ¼ 21:7 A (2) Capacity of the fuse required on 40 : 21:74 A=f100 % þ ð40  C  24  CÞ  ð0:15 %= CÞg ¼ 22:5 A Needs to choose a fuse its nominal capacity is 25 A more appropriate. Fog lamps system around 40 temperature in the computation capacity required the fuse (1) Capacity of the fuse required on normal temperature: 6:37 A=70 % ¼ 9:2 A (2) Capacity of the fuse required on 40 : 9:2 A=f100 % þ ð40  C    24 CÞ  ð0:15 %= CÞg ¼ 9:5 A Needs to choose a fuse its nominal capacity is 10 A more appropriate. In order to meet our design principles article 2 “The capacity of the fuse should be small enough so that a short circuit to fuse to protect conductors and connected systems” based on experience choice, 25 A fuse downstream all small lights back to route shall be not less than 2.0 mm2 was the sectional area, 10 A fuse downstream of the fog lamps back to route not less than 0.5 mm2 was the sectional area, to ensure the safety of the two system loop. Thus found that fog lamps system without problems, small lights system, there are some problems: (1) the total fuse

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capacity slightly small; (2) the relative 20 A fuse capacity requirements, small lights system back to their line diameter is fine. Face the second question, solution has two kinds: one idea: structure remains, bold line diameter; the other: change distribution structure, reduce the fuse capacity.

3 Question Analysis Solutions 3.1 Scheme i According to first idea, we can put small lights system of dozens of circuit are for 2.0 was the specification of the wires. The result is: (1) our problem solved; (2) part of our connectors for not pressure the specification of the wire was 2.0 should to choose again; (3) our costs more; (4) our wire for coarsens and the difficulty to assembly.

3.2 Scheme ii According to thinking two, changes the structure reduces fuse capacity. We in the small lights each function before a small loop increase the capacity of the fuse also is very good method. The little lamp system around 40 temperature in the computation capacity required the fuse (1) Capacity of the fuse required on normal temperature: 8:85 A=70 % ¼ 12:65 A (2) Capacity of the fuse required on 40 : 12:65 A=f100 % þ ð40  CÞ   ð0:15 %= CÞg ¼ 13:0 A Suitable for choose nominal capacity of 15 A fuse. Thus gain power distribution and control principle diagram as shown in Fig. 2. Although increased a insurance, but little light system loop conductor of the specification is reduced to not less than 0.85 was, so the result is: (1) our problem solved; (2) our connectors do not need to change; (3) our cost slightly increase; (4) our wire slightly coarsen.

3.3 Plan 3 Since the fog lamps system in a small lights system purpose is to do only in small lights open under the conditions of the fog lamps to open, so as long as do fog lamps relay of the client in small lights system control under also think meet the design requirements. We can design into another structure (Fig. 3).

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Fig. 3 The power distribution and control principle diagram 2

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Fig. 4 The power distribution and control principle diagram 3

This plan is to load the total insurance from fog lamps load of separation of the direct fuse connected to often power supply, total fuse become small lights system fuse, because of the fog lamps relay control power still needs to provide little lamp system, so the control effect remains the same. Compared with the scheme ii, achieve the result is same, but because do not need to provide power for fog lamps load small light relay burden reduced greatly, and save a fuse its nominal capacity is 25 A.

3.4 Plan 4 In the scheme based on the three will be modified, let little light load is divided into two parts; each one has a fuse its nominal capacity is 10 A, as shown in Fig. 4. Using this distribution structure can make sectional area of little lights system’s wire line requirement was dropped to 0.5 mm2, greatly reduced the cost of wire, and when one of small lights get a fault, the loop road in the upper reaches of the points blow out, another part can still work, the fog lamps can still work. Improve the reliability of the system, but its demand total four fuses position. Based on the above mentioned four schemes can be concluded that scheme for four compared the lowest cost, the highest of the reliability of the system, but should be increased to 2 fuse position. We electrical boxes fuse position is limited, and the specific choose plan 4 or scheme will depend on the three of the whole system resources situation and decide. According to the physical state, we have 2 or more reserved fuse position. This improvement chose the implementation play 4.

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Fig. 5 The power distribution and control principle diagram in test

4 Effect Validation As shown in Fig. 5 shows, the fuse Numbers were F21, F39, F40 and F8, of which F21 for small lights the system total fuse, F39 is fuse for branch 1 of small lights system, and F40 is fuse for branch 2, F8 is fuse for fog lamps system. Whether the Improvement has a good effect? Let’s give an experiment to the improve system. Experimental requirements: the system improved vehicle a car, one wire length of 100 mm sectional area of 0.5 mm2, one timer wire; The experiment steps: 1. List the test circuit experiment form; 2. Remove the need to test system with electric equipment of connectors; 3. Closed switch, directly in the wires into the harness provide connectors short sub test loop; 4. Check the protection of the circuit in 3 S whether the fuse blow out; 5. Hand feels test loop wire is burning; 6. The test results in the experimental form. For many circuit, the experiments of several representative loop only tested. The test results to Table 3.

5 Conclusion This paper mainly introduces a heavy trucks small lights system improvement of the specific case, through the case of calculation and analysis process express my opinion is that we do not only in design of performance requirements, the

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Table 3 short circuit experiment record Code number Equipment name

Fuse number

Fuses blowout in 3S

Wire heat

1 2 3 4 5 6 7 8 9 10

F39 F39 F39 F40 F40 F39 F39 F39 F8 F8

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

No No No No No No No No No No

Left door-side lamb Front left-side lamb Front left-side lamb2 Rear right-side lamb Body-side lamb Horn switch-lamb Fog lamb switch-lamb Fog lamb relay coil Front left fog lamb Rear fog lamb

production, the assembly, the cost, can use the resources comprehensive consideration, to fully diversify our thinking. A good play achieved only by constantly thinking, validation, and continuous improvement. Do a good product also is same.

References 1. Huang M (2003) Automotive electrical equipment. The peking University publishing house p 234–254 2. Wei C (2004) Automotive electronics.The Beijing Institute of Technology publishing house p 21–33

Development Trend Analysis of Automobile Electronic System ZhiRong Fan, Ying Xie, Cheng Yang, Yipeng Zhang and Jian Chen

Abstract Research and/or Engineering Questions/Objective: This chapter, based on the technology evolution theory, reviewed present situation of the automobile electronic system technology, based on this prediction that the electronic system in function, structure and new technology development trend. Methodology: Using on the S curve and technology evolution method based on TRIZ analyzing theory, also vehicle electronic system of china own brand passenger car, this chapter analyze developing tendency of vehicle electronic system with lamps, instrument, auxiliary parking, bus network. Results: Through a systematic analysis, the result of developing tendency of all the electrical and electronic systems is developed. Limitations of this study: As in short this chapter, development tendency of complete vehicle electronic system and specification of china own brand passenger car is not fully covered. What does the paper offer that is new in the field in comparison to other works of the author: This chapter analyzes the development of electrical and electronic system, by adopted S curve theory and technology evolution method based on TRIZ theory. Conclusion: Through the analyzing S curve of electrical and electronic system, a conclusion can be drawn that the electrical and electronic system will evolved towards platform, intelligent, and integration. The electronic specification of China own brand passenger car still has certain distance behind the frontier, yet still has a huge opportunity of development. Keywords Electronic system curve China own brand



 Technology evolution  Developing tendency  S

F2012-D02-024 Z. Fan (&)  Y. Xie  C. Yang  Y. Zhang  J. Chen Dongfeng Motor Corporation Technical Center, Wuhan, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_26,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction As a combination of electronic industry and car industry, automotive electronic is developing rapidly. At present, the western developed countries have account 15–20 % of electronic products manufacturing, when many characteristic functions are realized by the dependent of automobile electronic technology [1]. Automobile electronic not only promoted the development of the car industry, it also greatly promoted the electronic product market. Modern automobile electronic technology has shown an irreplaceable role in improving automobile power, safety, reliability, driving stability and comfort. At the same time, customers are more and more rational about function and performance requirements, regulations show increasingly harshness on reliability and quality, cost-down target is classified to design and development, manufacture, as well as the after-sales service, and in order to preemption market opportunities, the research and development time cycle is compressed. Based on the above factors, every famous carmaker is studying the development tendency of electronic system, to help research department developing electrical and electronic architecture platforms, at the purpose of reducing development cycle as well as development costs.

2 Analysis Methods 2.1 The S Curve of Technology Evolution The evolution of technology system is not random, but to follow certain objective law, like the evolution of biological systems, technology system also is facing a ‘‘natural selection’’. The S curve is developed by analyzing massive products. The product performance parameter evolution over time in law of S form curve, any product development process will experience the baby period, youth period, mature period and decline period, as shown in Fig. 1. Each product has its own core technology, in narrow sense the core technology is through the physical, chemical and geometry. If ever a new core technology substitute appears, two kinds of situations will appear as the following: (1) The new product will top existing product performance limit in performance; (2) The new products will exceed the original product at a higher rate and with lower prices, therefore the substitution process of product is also the substitution process of core technology, as shown in Fig. 2.

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Fig. 1 S curve of technology evolution

Fig. 2 S curve of product substitution

2.2 Technology Evolution Rule Technology system evolution theory is based on analyze historical data of a lot of the world patent and other technical engineering system. It is concluded by G. S. Altshuler the founder of TRIZ and other TRIZ experts. It mainly embodies in the process of technical system corresponds functions, technology system improvement and development tendency. In the classic technology evolution theory of eight different kinds of technology system evolution rules [2]: completeness rule, energy transfer rule, improving ideal rule, dynamic evolution rule, subsystem unbalanced evolution rule, micro level evolution rule, super system evolution rule, using evolution rule.

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Fig. 3 S curve of automobile electronic system evolutions

3 Electronic System Evolution Analyses 3.1 Electronic System Overall Development Analyses Automobile electrical system is an important part of the car, which has one of the performances of a direct impact on the car of the power, economy, emission, comfort and so on. Automobile electric development has three stages, first is automobile electronic products build by division components and integrated circuit, which has been eliminated now. Electronic devices from the scale integrated circuit to large scale integrated circuit, promote the rapid development of automobile electric product, which developed some special independent system, such as electronic control gas injection, antilock brake systems, and each system is linked through the hard wire, which is complex and inconvenience in maintenance. In order to perform a variety of functions of the comprehensive system and centralized control of vehicle integral system, there was such centralized control system as body control system and video entertainment system. Using one controller to focus on the control, each system will communicate buy network bus. As shown in Fig. 3.

3.2 Lighting System The source of the light from the original system of tungsten lamp to improved halogen lamp, then to xenon headlamp, until the fourth generation of LED light source, new light source performance continues to improve, as shown in Fig. 4. The lamps periphery system development is mainly in headlight intelligent and

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China own brand

Fig. 4 S curve of light lamps system

China own brand

Fig. 5 S curve of the development of the peripheral lamps organization

energy conservation, especially the AFS system and other humanized design, the headlamps controller work in different environment and conditions to control the front combination lamp, so as to achieve the purpose of safe driving, as shown in Fig. 5. From the Figs. 4 and 5 can be seen that the china own brand passenger car brand is still in the HID xenon lamp and headlight cleaning device, the next step is the development of the headlights with optical waveguide and AFS system.

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Fig. 6 S curve of the parking assistant system development

3.3 Parking Auxiliary System The most basic of parking auxiliary system form is single reverse radar, after that, video system was also used as parking assist device. As the continuous improvement of the car safety requirements of customers, parking assistant system becomes more and more important, and move to the all-round, detection means development, such as the front anti-collision radar, backing up dynamic auxiliary line, video image, side video images and automatic parking, its development curve as shown in Fig. 6. We can see from Fig. 6, at present the china own brand passenger cars are still in reverse video image stage, the next step should be reverse dynamic auxiliary line function.

3.4 Instrument System Today, most of the car instrument used digital processing technology. Typical applications are the stepping motor instrument, all kinds of digital instrument and LCD display information. From 3 in dot matrix screen to 7 in TFT color LCD screen, make meter visual tonal of drivers improved. Display information is more intuitive and easy to read. Recently, two to three years, full LCD screen appeared in the luxury car meter part and concept car, let instrument displays and interface entered a new area that, through the display screen, driver can see reverse images, map navigation, night vision displays, mobile TV etc. In the future, the meter will become information processing and display center. Through the instrument

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Fig. 7 S curve of combination instrument system development

information platform, drivers can not only understand the state of the vehicle and situation around the vehicle, but also through the intelligent Internet technology, understand the traffic and driving with relevant information. The development course is shown in Fig. 7. We can see from Fig. 7, the china own brand passenger car uses color LCD screen, the next step will be equipped with full LCD instrument.

3.5 Network Communication Technology Currently popular car bus: CAN, LIN, MOST, FlexRay, Bluetooth etc. CAN/LIN network is mature technology, low in cost; within the next 5 years, CAN/LIN network is still the mainstream of network technology; Very few luxury cars will use FlexRay bus; MOST are mainly applied in navigation, multimedia systems. As shown in Fig. 8, we can see from Fig. 8, at present the china own brand passenger car uses the combination of the network CAN/LIN, the next step may use FlexRay bus technology. The communication network topology is of two kinds: single segment and multisegment structure, with the improvement of car, electrical function increasing and the interaction between controller signals increasing, from single segment to many segment is an inevitable trend.

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Fig. 8 S curve of the network communication technology

4 The Technology Evolution Analysis of Car Electronic System Along with the computer technology, information technology and electronic technology, new materials and new technology advancing, automobile electric appliances product is also developing rapidly. More humane and intelligent automobile electric appliances will bring people more convenient and joy. Using the technical system evolution theory to analyze the electronic technology using trend, in conclusion, automobile electronic system development trend will mainly embodied in the following aspects: (1) EEA platform Based on the existing models, combined with the next 3–5 years electronic technology development trend, construct the relatively complete electronic function architecture, which has good expansibility portability and terrace of electronic structure foundation, makes different models design engineers to work under one platform according to different demands, save the development cost, shorten the development cycle, ensure the development of electric vehicle quality. (2) Network At present CAN/LIN network is a mature technology, low in cost. Within the next 5 years, CAN/LIN network will still be the mainstream of network technology, very few luxury cars will use FlexRay bus, MOST are mainly applied in navigation, multimedia systems. CAN network has been widely used in the development of domestic and foreign oem models, will still be the mainstream technology, and with the improvement of car configuration, electrical function increasing, and the interaction between

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controller signal increases, single segment to multi segment of the development is an inevitable trend. At the same time the Internet technology, optical fiber, Bluetooth technology and network technology will also be applied largely to cars, and further improving the car networking. (3) Intelligent Smart sensors and actuators, car level micro-processing technology have developed rapidly, the new control theory and method of usage has made car electronic, digital and intelligent become mainstream car engineering. Intelligent main is the adaptation to the change of environment and. In this state, oems need environment sampling information, such as AFS technology, biological recognition technology, automatic parking technology, etc. In order to meet the market growing demand, oems will improve the intelligent of future products, using AFS, Telematics and other new technology. (4) Modular/integration Through the integration to add control system function, which also divide function into several separate set function. Such as meter integrate gateway functions, which can realize the combination of the display and message routing, will combines the instrument panel and control module, make its have universal; Distributed car body control system, using one BCM and multiple units module, may provide more function, better meet body control function of humanity, comfort and safety requirements.

5 Summary With the rapid development of industry, automotive technology is developing towards environmental protection, energy saving, sustainable development, humanity, intelligent, high technology, function integration and other development direction. Through S curve analysis of the electronic system, and technology system evolution analysis, it can be concluded that the electronic system will change towards the platform, network, intelligent, module/integration development direction, China own brand passenger car still has certain distance behind the frontier, yet still has a huge opportunity of development.

References 1. QianJuGen. Domestic and foreign automobile electric appliances development trend ‘‘jiangsu electric’’ 2008 years we period 2. TianJie flowers. The S curve and technology system evolution law the library collections

Automotive ECUs Fault Diagnosis Modeling Based on the Fault Database Yanqiang Li, Yang Li, Zhixue Wang, Ruke Zhuang and Jianxin Li

Abstract The automotive ECUs is becoming more and more complicated, and so is the fault diagnosis. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on fault database. By making full use of data stream, we firstly extract symptom vector by processing data steam and pre-processing rules, and then we use the symptom vector to match the fault pattern in fault database, we use the unmatched vector as the test case of C4.5 decision tree algorithm to create the link rules between fault symptom and fault reason, and finally store the rules into the fault database. An example of ETCs is showed to testify the fault diagnosis method. The test result confirm the reliability and validity of this diagnosis method.







Keywords Fault diagnosis modeling Data pre-processing Decision tree Fault database

1 Introduction With the rapid development of automotive Electronic Control Unit (ECUs), the fault diagnosis becomes more and more complicated. And the link between fault and fault symptom becomes less obvious. Diagnostic Trouble Code (DTC) and in-vehicle fast data snap-shop is very useful to diagnose an ECU, but the fault F2012-D02-026 Y. Li (&)  Y. Li  Z. Wang  R. Zhuang  J. Li Shandong Provincial Key Laboratory of Automotive Electronic Technology, Institute of Automation Shandong Academy of Sciences, Jinan, Shan dong, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_27,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 To-be-diagnosed system

e(t )

u (t )

f (t )

system

h(t )

coverage of DTC is limited. For example, the DTCs of 2003 Accord only cover 37 % malfunctions. Additionally, sometime the faults indicated by DTCs cannot demonstrate the actual fault [1]. Although more and more diagnosis technology have been applied in this field, but those methods, such as [2–4], often boils down to the use of heuristics, associative case memories, or expert systems. These approaches are restricted with respect to the complexity of the diagnosed system and the faults to be detected [5]. This paper proposes a fault diagnosis approach based on fault database technologies in contrast to the above traditional approaches. Firstly, abnormal data stream is extracted from data stream and then a fault symptom vector is created by utilizing the data preprocessing approach, then we put the symptom vector into the fault database to match fault pattern, and we use the unmatched symptom vector as test case of decision tree to set up connection between fault symptoms and its failure reasons, and at last, we save to the fault database.

2 Description Generally speaking, fault diagnosis modeling is a complex process. The to-bediagnosed object can be abstracted as a system [6]. The system contains its hardware and software, and it has interaction with outside environment. The outside environment contains human factor, EMC factor, working environment factor etc. The system fault reason contains hardware fault, software fault or mechanical fault etc. The system can be abstracted as follows Fig. 1.The system has input information or input functions uðtÞ; such as driving command or data from sensors; the output information contains actuator command and fault information. In addition, the system has the other possible failure caused by environment factor eðtÞ and human factor hðtÞ. Because diagnosis object structure is complex under the condition of many interaction factors, it is difficult to implement the fault diagnosis. But through the

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273

Info &

data

System aim &design

System modeling & simulation

Analysis & conclusion

conclusion

Optimize the parameters Re-examine the framework

Fig. 2 Framework of Fault Diagnosis

establishment of a reasonable framework and idea of fault diagnosis, it is not complicated to solve this problem. The paper proposes fault diagnosis structure as shown in Fig. 2. Firstly to clear the operating mechanism and fault symptom of whole system based on available theoretical knowledge and experience, and then to determine the system framework and boundaries and parameters of the variable for solving the problem. Secondly to built the system modeling based on the first work. The input–output relationship should be described based on the mathematical and logic models. Based on those models, simulation should be implemented in order to adjust and optimize the parameters of those models. Thirdly to optimize the previous parameters and boundary parameters based on the history data and simulation result several times. Fourthly to re-examine the previous model framework and boundary definition based on the quantitative analysis several times in order to satisfy accurate requirements. At last to store the modeling conclusion.

3 Diagnosis Modeling 3.1 Electrical Throttle Control-System In this paper, we propose an example of Electrical Throttle Control-system (ETCs) which is the system that allow the ECM to precisely control the opening and closing of the throttle valve based on drivers input and is also interrelated with Traction Control ECU and Vehicle Stability Control ECU. The topology of the ETCs is as follows:

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The ETCs-I is composed of an accelerator pedal assembly, a throttle body assembly, and an ECM. The ECM contains CPU, throttle motor control drive circuitry, a power supply, and inputs from other functions. In addition, the ECM electronic fuel injection and ignition functions provide fuel and spark in the correct amounts and at the correct time to keep the engine running. All three (i.e., air mass, fuel, and ignition) are needed in the correct proportion and sequence for the engine to run otherwise power output is diminished and/or the engine stalls [7].

3.2 Diagnosis Modeling Fault diagnosis modeling is the core of the diagnosis. The paper proposes such the diagnosis process: picking up abnormal data flow from data stream and pre-process those data to extract fault symptom vectors based on the data pre-processing rules, putting vectors into the history fault database to match the same fault mode, those unmatched vectors are deemed as test case for decision tree, the decision tree creates diagnosis rules, and the rules are stored in the fault database. The working process is as follows [4]: 1. Firstly, we need to know the working principles of ETCs, and to analysis the possible fault reasons. The possible fault component concludes: Throttle position Sensor, Idle speed Sensor, Accelerator pedal sensor, Transmission gear Sensor, Throttle Motor, Cruise control System, Engine control System. 2. Secondly, we need to classify and check the data stream: the data stream is mainly divided into value information, control information and the other information. The premise of fault diagnosis is to judge whether the parameters are in a reasonable range, are stable and are change unreasonable. 3. Thirdly, we need to pre-process those abnormal data stream. The aim of data pre-processing is to extract useful information, and change data stream into symptom vector based on pre-processing rules, and the vector is the input of the fault database; 4. Finally, putting the symptom vectors into the history fault database which is the core component of fault diagnosis. The fault database is used to store fault symptom vector and its corresponding fault reason vector. If the input vector can match the fault mode, the fault reason can be found based on the fault reason vector; if it isn’t matched, the fault symptom vector is used as test case of decision tree. Though decision tree algorithm, the fault reason rule is created. With the accumulation of test cases, the fault database will become more effective. Figure 3 shows the fault diagnosis process (Fig. 4).

Automotive ECUs Fault Diagnosis Modeling Based on the Fault Database

275 Throttle Valve

Accelerator Pedal Position Sensors Idle Speed Control Sensor

ECM Throttle control

Throttle Control Motor

Cruise Control Transmission Shifting Control

Fuel Injection

Mass air flow

Ignition Coil

Fig. 3 Topology of ETCs-I

start

Data pre-process

Database with symptom & failure

Test cases

Decision tree No Cut set

Matches or not Rule set Yes

Diagnosis system Failure match

Fig. 4 Fault Diagnosis Process

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3.2.1 Data Pre-processing Based on the working principle of ETCs, we should analysis the possible failure and link between the fault symptom and fault reason [8]. According to the experience of domain expert knowledge, the fault symptom and fault reason of every possible fault component are encoded, Table 1 shows the fault symptom code of ETCs. Fault database is used to store fault symptom vector and its corresponding fault reason vector, the fault database needs to refer to a lot of experiment and test result. The expert should set the initial value based on their experiment firstly, however, the low accuracy of database should be modified constantly, thus, with the increase of the test cases, the database becomes more and more accurate and credible. Table 2 shows the relationship between fault symptom and its related reason vector. Fault occurs when abnormal data steam is generated. After extracting those abnormal data stream, the fault symptom vector can be created based on the fault component and its symptom, according to transacting rules of Table 2.

3.2.2 Decision Tree Algorithm Modeling The most influential decision tree algorithm is ID3 and C4.5 proposed by Quinlan in 1986. The core algorithm of ID3 is to take all unused attributes and count their entropy concerning test samples, to choose attribute for which entropy is minimum and make node containing that attribute. The C4.5 is the improved ID3 algorithm, using information gain ratio as the attribute selection criteria, to make up for the inadequacy of ID3 algorithm [9, 10]. The working process is as follows: 1. Calculate the exception information of set S: Suppose S is the data set of s subset, suppose s1 ; s2 ;    ; sm are m subsets belonging to E, the information entropy is as follows: Iðs1 ;    ; sm Þ ¼ 

m X si i¼1

s

 log2

si s

ð1Þ

2. Calculate the entropy of the subset S after a split over the A attribute: EðAÞ ¼ 

k X i¼1

ðsij þ    þ smj Þ=s  Iððsij þ    þ smj Þ:

ð2Þ

Automotive ECUs Fault Diagnosis Modeling Based on the Fault Database Table 1 Data Pre-processing Identifiers Failure symptom Electronics component Throttle Position Sensor (TPS)

f1

f2

Idle speed sensors

f3 f4

f5 Transmission sensors

f6

Pedal sensors f7

f8

f9 Throttle motor

f10

Cruise control f11 f12

f13

Small openings \3–5 between normal sensor values and DTC for sensors not within the range. Small openings \3between normal sensor values and DTC position does not match commanded DTC for sensors not within operational zone DTC for sensors not within operational zone, Engine stall

277

Possible failure reason Position sensor fail high, low, intermediate values. compromised power feed or ground to both sensors Incorrect learning of fully closed position, sensor voltage lower

Temperature sensor fail high, low, intermediate values Incorrect Sensing of Engine Load, Gear, Power Steering, A/C, Electric Load, Engine Speed Incorrect learning of throttle angle for Idle speed

Small openings \3–5 between normal sensor values and DTC limit DTC when selected Gear doesn’t Transmission shifting, torque match sensed gear ratio converter lockup throttle modulation DTC for high, low and outside Position sensor fail high, low, lane. None, if Pedal sensors intermediate values fail within operational lane DTC Engineered Fault in operational Incorrect learning value, Dual lane Valid pedal signal failures to specific voltages escapes detection, not DTC. that result in voltages within Electrical Failures should operational lane leave trace Dual failures that result in voltage Engineered Fault in operational within operational lane lane Valid pedal signal escapes detection, no DTC. DTC high current, transitory Motor shorted to power or small opening until DTC ground, h-bridge fail, lath-up activates fail-safe or transistor short mode \0.5 s Short of power, ground, open Failure of Steering Column disable function Switches or ECM input circuit DTC when both Brake Switch Failed Brake Switch poles do not agree. Cannot switch into Gear DTC for Speed Sensor, small Vehicle Speed indicate lower throttle openings to maintain speed 0.06 g acceleration to set speed (continued)

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Table 1 (continued) Identifiers Failure symptom Electronics component Software of ECU

Possible failure reason

Theoretical Fault Escapes Detection

f14

Software unilaterally opens throttle with Accelerator released., Idle Fuel Cut not active, Watchdog serviced, no EDAC error

Table 2 Fault symptom vector Num Fault symptom vector 1 2 3 _ n

Fault reason vector (U)

f1

f2

f3

…

f14

1 0 0 _ 0

0 1 0 _ 0

0 0 1 _ 0

_ _ _ _ _

0 0 0 _ 1

U1 U2 U3 _ Un

3. Calculate the gain of the attribute A is: GainðAÞ ¼ Iðs1 ; s2 ;    ; sm Þ  EðAÞ

ð3Þ

4. Calculate the gain of the subset S after a split over the A attribute: Gain Ration ðA; SÞ ¼ Gain ðS; AÞ=SplitInfoðS; AÞ SplitInfoðA; SÞ ¼ 

c X i¼1

ðjSj j=jSj  log2 jSj j=jSjÞ

ð4Þ ð5Þ

SplitInfo (S,A) expressing the breadth and uniformity of split set S according to attribute A. The highest attribute of the information gain ratio as the test attributes of set S is used to create a node. To create branch according to A property of all value or all intervals, so to divide the sample. 5. Cut Sets- the formal method are pre-pruning and post-pruning, and postpruning allows over fitting data, and then built the tree pruning; The prepruning method is difficult to estimate when to stop tree growth accurately, so the post-pruning method is more practical in the real problem. The paper use the post-pruning method to avoid the tree over growing and to avoid data over fitted, and using the test case itself to judging whether pruning or not.

Automotive ECUs Fault Diagnosis Modeling Based on the Fault Database

"

# f q Pr pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi [ z ¼ c qð1  qÞ=N

279

ð6Þ

N = quantity of test cases, f = E/N error rate (E is the N instances in the number of classification errors), q = real error rate, c = degree of confidence (C4.5 default value is 2.5), z is the corresponding standard deviation of the degree of confidence c. Suppose there 14 fault according to the current test samples. The fault reason is as follows: U1 = sensors shorted to the ground or power, U2 = Sensor supply voltage is too low resulting in the position value error, U3 = temperature value not within the valid range; U4 = Error sensing data leading to data collection error; U5 = incorrect learning of throttle opening, U6 = Torque conversion error; U7 = position sensor’s voltage lower or higher, U8 = Position sensor output error learning, U9 = Fault coverage for non-DTC torque, U10 = Electrical shorted to power or ground, H-bridge failure, U11 = steering wheel key failure or ECM input circuit failure, U12 = Brake switch failure, U13 = Speed error,U14 = watchdog action or login errors. According to the formula, we need to calculate exception information of all test cases; Then, calculating the information gain ratio of each attribute. And constructing every brunch of decision tree according to the formula above. After traversing the decision tree we get the following classification rules: IF throttle opening increases AND DTC occurs for throttle sensor not within the valid range THEN throttle sensor shorted to Power or Ground; IF throttle opening increases AND DTC occurs for pedal sensor not within the valid range THEN position sensor’s voltage lower or higher; IF throttle opening increases AND cruise control system shorted to power or ground THEN steering wheel key failure or ECM input circuit failure; IF engine temperature is too low AND ECM fault escape detection THEN watchdog action or login errors; IF engine temperature is too low AND pedal sensor opening small openings\3–5 between normal sensor values THEN temperature value not within the valid range; IF engine temperature is too low AND pedal sensor has no DTC and throttle small opening \3 to 5 THEN incorrect learning of throttle opening; IF engine temperature is too low AND throttle motor small-angle instantaneous detecting large current DTC THEN Electrical shorted to power or ground, H-bridge failure (Fig. 5).

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Fig. 5 Fault Decision Tree

Sudden unintended acceleration

Engine water temperature lower

Throttle opening increase

f14 f1

f11

f7

U14

2,5

U11

U1 U7

f3

U3

2

5

f5

f10

U5

U10

3.3 Experiment Result Fault phenomena of one type electronic throttle system is that throttle opening increases unexpected without any operation. After monitoring signals by using CANoe, we find the abnormal data stream comes from pedal sensor. Based on the data-preprocessing rules, we get the fault symptom vector f ¼ ½0; 0; 0; 0; 0; 0; 1;    ; 0 After put the vector into the fault database, the fault pattern is matched and the fault reason is the output of position sensor is too high or too low. The fault symptom disappeared after changing the position sensor.

4 Conclusion This paper proposes a fault diagnosis modeling structure in the first place, then it proposes a fault diagnosis algorithm based on fault database. The main process is to extract the abnormal data stream and change the data stream into fault symptom vector based on data pre-processing rule, and then put the vectors into database to match fault pattern, and we use the unmatched symptom vector as test case of decision tree to set up connection between fault symptoms and its failure reasons. At last, we store the rules in the fault database. The paper proposes ETCs as an example to test the fault diagnosis model, and the test result confirm the reliability and validity of the modeling method.

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Acknowledgments This work has been supported by Natural Science Foundation of Shandong Province, China (ZR2011FQ034).

References 1. Ya D (2009) Data analysis in automotive fault diagnosis. Machinery Industry Press, Beijing, pp 2–4 2. Yi J (2011) Research on fault diagnosis expert system of automotive engine based on ontology. Electrical Control Eng (ICECE), pp 5409–5412 3. Choi K (2006) Data reduction techniques for intelligent fault diagnosis in automotive systems. Autotestcon, pp 66–72 4. Namburu SM (2006) Application of signal analysis and data-driven approaches to fault detection and diagnosis in automotive engines. Systems, Man and ybernetics, pp 3665–3670 5. Stein B (2003) Model compilation and diagnosability of technical systems. In: Hanza MH (ed) Proceeding of the 3rd IASTED international conference on artificial intelligence and application (AIA 03), BenalmAqdena, Spain, pp 191–197, ACTA Press, Sept 2003 6. Xuesen Q (2005) A new discipline of science-The study of open complex giant system and its methnology. Urban Stud 12(5):1–8 7. Technical support to the national highway traffic safety administration on the reported toyota motor corporation unintended acceleration investigation. 2011 8. Li Y, Li Y (2010) Fault diagnosis of automobile ECUs with data mining technologies. Adv Sci Eng 2010:156–161 9. Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann Publishers, Burlington 10. Hui O, lebin L (2010) Research of paper metadata extraction algorithm based on C4.5. Comput Eng Des 2010(16):3708–3711

Secure Microprocessor Architectures: Solutions from the Semi-Conductor Industry Klaus Scheibert and Björn Steurich

Abstract Even if the question of manipulation protection is not always widely understood to date among some non-European vehicle manufacturers, this topic plays a particularly critical role among German premium vehicle manufacturers. As soon as the ignition key is turned, a number of electronic controllers (ECUs) start performing authentication queries by means of a challenge-response method (based among other things on the 128-bit AES standard). Both the necessary security keys and the process of data evaluation and processing must be protected from possible attacks. Based on the Secure Hardware Extension (SHE) of the latest 90 nm 32-bit TriCoreTM microcontroller generations, the future extension to the Hardware Security Module (HSM) is described for components based on the incoming 65 nm technology. The concept for this enhancement is based on work carried out in the framework of the European research project EVITA (www.evitaproject.org). The implementation is being performed by Infineon in close cooperation of its Automotive and Chip Card and Security divisions. Keywords Cryptography

 IP protection  Component protection  SHE  HSM

F2012-D02-029 K. Scheibert  B. Steurich (&) Infineon Technologies AG, Neubiberg, Germany e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_28,  Springer-Verlag Berlin Heidelberg 2013

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1 Motivation According to a publication by the MEMA Brand Protection Council [1], the global financial losses owing to forgeries amounted to 600 billion US Dollars in 2008. MEMA highlighted losses by the automobile industry of some 12 billion US Dollars (USA: 3 billion US Dollars, Europe: 9 billion US Dollars). The forecast (Frost and Sullivan) for 2011 predicted an increase to over 44.7 billion US Dollars. While mechanical plagiarism (from windscreen wipers to oil filters to brake pads) has accounted for the majority of this to date, the number of cases involving falsified electronic components is on the increase and, apart from theft and replacement of controllers as well as chip tuning, is resulting in significant losses in the automobile industry. Such offenses surely cannot be regarded as trivial if one considers that falsified or modified vehicle components: • Represent a not inconsiderable security risk both for the passengers and also for service personnel. The risk increases disproportionately with the complexity of the components. • Can result in the loss of valuable jobs. • Can endanger the image of established brands and lead to spurious liability claims. • Involve organized criminals. It must also be considered that the intellectual property both of the vehicle manufacturers and the suppliers lies to a large extent in the software and therefore can in the case of inadequate protection represent easy prey for fraudsters. The excerpt below is structured as follows: Following an overview of the current implementation and application of the SHE module, a detailed comparison with the future HSM module is performed. In addition, other security measures implemented by Infineon are explained in relation to hardware interfaces, storage protection and chip ID. The article concludes with an appraisal of other possible application areas, a summary and a projection of future developments.

2 Secure Hardware Extension The Secure Hardware Extension (SHE) is based on the Hersteller (manufacturer) Initiative Software (HIS) to which the OEMs Volkswagen, BMW, Audi and Daimler belong [2]. Infineon’s SHE implementation is based on the specification Version 1.1 dated April 01, 2009 of the HIS consortium (AK Security). The Secure Hardware Extension (SHE) is an on-chip extension for microcontrollers in the automobile area. The intention is to move the control of cryptographic keys from the application domain to the hardware domain and in so doing protect the secret

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keys from software attacks. In contrast to more robust solutions such as Trusted Platform Module (TPM) chips or smartcards, the specification declares that no resistance is needed in the face of physical attacks (such as differential power analysis (DPA), electromagnetic analysis (EMA), attacks with interference pulses). The priority is more so to reduce the risk of an attack to a commercially acceptable level. This means that the costs of a physical attack must be higher in any case than the possible financial gains that can be hoped to be achieved with a successful attack (e.g. through avoidance of global keys or series-specific passwords). Therefore, if a costly successful attack on a security system in a car only leads for example to the obtaining of a vehicle-specific crypto key, which cannot be transferred to the next automobile, then the effort required for a chip tuner for example is not in proportion to the effort invested in terms of the benefit gained. The primary declared objectives of HIS can be summarized as follows according to [3]: • Protection of cryptographic keys against software attacks • Provision of an authentic software and hardware environment (referred to below as a ‘‘secure boot’’) • Exclusive dependency of the target security level on the strength of the underlying computational algorithm and the confidentiality of the cryptographic keys • Distribution of ownership of keys to a number of electronic components (controllers) • Optimum flexibility with respect to provision and lower add-on costs (for example the possibility to exchange the key in the workshop)

2.1 Overview on Current Implementation The SHE module has thus far been implemented by Infineon on the TC1798, TC1793 and TC1791 microcontrollers in 90 nm flash technology. These microcontrollers are the three premium components of the AUDO MAX family [4]. With an internal clock frequency of up to 300 MHz, the components are based on the TriCoreTM CPU and were designed especially for powertrain and chassis applications. Apart from excellent real-time capability, development focused on the aspects of functional safety and data protection. Figure 1 shows the arrangement of SHE within the AUDO MAX architecture. The module comprises the following four main hardware components [3]: • A hardware accelerator for symmetrical block ciphers based on the encryption standard AES-128 [5] • A true random number generator (TRNG) for generating 128-bit random numbers • A memory for storing cryptographic keys and data

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Fig. 1 SHE architecture

• Control logic for managing the arithmetic operations and memory access within SHE. SHE (as well as HSM) relates to a hardware software co-design. The entire security concept only takes effect if the hardware is supported by corresponding application software and processes. The hardware components are described in detail below before their application is discussed in greater detail.

2.1.1 AES-128 Hardware Accelerator All cryptographic operations of SHE are performed based on AES-128. The Advanced Encryption Standard (AES) is a symmetrical encryption algorithm that was published as a successor to DES and 3DES in October 2000 by the National Institute of Standards and Technology (NIST). The AES-128 variant has a fixed block and key length of 128-bits each. A hardware accelerator was implemented for the AES-128 block ciphers in order to speed up the cryptographic operations. The main cryptographic operations supported by SHE include the secure encryption and decryption of messages as well as the authentication of messages and data of various communication partners (external and internal hardware components): • The AES-128 hardware accelerator supports the two operating modes ECB (Electronic Cipher Codebook) for individual 128-bit data blocks and CBC (Cipher Block Chaining) for larger data volumes (n 9 128-bit blocks) for encrypting and decrypting messages. Encryption and decryption of an individual 128-bit block is performed in both operating modes within \1 ls.

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• In cryptography, Message Authentication Code (MAC) refers to the generation of a digital fingerprint for authenticating a sent (unencrypted) message. It therefore provides protection against undetected manipulation of the sent message. A secret key issued to the sender and recipient is required beforehand. The sender generates the MAC based on the secret key and the message being transmitted (plain text). The message and MAC are transmitted. The recipient likewise generates the MAC from the message received and compares it with the MAC sent by the sender. The SHE module uses hash functions to create the MACs. ‘‘A hash function h can be defined here as a mapping h : A ! A00 , which can be used to convert character strings of any length (character block) x 2 A to character strings hðxÞ 2 A00 of a constant length n [5]’’. The function value h(x) is called the hash value, hash code or also message digest of x. An iterative hash function is also referred to as a compression function. SHE uses the method based on Miyaguchi-Preneel as the compression function in order to generate hash values by means of AES-128 block ciphers and secret keys. A MAC based on block encryption is also referred to as CMAC (Cipher-based MAC).

2.1.2 Random Number Generator The SHE module uses a pseudo random number generator (PRNG) to generate 128-bit random numbers for challenge-response authentication (see Sect. 3.2.2). The start value is generated by means of a true random number generator (TRNG). The task of a pseudo random number generator is to generate a bit sequence that cannot if possible be differentiated by means of statistical analysis from a real, random bit sequence. This means that: • The bits are distributed uniformly, therefore the same number of zeros occur as ones. • A specific bit in the sequence cannot be derived from the others, therefore is independent of the preceding and subsequent bits. The TRNG implemented corresponds in all operating modes to at least the classification ‘‘P1 medium’’ under [6]. The pseudo random generator uses the AES-128 hardware accelerator in the ECB operating mode with PRNG_KEY as the key for calculating the feedback function. The random number generator is used for challenge-response authentication and for generating the initialization vector for CBC coding.

2.1.3 SHE Memory Area The SHE module is attached to the peripheral bus and as the bus master can therefore initiate data transfer from and to other peripherals as well as memory areas. 48 Mbytes of RAM and a 2 9 8 kByte data flash have been implemented

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for storing confidential keys, MACs and temporary data. Access to this memory area is blocked for all other bus masters. SHE has write or read access to the separate flash area.

2.2 Application of the SHE Module 2.2.1 Secure Boot A main requirement with respect to SHE is the enabling of a secure boot process, i.e. the checking of authenticity of the internal flash memory content each time the microcontroller boots. A short software routine is executed for this purpose immediately after the reset (part of the Boot ROM code of the application processor) and before the actual application software. This routine checks a memory area, which is referred to as the SHE boot loader, by calculating a CMAC (Miyaguchi-Preneel process with BOOT_MAC_KEY as the secret key—see 3.1.1). The result is compared with the expected value (BOOT_MAC) stored in the secure flash area. If the calculation does not arrive at the expected result (e.g. through prior manipulation of the memory content or by booting from external memory), the keys can be locked explicitly in the flash memory (see 3.1.3). As soon as the initialization of the microcontroller has concluded, the SHE boot loader is first executed. This can in turn initialize the checking of the other memory areas. The concept is based on the idea of ‘‘core root of trust for measurement’’ (CRTM) as proposed by the Trusted Computing Group [7]. The size of the tested memory area can be freely selected. The AES hardware accelerator used allows checking of 128 kBytes of the integrated flash memory in \10 ms. A further variety of the ‘‘Secure Boot’’ operating mode is the checking of certain security critical software sequences in the background following the startup of the application. Delays while powering-up the controller can be avoided in this way and thus appropriately short response times can be achieved in the controller network cluster. Following authentication or unsuccessful authentication of a software sequence, it can then still be decided how a controller will react (for example because of an error entry in the error memory).

2.2.2 Challenge-Response Authentication The challenge-response method is a secure authentication procedure used by a communication partner based on shared knowledge. One partner presents a question (‘‘challenge’’) and another partner must provide a valid answer (‘‘response’’) to prove s/he knows certain information. Depending on the encryption method used, a variety of different methods can be used, all of which are based however, on the same basic principle. If one partner (generally referred to as Alice in cryptography) wants to authenticate him/herself to another partner (generally referred to as Bob), Bob for

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example sends a random number N (Nonce) to Alice (Bob therefore presents the challenge). Alice encrypts this number N with her password and sends the result back to Bob (and therefore delivers the response). Bob has meanwhile encrypted the same random number with the password known to him for Alice and compares the result of this encryption with the response that he gets from Alice. If the two encrypted messages are identical, Alice has successfully authenticated herself. What is important here is that the partners do not transmit the password, rather simply have to prove that they know it. Such a security request can take place, for example, during start-up and can recur periodically or for predefined events. A series of application cases arise of which three will be depicted below by way of example: • Electronic immobilizer: The immobilizer is activated automatically when the ignition is turned off. An RFID chip (radio frequency identification) is generally used to disable it again when switching on the ignition. In the case of the current third generation immobilizers, both the communication between the RFID transponder and the immobilizer (authentication of the approved driver) and the communication between the immobilizer and the engine control unit (release of vehicle/starting of engine) are secured cryptographically by means of challengeresponse authentication. • Component protection: Following conclusion of the secure boot process, the domain controller (gateway ECU) for example checks the authenticity of the controllers of the associated system domain. According to the manufacturer’s guidelines, a variety of controllers meanwhile participate in the authentication queries. In the case of stolen or exchanged controllers, the cryptographic keys do not match and the component protection prevents the actual application software from booting for example (incl. air conditioning, CD changer, dashboard displays, body gateway). • Protection against chip tuning: Chip tuning, i.e. the subsequent modification of application software supplied ex works or calibration data, seems at first glance to be a temptingly easy way to increase driving dynamics and improve performance. Increasing the performance by chip tuning general produces results that vehicle manufacturers would prefer to avoid. Such results include shorter lifespan, deteriorating exhaust emission values or higher wear and tear. This gives rise to quite a significant risk potential. The chip internal flash memory (embedded flash) in the microcontroller is used almost exclusively today for storing the application software and calibration data. This gives rise to two potential attack scenarios—replacement of the microcontroller by a copy or manipulation of the memory data by means of a programming device via external memory or debug interfaces. The first procedure can generally be eliminated owing solely to the complexity of the chip package used (and can moreover be suppressed by the component protection described above). Challenge-response authentication therefore reliably prevents replacement of the microcontroller for a newly programmed off-the-shelf device. Furthermore, a series of additional measures are provided in the microcontroller hardware that

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protect against unauthorized reading of the memory content and also prevent the code in the internal program flash (PFLASH) area from being modified in an unauthorized manner by third parties for the purpose of engine tuning. Some of these additional measures are described in greater detail in the next section.

2.3 Additional Protection Mechanism Among the additional protection mechanisms of the TriCoreTM microcontroller family is the possibility to block read and write access to the internal flash memory (Read and Write Protection), as well as the option to lock the external debug interfaces (depending on the respective boot mode and selected memory protection). The primary goal is to protect both the IP of the vehicle manufacturer and the supplier against ‘‘Reverse Engineering’’. If the entire memory area is blocked with respect to read access, this automatically protects the entire memory against write access also (this protection can be disabled separately for the data memory). Furthermore, a choice can be made between write and OTP (One Time Programmable) protection for each individual sector of the program memory. OTP protected sectors are locked in this case to prevent further deletion and from this point on offer ROM functionality. Readout protection as well as sector-specific write protection can be disabled temporarily for a driving cycle via password protection. If read protection is activated and the program boots from the internal flash memory, the external debug interfaces are locked automatically. Read protection can be canceled temporarily by means of a 64-bit password in order for example to change access rights or perform a programming action. Since only one (correct) attempt is permitted per boot cycle, up to 264 attempts are required in order to hack the password (with 2 ms per boot cycle, this could take theoretically up to 1.17 billion years, which corresponds to approx. 26 % of the estimated scientific Earth age).

3 EVITA 3.1 Project Overview The Hardware Security Module (HSM) presented below is a specific implementation of the hardware security extension presented in the framework of the European Research Project EVITA [8]. The aim is to create a secure system architecture for exchanging data within the vehicle as well as for external communication (vehicle to vehicle and vehicle to infrastructure). The objective here, as with SHE, is to protect the platform integrity, provide secure communication

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channels, access control, and detect and prevent infiltrations. In contrast to SHE, HSM is multi-tasking capable however and also offers programmability of additional cryptographic functions.

3.2 Overview of the Different HSM Variants In order to enable optimally cost-effective hardware solutions for the various application scenarios in the automobile industry, three different HSM variants have been specified [9, 10]: • The HSM full version for protecting the vehicle’s board net from external attacks in the case of V2X communication. The variety of communication partners requires continuous creation and checking of electronic signatures. By using asymmetrical rather than symmetrical encryption, the key distribution problem, i.e. the distribution of keys via an unsecure channel, is simplified considerably. The total number of required keys is also comparatively small (2 keys per party). These are two criteria that favor the use of asymmetrical encryption owing to the variety of communication partners involved in V2X communication. However, this goes hand in hand with significantly increased computational effort (100–100 times slower than equivalent symmetrical encryption) and relatively large key length. To fulfill these performance requirements, a highly efficient asymmetrical cryptographic processing unit is required for the full version. In addition, the AES-128 hardware accelerator is extended by additional operating modes in comparison with SHE. • The medium HSM variant for protecting on-board communication. Owing to the limited number of communication partners (approximately 50 controllers for a medium class vehicle) and the possibility to exchange the key, for example, during end-of-line programming, asymmetrical cryptography can be foregone with the medium variant. With the exception of the missing asymmetrical cryptographic module, the medium HSM variant is similar to the full version. Depending on the processing performance of the CPU used, there is also the possibility to execute fewer time-critical asymmetrical encryption operations in software. • The HSM light version for secure communication between controllers, sensors and actuators. Only a symmetrical cryptographic processing unit is required in this case and a hardware interface reduced by the corresponding range of functions.

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4 Extension to the Hardware Security Module A detailed explanation was given in the last section which security applications are candidates for the various HSM variants (Light/Medium/Full) in the framework of the EVITA project. The current implementation of the SHE module within the Infineon 90 nm AUDO MAX Premium Controller TC179x assumes a mixed form when classified in the existing EVITA nomenclature, which fits in somewhere between Light HSM and Medium HSM. When defining the next 65 nm powertrain microcontroller, Infineon took into account the customer’s requirements to develop some security functions in the direction of the Medium HSM hardware approach of EVITA.

4.1 Objective of Infineon Having committed itself in the case of the automotive microcontroller applications mainly to application areas such as Powertrain (PT), Hybrid and Electrical Vehicle (HEV and EV) as well as active and passive Safety, it stood to reason to use the security hardware classification described in the EVITA project as a basis and to align it to the customer’s requirements. The result was clear and excludes the use of a special asymmetric hardware extension as described in the Full HSM approach. In concrete terms, this refers in particular to future driving operation-related communication services for vehicle to vehicle communication, which by preference use asymmetrical cryptographical methods based on hardware accelerator algorithms such as ECC-256 (Elliptic Curve Cryptography) or special symmetrical AES hash methods such as WHIRLPOOL for ensuring a secure communication channel. The HSM implementation selected by Infineon will therefore focus on secure vehicle-internal communication. Compared with the already established SHE architecture, the HSM module is equipped however with a flexible, multi-tasking capable 32-bit processor. Furthermore, the 128-bit AES hardware accelerator is extended beyond ECB (Electronic Code Book) and CBC (Cipher Block Chaining), to include additional, higher-performance symmetrical processing modes such as CFB (Cipher Feedback), CTR (Counter Mode), OFB (Output Feedback for PRNG functions), GCM (Galois Counter Mode) and XTS (XEX-TCB-CTS).

4.2 Overview of the Implementation of the HSM The aim of the HSM implementation (Fig. 2) is to develop an optimally flexible and high performance freely programmable microcontroller security subsystem, which can perform its tasks autonomously in the background completely independently of the primary application.

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Fig. 2 HSM architecture

The implemented HSM IP essentially comprises the following IP blocks: 32-bit processor with up to 100 MHz CPU processor speed MPU (Memory Protection Unit) Dedicated, local 24-40 kByte SRAM area (ECC protected) Local ROM for autonomously booting the HSM HSM password or OTP protected PLASH code sectors (2 9 64 and 1 9 16 KB) Separate data flash (DFLASH) as key memory (non-readable and non-writeable for application software) • High-performance 128-bit AES hardware extension:

• • • • • •

– Supported modes: ECB, CBC, CTR, OFB, CFB, GCM and XTS – Data throughput: Minimum 25 MByte/s @ 100 MHz for secure boot (Hashing for SHE: At least 10 MByte/s) – AES pipelining with separate, associated contexts for processing five data streams in time multiplexing mode – Context, operating modes and up to eight keys can be combined arbitrarily • True Random Number Generator (TRNG) based on AIS 31 ‘‘class P2 High’’ classification • Defined bus interface with firewall to the application software • Register for defined interrupt generation in both directions • 2 9 16-bit general purpose timer. The HSM subsystem is started following a cold start (e.g. driving cycle with terminal 15 on) by means of an integrated local boot ROM and can start working in the background without any knowledge of the main application software.

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Nevertheless, communication is possible at all times with the software tasks implemented in the controller via the integrated defined application interface with firewall functionality. For example, all SHE functions can, as described in the specification Version 1.1 of the HIS Consortium (AK Security) [3], be emulated from a combination of software and hardware functions. Discussion in the AUTOSAR Consortium is likewise already focusing heavily on AUTOSAR compatible security extensions, which ultimately will communicate in the future with the HSM module via a security framework API layer that is yet to be defined.

4.3 Comparison with SHE As already mentioned in the last section, emulation of the complete SHE functionality with the accordingly specified features by means of the HSM hardware including a corresponding SHE software layer is possible at any time. In addition, the following functions are also offered in the HSM extension: • The 128-bit AES hardware accelerator unit was extended beyond the ECB and CBC modes already required in the SHE specification to include the CTR, OFB, CFB, GCM and XTS modes. • With five free contexts in the AES unit and up to eight different keys and in conjunction with a flexible processor software layer, the system is now multitasking capable in contrast to the existing SHE implementation. • 2 9 16-bit general purpose timers extend the flexibility of the system. • The freely programmable 32-bit CPU enables the implementation of asymmetrical cryptographic algorithms in software (e.g. ECC-256) if these can get by with a lower performance. This should suffice at least for less time-critical asymmetrical cryptography, which for example could come into question for secure communication between the electric vehicle and charging station or control unit and the workshop tester.

4.4 Applications of the HSM Module Further potential applications of the HSM extension compared with those already outlined in SHE Sect. 3.2 are: • Secure sensor communication e.g. on PSI5 bus in conjunction with an EVITA light module on a pressure senor • Secure communication to a workshop tester by starting with an asymmetric challenge-response authentication followed by a Deffi-Hellman to establish a session and then continued symmetrically. The necessary routines can thereby run on the freely programmable 32-bit CPU of the HSM module

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• Secure communication between electric vehicle (EV) and the charging station (implemented in a similar manner than the communication to the workshop tester) • Secure mileage storage to prevent tachometer turning

5 Summary and Outlook Based on the Secure Hardware Extension (SHE), the 90 nm TC179x powertrain microcontrollers already support a modular hardware and software security concept for automotive use. Thanks to the subsequent extension to the Hardware Security Module (HSM) of the 65 nm generation, new applications are opening up both in the area of engine and transmission control and in areas such as eMobility and active and passive safety. At this point we would like to thank the Hersteller Initiative Software (HIS) Consortium as well as our partners from the EVITA project for their consistently good and constructive cooperation.

References 1. MEMA Brand Protection Council (2008) Intellectual property: protecting valuable assets in a global market, Motor and Equipment Manufacturers Association (MEMA), USA, January 2. Hersteller Initiative Software (HIS), http://portal.automotive-his.de 3. Escherich R, Ledendecker I, Schmal C, Kuhls B, Grothe C, Scharberth F (2009) SHE: Secure Hardware Extension—Functional Specification, Version 1.1. Hersteller Initiative Software (HIS) AK Security, April 4. Hank G (2010) Mehr Leistung, weniger C02- Mikrocontroller-Architekturkonzept für Anwendungen mit hohen Sicherheitsanforderungen. Elektronik Automotive, Deutschland 5. Haenni R (2006) Kryptographie in Theorie und Praxis, 1st edn. Hochschule für Technik und Informatik Biel, Schweiz 6. Schindler W (2001) A proposal for: functionality classes and evaluation methodology for true (physical) random number generators, Bundesamt für Sicherheit in der Informationstechnik (BSI), Bonn, Version 3.1, September 7. Trusted Computing Group, https://www.trustedcomputinggroup.org/ 8. E-safety Vehicle Intrusion Protected Applications (EVITA) project, www.evita-project.org 9. Weyl B, Wolf M, Zweers F, Gendrullis T, Idrees MS, Roudier Y, Schweppe H, Platzdasch H, El Khayari R, Henniger O, Scheuermann D, Fuchs A, Apvrille L, Pedroza G, Seudié H, Shokrollahi J, Keil A (2010) Secure on-board architecture specification, EVITA Deliverable D3.2 10. Apvrille L, El Khayari R, Henniger O, Roudier Y, Schweppe H, Seudié H, Weyl B, Wolf M (2010) Secure automotive on-board electronics network architecture, In FISITA 2010 World Automotive Congress, Budapest, Hungary

Research on CAN BUS-Based Electronic and Electric Platform of Automobile Gouqing Tong, Lei Chen, Anzhi Yang, Fangwu Ma and Fuquan Zhao

Abstract Through research on Chinese and European regulations and the application trends of new electronic and electric technology of automobiles worldwide, combined with the current technology and high cost performance a Chinese domestic brand pursues, the feasible and reasonable electronic and electric appliances configuration and reasonable CAN BUS system are researched and designed. In addition, a set of automotive electronic and electric appliances configuration and the theoretical foundation platform system of CAN BUS application is established. Therefore, the above results shall serve as guideline and reference for the development of new models. Based on development of a new vehicle for Europe market, analysis and research were conducted on the worldwide new technology of automotive electronic and electric system and the regulations and markets in China and Europe; therefore it is understood as to how to apply the advanced automotive electronic and electric system in the world. Furthermore, the reasonable electronic and electric system configuration and CAN BUS technology application were implemented. Thirdly, with test equipment of the OEMs, suppliers and certification authorities, product research, test development, optimization and improvement of electronic and electric system were performed. Therefore, development and design of a certain model shall meet related requirements. Meanwhile, an automotive electronic and electric platform system based on CAN BUS and appropriate for a Chinese domestic brand was established. Currently, a vehicle electronic and electric platform system based on the development of this model has been established, which is used to guide the development of this model at the same time. The system development was completed and put into mass production. Its electronics and F2012-D02-031 G. Tong (&)  L. Chen  A. Yang  F. Ma  F. Zhao Zhejiang Geely Automobile Research Institute CO. LTD, Hangzhou, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_29,  Springer-Verlag Berlin Heidelberg 2013

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electrics meet European regulations, and the vehicle has passed European certification and acquired overseas certificate. Furthermore, the electronic and electric platform established out of the development of the vehicle has been gradually applied in the subsequent development of new models, which will be deepened and optimized in the future. Since it is the first time to discuss, study and set up the vehicle electronic and electric platform in this paper, inconsiderateness and faultiness may be inevitable. The AUTOSAR application on vehicle is also taken into account, while no actual operation or test is conducted due to limited conditions. Thus, further research shall be conducted once conditions are completed. And continuous supplementation and perfection for the platform system shall be implemented in the future. The vehicle electronic and electric platform is firstly established with a Chinese domestic brand, which will tremendously improve the efficiency and quality of the vehicle development. The vehicle electronic and electric platform of a Chinese domestic brand, which is applied in new models, proves feasible. With the expansion of new models and continuous application of new technologies, this platform will need continuous supplementation and improvement. Keywords Automotive electronic and electric platform performance Reliability Electronic control unit





 CAN BUS  Real-time

1 Introduction Since the 1960s, with the development of the electronic industry, automotive electronic control technology is widely used. The new automobile electronic systems grow rapidly, especially the application of large-scale integrated circuit and microcomputer control technology, which bring the epoch-making change. The CAN bus is first put forward by Germany BOSCH, which is a field bus for automobile application. The CAN is ‘‘Controller Area Network’’, which is mainly to solve the problems of the increasing automobile electronic system communication. The CAN bus is welcome by car manufacturers. Since the domestic brand has entered the vehicle industry, an early new car is developed based on this car market target, technical target of individual development. The many parts are different among different cars. Developing a new car needs a long life cycle and high cost. Without CAN BUS and other network systems, each electronic system is independent, which has itself all sensors. And when the domestic batch production cars are exported abroad, the adaptability development must be performed according to different regional’s and countries [1, 2]. Regarding the communization and internationalization, the laws and regulations in China and Europe, market demands and automotive platform should be considered when the automotive development is performed.

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2 The Electronic and Electric Configuration Settings to Adapt to the Regulations and Markets in China and Europe To realize the platform of the technologies and the markets, this paper analyzes the regulations and markets in China and Europe. The automobile electronic and electric system configurations are established as follows: (1) electronic control gas jet EGI with variable valve timing system; (2) four spokes steering wheel with audio switch; (3) ABS; (4) parking aid system; (5) driver and passenger seats with heating system; (6) HVAC with air purification function; (7) airbag; (8) audio or navigation: 2-Din sound + CD player or GPS; (9) central clock controller: remote control + voice alarm; (10) the voice alarm device actuates when speed is more than 100 km/h. (11) instrument cluster which has display and sound reminders; (12) visible alarm device: shows the driver or passenger didn’t fasten seat belt; (13) BCM device; (14) TPMS; (15) electric sunroof; (16) headlamps and other car lamps; (17) all kinds of switches; (18) vehicle wire harnesses; (19) all kinds of sensors; (20) windscreen wiper and washer; (21) all kinds of motor devices [3–6].

3 Vehicle CAN Network Analysis and Design 3.1 The Vehicle CAN Network System Structure The topological structure design is to realize all kinds of field bus foundation, which is also the first step of establishing car network. The topological structure is directly related to the performance of the network, real-time, reliability and cost, etc. The vehicle CAN and LIN topology structure solution: 1. The modules with CAN communications are: ABS, airbag, body function control module-BCM, instrument cluster, engine management system-EMS. The blue lines are CAN communication lines shown in Fig. 1. 2. The modules with LIN communications are: BCM, window regulator controller, sunroof. The red lines are LIN communication lines shown in Fig. 1 [7–10].

3.2 Communication Physical Interface 3.2.1 Transceiver Circuit A high speed transceiver in the CAN system is used at 500 kbs. An integrated circuit provides the interface between the CAN protocol controller and the physical bus. This device provides differential transmission capability to the bus

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Fig. 1 The vehicle CANS, LIN topological structure

and differential receiving capability to the CAN controller. Due to excellent EMC performance and ideal passive behavior in un-powered state, this interface also provides a low-power management, and supports remote wake-up. The ECU supplier has to design the PCB with space for optional loads for EMC and ESD protection. See Fig. 2 [11]. Transceiver Working Modes 1. NORMAL: In this mode, the transceiver is able to transmit and receive data via the bus lines CANH and CANL. 2. STANDBY: In this mode, the transmitter and receiver of the transceiver shall be switched off, and the low-power differential receiver shall keep monitoring the CAN bus line to identify a wake-up event (Dominant) on the bus.

Fault Tolerant Function The software and hardware layers combination for the CAN interface provides protection for: CAN HIGH wire interruption, CAN LOW wire interruption, CAN HIGH short-circuit to battery, CAN HIGH short-circuit to ground, CAN LOW short-circuit to battery, CAN LOW short-circuit to ground, CAN HIGH and CAN LOW mutually shorted.

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Fig. 2 Transceiver circuit

Table 1 CAN bus termination resistor Notation Unit Min. Nom.

Max.

Condition

RL

130

Min. power dissipation: 220 mW

X

100

120

Notice Based on the topology, the bit rate, and the slew rate, deviations from 120 X are possible. It is, however, necessary to check the applicability of other resistor values in each case Remark: The lower the termination resistor value is, the smaller the number of nodes in the network is

Termination Resistor See Table 1. Provision for terminal resistors can exist in all control units connected to the CAN network. However, only two control units are provided with resistor terminal after system design release. The location of the terminal loads will be defined according to the actual layout of the wiring harness as mounted in the car. In the HS-CAN Bus network, the terminal loads are BCM and EMS.

3.2.2 CAN BUS Voltage Level (High Speed CAN) See Fig. 3 and Table 2.

3.2.3 CAN Controller Compliance of Controller: It is up to the ECU supplier to decide which CAN controller will be used to connect to the CAN Bus system. However, the design of CAN controller shall fully comply with ISO-11898-1 and ISO-16845.

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Fig. 3 CAN BUS voltage level

Bit Time Setting: The CAN network system Baud Rate is 500 kbps +/-1 %.

3.2.4 CAN BUS Harness Wiring Parameters Physical Bus Cable Medium: The twisted pair of shielded wires shall be used for the CAN Bus backbone cable, max cable length is less than 4,000 mm (between 2 terminal loads) and length of untwisted parts of cable near connector side shall be less than 30 mm. Physical Bus Network Topology: The wiring topology of the CAN bus network shall comply with ISO-11898-2. The CAN bus cable stubs length shall be kept as short as possible.

3.3 Can Protocol Specification 3.3.1 General Protocol See Table 3.

3.3.2 Standard Frame Format CAN network frame format shall comply with CAN2.0a. See Fig. 4.

3.3.3 Error Handling 1. BIT ERROR: A unit sending the data on the bus also monitors the bus. A bit error has to be detected at that bit time, when the bit value which is monitored is different from the sent value.

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Table 2 HS-CAN bus voltage level measurement Item Notation Min. Nom. Common mode recessive state Common mode dominant state Differential volt recessive state Differential volt dominant state

Max.

Condition

V can-h-r V can-l-r -2V0 V can-h–d V can-l-d -2V0 V diff-r -0V120

2V5 2V5 3V5 1V5 0V0

7V0

Measure according to reference ground for each ECU

V diff-d

2V0

3V0

1V2

Table 3 Genernal protocol Layer Physical Layer

Data Layer

NETWORK Type Bus wire medium Data band rate Bit encoding type Synchronization BUS ACCESS Type Arbitration ACK field CRC field DATA length DATA frame

7V0 0V012

Measure between CAN-H and CAN-L

Specification High speed CAN BUS TWISTED PAIR WIRES-CAN-H, CAN-L 500 kbps NRZ Bit-stuffing CSMA/CD (carrier sense multiple access/collision detection) NDA (non-destructive arbitration) Acknowledge (ACK SLOT) 15 BIT CRC Max 8 bytes/64 bits Refer to follow chapter

Fig. 4 Standard frame format

2. STUFF ERROR: In CAN communication protocol, synchronization is done by STUFF BIT. The transmission node must insert an opposite bit level after fifth consecutive equal bit level. So the receiving node must detect stuff error when receiving sixth consecutive equal bit level. 3. CRC ERROR: A CRC error must be recognized if the calculated result is not same as that received in the CRC sequence. 4. FORM ERROR: A FRAME error must be detected when a fixed-form bit field contains one or more illegal bits. 5. ACKNOWLEDGE ERROR: An acknowledgement error must be detected by a transmitter whenever it does monitor a recessive bit at ACK SLOT.

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Table 4 CAN message data byte CAN message data byte Byte order

MSBYTE

Bits order

MS bits

LS bits

MS bits

LS bits

MS bits

LSBYTE LS bits

MS bits

LS bits

Identifier size 11 Bits

1 0–7

2 8–15

3 16–23

4 24–31

5 32–39

6 40–47

7 48–55

8 56–63

6. ERROR SIGNALLING: A node detects an error condition signals ERROR by transmitting ERROR FLAG.

3.3.4 Transmission and Reception of Basic Frame 1. Basic Frame Reception Rule: DLC observation (messages that are longer than expected are tolerated, shorter ones are discarded). Signal De-bounce treatment must be added for critical signals (e.g. airbag ECU-ACU Fuel Cut off Req. for Crash fuel cutting off in EMS and Crash unlock in BCM). 2. Basic Frame Transmission: In The HS-CAN bus, at least following frame transmission types shall be supported: Fixed Periodic, Event, Fixed Periodic and Event.

3.3.5 Communication Frame Format Overview of CAN Frame Format: In the HS-CAN BUS network, CAN frames Data Bytes shall comply with Motorola formats. See Table 4.

3.3.6 CAN Identifier Design CAN Identifier design—Identifier Range and Classification, as shown in Fig. 5.

3.4 CAN Bus Node Management The CAN NM is designed to provide services for application operation on the HSCAN bus. These services contain: control the transmission to Bus-Sleep Mode for power saving purpose, Bus-Off handling and recovery, failure mode operations [12].

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Fig. 5 Identifier range and classification

ECUs connecting to The HS-CAN bus can be classified as 2 types: 1. Ignition switched nodes: The ignition switch-related nodes (ECUs) will stop all CAN bus communication as long as ignition is switched off and CAN communication will begin only after ignition is switched on. 2. Non-Ignition switched nodes: The none-ignition switched nodes will still require CAN bus communication after ignition is switched off. The NM BusSleep Mode management is valid only for none-ignition switched nodes on the HS-CAN bus. However, Bus-Off handling/recovery and Failure mode operations shall apply to all kinds of nodes connecting to the HS-CAN. For the CAN NM state transition diagram, see Fig. 6, Tables 5 and 6.

3.5 Failure Mode Operations General Failure Mode Operations Requirements: After Node ECU reset, all CAN communication of this node shall be disabled until CAN related data initialization is completed (e.g. driver initialization OK). CAN Application Frame Transmission—Default Value: Critical ECUs shall keep the values of its application tx frames in secured RAM in order to not transmit bad data after ECU is reset or when CAN is switched off due to voltage supply failure. If the secured RAM data is corrupted (e.g. after code reset) a ROM initial data shall be transmitted for detailed information of default value of signals using secured RAM as storage. CAN Application Frame Transmission—Lost Signal Detection: A periodic CAN frame is detected as LOST with following situations: (1) It is not received since CAN is started and T-Rx-TIMEOUT elapses. (2) When it is received, the TRx-TIMEOUT time period is not effective. CAN Application Frame Transmission and Reception-Under/Over voltage: Full functionality of the HS-CAN bus shall be guaranteed from 8 to 15 V. If voltage supply is below 7 V, all CAN transmission shall be stopped. If voltage supply is above 18 V, all CAN transmission shall be stopped. In case voltage is between 15– 18 V and 7–8 V, all CAN data frames transmission shall be stopped while ECU shall be able to still receive data frames from CAN bus. In the case of voltage

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Fig. 6 CAN NM state transition diagram

Table 5 NM state transition condition Description Condition No. Condition 1 Condition 2 Condition Condition Condition Condition Condition Condition Condition Condition

Dominant detection on CAN bus/CAN controller interrupt Local NODE-sleep condition = WAKEUP (node application needs CAN bus communication) 3 T-WAEUP-WAIT elapse 4 T-WAKEUP-SYNC elapse 5 Local NODE-sleep condition = SLEEP (node application needs no more CAN bus communication) 6 ALL master nodes, SLEEP frames received 7 Any application frame received 8 T-SLEEP-WAIT elapse 9 T-KEEP-NORMAL-MIN-TIMEOUT elapse 10 Master notes, WAKEUP frames received

between 15–18 V and 7–8 V, CAN control frames transmission and reception shall be kept as normal. CAN Application Frame Transmission-Bus-Off: In case of Bus-Off, no periodic and event frame shall be able to transmit on HS-CAN bus. Any waiting periodic and event frame waiting in Tx buffer shall be cleared. After Bus-Off recovery, periodic and event frame transmission shall be processed again with latest state of the signal.

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Table 6 NM state transition actions Description Action No. Action Action Action Action

0 1 2 3

Action Action Action Action Action

4 5 6 7 8

Initialize CAN controller Start T-WAKEUP-WAIT Start T-WAKEUP-SYNC Master node transmit WAKUP control frames. WAKUP control frame shall be transmit periodically (T-WAKUPTX) within T-WAKEUP-SYNC to ensure whole bus is awake Start T-SLEEP-WAIT Master node transmit SLEEP control frame Start T-KEEP-NORMAL-MIN-TIME OUT Transmit ACTIVE control frame with periodically Stop transmit ACTIVE control frame

CAN BUS-OFF Handling and Recovery: All nodes connecting to the HS-CAN bus shall implement Bus-off recovery starting after Bus-off detection by node’s CAN controller. In case of the fatal BUS-off detection, any node shall restart the CAN controller initialization for a BUS recovery.

3.6 Self-Diagnosis Control Relating to Communication Lost Communication: ECUs connecting to the HS-CAN bus shall have selfdiagnosis control in case communication is broken: DTC shall be stored in ECUs EEPROM. Bus-Off: ECUs connecting to the HS-CAN bus shall have self-diagnosis control in case HS-CAN Bus-Off state is detected by ECU. The DTC shall be stored in ECUs EEPROM under following conditions: IGN from OFF ? ON transition has actually happened more than 6 s; Voltage supply is in the normal range for CAN bus communication (8 * 15 V); Bus-Off state lasts at least 6 s. Communication DTC Definition: Communication DTC definition should be defined according to the practice.

3.7 Diagnostic Control via CAN BUS Five ECUs connecting to the HS-BUS shall have ability to have diagnosis by a CAN based OBD tester connecting to the same bus. The five ECUs are BCM, ACU, ABS, Instrument Cluster and EMS.

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3.8 BCM CAN/LIN Bus Gateway BCM acts as a CAN/LIN Bus Gateway in the platform.

4 Prototype Car Trial-Produce and Test 4.1 Prototype Car CAN Function Validation Test Testing purpose: to check completeness and correctness of the prototype car ECU CAN signals on the validation car. The test result meets the design requirements.

4.2 The Vehicle CAN Bus Load Test (1) Testing purpose: The CANOE simulator is used to check whether the high speed CAN load factor meets the requirements. (2) Test equipment: Vector CANOE 5.1. (3) With ABS, ACU, BCM, EMS, instrument cluster. The test result is as follows Table 7. The test result shows that the bus average load rate is 9.13 %, the peak load rate is 9.33 %, sending 400 unit of data per second. The load rate \20 %, which belongs to the normal situation.

4.3 The Vehicle Electronic System Development Result At present, the vehicle electronic and electrical systems have been developed. This model car is put into massive production. This car complies with Chinese and European regulations, and can be sold in China and Europe, which verifies the feasibility of the passenger vehicle electronic and electric platform.

5 Summarize and Prospect To advance the internationalization strategy of a domestic national brand, it is necessary to build an automotive electronic and electric platform so as to reduce development cost and improve the development speed. This paper puts forward the platform of internationalization strategy for domestic brand to solve the problem of much data exchange in a vehicle. What this paper mainly has done is as follows:

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Table 7 CAN bus load rate

1. This paper has built an electronic and electric system configuration to comply with the regulations in China and Europe. 2. This paper has analyzed the vehicle CAN network system structure. And it has established the vehicle CAN and LIN topology structure, and a CAN technology platform. The fault diagnosis of the control modules has been researched. 3. The sample car CAN functions are tested, and the prototype car network management is tested, of which test result meets the design requirements. This model car is put into massive production. This car meets Chinese and European regulations, and can be sold in China and in Europe. 4. The establishment of the passenger car electronic and electric platform provides the platform basis for later automotive development, which can significantly shorten the development cycle, and to save development cost. 5. With the further development of automobile electronic and electric system, it is necessary to seek more advanced technology and more perfect passenger vehicle electronic and electric platform. The introduction of AUTOSAR technology will improve the car electronic and electric platform technology level. The next step is to optimize and deepen the passenger car electronic and electric platform.

References 1. Wang S, Xia Q, Li J (2005) Automotive electronics. Tsinghua University Press, Beijing 2. Germany BOSCH company, with Wei Chun Yuan translation (2004) Robert BOSCH GmbH automobile electrical and electronic control [M]. Beijing, Peking University press 3. Ma Y (2007) Automobile electric appliances and electronic control system. Mechanical Industry Press, Beijing 4. Wu H (2003) Automotive electronic control technology and vehicle LAN. Electronic Industry Press, Beijing 5. Zhuang J (2003) Car system integration and modular technology. Mechanical Industry Press, Beijing 6. Nan J, Liu B (2005) Car single-chip microcomputer and the bus technology. Beijing University of Science and Technology Press, Beijing

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7. Lei L (2004) Field bus control network technology. Electronic Industry Press, Beijing 8. Chakib J (1992) Digital Single Processing Selection Guide. Texas Instrument 9. Li D, Zhang D (2005) Automobile audio network system (CAN-BUS) principle and maintenance. Mechanical Industry Press, Beijing 10. Hu S, ROUCHE D (2006) The Automobile audio network (VAN/CAN/LIN) technology auspicious solution. Mechanical Industry Press, Beijing 11. Wu K (2002) CAN bus system design principle and application. Beijing University of Aeronautics Press, Beijing 12. Gan Y (2004) The field bus technology and its application. Mechanical Industry Press, Beijing

On the Application Development of 3G Technology in Automobiles Ying Lu, Wenqiang Chen, Xingmin Wei and Fuquan Zhao

Abstract Advances in wireless mobile communication and cloud computing, the technological development of remote information services of vehicles has evolved rapidly and almost everything in a vehicle is controlled by electronic systems in network, it is possible to provide new kind of Telematics services. This paper provides the current application of 3G technology in automobiles, explores the frontier and prospect of 3G application from the viewpoint of the vehicle OEM. With 3G technology, automobile will develop itself from a simple vehicle to a realtime information receiver and entertainment experience centre, and provide more intelligent, safe, environment-friendly and economical driving experience for the driver. Besides, it will be more easily to be diagnosed and maintained. Keywords 3G technology cation Automobile



 Telematics  Vehicle terminal  Mobile communi-

1 Introduction Reconstructing of telecom industry in May 2008 has made a rapid development of telecom in China, as well as 3G even 4G technologies. It makes the application of 3G technology in automobiles like Telematics as a typical representative of the Internet of Things that large scale effect and industry leading role in the automotive industry, it ushered in a lot of attention and support of others industry. F2012-D02-032 Y. Lu (&)  W. Chen  X. Wei  F. Zhao Zhejiang Geely Automobile Research Institute Co., Ltd, Zhejiang, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_30,  Springer-Verlag Berlin Heidelberg 2013

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There is no precise definition of the industry on the Telematics, China Science Lab Director of Shanghai General Motors Corporation Limited John Du thinks that Telematics consists of the network in automobiles that provide services for drivers and passengers, such as safety, GPS navigation, Traffic Information, POIs and infotainment and the Internet of Things in automobiles that integrate mobile vehicles and other smart devices [1]. Telematics is growing rapidly in Chinese market in recent 2 years, Apart from GM, BMW, Toyota and other joint ventures, many Chinese car manufactures have launched or are researching on self-made Telematics system to expand domestic market. More associated industries are also focus on Telematics research but less on the perspective of vehicle OEM. This paper presents the contrastive study of function settings and application status of main vehicle OEMs in China about their own Telematics system, and explores the frontier and prospect of 3G application in automobiles form vehicle OEM perspective.

2 Concept and Development of 3G Technology 2.1 Concept of 3G Technology 3G technology refers to 3rd generation mobile telecommunication, the main feature is to support higher speed data transfer, its’ current rate is range from several 100 Kbps to dozens of Mbps. It also combines mobile communication network with Internet, aims to achieve wireless roaming on a global scale, process and transmit sounds, images, audio/video streams, control data and other multimedia data, to achieve web surfing, video conferencing, e-commerce, online games and other applications. 3G applied to automobile gains a breakthrough on its original concept of communication, which refers to a new type of Telematics service mode, but the 3G technology also has been expanded that generally means the high-speed mobile communication technology, combining with in-vehicle networks, vehicle shortaware networks, wireless mobile networks and the Internet to be a flexible communication system, providing a perception of people-vehicle, road-vehicle, vehicle–vehicle and builds up a service system with information processing centre. From the standing of vehicle OEM, 3G is Telematics.

2.2 Development of Mobile Communication Mobile communication has grown rapidly, and there were three generations in short dozens of years with three waves. The first generation was analog mode, just provided voice telephony, and 1st wave of mobile was connecting people, the

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Fig. 1 Advance line of mobile communication [3]

strength of 1st mobile communication was its ubiquity, underpinned by its global interoperability. 2nd generation mobile communication could transfer few of multimedia data, and connecting the world’s population to the Internet, its strength was the exponentially increasing power of its networks. Now 3rd mobile communication is connecting everything in our lives, supporting higher speed data transfer like videos, audios etc. The next generation of mobile such as LTE and LTE-Advanced (4G) was also launched and constructed in several markets. All telecom carriers will adopt LTE in future of 5–7 years by forecast, the peak rate of LTE will be 170 Mbps and LTE-Advanced 1 Gbps [2]. Figure 1 shows the development stages of mobile communication. A few tries of mobile communication in automobiles were made in 2G era and more adopted in 3G era, it was clear that the quality and rate of 3G communication was fit for auto life which required high real-time driving and no distraction. It will be widely deployed when 4G or higher technology (e.g. LTE-Advanced) is in market.

3 Major Telematics Systems in China 3.1 Launch Opportunity and Service Providing Telematics is not a new technology, it was launched and commercialized in past 10 years in Europe and American, but bourgeoned and developed in past 2 years in China, then attracted many industries to pay attention and invest with its rapid growth. There were a few Telematics systems, their ecosystems were working in domestic

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market and navigation, safety, infotainment and convenience were provided. These systems mainly are On-Star, G-Book, InKaNet and Carwings. Former two were respectively working many years in North America and Japan, they were mature and consummate, especially in navigation and safety has formed unique character and system. The Telematics system of SAIC Roewe named InKaNet with speech recognition as part of its features and highlights with ahead of the design and quite imaginative, now has been introduced the latest version named iVoka. In addition, Dongfeng Nissan’s Carwings is a sort of personality revelation, because it provides a range of services to support eco-driving and ease congestion, thus enabling more fuelefficient driving, and has been fitted on its pure electric car LEAF, electric vehicles equipped with Telematics products bring a new Exciting situation to pollution and oil shortage era. With the General Motors’ joint venture business expansion and the improvement of car sales in China, Shanghai On-Star Telematics Co., Ltd was established by On-Star, LLC, Shanghai Automotive Industry Sales Co, Ltd and SGM in 2009, Shanghai On-Star provides safety and security services for select SGM models manufactured and sold in China [4]. G-Book was first introduced on Lexus RX350 models in China in 2009, and then was also equipped on Camry, G-Book consists of network communication, data processing centre and smart vehicle terminal, and provides base driver assistance and navigation for drivers, and brings great extension and expansion to traditional navigation, and it also provides news, rescue and attendant service. The G-Book service in the country is not operated directly by Toyota, but rather outsourced to the Beijing 95190 IT Co., Ltd. to manage and to operate; we can see the Toyota’s management thinking of the G-Book in China. The unique Roewe 350 InKaNet Intelligent Network Travel System becomes a window to connect with the wonderful Internet world, to achieve information retrieval, real-time traffic navigation and electronic road book, stock trading and community interaction, to meet with fashion technology and connectivity demand for consumers. Carwings first go to high-end Dongfeng-Nissan Teana vehicles in late November 2010 and given a Chinese name of ‘‘ZhiXing+’’. The major functions of the system could be divided into three categories by the official: Safety & security, which provides ‘‘fully monitored’’ accident rescue services. Automatic diagnostics and record of driving conditions to help car owners better understand and improve driving habits. There is abundant information including traffic information, news reports, and other information. Nissan launched its fuel-efficient systems based choice in the domestic market when the car sales is hot and sense of energy is clear. This will enable it to have a share in the market place.

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3.2 Models and Market Positioning As an advanced vehicle Telematics service system in China, On-Star is applied to each main model manufactured by SGM by the end of year of 2009. Currently, On-Star is equipped to the new Cadillac Seville SLS. On-Star is applied to Buick Regal, LaCrosse since 2010, Chevrolet Cruze 1.6T and 2011 models is also equipped. It is different from American market that On-Star Telematics service is just adopted on all of Cadillac models and some high-equipped models of Buick and Chevrolet. All new SGM models include a 1 year On-Star service plan, then charge by service packages with abundant combination forms which users chose, prince range from RMB480 to 3980, it can be seen from Equipped model and package cost that On-Star in China is still a high-end consumer, can’t do the civilians. The new Lexus RX350 is the first model with the G-Book system outside of Japanese market, Camry, the new Crown and other models are also equipped with this system. When the RX350 is listed, the deluxe edition sells 792,000 yuan, the premium version is priced at 858,000 yuan, and the new Crown price of more than 200,000 the Camry over 100,000 so the G-book is also equipped with high-end models. The G-book is free after 2 years to start charging, package year 1200, the same is not a cheap service. So G-book is also just equipped to the advanced configuration models. G-Book provides service for free first 2 years and RMB1200 yuan a year, it is not cheap. Carrying 3G Intelligent Network Travel System, ‘‘full-time online compact car’’—Roewe 350 global launched and announced the price of 5 models the same time. InkaNet. Intelligent Network Travel System is an optional package for all series. But Roewe is attractive in price, the charging method is also free for 2 years, and then charge for service according to package. When a new car is purchased at Nissan dealers, the navigation service is optional; registration for the service was free for 3 years. After the free period ends, the service is charge for by year. The new Teana is divided into Teana and Teana Duke of two series, a total of eight levels, the official guide price of 190,800–371,800 yuan, were equipped Carwings [5]. In addition, the highest models of Qashqai and March were also equipped. The Carwings is also as suction gold weapon of high-end models (Table 1). Through the above analysis and comparison about domestic Telematics products can be drawn, whether application models or charging scheme are both facing with top customers, new products come out, not mature enough and can’t be generally recognized by the public, small sales and customer traffic unable to bring the income of the scale, However, with its own brand depot, Telematics services to low-end extension to be income to the scale should be a trend.

316 Table 1 Contrasts of Telematics systems in China System Major function Merit and shortcoming Enough safety Automatic crash response, service emergency rescue assistance, vehicle Lack of condition detection report, entertainment stolen vehicle location, function turn-by-turn navigation, Lack of information hands-free calling service G-book Emergency report service Great extension and expansion to Remote maintenance services, traditional probe communications navigation traffic information, reading-out and Convenient recognition, e-commerce, elephonist service operator support service Just equipped to high-equipped models InkaNet Information retrieval, realFully intelligence time traffic, navigation, GPS navigation, electronic road book, stock abundance trading and community infotainment interaction service Lack of safety service Carwings Eco-driving, ease congestion, Perfect Eco-driving fuel-efficient driving, function safety and security, Abundance automatic diagnostics, infotainment abundance infotainment service service

On-Star

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Equipped models Cadillac new seville SLS, Buick new Regal, LaCrosse, Chevrolet Cruze 1.6T and 2011 models

Lexus, Camry, new Crown

Roewe350, MG5

Teana and Teana Dukehe, highest models of Qashqaik march

4 The Technical Implementation and Vision 4.1 System Architecture Reference to the International Telecommunication Union ITU-T recommendations of Things architecture, Telematics can also be divided into 5 layers: data-aware, network access, network traffic control, information services support and information services open platform: 1. Data–aware layer is related to the intelligent information exchange of the vehicles and their operating environment, the vehicles and the passengers form a mobile node, the driving and entertainment needs of the passengers is perceived by car terminal, and driving environment is perceived by car camera, gyroscope and reversing radar, and social information such as points of interest, accidents, incident is collected by the occupant, all of this information as a basis

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for accessing to services at the same time composed of the data resources of Telematics services. 2. Network access and network traffic control can be combined, provided condition for fusion of vehicle and network via infrastructure of GPRS, 3G mobile and wireless broadband, these kind of access is ultimately unified into the IP core network to be process transmission control, and then complete the information exchange of each node in the Telematics system. 3. Information services support layer contains many cloud computing centres, pushing, collecting, processing, and storage information, providing computing power and data resources for information services open platform, complete the open services of intelligent transportation, remote monitoring, vehicle information services. From the Telematics concepts and analysis of the above layers, as well as existing domestic system, it is more than one industry chain composed of the ecosystems, and needed to be based on the ‘‘Network’’ and ‘‘service’’, therefore, to be a member of the ecosystem will have to grasp the ‘‘node’’ attribute. Each node in the Industrial ecological chain or the ecosystem, regardless of industry giants such as General motor or monopoly authority like Google, can only play a specific role in the corresponding node, to earn their own interests. In this system, the manufacturers, suppliers, operators, TSP should find their own level of node locations and concentrate on doing their own products or services in order to ensure healthy and sustainable development of Telematics, an overview of the people concept is ‘‘large and comprehensive’’, they like the ‘‘through-train’’ service. This idea does not work in the Telematics industry indeed, no company have enough capacity or financial resources to run their own Telematics system independently. Based on the analysis above, the architecture of Telematics system from viewpoint of vehicle OEM should be concern as follows Fig. 2. Vehicle plant play a leading role in constructing Telematics system that related to its own brand, organizing and co-ordination suppliers inside system, operating vehicle OEM private cloud at the same time, OEM private cloud is isolated from other systems through the firewall, and mainly engaged in 4S services, remote diagnosis, anti-theft tracking which relies on the vehicle network, OEM technology and sales network services. For the service outside of private clouds, with the above mentioned node point of view as the guidance, select suppliers to cooperation, and strive for professional spirit and ability. For users, no matter how complex car networking architecture and how advanced the technology system it is, the user needs is the final service, everything else can be transparent to the user, the owner will not because of the vehicle equipped with Telematics system and are willing to pay for, but which services can gain through the system. Therefore, the Telematics construction should be based on services, ecological system of Telematics should be a service ecosystem consisting of a number of nodes.

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Fig. 2 Telematics system reference architecture

Fig. 3 Vehicle terminal hardware block diagram

4.2 The Vehicle Terminal Hardware Platform It can be knew from the Telematics network architecture, a complete Telematics platform is essentially a solution of system level, it is too big to comprehensively discuss the hardware needed, and it is hard to finish. Therefore, take OEM which the most care Telematics hardware as discuss object, discusses hardware framework and hardware cost performance contradiction. As shows in Fig. 3, the vehicle terminal hardware is composed of CPU, peripheral circuit and all kinds of interface circuit and Combining I/O system (screen, button, etc.). Terminal design development, it is restrained by choice of CPU decision, development way, development cost, performance. On the whole, the car terminal hardware is equivalent to a general-purpose computer, and has big difference to the traditional embedded processing system. It can be seen by the Fig. 3, the hardware of car terminal including many processing chip and peripheral circuit, the connection of all kinds of chips involves in various bus and interface, for instance: CAN, LIN, UART, GPIO, USB, MOST, I2S, BT656 and so on. Complex systems like this scale, if the hardware of all used the

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track-level devices, then you should not be overlooked its cost, for the car plants which earn a large proportion of the brand value can’t be overemphasized, but for domestic independent brand car plants, it brings some limits for comprehensive retrofitting. Forced by the pressure of cost, only with a way to drop and not to bring performance degradation, you can only reduce the device specifications, and from the track level down to the industrial level. Therefore, the contradiction between cost and performance at this stage is very sharp.

4.3 The Vehicle Terminal Software Platform Because of functional complexity and real-time computing of Vehicle terminal, the system has the function of process management, storage management, file management and network communication, tradition embedded program no longer meet the requirements of modern embedded operating system, there are many vehicle terminal operating system such as Meego, QNX, Android and so on are working in home market. Meego and Android use Linux kernel, only the upper layer to do their own personalized packaging. Microsoft Auto is the system used in the MyFord Touch system, and QNX is known for being the operating system in the floundering BlackBerry PlayBook Tablet. Linux is a robust and versatile computer operating system, which is used in popular mobile devices like those powered by Android. Additionally, it’s open-source, and will be coming to a car’s infotainment system soon. The development of software system using a layered architecture, generally divided into three layers, respectively, for the system layer, middleware service layer, application layer, system layer includes a variety of traditional drivers such as display drivers, I/O drivers, sound drivers and other drivers, while as automotive systems, need to integrate specific drivers, such as GPS-driven, CAN bus driver etc., there are some system libraries on these drivers, such as SQLite structured storage database, SSL security, OPENGL/ES graphics library, Web browser kernel of webKit, map and vector fonts, the FreeType functions, then the above, it is the services and applications, such as car navigation, entertainment, security, etc. Figure 4 is a typical automotive software architecture frame. The M (model) V (views) C (controller) architecture is a popular trend that use to do develop the software of application layer, M refers to the database and its operating model, V refers to the interface of UI, C refers to the service control logic layer, the MVC framework has become the mainstream model of software development framework. It is worth that the domestic software development giants, such as Yonyou, Neusoft, Kingdee, hovering in the edge of the Telematics industry, mainly due to the vehicle hardware terminal providers not only committed to the design and development of the hardware platform, but also software, HMI and application program from its bundled. Even brought a hardware terminal, visual hardware

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Fig. 4 Software architecture

design can be found unreasonable, the CPU is not designed wide space in the machine box, but crowded together in the narrow front end of the panel and power supply module of TFT screen and CPU is not designed heat sink, while idling the architecture and development experience of many years of professional software developers and hinder the development of the industry.

4.4 Development Status and Trends 4.4.1 Development Status Since Telematics’ introduction into China, for the large scale effect and huge market vacancy in domestic auto industry, it attracts many manufacturers and organizations to participate in. whether the telecom carriers, Telematics service providers, terminal providers or car makers, has been put massive manpower and physical resources to research and develop, though the future is bright, the development status looks very cloudy. Main status as follows: (1) Different standing points Efficient communication is the foundation of the entire Telematics, while the mobile communication technology and resource are still in the hands of the telecom operators. Seeing the big picture, the three largest domestic operators do not only provide high quality communication service, but also encroach on TSP

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market space taking advantage of the construction of 3G networks, and this leads to a not specialize in one subject situation. The car makers make profits from cars, but not from the car services and the car services are only value-added services and own low attention, but, the car makers have the leading rights to choose TSP, map and other services providers, while these providers’ directed clients are the ultimate users, not the car makers, this results in the leading rights of choosing products and services are in the hands of the clients who don’t care about the product, and the ultimate users who really care about these don’t have the rights to choose, and these certainly are unfavourable for positive market competition. (2) Small users scale In the past few decades, the domestic automotive industry has developed greatly, especially some independent brands have grown up and great effort have been made to let more people can afford the car, but compare to the citizen’s income level, buying a car still is not easy, add the domestic oil price stays at a high level, coupled with the extra expenses of the loading and opening Telematics systems, the users scale is rather small which is the current condition, the small user group cannot develop many applications and make the industry that depend on user scale cannot enter, for example Baidu, Google and some other internet company are wasting opportunities of the Telematics development in wandering, waiting and observing, the qualitative leap is waiting for the development of the quantity. (3) Less willingness to pay of users Every car makers provide comprehensive, various services to absorb customers which make the industry be grand and complete, but not expert and excellent enough, and this result in the less willingness of the customers to pay. On the other hand, the society is in critical period of transformation, the construction of social honesty is far from perfect, the insurance services are not universally accepted, bill package and one time pay are both not accepted. The users don’t believe that they can get the corresponding service after paying, and they tend to pay on times and demand. Totally speaking, chance and challenge coexist in current situation, well, this is a normal stage in every new thing’s emerging and developing, this need every relevant professions’ jointly contribute capitals and co-take risks to push development and run to make profits. (4) No successful experience in the field to learn Though Telematics has been developing for decades overseas and has mature model, it is not kind of technology that bring and ready to use. For example, there are more than 600 private rescue centres in German, and they have accumulated rich operational experiences under the competition, while, there are very little this kind of institutions in China, whether the car makers or the government, don’t show excess attentions. Or take another example, in the sparsely-populated America; the traffic is developed, while in Japan, the government supports planning. We don’t have these premise conditions, so we can take and directly use their

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products, and the industry at home need to learn their inner operating pattern. According to the reality of our country, carrying out ‘take’ rightly is needed, and this is not that easy to fulfill and is a challenge to ‘‘made in China’’ model. (5) High cost of 3G Up to now, the construction of 3G network doesn’t finish, the popularity of 3G technology is low, and the speed of communication is not ideal, all these leads to the high cost of operation, the services results is not recommended and the higher cost compared to the aboard. On the other hand, the amount of data of Telematics is quoted in M/s. The data amount is several M/s to download a song, and several hundred M/s to refresh the map, most of the 3G users could not afford these kinds of large amount of data transfer. (6) Infrastructure lags behinds the development The greatest needs applications at home are security and navigation. The fastgrowing economy and urbanization process make the urban topographic, infrastructure and road construction change fast, while, the relevant information and services could not keep pace with the development, for example, the rescue centres and the map data collection. Without the matching systems, the relevant Telematics services could not provide. On the other hand, people used to the bigger and more comprehensive management method, for example, the map providers are all dedicating to provide the national map, and do not deepen division and collaboration to accelerate coordinated development, these all limit the development of Telematics.

4.4.2 Development Tendency and New Application Area By analyzing the existing Telematics products and the relevant technology, we can conclude some useful conclusion. (1) Application trends of cloud computing For any car terminal hardware system, ether On-Star or G-book, the data operation and transfer speed are very limited, and the data storing is not optimistic. Geely GNetLink that has excellent terminal computational capability, are based on Intel Atom processor and can dual-display, it only reaches the level of Netbook. The property of Telematics is: the more meticulousness and more humanization of the services, the more computationally expensive. The application like the annual oil consumption information, intelligent route planning, which need to store and process a large amount of history data to get results, and this need a great deal of memory space and considerable computational capability. Another example like intelligent traffic, real-time road condition, also needs a great amount of process and collect data. These all can only completed by cloud computing. The properties of the cloud computing is that taking IT as service, providing it to users and run on a ‘pay-as-you-go’ basis. The new emerging TSP which has

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Fig. 5 Illustration of SoLoMo

prosperous market prospects services but has not ability to run data centre, renting some IT resources to lower the cost and devoting to provide services is a way to solve this problem. In summary, the development tendency of the cloud computing is very obvious, and there emerging various relevant service layers, in the future, the Telematics will not able to provide services without the supporting of cloud computing (Fig. 5). (2) SoLoMo informationization life SoLoMo is the main method of the future informationization life; it is the compound word of Social, Local and Mobile. With no doubt, so is the social development trend. Facebook, Twitter, RenRen and the recent Google ? all can prove that the life and communication is being community network. Lo means that the living space of people is rather large, but still limited. The local services covers a large part of the services, the application of 3G network is an indubitable fact with the popularity of the intelligent mobile phones and the development of the vehicle terminal. The development trend of SoLoMo points out direction for the design plan of the Telematics service products, except the security service, accessing and using community network, the localization navigation, intelligent traffic, interests searching and the interactive of the mobile devices such as intelligent mobile phones, PDA and U disk becoming the important point. (3) Smartphone auto impact The consumer electronics products have several weeks developing period and two or 3 years life cycle time, compared to the yearly developing period and decades life cycle time of cars, the cars developing cannot keep pace with the consumer electronics products, so using intelligent mobile phones in automobile platform or make the car be the attachment of the phones is a good solution. Toyota and Fort are good at the integration and application of Telematics system and intelligent phones. BMW, General Motors and Hyundai Motors also have phone products, the functions of this kinds of products are relative simple, mainly focus on the internet radio and social network. It can be predicted that more

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Fig. 6 The development orientation of speech recognition [6]

and more applications of the phones and integration of Telematics system and intelligent phones will emerge with the development of the technology. Mobile phone is a really convenience payment tool; it has many added values, and the development of its application is very fast, the integrated of mobile phones and cars will provide each diver his own environment according his phone. The integrated of cars and phones are the most economical way and an important develop tendency. (4) Speech recognition technology The interactive of the driver and the car while the car is in motion is a serious security hidden danger, how to avoid the danger to the drivers becomes an important research project. Once the iPhone 4S SIRI emerge, it brings a great shock to everyone, its core technology contains Natural language recognition, artificial intelligence and database technology, perfect experience of intelligent speech recognition need powerful cloud computing centre to support, application in vehicle have to consider antinoise character, dialect recognition etc., Fig. 6 is the key point and development orientation of speech recognition There is a great achievement of domestic speech recognition, e.g. SAIC Roewe launched its new edition of InkaNet iVoka which is absorbed in speech recognition and has a commercial accidence achievement, the speech recognition will be the most great role in the human-vehicle interactive. (5) Telematics of Electric Vehicle The history of electric vehicle can be cast back from 1810 to 1820, came through from flourish to stagnant and flourish again. With lack of oil, polluting environment, electric vehicle attracts attention again. Carwings was equipped to Nissan LEAF which is a full electric vehicle, from inside Nissan LEAF, it’s easy to see the vehicle and battery status, as well as charging station locations and when we need them. Set the timer once to charge each night during off-peak hours if we like. Connect and communicate with Nissan LEAF remotely through the

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smartphone or laptop. Pre-set A/C functions from any web-enabled phone or computer. Electric vehicles and electric vehicle Telematics exist, try to run the car as a concept car and a small amount of focus on battery charging, use, and monitoring services, mainly to find the charging station, power management, energy saving, It is precisely because the electric car service facilities extremely lacking, but with electric cars and car networking, taking advantage of environmental and energy voices will certainly return to the stage of history.

5 Conclusion Through analyzing and comparing the mainstream products like On-Star, G-book, InkaNet etc., we have drawn the conclusion of the main functions, emergence timing, application models, market positioning, current development status, development tendency and existing problems. Analyse the actual development situation of Geely and the practical problems encountered during the development process, such as the tariff issue, TSP problem, the collocation of software system and hardware platform, we have come to a conclusion that the 3G technology application on the automobile has a broad prospect and the application of multiple modes, such as better application of cloud computing technology, which will effectively realize the sharing of the resources and information, enabling a more effective mixture among the car, office and home, breaking the traditional vehicle definition, and will obviously guide the direction and lay the foundation of 3G application in automobiles.

References 1. Du J (2011) Che Lian Wang and its industry implication. Shanghai. Telematics@China2011, 12.8 GM China Science Lab 2. Ton Brand. Network Technology Overview. Shanghai GSMA 2011.12.8 3. Cao Z (2010) Modern communication technology overview. TsingHua University, Beijing 4. http://www.onstar.com.cn/index.html, SGM On-Star official site 5. http://www.carwings-nissan.com.cn, DongFeng Nissan Carwings official site 6. http://www.vcyber.cn

A Typical Application of FlexRay Bus in the Vehicle Yandong Dong and Wanrong Wang

Abstract Objective. FlexRay is a new and high-powered bus which is designed to achieve the purpose of ‘‘X-by-Wire’’, may be one day, the CAN bus will be replaced by FlexRay bus. Although this is a high speed and reliable bus, but it is very difficult to apply this bus in the vehicle. The cost is too high, by now on, only a few top grade cars like Audi A8 and BMW X7 use the FlexRay bus. We are doing the research about how to apply the FlexRay in a low cost and feasible way. Methodology. FlexRay is a high cost bus: 1. The FlexRay controller and the MCU which support FlexRay communication is very costly. 2. The software design is very difficult, and there are no uniform guidelines to develop the system. Our research focus on the vehicle network’s strategy, the uniform FlexRay develop standards as well as the tool chain, the uniform diagnostic strategy and software reprogram strategy. We can use the FlexRay bus as a subnet of the vehicle. The important module like ECM, TCM, ABS module use FlexRay, other module like BCM, SDM, IPC use CAN, there will be a central gateway to transmit the signals between the FlexRay bus and CAN bus. If more and more module suppliers can follow our standards, the cost will be low and the system development will be easy. Results. Based on the ISO and FlexRay Union’s documents, we have developed some standards for the FlexRay, these standards contains the FlexRay Physical layer standard, the Protocol layer standard, the net management standard, the Communication layer standard and the Diagnostic layer standard. These standards have established main parameter and many other important schemes for the FlexRay bus. The modules which are developed based on these standards can communicate on the same FlexRay bus with no error. We are developing the F2012-D02-033 Y. Dong (&)  W. Wang Pan Asia Technical Automotive Center Co., Ltd., Shanghai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_31,  Springer-Verlag Berlin Heidelberg 2013

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central gateway module which can transmit the signals from CAN bus to FlexRay bus, these signals contain the cycle signals the diagnostic signals and the programming signals. Use the gateway, we can diagnostic and programming the FlexRay modules through the CAN bus. Limitations of this study. We are now designing the entire system and the central gateway module, we have not enough FlexRay modules to do the network testing. We have to use the diagnostic equipment to simulate the entire network. What does the paper offer that is new in the field in comparison to other works of the author: Only few vehicles use FlexRay bus. We will develop a low cost and feasible architecture and network strategy to apply the FlexRay bus in the vehicle. Conclusion: Although the FlexRay bus is complex bus, we still can find a low cost and feasible way to apply the bus in the vehicle. Keywords Flexray

 Network  Gateway  CAN  UDS

1 Overview FlexRay is the high-powered bus in the future. It is Time Division Multiple Address (TDMA) to ensure the FlexRay communication precision, compared with CAN, it has much higher speed (more than 10 Mbps), more flexible topology, higher bus load (more than 90 %), and more safety (support two channel redundancy communication). The technical standard about FlexRay: • • • • • •

The speed is more than 10 Mbps; Dual channel, high speed, deterministic, fault tolerant; The bus length can reach 72 m; Deterministic communication (Time Division Multiple Address); Distributed clock synchronization; Flexible topology;

As a new network technology, FlexRay is more complicated, require high qualified communication wire. On the standard of communication, Flexray union has made a detailed definition of physical layer and data link layer, but FlexRay union did not define the communication layer and diagnose layer of FlexRay [1]. Because FlexRay’s agreement is far complicated than CAN and it has many communication parameters, every factory has not form the unified and applied agreement standard, which is not good for the promotion of FlexRay. This article schemes out a more detailed FlexRay agreement standard on basis of the existed FlexRay standards. The article has a much detailed definition of physical layer, data link layer, communication layer, diagnostic and key parameters. The aim of this article is to make FlexRay more convenient for the use of engineering.

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Fig. 1 FlexRay topology

On the basis of this standard, the article puts up with a typical FlexRay scheme, defines the topology. Here this topology needs a central gateway to link CAN and FlexRay. Because the working style and message format of CAN and FlexRay has a big difference, the article puts up with a transition way between CAN and FlexRay. The way can solve the problem of message route, reprogram, network management and diagnostic.

2 The Specifically FlexRay Protocol for the System 2.1 Physical Layer FlexRay topology is shown in Fig. 1, the parameter is shown in Table 1.

2.2 Data Link Layer 2.2.1 Parameters All ECU use dual channel, fault tolerant, all ECU baud rate is 10 Mbit/s. When the baud rate is 10 Mbit/s, The global sample clock period gdSampleClockPeriod is 0.0125us [2].

2.2.2 Period As shown in Fig. 2, except the startup stage, the period is continuous and contain fixed time slot. gMacroPperCycle and gdMacrotick are defined in Table 1; Communication period counts is from 0 to cCycleCountMax = 63 (Table 2).

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Table 1 FlexRay topology parameters Parameter Symbol Min Median

Max

Unit

Comments

Wire length ECU number

24 22

m Count

Tow ECU’s distance The max ECU number

L1 n

0.1 0

– –

Communication cycle N-1

Communication cycle N+1

Communication cycle N gdMacrotick

Macrotick level

1

n

n+1 gMacroPerCycle-1

Microtick level pdMicrotick

pMicroPerMacroNom

Fig. 2 FlexRay communication cycle Table 2 The communication cycle parameters Parameter Min Median Max

Unit

Comment

gdCycle gMacroPerCycle gdMacrotick gdMaxMicrotick

us MT us us

Cycle time The MT number in a cycle The general MT time The max time of uT

1 0.0125

5,000 3,636 1.375 0.025

16,000 16,000 6 0.1

Time Communication cycle N-1

Communication cycle level

Communication cycle N

Static segment

Dynamic segment

Communication cycle N+1

Symbol Window

Network idel time

Fig. 3 FlexRay communication cycle structure

The communication cycle contain Static segment, Dynamic segment, Symbol Window, Network idle time (Fig. 3).

2.2.3 Static Segment In static segment, ECU use static Time Division Multiple Address to adapt to the communication.

A Typical Application of FlexRay Bus in the Vehicle Static segment

Communication cycle level Static slot Arbitration grid level

331

Slot counter 1

Dynamic segment

Symbol Window

Network idel time

gNumberOfStaticSlots Slot counter 2

Slot counter n

gdStaticSlot Macrotick level

t

Fig. 4 Static segment

Table 3 Static segment time parameters Parameters Min Median Max

Unit

gNumberOfStaticSlots 2

91

1,023 -

gdStaticSlot gdTSSTransmitter gdActionPointOffset

24 9 2

661 15 63

4 6 1

MT gbBit MT

Comment The number of static slot in the static segment The static slot length

As shown in Fig. 4, all the static slots are equal. Static segment contain some static slots which can be configured. All the static slots can be constituted by some MT [3] (Table 3).

2.2.4 Dynamic Segment As shown in Fig. 5, dynamic segment is constructed by some mini slots. Each mini slot can be divided into some MT. The number of mini slot in dynamic segment is given the name dNumberOfMinislots, this parameter is a global variable. The parameter gdMiniSlot which shows the length of mini slot, is a global variable, all these parameter should be given the value as Table 4.

2.3 Communication and Diagnostic 2.3.1 Addressing Mode of Diagnostic The addressing mode show the target address and source address in FlexRay network. The format is shown in Table 5. The diagnostic message is in dynamic segment, the message ID is target address, the addressing mode in data link layer is shown in Fig. 6.

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Communication cycle level

Static segment

Dynamic segment

Mini slot Arbitration grid level

MiniSlot 1

Symbol Window

Network idel time

gNumberOfMiniSlots MiniSlot 1

MiniSlot n

gdMiniSlot

Macrotick level

t

Fig. 5 Dynamic segment

Table 4 Dynamic segment parameters Parameters Min Median

Max

Unit

Comments

gNumberOfMiniSlot gdMiniSlot

7,986 63

MT

The number of Mini Slot The length of Mini Slot

Table 5 The Addressing mode format

0 2

289 5

Byte1

Byte2

Byte3

Byte4

15 C_TA

0

15 C_SA

0

Fig. 6 Target address in dynamic segment

FlexRay Communication Layer PDU TargetAddress SourceAddress PCI

Data

MessageID Payload Preamble Indicator

Data Header

Payload

Data

Slot-ID Dynamic Segment-Slot

t

All the ECU should support function addressing and physical addressing. Physical addressing is used in the condition that tester diagnose only one ECU (1:1).Function addressing is used in the condition that tester diagnoses many ECUs at the same time (1:n). Function addressing mode can only support single diagnostic frame. All the diagnostic messages should be transmitted in channel A.

A Typical Application of FlexRay Bus in the Vehicle Fig. 7 Un segmented unacknowledged message (known message length)

CL

333 DL

DL

CL

C_Data.req StartF

C_Data.con

rame

C_Data.ind

CL…Communication Layer DL…Data Link Layer

2.3.2 Communication Layer The communication layer should follow ISO10681-2 and the standard bellow. OSI transport layer and network layer together constitute the communication layer. The ECU should support the communication mode as bellow: 1. Un segmented unacknowledged message (known message length), as shown in Fig. 7; 2. Segmented unacknowledged message (known message length), as shown in Fig. 8; When tester communication with ECU in FlexRay network, the message must be routed by the Gateway, only PCI need to be changed, this must follow the standard bellow: 1. When the data is less than 7 bytes, use single frame to transmit; 2. When the data is more than 7 bytes, use multi frame to transmit, and must follow the standards bellow [4]: STF (Start Frame) should transmit 6 payload bytes, FPL is 6; CF (Consecutive Frame) should transmit 7 payload bytes, FPL is 7; CFEOB (Consecutive Frame EOB) should transmit 7 payload bytes, FPL is 7; LF (Last Frame) should transmit less or equal to 7 payload bytes, FPL is less or equal to 7; • The max length of the multi frame transmission is 4,095 bytes (ML is 4,095 bytes); • If the number of payload data length is odd, 0 9 00 must to be used to fill the data to reach even number. • • • •

2.3.3 Diagnostic Services Overview of enhanced diagnostic services shown in Table 6.

334 Fig. 8 Segmented unacknowledged message (known message length)

Y. Dong and W. Wang CL

DL

DL

CL

C_Data.req StartF

rame

C_DataSTF.ind

CTS

ontrol_

FlowC Cons

ecutiv

eFram

e

Cons

ecutiv

eFram

Cons

e

ecutiv

eFram

e_EO

B

S ol_CT

ontr

FlowC Cons

ecutiv

eFram

e

Cons

ecutiv

eFram

Cons

e

ecutiv

eFram

e_EO

B

CTS

ontrol_

FlowC Cons

ecutiv

eFram

e

LastF

rame

C_Data.con

C_Data.ind CL DL

Communication Layer Data Link Layer

3 FlexRay Typical Application 3.1 FlexRay Architecture Scheme Now most of vehicles have CAN network, for FlexRay network, the most convenient way is change the existent CAN network into FlexRay network, the communication speed and quality will be improved, the system performance will be improved. As shown in Fig. 9, the architecture contains the Gateway, the diagnostic interface, the Chassis CAN network and the Chassis FlexRay network. The CAN network and the FlexRay network contain the same electronic modules. Only one network can work at the same time, the other is the backup network, vehicle can either use the CAN network or use the FlexRay network. Only upgrade these electronic modules to support FlexRay, the vehicle will use FlexRay to replace CAN. This Gateway can support 4 CAN network, the Body CAN network, the Chassis CAN network, the Power train CAN network and the diagnostic CAN network.

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Table 6 Enhanced diagnostic service [5] Service Session Supported SID (Hex) Default Other Fun Phy Diagnostic and communication management 0 9 10 DiagnosticSessionControl 0 0 9 11 ECUReset 0 0 9 27 SecurityAccess – 0 9 28 CommunicationControl – 0 9 3E TesterPresent 0 0 9 85 ControlDTCSetting – Data transmit 0 9 22 ReadDataByIndetifier 0 0 9 23 ReadMemoryByAddress 0 0 9 2A ReadDataByPeriodicIdentifier 0 0 9 2E WriteDataByIdentifier – 0 9 3D WriteMemoryByAddress – DTC 0 9 14 ClearDiagnosticInformation 0 0 9 19 ReadDTCInformation 0 Input output control 0 9 2F InputOutputControlByIdentifier – Up load/down load 0 9 34 RequestDownload – 0 9 36 TransferData – 0 9 37 RequestTransferExit – RoutineControl 0 9 31 RoutineControl –

Fig. 9 Architecture

Cvt Dependence

0 0 0 0 0 0

H H – H H H

H H H H H H

M M U U M U

– – – – – –

0 1 0 1 1

H – – – –

H H H H H

M U U U U

– – – 0 9 22 0 9 23

0 0

H H

H H

M M

– –

1

–

H

U

–

1 1 1

– – –

H H H

U U U

0 9 36, 0 9 37 0 9 34, 0 9 37 0 9 34, 0 9 36

1

–

H

U

–

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The Gateway can also support 1 FlexRay network which can fulfill the function of the Chassis CAN network.

3.2 FlexRay Protocol and CAN Protocol Exchange 3.2.1 Gateway The Gateway is the kernel module of the whole network, it can connect 4 CAN network and FlexRay network together, to fulfill the signal routing between different network. The Gateway support not only CAN protocol but also FlexRay protocol, it can route the period message and diagnostic message.

3.2.2 Period Message Routing Between CAN and FlexRay CAN protocol is simple, each CAN message contain 8 bytes, each CAN message has the only CAN ID, all the CAN ID is different; The FlexRay is different from CAN, FlexRay contain static segment and dynamic segment. The message in static segment is cycle message; the message in dynamic segment is usually diagnostic message. The max data length of FlexRay message is 254 bytes [6]. When route the FlexRay message of static segment to CAN network, there are many ways. If the payload data length is 32, this message can be split into 4 different CAN message, if the payload data length is 80, this message can be split into 10 different CAN message. In static segment, all the message data length should be the same. When route the cycle CAN message to FlexRay network, if the FlexRay payload data length is 32 bytes, then 4 CAN message should be packed into one FlexRay static message. When the period time of the 4 CAN message is different, the shortest period time should be used as the FlexRay message’s period. For this system, the FlexRay static message’s payload data length is 16 bytes. FlexRay is different from CAN, CAN is based on event, FlexRay is based on time, the CAN message period time is not very precise, but the FlexRay message period time is very precise. Use CAN id, each CAN message can be exactly identified, but the FlexRay Frame ID cannot exactly identify the message, now the FlexRay schedule is needed. Based on the schedule, use FlexRay frame ID and FlexRay cycle count, tester can distinguish which module the message is come from. FlexRay cycle count is from 0 to 63, so the static message period time should be the multiple of one cycle time. In this system the single cycle time is 5 ms, the entire communication cycle time is 5 ms*64 = 320 ms. Thus, the static message period time should be the multiple of 5 ms, such as 5, 10, 20 ms, and so on. If the CAN message period time is 12 ms, when route this message to FlexRay network, the period time should use 10 ms, less than 12 ms.

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Fig. 10 Communication between Tester and ECU

3.2.3 Diagnostic Message Routing Between CAN and FlexRay The CAN diagnostic transport protocol should follow ISO15765-2, the application protocol should follow ISO14229-1. The FlexRay diagnostic transport protocol will follow ISO10681-2, the application protocol should follow ISO14229-1. For this system, tester can only diagnostic the network through CAN bus, the diagnostic message must be routed to the FlexRay bus by Gateway, one CAN diagnostic message versus one FlexRay diagnostic message. The CAN diagnostic message contain 8 bytes data, the FlexRay diagnostic message contain 16 bytes payload data. The following example will show how the CAN diagnostic message converted to the FlexRay diagnostic message by Gateway. Condition: Such as UDS $22 service, read PID, tester send the CAN message: 241 03 22 C0 00. ($241 is the ABS module which is on FlexRay bus, $03 is the data length). Response from CAN: 641 10 0A 62 C0 00 00 00 00. Tester send: 241 30 00 00 (flow control). Response from CAN: 641 21 01 03 FF FF 00 00 00 [7, 8]. Process: $241 is ECU ID, the ECU is on FlexRay bus, $641 is tester ID, the dataflow can be shown in Fig. 10 and Table 7. • Red data in Table 7: for transport layer, red data is available data • Blue data in Table 7: transport layer protocol control message • Black data in Table 7: filling data (follow ISO10681-2) Message 1 and message 2 are single frame request for PID, $02 41 is ECU address, $06 41 is tester address, $40 is FlexRay STF (Start Frame) communication control message, $03 is the frame payload length (FPL), $00 03 is single frame or multi-frame max data length (ML). Message 2 is single frame transmission. Message 3 is a multi-frame transmission, this is multi-frame response to tester. $06 41 is tester address, $02 41 is ECU address, $40 is STF, $06 is FPL, $00 0A is ML, the other 6 bytes is payload data.

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Table 7 Data flow example Message ID 1 2 3 4 5 6 7 8

03 02 06 10 30 02 06 21

22 41 41 0A 00 41 41 01

C0 06 02 62 00 06 02 03

00 41 41 C0 00 41 41 FF

00 40 40 00 00 83 90 FF

00 03 06 05 00 00 04 00

Data Flow 00 00 00 03 22 00 0A 62 00 00 00 00 0F FF 00 0A 01 00 00

C0 C0

00 00

00 05

03

FF

FF

00

00

Message 5 is the flow control message sent by tester, message 6 is the flow control message for FlexRay which is converted from Message 5. $02 41 is ECU address, $06 41 is tester address, $83 is flow control message which means continue to send (CTS), $00 is BC (Bandwidth Control), $0F FF is communication layer buffer which is only used in FlexRay communication layer. Message 7 is continue frame sent by ECU, $06 41 is tester address, $02 41 is ECU address, $90 is last frame (LF), 04 is payload data length (FPL), $00 0A is max data length (ML), the other 4 bytes is payload data.

4 Conclusions This article advances the FlexRay standards which have been proved to be all right in simulation test. If more company can unite to use the same standards, the cost will be low, this is very important for the cosmically application of FlexRay. The scheme that based on the Gateway, use FlexRay to replace the CAN network, is very efficiency for the development of FlexRay. This scheme can save time for developing and is very convenient for the FlexRay generalization. The conversion scheme between CAN and FlexRay proposed by this article, can realize the CAN protocol transfer to FlexRay protocol with no error. This conversion scheme can resolve the diagnostic problem, the net management problem and the reprogramming problem between CAN and FlexRay.

References 1. FlexRay communications system protocol specification version 2.1 2. FlexRay Consortium (2009), FlexRay communication systems protocol specification, Version 2.1 Revision D 3. FlexRay_ EPL-Specification_ V2.1_Rev_D2_N010 http://www.flexray.com/FlexRay_ EPLSpecification_ V2.1_Rev_D2_N010.pdf 4. ISO10681-2, Road vehicles—communication on flexray—Part 2: communication layer services

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5. ISO14229-1 Unified diagnostic services (UDS)—Part 1: specification and requirements (Release 2006 12-01) 6. FlexRay_ EPL-Application Notes_ V2.1_Rev_D_N009 http://www.flexray.com/FlexRay_ EPL-Application Notes_ V2.1_Rev_D_N009.pdf 7. ISO15765-2: Road vehicles—diagnostics on controller area networks (CAN)—Part 2: network layer services 8. ISO15765-3: Diagnostics on controller area network (CAN)—Part 3: implementation of unified diagnostic services (UDS on CAN) (Release 2004 10-06)

Development of Controller Diagnostic System Based on ODX Li Li and Shanzheng Tang

Abstract OBD is an important technology for insurance of automotive safety and emission. The requirement for automotive safety and emission is getting higher, so more attention has been paid on OBD technology. The paper presents an instruction to the development of controller diagnostic system based on ODX (Open Diagnostic Data Exchange). According to the property of ECU I/O signals, using this method to build a diagnostic model of BCM (Body Control Module) in a certain automotive. The test results show the diagnostic model based on ODX is easy to embed, and will help OEM or after-sales tool supplier to explore their diagnostic tools. Keywords ODX

 Diagnostic system  Emission  OBD  Controller

1 Foreword OBD is On Board Diagnostic System, at first legislated from CARB in 1985, then carry out from 1988. SAE draw up the standard of OBD-I in 1988 [1]. From 1980s the United States, Japan, Europe, and other major auto makers start to use OBD system in their production of automotive. The reason for more attention on OBD is the high requirement for emission. The OBD systems monitor the devices in automotive that related to the emission, and through the connector (SAE J1962) to exchange data from diagnostic tool. F2012-D02-036 L. Li (&)  S. Tang SAICMOTOR Commercial Vehicle Technical Center, Shanghai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_32,  Springer-Verlag Berlin Heidelberg 2013

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2001

2002

ASAM MCD-2D 1.2.2 ASAM MCD-2D Basic Working Draft (ODX 1.1.4) MCD Harmonization

2003

2004

2005 2006

ASAM MCD-2D ODX 2.0

ODX 2.0.1 ISO CD 22901-1

ODX 2.1.0 ISO DIS 22901-1 ODX 2.1.1 in progress

Fig. 1 Development process of ODX

ECU in every phase of automotive design-manufacture-aftersale, it may exchange data with different tools through OBD connector [2]. The data formats in different tools are obviously very important. This paper presents an instruction to the development of controller diagnostic system based on ODX (Open Diagnostic Data Exchange), and build BCM database for a certain automotive in ODX, through the validation to prove the consistency.

2 Open Diagnostic Data Exchange ODX (Open diagnostic data exchange) at first raised by ASAM (Association for Standardization of Automation and Measuring Systems), and released by ISO (International Standards Organization) in March 4th, 2009 as standard ISO229011:2008, according to ODX 2.2.0 as the standard of diagnostic data exchange. ODX data format release process as the Fig. 1. ODX standard consists of all the diagnostic data model from automotive and ECU, for example, DTC (diagnostic trouble code), data parameters, identification data, input–output parameters, ECU coding; ECU diagnostic communication protocol; Data-link layer communication parameters; ECU flash data format; related connector description etc. Using the database of ODX standard can make sure the database from every major auto makers independent on the diagnostic tool that from any tool supplier. Ensure the consistency of the database in the line of automotive design-manufacture-aftersale. Currently there are many automotive makers, as BMW, GM; there are many ECU suppliers, as Bosch, Siemens-VDO; and many diagnostic tool suppliers, as Vector, Softing, ESG, ETAS etc. support the ODX.

Development of Controller Diagnostic System Based on ODX Fig. 2 ECU surrounding components

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Switch

Light ECU M

Sensor

Motor

3 Design and Application of OBD System 3.1 Legislated On-Board Diagnostics The OBD system that related to emission is monitoring the signals and devices that belong to engine in automotive. This function is implemented by ECU (Electronic Control Unit). Any components or subsystems’ fault that may course the emission exceed the legislation limit, the ECU will exactly diagnostic the failed components or subsystems, and then do the failure reminder. In the standard of OBD-II, the main diagnostic systems that related to emission are monitoring subsystems as three-way catalytic converter, lambda sensor, engine knock and fuel oil system [3].

3.2 Vehicle Manufacturer Enhanced Diagnostics Besides the legislated On-Board Diagnostic, the electronic monitoring system defined as enhanced diagnostic. Normally use the ECU in body to achieve the function. The body ECU monitors the surrounding components and devices, include input outputs, then analysis the signals based on the diagnostic software, in order to identify whether the surrounding components have fault. Then to judgment the reason of the fault, record the corresponding diagnostic trouble code (DTC). ECU surrounding components can be divided into input component, such as switch, sensor; output component, such as engine, actuator etc. Different surrounding components considering use different monitoring strategies. Figure 2 is the example of the surrounding components.

3.3 Input Component Monitoring When the input monitoring component is switch, the fault monitoring flow is showed in Fig. 3. The switch divided into self-locking and non self-locking, the status of selflocking switch cannot judgment the fault condition, so generally do not do the fault detection. The non self-locking switch after the execution of a signal, will recovery the signal. If the signal can’t recovery to the initial status after the conversion, then consider the switch has fault.

344 Fig. 3 The monitoring flow of switch

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Switch Status

Yes High Voltage 1s, then Low Voltage

Normal

No

Fault

Record the Locked Rotor Fault (DTC)

When the input component is sensor, the MCU collect the voltage and current of the sensor. In initial ECU have defined the valid range of each sensor, when ECU monitor the signal exceed the specified valid range, can be judged one of the following faults: sensor cable is loose or damaged, the wiring is short to ground or short battery, the sensor is failure.

3.3.1 Output Component Monitoring The output component means the actuator connected to the ECU. ECU monitor the signal of the actuator, read back to the MCU, then judgment whether the actuator or the actuator loop have the fault. Because the majority of the electrical actuators are electromagnetic coil, such as motor, so detect the actuator can measuring the resistance of the coil. The abnormal resistance will cause the voltage read back to the MCU in abnormal status.

4 Diagnostic Application Design in Body ECU The OBD function of body ECU is focus on the enhance diagnostic, each vehicle manufacturer always design the system based on the feature of the vehicle electrical system. This session, will engage in the diagnostic system design for the body controller of a vehicle platform. At first, according to the feature of the BCM (Body Controller Module) surrounding component, distinguish the types of signals. Confirm the signal resources that need to detect. Figure 4 list some signal resources of the BCM.

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12V Hazard Switch

Hazard Light A/D A/D

Door Contact Pin

IS1

A/D A/D

5V Wiper Intermit Switch

IS2

HSD Chip

Rear Compartment Light

MCU A/D

12V IS1 A/D

LSD Chip

12V

Motor M

Fig. 4 Some signal resources of the BCM

1. Hazard Switch, it is a non self-locking switch, when detect the signal always in the high voltage, and then can consider the switch is in the fault. The door contact switch is self-locking switch, so cannot judge the fault condition. 2. Wiper Intermit Switch, it is analog input signal, when MCU monitor the signal exceed the specified valid range, can be judged in fault. The input signal of the wiper intermit switch is the resistance value, through the internal pull-up resistor voltage divider, the resistance value invert to voltage value. The valid signal of the wiper intermit switch have two ranges 0  10KX and 20  50 KX(its just for example, normally the ranges are more than two), the valid value that read by MCU also have two ranges, assume as 0.1 * 1 and 2 * 4.9 V, when exceed the different range, judged as different fault, the detailed type of the fault listed in Table 1. 3. The output component of the BCM is controlled by the LSD and HSD that have the function of read back function. The chip of LSD or HSD according to the read back value of IS1 IS2, feedback to the A/D of MCU, MCU judge whether the actuator have fault. Based on the above design program, the BCM may have the following failures.

5 Verification and Validation Based on the ODX model, build up the ODX database of the BCM diagnostic function model. Then do the verification and validation.

346 Table 1 List of BCM DTC DTC Failure description B1062 Wiper intermit input out of range B1063 Wiper intermit switch short to ground Check wiring of switch the B1064 Wiper intermit switch short to battery Check wiring of switch the B1071 Hazard light open B1072 Hazard light short to ground Check wiring the B1090 Compartment light open B1091 Compartment light short to ground Check wiring the B1092 Motor loop open B1093 Motor loop short to battery Check wiring the B1094 Hazard light switch stuck

Table 2 The format of K line Fmt Tgt Src Max.4 byte

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Failure detect method

Failure type

Fault maintenance method

Input signal out of 0*1 or Invalid Replace the switch 2*5 V signal Input signal always 0 V Short to ground

Input signal always 5 V

Short to

Output signal read back current is 0 A Output signal read back voltage is 0 V

Open Check the wiring or signal replace the light Short to ground

Output signal read back current is 0 A Output signal read back voltage is 0 V

Open Check the wiring or signal replace the light Short to ground

Output signal read back current is 0 A Output signal read back voltage is 5 V

Open Check the wiring or signal replace the motor Short to battery

Input signal of switch always high voltage

Invalid Check the switch signal

Len

SID Max.255 byte

battery

Data

CS 1 byte

Fmt frame byte, Tgt target address, Src source address, Len length, SID diagnostic service id, Data data byte, CS Checksum

5.1 Definition of the Communication Parameters The tester and ECU need define the same communication speed (baud rate), address ID and message format. Then the message can be received successfully in K line and have been analyzed. These communication parameters are defined as basis parameter. It has been set up in the COMPARAM-SPEC model of the ODX

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data structure. The baud rate of K line normally set up as 10.4 Kbps. According to the protocol of ISO 14230, the format of K line is defined as the Table 2. The first four bytes of the message can use different formats, according to different manufacturers’ definition. This BCM define the message length in bit5bit0 of Fmt. The address and other signal is defined as follow: 1. Address separate to target address and source address, ODX database is mainly used in diagnostic tool, so for the tool, the target address is ECU, defined as 35H(hex), the source address is diagnostic tool, defined as F1H(hex). 2. The header of the message has three bytes, and only support physical addressing(point to point), define the Fmt as 80+ data length(include SID) in the database. 3. Data byte area is composed of SID and its corresponding data, not exceed 255 bytes.  4. Checksum and byte satisfy the following formula: \CS [ i ¼ \CS [ ði1Þ þ \BYTEi [ g mod 256; i  1. Among, \CS [ 0 ¼ \BYTE0 [ . Before the diagnostic SID request, the ECU has been fast initialized by the tester. Using the 25 ms high and low voltage signal as the wake signal, inform the target node the tester want to communication. Through the start communication request SID 81, build the consistency between the tester and tool with tester address, ECU address and message header etc. After the initialization, the communication will maintain, until exceed the certain time, and the tester have not send any request, the communication will stop. If the tester want to communication again, it need to send wake signal again.

5.2 Definition of Service Request ID According to the definition, clarify the services that supported by ECU. Build these parameters in DIAG-LAYER model in ODX, then inherit the COMPARAMSPEC, make communication parameters consistency with the vehicle parameters. Each diagnostic service ID includes request service and response service, and the response service includes positive response and negative response. Response service and request service have certain regularity. Such as, RespSID ¼ ReqSID þ xxH, ReqSID is request service, RespSID is response service, xxH is a fixed Hex value. In protocol KWP2000 xxH is40H. Request message: ReqSID+request information Positive response message: RespSID+positive response information Negative response message: 7F þ ReqSID þ NRC, NRC is negative response code.

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Fig. 5 Interface of the diagnostic test

Different service contain different meaning and length, each service also have different NRC. After build the relationship between the request message with the positive response and with the negative response message, add all these definition to the ECU’s diagnostic parameters. Then the finished diagnostic database can be exported as an ODX format, the format of ODX has the following eight types: 1. 2. 3. 4. 5. 6. 7. 8.

odx-c (COMPARAM-SPEC) odx-d (DIAG-LAYER-CONTAINER) odx-f (FLASH) odx-m (MULTIPLE-ECU-JOB) odx-v (VEHICLE-INFO-SPEC) odx-e (ECU-CONFIG) odx-fd (FUNCTION-DICTIONARY) odx-p (Contains One, or Multiple ODX Files and Other Files/Data).

According to the definition of the database, it can be export the corresponding ODX file.

5.3 Verification and Validation Using of Company Softing’s tool DTS Monaco, import the ODX database. Carry out the diagnostic testing for a real ECU. In tester have defined the K-Line communication manner, after the faster initialization between the tester and the

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ECU, the communication has set up, and then can do the verification. Figure 5 is the picture of the test. Choose the request service, click transmit, the interface show the request message and the response message from ECU. It has showed the request message and response message of the TesterPresent service after the communication set up. And show the request and response of readDTCByStatus. Req 3E Resp 7E Req 18 00 FF 00 Resp 58 06 90 62 24 90 71 64 90 90 48 90 93 48 90 94 48 90 95 48. Translate it is DTC B1062 B1071 B1090 B1093 B1094 B1095. On the basis of this database, in development phase it can realize test of application diagnostic service, the test of communication time parameters in data link layer, the test of ECU DTC and the test of DTC status etc. The database exchange to the supplier of the manufactory tester, it can realize the function of EOL test, ECU coding etc. The database exchange to the dealer of the manufactory, it can realize function of repair auxiliary, read the DTC code and change ECU etc. Also the supplier of ECU can use this database in diagnostic software integration; it can save the cost of the development. And it is benefit for the software developer, they can deal with the ODX database directly, do not need master more ODX professional knowledge.

References 1. Zechang S (2006) Vehicle network and electric drive control. Tongji Univesity Doctoral Programmes, Shanghai 2. ISO 22901-1, International Organization for Standardization (2008) Road vehicles—open diagnostic data exchange (ODX)—part1: data model specification 3. Wang X, Wang S (2006) The theory of on-board diagnostic and employment. Automobile Electric

Magnetic Circuit Design for Improving Performance of In-Wheel Type IPMSM Byeong-Hwa Lee, Jae-Woo Jung, Kyu-Seob Kim and Jung-Pyo Hong

Abstract This paper presents rib design in the rotor of IPMSM to increase maximum power in the field weakening region without variation of PM usage, conductor and size. Rib design is critical issue in the design of IPMSM due to leakage flux of permanent magnet and dominated by mechanical strength, therefore structural analysis should be conducted and minimum thickness should be selected. Rib design also affects to the inductance and saliency ratio and results in the variation of the field weakening performance and output power. With determined rib thickness, motor parameters and other motor characteristics according to rib length are presented in this paper. By redesigning rotor ribs, 6.2 % of increased maximum power in the field weakening region is achieved. Finite element analysis (FEA) and equivalent circuit analysis (ECA) are used for parameter and performance estimation.



Keywords Characteristic current Interior permanent magnet synchronous machine In-Wheel type motor Magnetic circuit design





1 Introduction Recently as the interest about the energy savings in the various industrial fields is increasingly raised, the high efficient electric motors have been demanded. The field of interior permanent magnet synchronous machine (IPMSM) is especially F2012-D02-041 B.-H. Lee (&)  J.-W. Jung  K.-S. Kim  J.-P. Hong Department of Automotive Engineering, Hanyang University 222, Wangsimni-ro, Seongdong-gu, Seoul 133-791, Korea e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_33,  Springer-Verlag Berlin Heidelberg 2013

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getting larger because both magnetic torque and reluctance torque can be used simultaneously. Due to possibility of field weakening operation through current vector control, IPMSM is suitable for In-Wheel type motor which demands high power density and high efficiency [1, 2]. When IPMSM is applied to the In-Wheel type motor, two-problems occurs. First, the price of permanent magnet that occupied 30 % of price of one IPMSM is the most serious problem. Due to this problem, hybrid traction motor has recently a tendency that reluctance torque occurred by d, q-axis inductance difference is considered to be more crucial than magnetic torque generated by permanent magnetic flux. Second, EMF increases linearly in proportion to the angular velocity of rotor and then reaches the limit which is IGBT breakdown voltage. The problem occurs in obtaining the maximum power in field weakening region due to limit in usage of permanent magnet. In the field weakening region, if the voltage ellipse moves to the left, the voltage ellipse will cross the upper constant torque locus. Finally, the maximum power is increased in the field weakening region. In order to move the voltage ellipse, a characteristic current should be changed. There are two kinds of alternatives for this purpose. First of all, increasing no-load linkage flux is one of the methods by using more permanent magnet. Secondly, by reducing d-axis inductance, the voltage ellipse is shifted to the left. Reducing the d-axis inductance is easily achieved by rib redesign. This paper presents rib design in the rotor of IPMSM to increase maximum power in the field weakening region without variation of PM usage, conductor and size. Rib design is critical issue in the design of IPMSM due to leakage flux of permanent magnet and dominated by mechanical strength, therefore structural analysis should be conducted and minimum thickness should be selected. Rib design also affects to the inductance and saliency ratio and results in the variation of the field weakening performance and output power. With determined rib thickness, motor parameters and other motor characteristics according to rib length are presented in this paper. By redesigning rotor ribs, increased maximum power in the field weakening region is achieved. Finite element analysis (FEA) and equivalent circuit analysis (ECA) are used for parameter and performance estimation [3].

2 Calculation of Voltage Ellipse in IPMSM The IPMSM is commonly analyzed using two axis theory. The d-axis is defined in the direction of the rotor permanent magnet flux linkage phasor so that the orthogonal q-axis is aligned in with the open circuit EMF phasor [4, 5]. The equivalent circuits for IPMSM based on a synchronous reference frame including iron losses are presented in Fig. 1. The mathematical model of the equivalent circuit is given in the following equations. The d, q-axis voltages and currents are given by (1, 2, and 3).

Magnetic Circuit Design for Improving Performance Fig. 1 Equivalent circuit of the IPMSM model in the dq reference frame, (a) d-axis equivalent circuit, (b) q-axis equivalent circuit

id

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Ra

iod

ωLqioq

icd Rc

vd

vod Ld

(a) d-axis equivalent circuit

iq

Ra

ioq

ωLd iod

icq vq

Rc

voq

ω

a

Lq (b) q-axis equivalent circuit Fig. 2 Voltage ellipses and constant torque locus

Va ¼ 

vd vq



qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi v2d þ v2q ;

Ia ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffi i2d þ i2q

       Ra vod iod Ld 0 iod ¼ Ra þp þ 1þ v ioq 0 L ioq Rc oq q        vod 0 0 xLq iod þ ¼ ioq voq xLd 0 xwa 

ð1Þ ð2Þ ð3Þ

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Start

Load analysis with i d , iq

Current

PM

On

On

FEM Calculation of extra parameters Rc (FEM), Ra

Calculation of L d , L q profile FEM

Current

PM

Off

On

Calculation of Ψ a profile

End

Fig. 3 Overall process of equivalent circuit analysis

where, id and iq are the d, q-axis component of the armature current, icd and icq are the d, q-axis component of iron loss current, vd and vq are the d, q-axis component of terminal voltage, Ra is the armature winding resistance per phase, Rc is the iron-loss resistance, Wa is the flux linkage of PM per phase root mean square (rms), Ld and Lq are d, q-axis inductance. The Eqs. (2.1, 3.1) can be obtained by applying the assumptions that the state is steady and the terms of resistive voltage drop are small at high speeds in Eqs. (2, 3). vd ¼ xLq ioq

ð2:1Þ

vq ¼ xðLd iod þ wa Þ

ð3:1Þ

When the Eqs. (2.1, 3.1) are applied at Eq. (1), the voltage ellipse equation is obtained like (4). ! 2 0 12 id þ wLda iq ð4Þ þ @w A ¼ 1 w o

o

Ld

Lq

3 Shift of the Center of Voltage Ellipse The voltage ellipse and current circle trajectories from inverter and motor establish operating limits of the drive system. The control strategy must select these commands that satisfy both trajectories [6].

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Fig. 4 Different rib thickness of two IPMSM model, (a) base model, (b) rib-redesign model

Table 1 Specification of base model

Values Number of pole and slots Winding method DC link voltage Input current Max speed Max power

8/48 Distributed winding 240 VDC 220 Arms 11000 rpm 35 kW

The center of voltage ellipse is called a characteristic current that is the ratio of No-load linkage flux to the d-axis inductance. The characteristic current is given by (5). Ich ¼ 

wa Ld

ð5Þ

When the characteristic current is placed within current circle, it is possible to use the field weakening control. Figure 2 is shows voltage ellipses and constant torque loci. If the characteristic current is shifted from A to B, the voltage ellipse will cross the upper constant torque locus. Finally, the maximum power is increased in the field weakening region. The characteristic current is shifted to left side by twoconditions. First condition is the increase of no-load linkage flux by more usage of PM. However the usage of PM is limited in IPMSM due to some problems. Second condition is the decrease of d-axis inductance by increase of d-axis reluctance. This is useful for improving maximum power by simple redesigning rotor ribs. In this paper, the method improving the maximum power without more usage of permanent magnet is suggested. The suggested method is the shifted of the characteristic current through the rib redesign of IPMSM. The detailed analysis results of using the finite element analysis (FEA) and equivalent circuit analysis (ECA) will be presented.

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(a)

80

Back EMF [ V ] @1000rpm

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60

Phase back EMF : 21.8 [Vrms]

Line-line back EMF : 37.7 [Vrms]

40 20 0 -20 -40 -60 -80

0

60

120

180

240

300

360

Electrical angle [ o ]

Back EMF [ V ] @1000rpm

(b)

80

Phase back EMF : 22.1 [ Vrms ]

60

Line-line back EMF : 38.2 [ Vrms ]

40 20 0 -20 -40 -60 -80

0

60

120

180

240

300

360

Electrical angle [ o ] Fig. 5 The back EMF of two IPMSM, (a) base model, (b) rib-redesign model

4 Calculation of Parameters in IPMSM 4.1 Overall Process of Equivalent Circuit Analysis Overall process of equivalent circuit analysis (ECA) is shown in Fig. 3. The difference according to current intensity is considered calculating the flux linkage. Profile of d, q-axis inductance and current, current phase angle are calculated to consider nonlinearity [6]. The d, q-axis inductance can be computed by relation between intensity of flux linkage and status under no-loaded and loaded condition through Eq. (6). Ld ¼

wa  wo cos a w sin a ; Lq ¼ o id iq

ð6Þ

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d,q-axis inductance [mH]

(a) 2.0

d-axis inductance q-axis inductance

1.8 1.6 1.4 1.2 1.0

Current Increase

0.8

Direction

0.6 0.4 0.2

30

40

50

60

70

80

Electrical angle [ o ]

d,q-axis inductance [mH]

(b) 2.0

d-axis inductance q-axis inductance

1.8 1.6 1.4 1.2 1.0

Current Increase

0.8

Direction

0.6 0.4 0.2

30

40

50 60 Electrical angle [ o ]

70

80

Fig. 6 d, q-axis inductance of Two IPMSM, (a) base model, (b) rib-redesign model

4.2 Different Rib Thickness of Two IPMSM Model Two IPMSM models which have different rib thickness are shown by Fig. 4. IPMSM model which has a specification as Table 1 is shown by first figure. The model which has reduced rib thickness by redesigning is shown by second figure. The maximum output which increased in weakening region can be confirmed and the characteristic of IPMSM which is changing can be observed by FEA and ECA reducing rib thickness.

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(a)

4.0

Saliency ratio

Saliency ratio (Lq/Ld)

3.5 3.0 2.5 2.0 Current Increase

1.5

Direction

1.0

30

40

50

60

70

80

Electrical angle [ o ]

(b)

4.0

Saliency ratio

Saliency ratio (Lq/Ld)

3.5 3.0 2.5 2.0 Current Increase

1.5

Direction

1.0

30

40

50

60

70

80

Electrical angle [ o ] Fig. 7 Saliency ratio of Two IPMSM, (a) base model, (b) rib-redesign model

Table 2 Compared of the Parameters of Two IPMSM Parameter Base model

Rib-redesign model

Flux linkage d-axis inductance Characteristic current Max power

0.0912 Wb (1.4 %) 0.424 mH (-6.4 %) 215 Arms (8.2 %) 36.1 kW (6.2 %)

0.090 Wb (0 %) 0.453 mH (0 %) 198.7 Arms (0 %) 34 kW (0 %)

4.3 Overall Process of Equivalent Circuit Analysis The characteristic current which is moved by redesigning rib lastly can be confirmed. The back EMF which is calculated by flux linkage under no-load condition

Magnetic Circuit Design for Improving Performance Fig. 8 Shift of the characteristic current within the current limit circle

359

iq

Current Limit Circle 245A rms

Characteristic Current of Base Model

id Characteristic Current of Rib-Redesign Model

is shown by Fig. 5. 1.3 % of increase in back EMF can be known by reduction of magnetic flux leakage decreasing rib thickness. The d, q-axis inductance and saliency ratio are shown by Figs. 6 and 7. Q-axis inductance increased in small quantity, but d-axis inductance decreased. The components of reluctance torque increase as saliency ratio which is calculated by the ratio of q-axis inductance and d-axis inductance. The larger maximum power can be obtained by reducing rib thickness eventually.

5 Result Parameters of two IPMSM which have different rib thickness are confirmed by FEA and ECA. D-axis inductance which is one of the IPMSM parameter is most influenced by reduction of rib thickness therefore it decreases sharply. Characteristic current moves to the left with same size of voltage ellipse. As a result maximum power increases owing to the intersection of the voltage ellipse and constant torque locus. Flux linkage, d-axis inductance and characteristic current calculated by FEA and ECA are shown by Table 2 The result that increased characteristic current moved to the left can be known within the current limit circle by Fig. 8.

6 Conclusion In this paper, the method improving the maximum power without more usage of permanent magnet is suggested. The suggested method is the shifted of the characteristic current through the rib redesign of IPMSM. As the rib is redesigned,

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d-axis inductance is largely decreased. The analysis results of using the FEA and ECA is shown. As a result, by redesigning the rotor rib, maximum power increased by 6.2 %. Due to reduction of rib thickness, the stress concentration can increase at the rotor rib. Therefore structure analysis is sometimes demanded.

References 1. Lee J-Y, Lee S-H, Lee G-H, Hong J-P, Hur J (2006) Determination of parameters considering magnetic nonlinearity in an interior permanent magnet synchronous motor. IEEE Trans Magn 42(4):1303–1306 2. Hur J, Km B-W (2010) Rotor shape design of an interior PM type BLDC motor for improving mechanical vibration and EMI characteristics. JEET 5(3):462–467 3. Lee B-H, Kwon S-O, Sun T, Hong J-P, Lee G-H (2011) Modeling of core resistance for d-q equivalent circuit analysis of IPMSM considering harmonic linkage flux. IEEE Trans Magn 47(5):1066–1069 4. Jung J-W, Lee J-J, Kwon S-O, Hong J-P (2009) Equivalent circuit analysis of interior permanent magnet synchronous motor considering magnetic saturation. Electric Vehicle Symposium 24 (EVS) 5. Dutta R, Rahman MF (2004) Investigation of suitable vector control techiques for low voltage IPM machine in 42 V system. In: Proceedings IEEE Industrial Electronics Society 30th Annual Conference Paper, vol 3. pp 2724–2728 6. Fu ZX (2003) Pseudo constant power times speed operation in the field weakening region of IPM synchronous machines. In: Proceedings IAS Industrial Application 38th Annual Conference paper, vol 1. pp 373–379

A Study on the Noise Reduction of Electrical Power Steering in Surface Permanent Magnet Synchronous Motor Do-Jin Kim, Hyeon-Jin Park and Jung-Pyo Hong

Abstract Electrical Power Steering (EPS) is designed to use an electric motor to reduce effort by providing steering assist to the driver. EPS have advantages which are economical and eco-friendly compare with hydraulic power steering, because the power steering oil is not used and efficiency of vehicle increased to 3*5 %. Since EPS is connected to handle of vehicle, acoustic noise and vibration have an effect on the driver directly. So study on the acoustic noise and vibration reduction of EPS is proceeding. In this paper, acoustic noise and vibration reduction of motor are generated by electro-magnetic exciting forces between stator and rotor. The magnetic noise is defined as noise generated from vibrations due to electromagnetic exciting force. The electro-magnetic exciting force affect on stator yoke, and the acoustic noise and vibration of motor generated by deformation of stator yoke. In order to consider mechanical characteristic, the natural frequency mode of stator is calculated using FEA to avoid resonance. In order to verify proposed method, the proto and improved model are manufactured and experimented.





Keywords Acoustic noise Electrical power steering Surface permanent magnet synchronous motor Electromagnetic exciting force Deformation





F2012-D02-043 D.-J. Kim (&)  H.-J. Park  J.-P. Hong Department of Automotive Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Korea e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_34,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction Acoustic noise of motor can be classified into three sections. First of all, there are higher space and time harmonics eccentricity, phase unbalance, slot opening, magnetic saturation, and magnetostrictive expansion of the core laminations on electrical noise. Secondly, there are mechanical noises associated with the mechanical assembly. Thirdly, there are aerodynamic noises associated with flow of ventilating air through or over the motor. The electro-magnetic exciting forces affect on stator yoke, and the noise and vibration are generated by deformation of stator yoke. Therefore, the normal and tangential forces are calculated using air gap flux density, and the effect is analyzed by calculating deformation of stator yoke. In order to calculate electro-magnetic exciting forces which affect on the acoustic noise, the FEA simulation is conducted. Numerous analyses have been developed in this field over years. Harmonic fluxes, for instance, have been investigated through search coils and the FEA. From these harmonic fluxes, it is possible to calculate electro-magnetic exciting forces using Maxwell’s stress equations. Most papers deal with electro-magnetic exciting forces and their modes. Kako analyzed modes of electro-magnetic exciting forces and modes of natural frequency in induction machines [1, 2]. The electro-magnetic exciting force and vibration have been studied numerically in synchronous machines and SRM [3–5]. The acoustic noise is caused by electro-magnetic exciting force such as radial and tangential forces which deform the stator core [6]. In order to calculate electro-magnetic exciting forces which affect on the acoustic noise, the 2-D simulation is operated using sinusoidal current considering load condition. The exciting forces are put in the surface of tooth of stator and the quantity of deformation of stator yoke is calculated. In order to reduce acoustic noise, the diameter of rotor and width of tooth are decreased in this paper. The characteristic of motor is satisfactory and the deformation of stator yoke is reduced. To satisfy characteristic of motor, the permanent magnet decreased by 20 % compared with proto model, series turns per phase increased to satisfy no load back EMF, when the rated speed is 1,500 rpm, the rated torque is 2.6 Nm. In order to verify suggested method, designed motor is manufactured and experimented. Between initial and improved model which is designed by decreasing the usage of permanent magnet. The motor is 6 pole 9 slot and surface permanent magnet type motor.

2 Design Process 2.1 Specification of Motor The specifications of analysis model are shown as Table 1. The analysis model which consists of 6 pole/9 slots and concentrated windings is driven by BLAC

A Study on the Noise Reduction Table 1 Specifications of tested motor

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List

Values

Number of pole slots Rated current (Arms) Series turns number per phase (turns) Rated speed (rpm) Rated torque (Nm)

6/9 45 16 1,500 2.6

operation and rated speed and rated torque are 1,500 rpm and 2.6 Nm, respectively. The initial and improved models which have the same characteristics but different stator and rotor structures are studied by this process. Finally, the calculated results are verified by an experiment. The cross-section of motors is shown in Fig. 1.

2.2 FEA Simulation In order to reduce acoustic noise, the rotor decreased to 17 % in comparison with proto model. The series turns per phase increased to satisfy no load back Electro Motive Force (EMF), torque. When the rated current is injected, the characteristic of motor is satisfied under the load condition 1,500 rpm and 2.6 Nm are shown as Fig. 2. In case of M2 model, the copper loss decreased because the torque is lager than other models.

2.3 Spectrum Analysis The electromagnetic exciting force which causes the noise and vibration of motor is analyzed in this paper. First of all, the flux density in the air gap is calculated by result of FEA. Secondly, the radial force and tangential force are calculated by using Maxwell Stress Tensor. The tangential force is ignored because it is smaller than radial force. Accordingly, the radial force is used to analyze acoustic noise and vibration. Temporal and spatial harmonic components of electro-magnetic exciting force which is calculated can be analyzed by applying Fourier transform. In the harmonic analysis using the electromagnetic force F as an example, first of all, the k-th space harmonic component is separated from the field analysis result as a function of the circumferential position xs of the stator coordinate at each time t in the field analysis. The separated electromagnetic force Ft ðxs Þ can be expressed as follows [7, 8]. X Ft ðxs Þ ¼ Fk sinðkxs þ ak Þ ð1Þ k

where Fk is the amplitude of the k-th space harmonic components, and ak is the phase angle. Equation (6) means only space harmonic components at each time-step.

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(a) Proto model

(b)M1 model

(c) M2 model

Fig. 1 Cross-section view of analysis models, a Proto model, b M1 modell, c M2 model Fig. 2 Torque according to the models @ 45 Arms, 20

Proro

3.0

M1

M2

Torque [ Nm ]

2.5 2.0 1.5 1.0 0.5 0.0 0

60

120

180

240

300

360

Electrical angle [ o ]

Next, the v-th time harmonic component is separated from the k-th space harmonic component at every circumferential position xs of the stator coordinate. The separated harmonic component Fks1 is presented as follows. X Fkx ðtÞ ¼ Fkv sinðvxt þ akv Þ ð2Þ k

Where, x is the electrical angular frequency, Fkv is the amplitude of the v-th time for the k-th space harmonic components, and are the phase angle. In the proposed method, each harmonic component is separated into positive-phasesequence and negative-phase-sequence harmonic waves which are functions of the circumferential position xs and time t. At the two circumferential positions xs1 and xs2 of the stator coordinate, the v-th time, for the k-th space harmonic electromagnetic force Fkv1 and Fkv2 are given as follows. Fkv1 sinðvxt þ akv1 Þ ¼ Fkvp sinðvxt  kxs1 þ akvp Þ þ Fkvn sinðvxt þ kxs1 þ akvn Þ Fkv2 sinðvxt þ akv2 Þ ¼ Fkvp sinðvxt  kxs2 þ akvp Þ þ Fkvn sinðvxt þ kxs2 þ akvn Þ

ð3Þ

where akv1 and akv2 are the phase angles of Fkv1 and Fkv2 ; respectively; Fkvp and Fkvn are the amplitudes of the positive-phase-sequence and negative-phasesequence v-th time for the k-th space harmonic components, respectively; akvp and

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akvn are the phase angles of the positive-phase-sequence and negative-phasesequence v-th time for the k-th space harmonic components, respectively. By solving (3) simultaneously, the amplitudes and the phase angles of the positivephase-sequence and negative-phase-sequence harmonic components can be obtained. In this process, the circumferential positions xs1 and xs2 are chosen where each absolute value of the harmonic amplitude by (3) is the maximum or minimum, though any position can be chosen freely. Consequently, the electromagnetic force F ðxs ; tÞ by the above FEM analysis can be expressed as (4). XX F ðxs ; tÞ ¼ ½ Fkvp sinðkxs  vxt þ akvp Þ þ Fkvn sinðkxs þ vxt þ akvn Þ ð4Þ k

v

2.4 Modal Analysis In order to consider mechanical characteristics and resonance, the natural frequency and mode of each component of motor assembly are calculated using FEA with material and geometry. Te designed model can avoid a resonance of motor by mismatching frequency of electromagnetic excitation force to the mechanical natural frequency (Fig. 3). Based on the principle of Hamilton, the modal analysis can be described in (5) with considering the un-damping system and external force free (Fig. 4). ½Mf€xg þ ½Kfxg ¼ 0

ð5Þ

where x is the vector of the displacement, ½M is the mass matrix, and ½K is the stiffness matrix. As the vibration is estimated, the deformation analysis requires consideration of damping and applied force vector. Thus, the mechanical system is expressed as (6). ½Mf€xg þ ½Cf_xg þ ½Kfxg ¼ fFðtÞg

ð6Þ

where ½C is the damping matrix and fFðtÞg is the applied force vector [9]. In this study, the vector FðtÞ only consists of the harmonic components of the calculated local force, because the DC component has no effect on the vibration.

2.5 Simulation of Deformation The deformation analysis is calculated using commercial tool ANSYS. The radial force is applied on the stator tooth surface and shown in Fig. 5a. The deformation of stator is calculated by radial forces which are generated in the air gap and is shown in Fig. 5b. The deformation of proto model is larger than other models. The

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(a)

(b)

(c)

Fig. 3 Natural frequency mode of stator @ proto model, a 1110 Hz @ 2mode, b 2835 Hz @ 3mode, c 4587 Hz @ 4mode

(a)

(b)

(c)

Fig. 4 Natural frequency mode of stator @ M2 model Modal analysis @ M2 model, a 978 Hz @ 2mode, b 2395 Hz @ 3mode, c 3625 Hz @ 4mode

measured point

Y Z

X

Deformation [m]

15.0n

Proto

M1

M2

10.0n 5.0n 0.0 -5.0n -10.0n -15.0n 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Time [s]

(a)

(b)

Fig. 5 Deformation according to the radial force, a Radial force distribution, b deformation according to rotor position

A Study on the Noise Reduction

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deformation [ m ]

10n

M1

M2

1n 100p 10p 1p 1

6

11

16

21

harmonic order

Fig. 6 Harmonic analysis @ measured deformation

(a)

(b)

Fig. 7 Experimental results of motors, a proto model, b M2 model

acoustic noise reduction is predicted using compare the deformation according to the harmonic analysis. The deformation quantity of M2 model is smaller than other models is shown in Fig. 6.

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3 Experimental Results In order to verify calculation method, the experiment for measurement of noise is conducted. The motor which is with the wheel is experimented according to the change speed and measured 1 m away from the configuration of experiment by microphone. Figure 7 shows the noise experiment results measurement by 1/3 octave band. The motor which has 6 pole 9 slot is generated by 18th harmonic noise and compared initial and improved model. The noise of improved model is less than initial model with down 5 dB.

4 Result In this paper, the deformation is related to the deformation of stator. In order to reduce acoustic noise, the deformation of stator is calculated using radial forces which are generated from the air gap. The acoustic noise of motor decreased through the deformation reduction of stator core. The deformation of improved M2 model is smaller than other models using harmonic analysis. This proposed method is conducted to reduce acoustic noise.

References 1. Ishibashi F, Kobayashi K (1990) Experimental study of harmonic magnetic flux of small squirrel cage induction motor. Electr Eng Jpn 110(6):118–127 2. Ishibashi F, Noda S, Mochizuki M (1998) Numerical simulation of electromagnetic vibration of small induction motors. IEE Proc Power Appl 145(6):528–534 3. Ha K-H, Kim Y-K, Lee G-H, Hong J-P (2004) Vibration reduction of switched reluctance motor by experimental transfer function and response surface methodology. IEEE Trans Magn 40(2):577–580 4. Hong J-P, Ha K-H, Lee J (2002) Stator pole and yoke design for vibration reduction of switched reluctance motor. IEEE Trans Magn 38(3):1295–1309 5. Jung J-W, Lee S-H, Lee G-H, Hong J-P, Lee D-H, Kim K-N (2010) Reduction design of vibration and noise in IPMSM type integrated starter and generator for HEV. IEEE Trans Magn 46(6):2454–2457 6. Lee S, Park I, Lee K (2000) Comparison of mechanical deformations due to different force distributions of two equivalent magnetization models. IEEE Trans Magn 34(4):1368–1372 7. Hirotsuka I, Tsuboi K (1989) Experimental study on the electromagnetic vibration caused by the slot combination of a squirrel-cage induction motor. T IEE Japan 109-D(5):347–354 8. Goeras JF, Wang C, Lai JC (2006) Noise of polyphase electric motors. CRC Press Taylor and Francis Publishing Co, UK 9. Mikami H, Ide K, Arai K, Takahashi M, Kajiwara K (1999) Dynamic harmonic field analysis of an inverter-fed induction motor considering all harmonic components in the secondary current. IEEE Trans Energy Conver 14(3):464–470

Power Distribution Design of Passenger Vehicle Xianming Wang

Abstract The chapter discussed the method of power distribution of passenger vehicle, and indicated the key points of power distribution. Via analyzing the property of electronic and electrical parts in vehicle, we ascertain the load characteristics of electronic and electrical parts, define the load types of different electronic and electrical parts, define the application strategy of fuses and the original power distribution draft. According to the numbers and power of load, calculate the capability of fuse and choose the type of fuse. Then, via the capability of fuse, calculate the diameter of wire connecting with the fuse. Finally, summarize all the information to design the particular power distribution drawing. The process method and key points of power distribution discussed in the chapter are based on an economical car of FAW R&D Center, and the design result is validated by the economical car. The chapter mainly discussed common fuse and wire diameter selection, did not contain some special fuse such as main fuse selection principle or contain the layout and number of fuse. Nowadays, there’re few articles about the power distribution study. However, this chapter discussed the power distribution systematically in order to assist the design of power distribution and guide the engineering design of power distribution of passenger vehicle. Power distribution is the key point of electrical system design. The chapter discussed the process, method and key points of power distribution. It is verified by an Economic car. This chapter discussed the power distribution in order to assist the design of power distribution and be helpful of the engineering design of power distribution about passenger vehicle. Keywords Power distribution

 Fuse  Wire diameter

F2012-D02-045 X. Wang (&) China FAW Co., Ltd. R&D Center, Changchun, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_35,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction The object protected by fuse is harness, not electronic and electrical parts connected by wires. The principles of circuit protection are: Each circuit must be protected; Key circuit and common circuit must be protected separately; Noise circuit and clean circuit must be protected separately; Fuse should be shared according to above requirements, but not to increase the capacity of fuses.

2 Content and Process Figure 1 indicates the content process of power distribution design: Three key points of power distribution: (1) combine products which can share fuses; (2) choose fuse type and calculate the capacity of fuse; (3) choose wire type and calculate wire diameter.

3 Electronic and Electric Parts Classification All electronic and electrical parts are usually classified as three types before the power distribution design, they are safety components; important components and comfort components. Safety components refer to the parts which influence the safety of vehicle directly. Important components refer to the parts which influence the driving of vehicle and the safety of vehicle partially. Comfort components refer to the parts which improve driving comfort, do not influence important performance. According to the purpose of parts, they can be divided into common load and temporary load. Common load refers to the electronic and electrical parts which working current lasts long time in work condition; Temporary load refers to the electronic and electrical parts which working current lasts short time in work condition, and wire harness does not generate heat in long time. According to characteristics of parts, it can be divided into resistance load, inductance load and controller load. Resistance load refers to the parts which working current is smooth in whole work condition, including starting and turning-off moment; Inductance load refers to the parts which working current has a peak value in whole work condition, including starting and turning-off moment, this peak value differs greatly from stable working current; Controller load refers to the electronic and electric parts which have complex PCB (Table 1).

Power Distribution Design of Passenger Vehicle Fig. 1 Content and process of power distribution

371 ascertain electric function and requirement of whole vehicle ascertain all the loads and special requirements of Electronic and Electrical Products Classify all the Electronic and Electrical Products

analyse function and characteristic of Products combine products can share fuses define power mode of products sketch power distribution drawing. choose the type of fuse and calculate the capacity of fuse choose wire type and calculate diameter improve power distribution drawing and check it electric principle design of vehicle

4 Combine Products According to the classification above, we have the principles of fuse strategy: Safety components must have own fuse; Important components can share fuse, but the fuse can’t be shared by load with different purpose and characteristics. Although the purpose and characteristic are same, the fuse can’t be shared by the parts that have different functions; Comfort components can share fuse if necessary. If the parts have different purposes, characteristics, and there is wide gap among their working current, we should avoid combining these parts in one fuse. The fuse also can’t be shared by the parts that have different characteristics and high working current. After the combination, we can design the fuse application strategy and obtain the strategy table.

Engine ECU • ABS ECU • Brake lamp Reversing light Trunk light Room lamp Throttle sensor Cooling fan Lambda sensor Fuel pump Canister solenoid valve Injector Ignition coil Horn Door lock motor Starter Wiper motor Generator Audio system Reserve power supply Cigar lighter Rearview mirror AC controller Air bag ECU •

Safety components

•

• • • • • • • • • •

• •

Important components

• • • • •

•

• •

Comfort components

Table 1 Electronic and electrical parts classification of an economic car Parts name Types

• •

• • • • •

• • • • • • • • • • • • •

Common load

Purpose

•

• • •

Temporary load

• •

•

• • • •

Resistance load

Characteristic

•

• • • •

• • • •

•

Inductance load

• •

•

•

•

• •

(continued)

Controller load

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Safety components

Types

Windshield washing motor TCU • Head light Steering lamp Fog lamp AC compressor Reversing radar Window regulator motor IMMO Roof window controller Instrument Defrost Blower Window regulator controller

Table 1 (continued) Parts name

• • •

Important components

• • • • • •

• • •

•

Comfort components

• • • • • •

• • • • • •

Common load

Purpose

•

•

Temporary load

•

• • • •

Resistance load

Characteristic

•

•

•

Inductance load

•

• • •

•

•

Controller load

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Table 2 Fuse application strategy of an economic car Fuse Parts connected Rank Serial number

Name

1 1

1 2

1 2 2

3 5 6

2 2 1 1 1 1 1

7 9 4 8 10 11 12

1 1 1 1 1 1

13 14 15 16 17 18

1

19

1 2

21 34

2 1 1 1 1

35 24 25 39 40

1 1 1 1 1 1 1

41 42 43 44 45 46 47

Main fuse Electronic throttle Main relay Canister Lambda sensor Injector ECU ECU Cooling fan Brake lamp Left head light Right head light TCM Starter ABS1 ABS2 Reserve Front fog lamp Rear fog lamp/horn Blower Window regulator Roof window IG. KEY1 IG. KEY2 Air bag Reversing lamp/TCM Start Ignition signal Wiper AC Ignition coil Cigar lighter Reserve power supply

Generator ECU

Canister valve Front and rear oxygen sensor Injector ECU ECU, main relay coil Cooling fan motor Break lamp, high braking light, shifter Head light, light adjust motor Head light, light adjust motor TCM Starter drawing switch ABS ECU ABS ECU Left/right fog lamp Rear fog lamp/horn Blower Window regulator controller Roof window controller

Air bag ECU Reversing lamp, reversing radar controller, gear sensor Starter relay coil Blower relay, fuel pump relay Wiper motor, washer motor AC controller, AC compressor relay Ignition coil Cigar lighter Reserve power supply

(continued)

Power Distribution Design of Passenger Vehicle Table 2 (continued) Fuse Rank Serial number

Name

1

50

1 1 2

51 36 26

1 2 2

22 28 29

2 2 1 2 2

30 31 23 32 33

1

37

1

38

Rearview mirror Shifter Defrost signal AC pressure switch ROOM1 Room lamp Rear defrosting Door lock Steering lamp ROOM2 Audio Instrument cluster Instrument cluster 2 IG.(S)

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Parts connected

Rearview mirror, audio Shifter BCM AC pressure switch

BCM Rear windshield defrosting BCM BCM Audio Instrument cluster, IMMO, Window regulator, auto AC controller Sport mode switch, snow mode switch, instrument cluster, headlight level adjusting switch, AC compressor relay IMMO controller, roof window controller, BCM, ABS ECU, Window regulator controller

5 Define Power Mode and Draft Power Distribution Drawing The power mode of passenger car has four types: B+\ACC\IG (IG1 and IG2)\ST. Before defining the power mode of electronic/electrical parts, there are three points to be confirmed: The part’s own requirement for the power mode; Some parts don’t have requirement for power mode, but we should define their power mode by function and experience; The use of relay. After defining the power mode of parts, according to the fuse strategy, we can draft the power distribution drawing, the draft include the following information: Plan of wires connecting among power related products, such as battery, starter, generator and so on; Position of fuses in wire harnesses; Definition of power mode of each part and the using of relay; Combination of parts (Table 2).

6 Type of Fuse As for power distribution design, fuses are often divided into two types: fast fusing fuse and slow fusing fuse (Figs. 2, 3, and 4).

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Fig. 2 PEC fast fusing fuse [1]

Fig. 3 PEC slow fusing fuse [1]

Fig. 4 Fast fusing fuse operating characteristics and slow fusing fuse operating characteristics [1]

Circuit design usually includes main circuit and secondary circuit. Main circuit is connected by many parts, so in working condition, the frequent change of function causes frequent change of working current; secondary circuit is connected by less products and the working current is changed slowly. So we have the principle of fuse-choosing as follows: Slow fusing fuses are used in circuits that have high impact current and lock rotor current; Slow fusing fuses are used to protect main circuit; Fast fusing fuses are used to protect secondary circuit.

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Fig. 5 Fuse I2t characteristics drawing [1]

Fig. 6 The changes of rated current of fast fusing fuse and slow fusing fuse with temperature [2]

Table 3 Reference value of life correction factor and temperature correction factor of fuse [2] Fuse Life correction factor (%) Temperature correction factor (%/C) Fast fusing Slow fusing

70 50

0.15 0.18

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Fig. 7 AV wire smoking and temperature characteristics [3]

7 Determination of Fuse Capacity Formula for working current calculation: I¼

P VV  : V V

ð1Þ

where I is working current; P is rated power of load; V is rated voltage of load; VV is working voltage, value is 14 V. In theoretical condition, fuse can last infinite time by rated current, but in practice, fuse will be fused by limited times even it is loaded by rated current after being impacted by certain time of rated current, so fuse capacity calculation should consider the life of fuse and use life correction factor to calculate accurately fuse capacity. We must make sure the life of fuse is longer than vehicle life when choose the fuse. Fuse I2t characteristics show the life of fuse (Fig. 5). With the raise of temperature, the rated current will be changed. Each fuse has its characteristic curve in which its rated current changes with temperature (Fig. 6). By considering the environment temperature and adding temperature correction factor, we can calculate the fuse capacity: IF ¼

I 1  d 1  ð TV  TF Þ  g

ð2Þ

where: IF is fuse capacity; d is life correction factor of fuse; TV is environment temperature of fusion; TF is rated temperature, normally is 23 C; g is temperature correction factor of fuse. Table 3 shows the life correction factor and temperature correction factor of fast fusing fuse and slow fusing fuse. In our design process, the value is reference.

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time(s)

wire smoking characteristics

fuse operating characteristics current(A)

Fig. 8 Relationship of fuse and wire characteristics

time(s)

Fig. 9 Relationship of fuse and wire characteristics

wire smoking characteristics

fuse operating

A

characteristics current(A)

Fig. 10 AVX wire smoking and temperature characteristics [3]

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Fig. 11 Example of power distribution

8 Determination of Wire Diameter and Wire Type An important characteristics of wire is smoking characteristics. Wire with different diameter can withstand different current, and smoke emission time is different (Fig. 7). After definition of fuse, we should calculate the wire diameter by fuse capacity. We must make sure that before fuses are fused, the wires can’t be burnt due to overheat. Therefore when we define wire diameter, we shall compare fuse operating characteristics with wire smoking characteristics, to must make sure wire smoking characteristics is above the fuse operating characteristics in the figure under the same current. In Fig. 8, the choosing of wire diameter is suitable, for the fuse is fused before the wire smokes, which ensures safety of the wire. In Fig. 9, the choosing is bad, for in A area, the fuse can’t protect wire harness, the wire is smoking, but fuse is not fused. The raising of environment temperature also leads to deterioration of current withstanding capacity of wire. After wire diameter is calculated by wire smoking characteristics, we must check if the diameter is suitable at the working temperature to make sure wire safe. If the result is not suitable for high environment temperature, we should increase the diameter. If the environment temperature is too high to choose a suitable diameter, we must choose high temperature resistant wire (Fig. 10). Before power distribution design, we should measure the environment of wire harness, and the measurement result is used as reference of design.

9 Improve Power Distribution Drawing The final power distribution drawing should include the following contents: Load characteristics of all electronic and electrical parts to distribute power supply, such as maximal power, maximal current, peak current and lock rotor current; Plan of

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wires connecting among power related parts, such as battery, starter, generator and other electrical parts; Position of fuses in wire harnesses, type and capacity of fuse; Power supply mode of each electronic and electrical parts and use of relay; Combination of parts; Wire type and wire diameter that connect all parts (Fig. 11).

10 Conclusion This chapter shows the way to calculate fuse capacity, define fuse type, calculate wire diameter and define wire type. It is verified by an Economic car. This chapter discussed the power distribution in order to assist the design of power distribution and be helpful of the engineering design of power distribution about passenger vehicle.

References 1. PEC Automotive fuse catalogue 2. JASO D 610-93, Selection of fuse-links for automobile wiring 3. JASO D 609-2001, Automotive parts-Current capacity of low tension cables

Part III

Software and Hardware Development

Study on the Performance Modeling Approach for Automotive Embedded Control Software Xiaofeng Yin, Jingxing Tan, Xiuting Wu and Qichang Yang

Abstract With the ever increasing complexity of automotive E/E (Electrical and Electronic) systems, model-based development techniques have been more and more widely used in the current development process of automotive embedded control software. Regarding the safety–critical automotive control systems with hard realtime characteristics, modeling timing and resource related performance and carrying out timing analysis for the control software at an early design stage play a crucial role to guarantee the quality of software as well as improve the cost-efficiency.





Keywords Meta-model Performance Modeling language time system Automotive control software



 Embedded real-

The motivation of this study is to investigate an appropriate performance modeling and timing analysis approach that can be integrated into the currently used modelbased development tool chain. A performance modeling language for automotive embedded control systems (PMOLACS) at high level of abstraction was put forward using meta-modeling technique, which consists of three different meta-models corresponding to software structure (SWS), target platform system (TPS), and run time system (RTS), respectively. The SWS meta-model defines the modeling paradigm of the constituent, interactive behaviors, timing characteristics and resource requirements of software components, the TPS meta-model defines the modeling F2012-D03-004 X. Yin (&)  X. Wu  Q. Yang Institute of Automotive Engineering, Xihua University, Chengdu, China e-mail: [email protected] J. Tan Department of Science and Technology, Xihua University, Chengdu, China SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_36,  Springer-Verlag Berlin Heidelberg 2013

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paradigm of the constituent, timing characteristics and resource constraint of the hardware and real-time operating system (RTOS) of the target platform, and the RTS meta-model defines the modeling paradigm of executable software system. The algorithms regarding translating existing functional model with timing and resource requirements together into the SWS model, building TPS model, mapping the components of the SWS model to the processors of the TPS model to form the tasks of the RTS model, and timing analysis, will also be discussed.

1 Introduction To meet the increasing demands on vehicle performance, such as drivability, safety, power, fuel economy, emission, as well as comfort, etc., a large number of embedded control units have been applied to the vehicle and the architecture of automotive E/E (Electrical and Electronic) systems has being become more and more complex. How to manage these complex E/E systems and ensure their performance requirements such as timing and resource constraints while their functional requirements are implemented and verified is a big issue in the current model-based development process, since the current automotive embedded software development process pays little attention to the non-functional requirements, especially the timing constraints, until the end of the development process—testing code on the target platform, although the algorithms related to the function of the system under development can be tested early through rapid control prototyping (RCP). If the system’s ability to meet timing constraints could be analyzed formally in the early design process, it is possible to avoid the hidden errors to be left in the final product software due to lacking of direct timing analysis that may hurt the driver and passengers and/or destroy the vehicle, and to avoid costly late-stage redesign of the software that may postpone the delivery of the product software. With respect to the complexity management of automotive E/E systems, AUTOSAR (AUTomotive Open System ARchitecture) provides a set of software infrastructure to enable the reuse and exchangeability of software modules between OEMs and suppliers through standardization of the software architecture of ECUs (electronic control unit) [1]. However, the main attention of AUTOSAR at present is focused on the implementation of software function. Some other model-based design tools are also widely used in the development of automotive embedded control software. For instance, MATLAB Simulink/ Stateflow [2] is used to design the control algorithms and then corresponding source codes are generated by a specific code generator such as Real-Time Workshop (RTW). As mentioned above, since the control algorithms can be optimized using RCP technique, the functionality of the controller can be tested at an early design stage. However, the direct formal verification of timing-related performance for embedded control software still can not be conducted in the current model-based development tool chain.

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In this investigation, the meta-modeling technique was used to construct a performance modeling language for automotive embedded control systems (PMOLACS) that could model the timing constraints and resource requirements of software components, the resource constraints of the hardware that will be the target platform for the product software, as well as the virtual runtime system that could be used as a basis to analyze the schedulability of each task. In addition, a number of software modules (also called interpreters) implementing the algorithms of functional model reuse, component assignment, task forming, priority assignment and timing analysis were integrated into the modeling environment configured by the PMOLACS paradigm.

2 Modeling Requirements Related to Timing and Resource In order to model the performance of automotive embedded control software, two main factors must be taken into consideration, i.e., timing and resource. Since most automotive embedded control systems (especially safety–critical system) are hard real-time systems, which means the completion of each task must meet its deadline, otherwise disastrous accident may occurs. On the other hand, the hardware resource of embedded controller is usually limited for the purpose of cost reduction. Therefore, the timing properties, resource requirements, and resource constraints must be described by the PMOLACS paradigm.

3 Performance Modeling for Automotive Embedded Control System 3.1 Modeling Approach In this study, the automotive embedded control domain specific performance modeling language PMOLACS has been defined by a UML-based meta metamodel which defines a set of generic meta-modeling concepts including Folders, Models, Atoms, References, Connections, Sets, etc. [3]. These generic modeling concepts have been used to define the PMOLACS paradigm which is specified by a set of meta-models that can be further used to configure the modeling environment for automotive embedded control systems. In other words, we use the UML-based generic meta meta-model to define the meta-models of PMOLACS language, and the latter is then used to configure the generic modeling environment, and finally the configured modeling environment can be used to construct the models of automotive embedded control systems.

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3.2 Performance Modeling for Automotive Embedded Control System PMOLACS paradigm defines three meta-models for automotive embedded control system, i.e., (1) the software structure (SWS) meta-model that defines the modeling paradigm for software components, interaction between software components, timing properties and resource requirements of software components, (2) the target platform (TP) meta-model that defines the modeling paradigm for the constituent of hardware environment and real-time operating system (RTOS), timing features, and resource constraints of the target system, and (3) the runtime system (RTS) meta-model. As specified in the SWS meta-model, the software system consists of a number of sub-systems that further consist of a number of software components. The execution time, priority, and required memory are captured by the attributes of the modeling element of software component. While the system deadline and execution period are captured by the attributes of the modeling element of sub-system. And the connection between sub-systems, between software components, or between subsystem and software component are described by association classes that can be divided into data connection and event connection, which have attributes describing the size of data passed and the size of data communicated, respectively. As specified in the TP meta-model, the target platform system consists of a number of real-time operating systems (RTOS), central processing units (CPU) and networks which are further classified into CAN, LIN and FlexRay. Each CPU only has a unique RTOS associated with it. The hardware resource constraints such as the maximum memory, the minimum size of assignable memory, and the upper bound of utilization are captured by the attributes of the modeling element of CPU, which the speed (baud rate) and utilization bound of network are captured by the attributes of the modeling element of CAN, LIN and FlexRay. And the timing features such as the context switching overhead, scheduling overhead, timer overhead, timer resolution, etc., are described by the attributes of the modeling element of RTOS. As specified in the RTS meta-model, the runtime system consists of a number of logical tasks that are used to group a number of tasks together, and the task further consists of a number of actions which are corresponding to the software components defined in the SWS meta-model. The execution time and required memory of software component are captured by the attributes of the modeling element of action. And the scheduling policy that may be preemptive, non-preemptive, or mix-preemptive (a policy defined by OSEK specification [4]), the response delay, the deadline, and priority of each task are described by the attributes of the modeling element of task. In addition, the logical task may have one or more triggers (corresponding to timer) which are used to periodically invocating tasks. The timing properties such as deadline and minimum period are captured by the attributes of the modeling element of timer.

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4 Integration Algorithms with PMOLACS Towards Timing Analysis 4.1 Functional Model Importation After the PMOLACS paradigm is defined, it is used to configure the generic modeling environment to build automotive domain specific performance modeling environment (PMOLACS modeling environment). The modeler can either build the SWS model for a specific automotive embedded control application and input performance parameters manually or reuse the existing functional model built by Simulink and add performance parameters automatically. To reuse the existing Simulink model, a software module has been developed, which translates the atomic level functions of Simulink model into an equivalent model by replacing the mutex, busses, and goto blocks in Simulink with their equivalent connections in PMOLACS. The timing constraints and resource requirements such as required memory, execution time, and execution rate for each function is also input simultaneously. And the models with a number of hierarchical levels in Simulink are translated into a flatten model in PMOLACS.

4.2 Component Assignment Once the SWS model is built either manually or automatically, the modeler could construct the TP model using TP meta-model to define the architecture of the target platform, such as how many processors will be used for the specific application, what kinds of networks will be used for each processor, and what kind of RTOS will be worked on each processor. During the process of target platform modeling, the modeler also needs to define the parameters of resource constraints and timing features manually in the PMOLACS modeling environment. A software module has been developed to implement software component assignment based on the built the SWS model and the TP model, which maps each software component in the SWS model to one of the processors defined in the TP model on condition that the resource constraints can all be satisfied. Two different algorithms have been implemented in the component assignment module: one is load balancing that tries to balance the loads of different processors, the other is communication minimizing that tries to minimize the amount of communication across different processors.

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4.3 Task Forming Regarding grouping the software components together to form RTOS tasks, there exist conflicting strategies. If the task contains many components, the overhead of context switches will be reduced. If the task contains few components, the overall response time may be reduced. In the implementation of task forming in this investigation, the components having same execution rate, being assigned on a same processor, and not forming dependency loop are grouped together to form a task, for the purpose of reducing the overhead of context switches.

4.4 Priority Assignment Three different algorithms have been implemented to assign the priorities for each task including: (1) the deadline-monotonic (DM) policy that assigns the task with the shortest deadline the highest priority [5], (2) the rate-monotonic (RM) policy that the task with the shortest cycle duration the highest priority, and (3) a combined policy that first assigns priorities according to the RM policy and then the DM policy is used to assign priorities to the tasks that have the same priority assigned by the RM policy. In addition, the modeler still can define the priority for each task in the PMOLACS modeling environment manually. Once all tasks have priorities assigned, the process of transforming the SWS model into the RTS model is completed and then the timing analysis can be performed based on the resulting RTS model to determine if each task meets its timing constraint.

4.5 Timing Analysis The timing analysis algorithm consists of the following three main steps: (1) constructing task timing graph according to the priority, scheduling policy, and interactive relation of the task; (2) calculating the response time for each task, with regard to a specific task, this is done by summing up the response time of the direct predecessor of that task, the execution time of all tasks that preempt that task, the execution time of that task, the overhead used by task scheduling and context switching from the commencement to the completion of that task, via traversing all of the input concurrent links of that task on the task timing graph; (3) evaluating the schedulability for each task, if the response time of each task is not greater than the deadline of that task, the RTS is schedulable, otherwise, the design of the specific automotive control software needs to be refined such as re-assigning priority, modifying the architecture of target platform, or adjusting component mapping algorithms.

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4.6 Algorithms Integration The algorithms of functional model importation, component assignment, task forming, priority assignment, and timing analysis have been developed and implemented using Visual C++. The software modules corresponding to these algorithms have been built as dynamic link library and then registered as components in the PMOLACS modeling environment to work together with the PMOLACS paradigm.

5 Conclusions and Future Work Aiming at performance modeling and timing analysis for automotive embedded control software at an early design stage, a performance modeling language PMOLACS is constructed using meta-modeling technique via the generic modeling environment, which is further used to configure an automotive embedded control domain specific modeling environment. With the implementation of a number of algorithms related to model reuse, model transformation, and timing analysis, the resulting PMOLACS modeling environment can be integrated into the current mainstream development tool chain for automotive embedded control system as an performance modeling, timing analysis as well as design automation or recommendation tool. As part of our future work, integration of the output of RTS model with the source code generated from Simulink model in compliance with the state-of-theart standards such as AUTOSAR still needs to be further investigated. Acknowledgments The work reported in this paper was supported in part by the National Natural Science Foundation of China (Grant No. 60970072), the Scientific Research Fund of Sichuan Provincial Education Department (Grant No. 10ZA100, KYTD201003), and the Research Fund of Key Laboratory of Automotive Engineering of Sichuan Province (Grant No. SGXZD9902-10-1).

References 1. Bunzel S (2010) Overview on AUTOSAR cooperation. In: 2nd AUTOSAR open conference, Tokyo, Japan, May 13 2. Mathworks Website: http://www.mathworks.com 3. Ledeczi A, Maroti M, Bakay A, Karsai G, Garrett J, Thomason C, Nordstrom G, Sprinkle J, Volgyesi P (2001) The generic modeling environment. In: IEEE international workshop on intelligent signal processing (WISP’2001) 4. OSEK/VDX. OS 2.2.3. 2005. http://www.osek-vdx.org 5. Burns A, Wellings A (2001) Real-time systems and programming languages, 3rd edn. Addison Wesley, New Jersey

Knowledge Based Engineering to Support Automotive Conceptual Design and Automatic Control Software Development Fengnian Tian and Mark Voskuijl



Keywords Multi-model generator Knowledge based engineering integration Conceptual design Electric vehicle





 E/E system

1 Background The global motor vehicle production is rising steadily year by year. These vehicles have an increasing amount of electronic components and associated control software. As a result, the control software development becomes a key aspect and time consuming part of the design. In contrast to the rising production numbers, the number of safety recalls is in fact decreasing steadily [1]. However, it should be noticed that the number of vehicles being recalled because of failures in the electronics, either software or hardware, was in fact increasing significantly in the period 2000–2010, which is shown in Fig. 1. To some degree, the increasing electronic malfunctions on automobiles can be explained by the fact that the development processes of vehicle and the development process of the related Electric and Electronic Systems (E/E systems) is not integrated. In other words, because almost all functions on the vehicles are electronically controlled nowadays, the complexity of overall E/E systems rise sharply along with the increasing number of vehicle variants. The established vehicle development processes which toward to efficiently create high quality mechanical systems cannot deal with the problem of high complexity of the E/E systems [3]. Moreover, the analysis and specification for the architecture of the logical system, the technical system and the software itself includes many repetitive processes in conventional

F2012-D03-005 F. Tian (&)  M. Voskuijl Delft University of Technology, Delft, The Netherlands e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_37,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 Comparison of the global vehicle production, the potential number of vehicles recalled and the number of units affected due to electronics [1, 2]

automotive software development [4]. Those repetitive processes are time consuming and are prone to errors. System engineering is introduced by the Original Equipment Manufacturers (OEMs) as a sustainable approach to the development of reliable E/E systems. Nevertheless, although it is well known that the logical system architectures, technical system architectures as well as software functions of the vehicle E/E systems should be integrated into the complete vehicle development [3], a number of issues have still to be solved in order to release the full system engineering potential. Firstly, Computer Aided Design (CAD) tools, which are widely used by the conventional mechanical dominated vehicle development processes, are not suitable for developing the logical and technical system architecture of the E/E systems. For example, the logical system architecture is usually described by graphical notations, such as block diagrams and state machines [4], which is not available in the CAD tools. Moreover, after the specification of the logical system architecture, the technical system architecture must consider all constraints of a technical and economic nature, as well as those concerned with organizational structure and manufacturing technology [4]. Only a few of those constraints are related to about geometry. Most of them are engineering disciplines, which are difficult to capture by CAD software. Secondly, the software had to be tuned several times due to any changes from the logical or technical system architecture, which is also difficultly supported by the CAD tools. Usually, the inputs of software functions are parameters and variables which describe the physical nature of a component or an assembly. For instance, it is well known that the axle load distribution has effects on the handing stability, Anti-

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lock Braking System (ABS), etc. Nevertheless, such information cannot be read directly from the geometry model but requires computational analysis. Therefore, the objective of the current research study is the development of new design methods and tools that allows the designer to take the development of the E/E systems including logical system architecture, technical system architecture and control software, into account already in the conceptual design stage of novel vehicles. The aim of these new methods and tools is to reduce the development time of these novel more electric vehicles and to create more consistent control software. The proposed design methods and tools can in principle be applied to any dynamic system with a high level of software integration, such as e.g., unmanned aerial vehicles.

2 Methodology The methodology used in this research is based on the Design and Engineering Engine (DEE) concept, which has been developed by La Rocca [5]. The DEE is a modular computational design system to support distributed multidisciplinary design and optimization. It can support the design process of complex products and accelerate the process through the automation of non-creative and repetitive design activates [6]. Previously, the DEE is developed mainly for aircraft design. In this research, we expand the border of DEE to the automotive conceptual design, including E/E systems. A schematic representation of the proposed DEE is shown in Fig. 2. As can be seen, the DEE is composed of several software modules. The first module is the Initiator, which is responsible for the initialization of the values of input parameters for the subsequent modules, such as wheelbase, track, weight, engine type, etc. It can work both in knowledge mode or custom mode. Under the knowledge mode, the user inputs general product planning, like vehicle type or number of passengers, and then the Initiator searches the design database to find similar existing designs, and produces the parameters automatically. The user could also directly select available assemblies from the component database to build a prototype vehicle under the custom mode. The next element of the system, named Multi-Model Generator (MMG) is the heart of the DEE. The MMG is a Knowledge Based Engineering (KBE) application, which is able to model different automobile configurations and configurations’ variants. Besides modeling high fidelity geometric models, the MMG can also output specific data for various analysis tools directly and automatically. For example, the MMG can generate the complex surface model of the car body, and then translates that model into clouds of points or panels which are required by the aerodynamic analysis tool. The model abstractions produced by the MMG are analyzed by corresponding analysis tools. If the results meet the requirements in each disciplinary area, the process is stopped and the output is a final design for the detailed design stage; otherwise, the DEE will generate a new vehicle configuration or variant and perform iteration again. An optimization process can be included in the iteration.

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Fig. 2 Overview of the DEE

However, that is not the focus of the current study. It should also be noticed that there is an agent based communication model [5] connecting the MMG and the multidisciplinary analysis tools in Fig. 2. The communication model not only exchanges the data and information between various DEE models, but also controls the overall process. In this paper, a communication model is established between the MMG and MATLAB for the purposes of dynamic and control system simulation, which is shown in test case section. The communication model makes it possible to have elements of the DEE on different computers, which can be located even in different companies/institutes. Thus it also allows collaborative design efforts. The DEE for automotive conceptual design is developed in the Genworks’ General-Purpose, Declarative, Language (GDL), which is based on the ANSI

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Fig. 3 Correspondence between source code and web-centric user interface for the MMG

standard version of Common LISP. GDL is particularly effective at representing complex systems, including three-dimensional geometric models and design process [7].

3 Integration Automotive E/E Systems Development into Vehicle Conceptual Design 3.1 A Brief Introduction of MMG for Automotive Conceptual Design A Multi-Model Generator (MMG) for automotive conceptual design has been developed in this work. Besides complex geometry modeling, the MMG is able to capture the methods, process as well as various engineering disciplines. It can not only provide various models for multidisciplinary analysis but also support the automation of repetitive processes. In general, the MMG which is a tree structure is composed of a component library and a rule base. All the components necessary to complete a vehicle conceptual design are defined as objects in the component library. Every object is a parametric model with required inputs. The user can specify those inputs to get an object variant. Objects can be fitted together with designed methods to create a component. Several components can be put together to generate an assembly. For example, as one can see in Fig. 3, the manikin template included in the accessories (indicated

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by red arrow) is an assembly of head, torso and leg. The head contour is a component. The user can specify the length of the X, Y, and Z axes of the head contour in the custom input interface. The position and the reclined angle of the head contour are defined by the attributes: center and :orientation (marked by purple box) relative to the vehicle grid. The rule base is designed to contain the requirements from various scientific areas. Those requirements are abstracted as one or a combination of the rules in terms of logic, math, geometry, configuration selection and communication. As mentioned before, besides geometry objects, there are also several rules integrated in the manikin template, such as hip angle check (pointed by green arrow). If we change the back angle A40 (displayed as manikin-1st-A40 in the red dash line box) from -25 to -27, the ‘angle’ in the green dash line will increase from about 98.57 over 100 immediately, which exceeds the range of recommended comfort hip angle from 86 to 100 [8]. The value of ‘violated?’ will change from current ‘nil’ to ‘t’ automatically in the interface, which means this rule is violated. A part of the source code of the MMG and corresponding web-centric user interface is shown in Fig. 3. The GENWORKS GDL also provides the bottom layer connections from the source code compiler to the web-centric user interface. Such an interface can be loaded in any internet browser and keep updating with the compiler. As a KBE application, components and rules in the MMG are tightly integrated. If an input to the component is changed in the user interface, any rules and components which directly or indirectly depend on that input will automatically re-evaluate themselves and show results in the interface immediately. Rules and objects which are not affected by a modification will avoid the re-evaluation [9].

3.2 Integration Logical and Technical System Architecture of E/E Systems into the MMG In general, the logical system architecture determines the performance the system will deliver but avoid the specific manner of its implementation [4]. In this study, the logical system architecture is summarized as a design discipline and included into the rule base of the MMG. Similarly, the physical parts of the technical system architecture are modeled as combination of objects included in the component library. The various constraints which have to be fulfilled by the technical system architecture are also defined as disciplines comprised by the rule base of the MMG.

3.3 MMG Supports for Software Function Development Besides geometry, the MMG can also output specific data for various analysis tools, which is capable of supporting the validation of software functions. The

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MMG can save the properties of the objects or pre-processing results in a common data format such as XML or ASCII, which can be used to set the values of parameters and variables needed by the software functions. For example, once a modification happens on the main assemblies, the MMG will re-calculate the axle load distribution immediately and send the data to the software functions. Then the results from the software validation could feedback to the MMG again to check whether some rules are violated. With the communication model of the DEE, such processes can be automatically finished repeatedly, which is suitable for getting the optimized solutions.

4 Test Case 4.1 A Novel Electric Vehicle Configuration It is well known that the driving range of electric vehicles is always a critical technical specification for the designer, car manufacture and consumer. Nowadays, the driving range is limited by the battery pack equipped on the vehicle. If the aerodynamic drag and the curb weight can be lowered simultaneously, the driving range can be increased significantly, especially at high speeds. In the current research study, a novel electric vehicle configuration named A-line, where passengers are seated in line, is proposed. Compared with a conventional vehicle configuration, the advantages of this novel configuration are threefold. First of all, the vehicle will have a significantly lower aerodynamic drag due to a small frontal area. Second, the curb weight can be decreased because it is designed for two or three person seated in line. Finally, it will require a much smaller parking space because of its narrow track. In principle, several A-line vehicles can drive on the road side by side at the same time, which has the potential to alleviate traffic congestion. Besides all the advantages mentioned above, the vehicle is an excellent test-case for the novel design system proposed in this research study because; (1) it requires the integrated design of a novel E/E system, and (2) the concept is new, so one cannot rely on existing designs. Two A-line models have been generated by the MMG, which is shown in Fig. 4. Their technical specifications are list in Table 1. It can be seen that both models have a smaller frontal area and curb weight than conventional configurations.

4.2 A Communication Model of the DEE In order to validate handing stability and range of the A-line models above, a communication model has been established in this paper, as depicted in Fig. 5. Firstly, the MMG generate a xxx.csv file which includes the all the values of the

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Fig. 4 The geometry of A-line generated by the MMG (1—electronic control unit (ECU), 2-DC–DC—converter, 3—battery pack, 4–charger, 5—cargo, 6—seat assembly, 7—passenger manikin template, 8—driver manikin template, 9—steer wheel, 10—electric motor) Table 1 Specifications of the A-line vehicles with two or three passengers A-line Model Two passengers Three passengers Dimensions

Weight

Wheelbase [mm] Track F/R [mm] Overall length/width/ height [mm] Ground clearance [mm] Frontal area ½m2 Š Curb weight [kg] Axle load distribution

Performance Pemax [kW] Temax [Nm] Vmax [km/h] Battery Type Number of cells Nominal capacity [Ah] Nominal voltage [V] Mass [kg] Capacities Seat capacity Cargo volume [L] Accessories Tire

2,400 1,050 3,250/1,250/1,455

2,700 1,050 3,700/1,250/1,485

150 1.54 855 59 % (empty) 52 % (full) 80 220 120 LiFePO4 Lithium 80 70 3.2 200 2 220 205/55 R16

150 1.57 1,110 60 % (empty) 48 % (full) 100 240 120 LiFePO4 Lithium 80 70 3.2 200 3 200 (rear seat up) 205/55 R16

parameters and variables required by MATLAB simulation. Then a server is opened in MATLAB, establishing a local host for the MMG (Our server is established based on a common lisp interface to MATLAB [10]). At this moment, MATLAB commands can be written in the source code compiler of the MMG to

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Fig. 5 Communication model between the MMG and MATLAB

control the objects in MATLAB, such as an M-file or a SimMechanics (multibody dynamics) model. After the execution of the M-files or MATLAB simulations, the results can be saved in any structure data type and read by the MMG. In the next step, the rule base will be re-evaluated again to check whether some rules are violated. If all the rules are satisfied, the whole process is finished. Otherwise, the MMG can start a new iteration.

4.3 Validation of Handing Stability Because of the narrow front and rear track as well as a short wheelbase, the handing stability of the A-line vehicles needs validation. Usually, the roll angle of vehicle body should be around 3 degrees, no more than 7 degrees when the vehicle is turning at 0.4 g centripetal acceleration with a constant speed and turning radius. Furthermore, the pitch angle should be smaller than 3 degrees when the vehicle is braking at 0.4 g [11]. Therefore, those two rules are set as requirements included in the rule base of the MMG. We build a dynamic model of the A-line in SimMechanics of the MATLAB. Through the communication model in Fig. 5, all the values of parameters and variables needed by the dynamic simulations are provided by the MMG according to Table 1.

4.3.1 Roll Angle Validation The initial condition of the roll angle validation simulation is the equilibrium position at standstill. After one second, the vehicle is accelerated and a turn is

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Fig. 6 Roll angle inspection for A-line vehicles

initiated until the desired condition (velocity 6 m/s and turn radius 10 m) is obtained. The resulting roll angle simulation for the A-line vehicles, with different loading conditions is shown in Fig. 6. It can be seen that both A-line configurations comply with the roll angle requirement for all loading or empty conditions.

4.3.2 Pitch Angle Validation The validation of the pitch angle requirement is related to the vehicle braking. In order to show the integration of the E/E systems and the MMG, an ABS module is built both in the MMG and SimMechanics. Because the focus of the current study is the integration of the two systems, the controller of the ABS module in the SimMechanics is simply set up based on the error between actual slip and desired slip. As described in Sect. 4.1, first, the logical system architecture of the ABS is defined as several rules in the MMG, like maximum braking distance and pitch angle of vehicle body. Second, the physical plant representations of the technical system architecture of the ABS are generated by the MMG, which includes wheel brakes, hydraulic modulator, sensors, etc. Third, together with the entire parameters list in Table 1, some physical plant attributes needed for the tuning of software functions are also transferred from the MMG to the SimMechanical plant through the communication model. For example, as one can see in Fig. 7, the pressure of hydraulic modulator set in the web-centric user interface is sent to the model in MATLAB by the .csv file. The dynamic model starts to brake at 2 s with the initial speed 100 km/h. The results of the ABS simulations are shown in Fig. 8. Similar to the roll angle validation, all the configurations of the A-line vehicles satisfy to the pitch angle requirements.

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Fig. 7 The ABS module in the MMG

Fig. 8 Pitch angle inspection for A-line vehicles

4.4 Validation of Range The main advantage of the A-line configuration is its large driving range. In order to demonstrate this, the driving range of the A-line vehicles is compared with the General Motors EV1, whose batteries were rated at 60 amp-hours (18.7 kWh) at 312 V [12]. To give a fair comparison with current batteries for which data is available, we use 70 amp-hour (17.9 kWh) Li batteries at 256 V. The range calculation method is based on the work of Larminie [13]. Both EV1 and A-line vehicles are tested in a simplified federal urban driving scenario. It is supposed that most electronic accessories are switched on, such as headlights, radio and heater.

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Fig. 9 A comparison of the distance travelled between the A-line vehicles and General Motors EV1

The drag coefficient is estimated 0.33 for A-line vehicles. Both two A-line models above are compared with the General Motors EV1, which is shown in Fig. 9. It is apparent that when 80 % discharge is reached, the A-line model with two passengers has already travelled for 169.4 km, followed by the three passengers’ model (155.9 km). The General Motors EV1 reaches 121.3 km, which is in agreement with the official driving range published for the EV1 [11].Thus, a 28 % driving range improvement is obtained by a change in the vehicle configuration whilst keeping other design parameters such as the type of batteries, motor, etc. identical.

5 Conclusion The concept of DEE for automotive conceptual design has been proposed in this paper. The MMG which is a KBE application has been developed in this work. It has been proven that the MMG can generate models both for conventional and novel electric vehicle configurations and variants. Moreover, the MMG is able to support the development of automotive E/E systems from the logical, technical system architectures to the software functions, integrating the E/E systems into the automotive conceptual design. Finally, a novel electric vehicle configuration named A-line has been tested for handing stability and range. It has been validated that the A-line can drive much longer range than conventional electric vehicles.

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References 1. OICA (2011) World Motor Vehicle Production[EB/OL]. [2012-02-01]. http://oica.net/wpcontent/uploads/total-2011.pdf 2. safercar.gov (2011) Flat File Copies of NHTSA/ODI Databases[EB/OL]. [2012-02-01]. http://www-odi.nhtsa.dot.gov/downloads/. 3. Weber J (2009) Automotive development processes. Springer, Berlin, p 64 4. Schäuffele J, Zurawka T (2005) Automotive software engineering. SAE International, Warrendale, p 167 5. La Rocca G, van Tooren MJL (2007) Enabling distributed multi-disciplinary design of complex products: a knowledge based engineering approach. Design Res 5(3):341 6. van Tooren MJL, Nawijn M, Berends J et al (2005) Aircraft design support using knowledge engineering and optimisation techniques. In: 46th AIAA/ASME/ASCE/AHS/ASC Structures, structural dynamics and materials conference, Austin, Texas 7. GENWORKS. Genworks International[EB/OL]. [2011-09-20]. http://www.genworks.com 8. Fenton J (1998) Handbook of automotive body and systems design. Redwood Books, Great Britain, p 41 9. Cooper J (2004) Knowledge base techniques in web applications: a tutorial for genworks’ GDL/GWL, p 46 10. Fenton J (1998) Handbook of automotive body and systems design. Wiley, London, p 41 11. Liu YX (2001) Automotive design. Tsinghua University Press, Beijing, p 40 12. Wikipedia. General Motors EV1[EB/OL]. [2011-09-01]. http://en.wikipedia.org/wiki/ General_Motors_EV1 13. Larminie J, Lowry J (2003) Electric vehicle technology explained. Wiley, West Sussex, pp 203–204

Development of an AUTOSAR Software Component Based on the V-Model Dieter Nazareth and Robert Siwy

Abstract The number of software supported systems in vehicles is constantly growing. All carmakers have more or less problems to handle the high number of software functions. Because, traditionally, each function is implemented by a separate control unit, also the number of control units has reached a tremendous level. The key points to master this situation are reusing und integration of functions. The AUTOSAR standard supports both approaches by defining standardized interfaces for software components. This chapter describes the approach to develop software components along the well-known V-Model. All process phases, from the analysis phase to the test phase of the function oriented development process are shown. Furthermore, all tools supporting the different phases are demonstrated. Keywords Automotive software engineering Function oriented development

 AUTOSAR  V-Model  Reuse 

1 Introduction The past decade has seen a dramatic increase in the number of electronic invehicle functions. This increase mainly occurred because of new safety and driver assistance functions. Also the introduction of hybrid power trains led to a bunch of F2012-D03-009 D. Nazareth Landshut University, Landshut, Germany R. Siwy (&) BMW Group, Munich, Germany e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_38,  Springer-Verlag Berlin Heidelberg 2013

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new functions, e.g., battery management function. As a consequence, the number of control units of a modern car has reached a dimension which can be hardly managed anymore. To stop this growth of control units today’s automakers need to integrate a multitude of electronic functions into one control unit. Integration, however, means that we shift the complexity from the network level to the control unit level. The former simple relationship, that one control unit implements one driver function is not valid anymore. Nowadays one control unit has to host several unrelated functions and the operating system has to ensure that all functions are executed at the right time and without interference. Besides integration of functions also the reuse of functions is getting more and more important. Many functions can be used across several model series. This does not only save development time and costs, but also makes the function more mature and reliable. However, reusing a function does not mean to reuse the control unit. The same function can be used in different control units from different suppliers. And it has to be ensured that individual functions, perceived by the driver, remain unchanged in their behaviour across several model series.

2 AUTOSAR AUTOSAR is a partnership of the automotive industry with the goal to standardize the development of automotive software [1, 2]. One goal of the partnership is the increased scalability and flexibility to integrate, transfer and reuse functions. This is achieved by the new AUTOSAR architecture shown in Fig. 1. In the center is the Runtime Environment (RTE) separating the basic software from the application software. The application software consists of Software Components (SWCs) with a well-defined communication interface. Any communication to the outside can only be made via this interface. This ensures the transferability and reusability of the components. However, it is essential that a user function is decomposed into a number of software components in the right way to achieve that goal.

3 From ECU Oriented Development to Function Oriented Development To handle the never ending increase of electronic functions in a vehicle on the one hand it is essential to reuse software for as many car models as possible. Software, however, can only be reused if it is disembedded from the hardware and it has a welldefined interface. On the other hand, to handle future vehicle networks the still growing number of control units has to be stopped. The only way to achieve this is to integrate more functions into one physical control unit. Hence, we have to shift our

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Fig. 1 AUTOSAR architecture

methodology from hardware oriented development to function oriented development. The AUTOSAR framework was developed to support this new methodology. However, AUTOSAR does not care about the behaviour of a software component, i.e., the functionality inside the component. Here a model based development using ASCET [3] from ETAS is performed. Furthermore the whole development had to be embedded in the V-Model development process used at BMW. The combination of AUTOSAR and model based development is very successful in the body area. Here we have many matured functions that can be reused across different vehicle platforms, like e.g., the rear view mirror function or the central locking function. Furthermore, in the body area most functions are distributed over several control units. At BMW all those models are nowadays managed in a central database and reused for many platforms [4]. The AUTOSAR software components are then given to the different suppliers who have to integrate them into different control units. Because the behaviour was developed in model based way it is easy to implement the components on different hardware platforms. The reuse of software components does not only save money. It also increases the quality of the functions because in software development matured software means that all problems already have been detected and eliminated. The reuse of functions is very effective if the function is quite stable and matured. For new and innovative functions that will change quickly in the future, the aspect of reuse is not so important. However, in this case the possibility to

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Fig. 2 V-Model

separate the hardware independent functionality from the hardware dependent enables the OEM to focus on the customer relevant development and to have control over his intellectual property.

4 Development Process of AUTOSAR Software Components Along the V-Model The V-Model is a well-known and relatively simple process model for the development of software. In this chapter we take the classical V-Model shown in Fig. 2 and adopt it the needs of AUTOSAR. In the next sections the four phases are explained in more detail with their input and output artefacts, and the tools used to create the artefacts.

4.1 Analysis In the analysis phase (see Fig. 3) usually the requirements are specified, i.e., what the function should do. This is now done in the context of software development. Here we have two documents as input. One is the functional requirement specification from the driver’s point of view. The other one is the requirement specification for the intended control unit of that function. This document, e.g., contains requirements for the sensors and actuators. In the analysis we extract all functionalities that can be developed independently of the hardware and reused for further car series. Usually this is the functionality that can be experienced by the driver. It is important that those customer relevant parts of the function are designed and implemented by BMW. In this phase the interface of this functionality is already specified in a logical way by the signals going in and out. Here the tool Doors [5] (see screen shot in Fig. 4) is used.

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Fig. 3 Analysis phase

Fig. 4 Doors screen shot

4.2 Design Based on the informal specification in the requirement specification, in the design phase the software components with their interfaces have to be defined (see Fig. 5). Usually the whole customer relevant functionality of a body function is modeled by one AUTOSAR atomic software component only. The decomposition into subfunctions is not handled in the AUTOSAR approach, but later in the implementation phase using modules. The result is written to some ARXML file called Software Component Description. This is an AUTOSAR standardized file based on an XML file format. In general an XML file can be written by a simple text editor. However, it is much more convenient to use an AUTOSAR authoring tool. Such a tool helps to stick to the fixed syntax and supports the user by offering plausibility checks. At BMW the tool Vehicle Systems Architect (VSA) [6] from Mentor Graphics is used as an authoring tool. Figure 6 shows a screen shot of the VSA Editor. In the design phase the incoming and outgoing signals specified in the requirement specification are formally defined and grouped together to so-called AUTOSAR interfaces.

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Fig. 5 Design phase

In general, AUTOSAR offers two kinds of communication interfaces: • Sender/Receiver Interface. This interface offers an asynchronous communication with other components. The sender does not know the number of receivers and does not get any receipt. It is quite similar what usually is used on the CAN bus. • Client/Server Interface. This can be compared to a remote function call. Usually the call is synchronous, i.e., the client waits until the server executes the service. Besides the communication interfaces also the interfaces to the ‘‘internal behaviors’’ are specified in this phase. These interfaces are called runnables in the AUTOSAR approach. Behind the runnables are the behaviors which are later in the implementation phase specified in a model based way. A software component can have many runnables. The decision which behavior is modeled by which runnable is an essential decision in this phase.

4.3 Implementation In the implementation phase (see Fig. 7) the internal behavior of the software component is implemented. This could be done by programming it in C with the help of a traditional development tool. Nowadays, however, usually a model based development is used in this step. Then the software component description has to be imported into the designated modeling tool. In our development process we are using the ASCET Tool family [7] which offers an AUTOSAR Importer. This one extracts all necessary information, i.e., the software component with its interfaces and runnables and makes it available in the model editor. Now, in the model editor the behavior described informally in the requirements specification can be implemented by appropriate graphical descriptions, like e.g., data flow diagrams or finite state machines. Figure 8 shows a screen shot of an ASCET model with two AUTOSAR interfaces on the left hand side. With ASCET-SE it is also possible to generate C-Code out of the model which fits the needs of an AUTOSAR software component. However, the code comes without the declaration of the RTE access functions because the prototypes of these functions are included in the RTE code, which is a part of the integrating control unit. As a consequence, it is not possible to build an executable program for one software component. The further processing of the source code, i.e.,

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Fig. 6 AUTOSAR editor

Fig. 7 Implementation Phase

compilation of the source code and linking of the object files is then done with a conventional development environment.

4.4 Test In the test phase (see Fig. 9) the software component has to be verified against the requirement specification. Testing is done via the AUTOSAR interfaces of the component.

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Fig. 8 ASCET screen shot

Fig. 9 Test phase

In a first step test cases have to be implemented which have to stimulate and monitor the AUTOSAR inputs and outputs. These test cases have to be created according the requirement specification. The test cases have to stimulate the inputs of the software component and check the outputs against given values. As the software component is tested according to its requirement specification without considering its internal structures this is a typical black box test.

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Fig. 10 TcEd screen shot

Fig. 11 Messina screen shot

The test cases can be written with cUnit [8] for example. A more traceable method instead is according to [9] to write the test cases with a special BMW internal editor called TcEd in an XML format. The advantage of this editor is that it is able to parse the software component description file and provide all interface signals. Then the stimulation and monitoring of the interfaces can be easily described with this editor. A screen shot of this editor can be seen in Fig. 10. To execute the test cases, the test object has to be compiled and linked. But as mentioned before, the C code of an AUTOSAR software component does not contain the RTE access function definitions because the definitions are done in the RTE. So for compiling the software component, all RTE access functions must be implemented. Therefore, the compilation of the software component is done in a special AUTOSAR test environment like Messina [10] or AUTOSAR Builder [11]. These test tools generate a so-called Single Sided RTE which contains the RTE access functions on the one side to connect to the software component and conventional set/get-functions on the other side to facilitate the write and read accesses to the AUTOSAR interface. In Fig. 11 you can see a screen shot of a test run in Messina.

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As the connection to software components can only be done thru the described interfaces, it is not possible to monitor signals from the inside of the software component. Therefore it is much more effort for performing white box tests by using a mixture of a self-implemented RTE and a conventional test framework like Unit.

5 Conclusion The growing complexity of vehicle software development can only be managed by modern software engineering techniques. The AUTOSAR methodology together with model based development is a proven combination at BMW to develop body functions. In this paper the integration of this combination into the well-known V-Model was shown. Each process step was explained in detail with all artifacts. AUTOSAR has standardized the development of automotive software and supports to disembedded the software from the hardware by offering software components with well-defined interfaces. Separating the hardware independent parts of a function from the hardware dependent helps to reuse software over many vehicle platforms. Reusing software components helps to increase software quality despite of the growing complexity of today’s car functions. The shift from control unit oriented development to function oriented development is the future for vehicle network development. Especially in the body area where reuse and distribution is more and more important this will decrease the development costs and increase the quality of the functions.

References 1. AUTOSAR: www.autosar.org/ 2. Kindel Olaf, Friedrich Mario (2009) Softwareentwicklung mit AUTOSAR—Grundlagen, Engineering, Management in der Praxis, dpunkt.verlag 3. Escherle Thomas (2011) Development of an AUTOSAR Software Component for a Convertible Top Control Based on a V-Model. Master Thesis, Landshut University 4. Siwy Robert (2005) Unterstützung der modellbasierten Funktionsentwicklung durch die Modellbibliothek, 25. Tagung Elektronik im Kraftfahrzeug, Tagungsband, München, ISBN 3-8169-2499-9 5. DOORS: www.ibm.com/software/awdtools/doors/ 6. Vehicle Systems Architect: http://www.mentor.com/products/vnd/autosar-products/volcanosystem-architect 7. ETAS GmbH: White Paper ASCET-SD, Stuttgart 2002 8. CUnit: cunit.sourceforge.net 9. Schiele Peter, Siwy Robert (2008) Test und Absicherung von Softwaremodulen mit AUTOSAR konformen Schnittstellen in der Modellothek, Stuttgart 10. MESSINA: www.berner-mattner.com/en/berner-mattner-home/products/messina/ index.html 11. AUTOSAR Builder: www.3ds.com/de/products/catia/portfolio/geensoft/geensoft-productlines/autosar-builder

MDG1: The New, Scalable, and Powerful ECU Platform from Bosch Johannes-Joerg Rueger, Alexander Wernet, Hasan-Ferit Kececi and Thomas Thiel

Abstract Bosch is developing an all new, scalable, and powerful platform of electronic control units (ECU) for powertrain control; launch date will be end of 2014. With this, Bosch will introduce a new powerful microcontroller generation from three different suppliers with multi-core technology which will fulfill future demands with regard to computational power. Besides this and several other innovations in hardware, Bosch will introduce a fully Autosar 4.0 compliant base software and step-by-step an Autosar 4.0 compliant application software. Our customers will have a chance to realize a seamless transition to the new ECU platform and introduce Autosar on individual timescales. Other functional enhancements will, for example, be in the field of vehicle-wide energy management. With MDG1 not only innovations in hardware and software will be introduced but in the area of processes as well. We will introduce IT standards in order to improve efficiency particularly with the integration of customer software (software sharing) and model-based development for the application software. This will bring the development efficiency—particularly for the cooperation with our customers—to a new level. With the new MDG1 Bosch will set a new standard in the market.



Keywords Electronic control units Dual core microcontrollers Model based development Energy management



 AUTOSAR 

F2012-D03-011 Powertrain Control Unit Platform MDG1. J.-J. Rueger (&)  A. Wernet  H.-F. Kececi  T. Thiel Robert Bosch GmbH, Diesel Gasoline Systems, Electronic Controls, Gerlingen, Germany e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_39,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 MDG1 is the new scalable ECU platform for all powertrain applications. MDG1 is the unified successor of former Bosch ECU generations EDC17 for Diesel engines and ME(D)17 for gasoline engines

Bosch as not just the leading global automotive supplier but leading in the field of electronic control units (ECU) as well has business with practically all customers and in all segments around the globe. In order to support our customers also in the future, to fulfil their functional requirements and to have cost-optimized solutions available we will be introducing a new ECU platform called MDG1. With this, we will respond to the following questions: How can a new ECU platform revolutionize the powertrain control business and satisfy the OEM demand of high degree of backward compatibility to the current ECU generation at the same time? And how can an ECU platform be realized that allows a perfect functional fit and an cost-optimized solution for any powertrain application, from 1 to 12 cylinders, from 2-wheelers via all kinds of passenger cars to heavy commercial and off-highway vehicles, and from internal combustion engines to hybrid and electric vehicles? (Fig. 1). Bosch’s answer to these requirements is the all new, scalable, and powerful ECU platform MDG1. MDG1 is the unified successor of former Bosch ECU generations EDC17 for Diesel engines and ME(D)17 for Gasoline engines. With the MDG1 platform, a nearly completely unified chipset can be used to manage Diesel or a Gasoline engines. Even the powerstage to drive solenoid injection valves could be unified by using a new programmable device. The core novelty of the new MDG1, however, is the new microcontroller family and the corresponding software architecture.

1 MDG1 Microcontrollers For the new MDG1 Bosch requested from its suppliers the most powerful and most scalable microcontrollers that have ever been used in powertrain applications, delivering the computational resources for today’s and future innovative control functionalities.

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Fig. 2 Schematic description of the MDG1 microcontroller architecture on the basis of the high-end device 4

MDG1 is the first generation of Bosch powertrain electronic control units that uses microcontrollers from different suppliers which are completely interchangeable between the suppliers. This results in a multi-supplier strategy that ensures a maximum level of supply reliability. With microcontrollers in 65 and 55 nm technology from Infineon, Freescale and ST Microelectronics, Bosch is cooperating with three suppliers who reliably delivered parts for Bosch’s last three successful ECU generations. Across all three suppliers there will be 4 performance classes of microcontrollers available. Bosch calls them uniformly Device 1 to Device 4, while Device 1 is for the low-end segment and Device 4 is the high-end controller ready to support even most demanding applications. Figure 2 shows a schematic description of the Device 4 microcontroller. Similarly to the development of CPUs in personal computers some years ago, the necessity to increase of computational power can not be fulfilled any more by increasing the clock speed only as we are hitting the technological limits regarding power dissipation and memory bandwidth. Consequently, we need to introduce multi-core microcontrollers also in embedded systems. For the MDG1, Bosch has chosen a configuration as depicted in Fig. 2 with a dual application core (Core 0, Core 1), a safety core (Core 2), and a peripheral core. This architecture does not only have benefits for the computational power but also fulfils state of the art requirements for functional safety which becomes more and more important in automotive applications by introduction of high standards,

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such as the ISO26262 standard. The safety core included in MDG1 microcontrollers (core 2 redundant to core 1 in Fig. 2) is a basic precondition for allowing building up an ISO26262-compliant ASIL-D system. Access security has been a hot topic for automotive electronic control units for years. Illegally tempered units can cause damage and may drastically reduce the engines lifetime. In order to set a new milestone in the history of access security, MDG1 is the first ECU generation that offers a dedicated microcontroller unit called Hardware Security Module (HSM, see Fig. 2). While today’s security mechanisms for tuning protection and immobilizer functionality are based on software only, the programmable HSM introduces hardware implemented security algorithms with a 2,048 bit encryption. A special feature of all MDG1 microcontrollers is the all new General Timer Module (GTM, see Fig. 2). This module, developed by Bosch is responsible for the exact execution of time-critical operations, such as angle synchronous injection or ignition in combustion engines. The absolutely identical GTM in the silicon of all three suppliers is the basic precondition for the multi-supplier strategy of MDG1 microcontrollers. Furthermore, in addition to well established interfaces such as CAN, FlexRay, SENT, LIN from former ECU generations, MDG1 offers innovative new interfaces such as PSI5 and Ethernet which will be integral part in future powertrain communication networks.

2 MDG1 Software The requirements for the MDG1 software architecture are partly contradictory and exhibit a special challenge to Bosch’s engineers. On one hand we want the software to be backward compatible to Bosch’s previous ECU generations allowing an easy and seamless migration to MDG1. On the other hand the software shall be prepared to support future trends such as the increasingly important use of software from different sources (called software sharing), the AUTOSAR standard and functional enhancements like vehicle energy management. Though the application software shall be as unchanged as possible, with the introduction of a new microcontroller family the hardware-near so-called basesoftware (BSW) needs to be adapted. Bosch decided to introduce a completely new developed BSW which is fully compliant to the AUTOSAR standard 4.0. This BSW comprises mainly the operating system, the microcontroller encapsulation and service features. One important reason for choosing the AUTOSAR 4.0 standard and not the co-existing 3.2 version is the multi-core support of the 4.0 standard. It was important for us that we do not force all customers to adapt the AUTOSAR standard in application software with the introduction of MDG1. With the help of an adapter layer (see MEDC17 adapter in Fig. 3) application software (ASW) from previous Bosch ECU generations ME(D)17 and EDC17 can be reused and combined with AUTOSAR 4.0 compliant application software modules. This allows all customers

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Fig. 3 MDG1 software architecture with AUTOSAR compliant base software (BSW), backward compatible application software (ASW) and microcontroller adapted complex device drivers

an individual decision if and how much AUTOSAR application software will be used and offers a smooth migration scenario. In order to make legacy software usable in the MDG1 generation it has to undergo some non-functional changes to eliminate hardware or compiler dependencies and to ensure data consistency in a multi-core environment. Without unwanted side effects like change of calibration data label names, this so-called ‘‘MDG1-ready’’ application software can be used in MDG1 as well as in legacy electronic control units. Thank to the unified GTM in all microcontrollers also a single set of Complex Device Drivers (CDD) will be used in the MDG1.

3 Openness and Transparency One of the major trends in the development of powertrain application software is the increase of software sharing between OEM, ECU supplier and sometimes even 3rd party software suppliers. The success factor for software sharing is efficient cooperation between involved development partners. Off course, Bosch has a huge experience in software sharing and is supporting this business model with many of its customers in all kinds of different ways. The intention with MDG1 is to further improve the efficiency of this process. Despite the valid interest of each party to protect its intellectual property (IP), the interfaces and the global structure need to be openly communicated. Bosch’s strategy to enhance software sharing efficiency is to provide a stable software architecture and reliable interfaces. The transparent and standardized interface

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Fig. 4 The publication of Bosch software architecture VeMotionSARTM and its interface specifications in internet in a simple and understandable way

descriptions and architecture guidelines of AUTOSAR 4.0 are the first key for successful software sharing. To gain the full efficiency benefit, Bosch is going one step beyond and publishes its own concretion of the AUTOSAR architecture for the powertrain domain, called VeMotionSARTM (Fig. 4), free accessible for anyone in the internet (www.bosch-vemotionsar.com) in a simple and understandable way (see Fig. 4). In addition to that, Bosch is preparing a reference implementation of the AUTOSAR Vehicle Network Communication (ComStack) and Memory Stack (MemStack) as well as main parts of the System Services (SysSW, see Fig. 4) to be available as source code for the AUTOSAR partners free of charge. Bosch will do this as an approach to reduce variability of important but usually common software parts which are not relevant for competitive differentiation. Furthermore, this will strengthen AUTOSAR as a global standard in the market.

4 Innovative Software Development Mechanisms Software development is a fast evolving technology and development efficiency is key as the number of development engineers is limited and costs need to be contained. Therefore, we analyze continuously methods and technologies from

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Fig. 5 Innovative software development mechanism: model-based development in close-loop system

other areas of software business regarding practicability and benefit for embedded software development. One important method is the model based development (MBD). Graphical models already have a good representation in automotive control systems but this will increase even further. Though MBD is not new in MDG1 we are convinced that the full potential of this method is not utilized yet. Based on the strong and open architecture of VeMotionSARTM there will be a simplified model of the complete MDG1 application software that can easily be used for rapid prototyping of new functionalities. Easy to integrate based on the standardized interfaces new functionalities can be simulated on system level in a modeled closed-loop environment in a very early state (see Fig. 5). This allows verification, optimization and even pre-calibration of a new functionality without the effort of coding and integration and without the availability of a real ECU hardware, an engine or a vehicle. As soon as the functionality reaches a stable state it can be processed to source code in order to being integrated into a real ECU for final validation and calibration in real-life application. Integration will be a plugand-use procedure since the interfaces in simulation and real ECU are exactly identical VeMotionSARTM interfaces. In combination with the support of state-of-the-art tools for modeling MatlabTM/ SimulinkTM as well as ASCETTM MDG1 will offer various possibilities of software sharing. Depending on the preferences of our customers and development partners functionalities can be exchanged on model, source code or object code level. With the combination of software mechanisms described above and the new powerful microcontrollers of MDG1 we will fulfill the need for efficient and costoptimized cooperation models as well as the introduction of innovative software functionalities. These innovations include besides enhancements of internal combustion engines new electrification concepts.

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Fig. 6 Vehicle-wide energy management considers multiple information

5 Innovative Software Functionalities Over the lifetime of the MDG1 platform, new software features will be implemented to cover future functional enhancements and customer requirements. This will include, for example, a vehicle-wide energy management with the goal to achieve an overall optimization of energy consumption on vehicle level (Fig. 6). Since we see an increasing diversification of powertrain architectures with e.g. numerous combinations of internal combustion engines and electric motors, upcoming vehicles consist of a high number of energy sources, energy storages and energy sinks. Such a complex and flexible system can only be mastered with flexible vehicle-wide energy flow models. And the system does not only include the vehicle itself but also its environment. With information from sensors (e.g. radar, acceleration, gradient etc., called ‘‘nearfield data’’) as well as information from the distance (e.g. GPS, radio traffic service, internet etc., called ‘‘far-field data’’) situation-depending operation strategies to minimize energy consumption can be chosen. With the help of statistical analysis of driving cycles even habits of the driver can be taken into consideration in the optimization process over vehicle lifetime in the field.

6 Customer Benefit and Conclusion With the latest, scalable microcontroller technology and the clear commitment to AUTOSAR, Bosch’s new MDG1 generation offers customer-oriented and costoptimized solutions for all future powertrain control challenges. The main customer benefits can be summarized as follows:

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• Common platform for all powertrain applications, from Gasoline to Diesel and to Electrification • Top performance and scalability in hardware and software for current and future customer requirements • Reliability and stability of microcontroller supply • Functional safety compliance with ISO26262 up to ASIL D if required • Extremely high degree of backward compatibility of application software to previous ECU generations • Customer individual, highly efficient migration to the new platform and to full AUTOSAR compliance • Innovation in software development mechanisms to improve development efficiency • New cooperation models with customers and more efficient project specific development and calibration • Support of future Electronic/Electrical (E/E) architectures enabling on a vehicle level increased energy efficiency, reduction of CO2, and toxic emissions With the new ECU generation MDG1, Bosch will define a new standard in the industry. Not only is this the most scalable ECU platform ever but it enables a smooth transition from previous generations and will bring efficiency gains in for the cooperation in software sharing. In addition, it will support the latest technology with AUTOSAR 4.0 and new software features such as vehicle energy management. With the publication of our basic software architecture in the web we want to support all our customers in their own software development. No matter whether a customer wants to use the complete Bosch software or integrate own or third-party software modules—Bosch is the most experienced and best partner. With the new MDG1 which will be launched end of 2014, Bosch will set a new milestone and continue with its successful ECU business.

Context-Aware Middleware for Vehicular Applications Jian Wang, Weiwen Deng and Peng Zhou

Abstract Developing vehicular distributed applications faces many challenges because most of them apply their specialized communication protocols and technical standards. We propose Context-Aware Middleware for Vehicular Applications (CAMVA), which can react to around environments adaptively and timely. CAMVA uses components based design pattern, and is optimized a lot in terms of vehicular complexity and special requirements of vehicular applications on security and immediacy, by which software programmers can develop and deploy vehicular applications quickly and reliably through assembling, plugging and articulating the existing components even though they are not familiar with the bottom details. CAMVA is located between application level and operation system level, and is composed of collection layer, core layer, running layer, component container, and component library. CAMVA realizes context-aware ability and supports complex vehicular environments. CAMVA behaves better in immediacy, expansibility, static configurability, and dynamic adaptability aspects, so it can achieve strict requirements of intelligent vehicles on middleware.



Keywords Middleware Vehicular applications communications Data collection





Context-aware



Vehicular

F2012-D03-012 J. Wang (&)  P. Zhou College of Computer Science and Technology, Jilin University, Changchun 130012, China e-mail: [email protected] W. Deng State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, China SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_40,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction With the quick development of vehicular electronic and vehicular communications, the approach to efficiently develop and deploy the distributed applications in vehicleto-vehicle situation attracts more and more attention [1]. Middleware can realize the interoperation between heterogeneous systems [2], abstract and encapsulate the universal processes into a uniform supportive layer, such as network communication, security and transaction services [3–5]. Vehicular networks have many different characteristics from the wired works, such as limited bandwidth, frequently changed topology and high requirements on security. Herein the middleware applied in vehicular networks has to meet the following requirements: 1. Instantaneity. Most the safety applications exert rigorous requirements on time delay. 2. Stability. The quick moved and changed vehicular driving environment demands that middleware should behave better stably and invulnerably. 3. Adaptivity. The frequently changed driving situation also requires that middleware should dynamically change configurations in order to be adaptive to surround changes. We design and implement a vehicular-network-oriented middleware CARMVE (Context-Aware Reflective Middleware in Vehicular Environment). CARMVE uses multi-component structure and markup language based synchronization protocol, in order to improve real-time and stability, and then to achieve the requirements of intelligent vehicles on middleware system.

2 System Architecture Figure 1 gives the system architecture of CARMVE which locates between application layer and operation system layer. It is composed of collection layer, core layer, running layer, component container, and component library. Collection layer is to collect and measure the current states and parameters, such as network state, device running state, users’ profile, software state, and surround environment state. It is composed of many separated collection components. Core layer is the key part of CARMVE. It is composed of script parser, adaptivity management and context-aware management. Script parser initializes the application configuration by parsing and analysing application configuration script defined by XML language. The application configuration script is exampled in Fig. 2. The label component lists all components used in applications, and the label rule gives all the rules. Context-aware management is responsible for organizing and computing the context information collected by collection layer, and then decide whether a adaptivity action is launched or not. After a adaptivity action is launched, adaptivity management searches the corresponding component instance or application in running layer according to user-defined adaptivity rules, and attempts to finish adaptivity process by modifying attribute values and changing behavioral structures.

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Application Layer App. 1

…

App. 2

App. n

Component Container

Running Layer Reconfiguration 2

Reconfiguration 1

…

Component Library

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Core Layer

Reconfiguration n

Adaptivity rules Script parser

publish

Context-aware management search

App. Conf. Script

Subscribe rules

notification

Collection Layer Network state

Device state

User Decision Engine Running profile state

… …

Environme nt state

Operation System and Communications Fig. 1 System architecture of CAEMVE Fig. 2 The configuration script

…

< Trigger >

…

Running layer provides a reconfiguration to every application. The reconfiguration encapsulates the necessary component instances and some component chains which correspond to adaptivity rules. Only one component chain stays in active state at the same time. The reconfiguration realizes the adaptivity process by

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changing the attribute value of component instance or activating the proper component chain. Component container is a special part in CARMVE. All the applications based on CARMVE are running as a component chain in the component container. Component container provides running environment to other CARMVE component, receives the registration and cancelation of components, publishes the component services, and so on. Component library records every registered component. The developers could resemble the existing components and then generate a highly efficient and highquality vehicular distributed software system. In practical, the subscribed adaptivity actions are always not provided only by a single context-aware component, but by more than one context information. For example, in automatic tracing system, when the velocity and distance meet a certain relation, the system needs to adapt acceleration, deceleration, stop, launch, and others. Herein, script parser generates a transaction tree for every application configuration script. Figure 3 shows the example of transaction tree. Contextaware management executes the realized subscribe and publish according to the transaction tree.

3 System Instance We explore the role of CARMVE in vehicular communications by using the case showed in Fig. 4. Vehicle A and vehicle B are driven in the same direction, vehicle A is in ahead, and vehicle B is in behind. At one time, vehicle A finds some dangerous areas in ahead, but because of blocked sight, vehicle B can’t detect the danger. So at this time, vehicle B may discover the potential danger by using the image data transmitted from vehicle A. However, the network environment between two vehicles is dynamically changed, such as bandwidth, so the transmission process needs to be changed in real time according to network situation, such as compressing ratio for real-time and clarity during image transmission. The applications based on CARMVE could solve this problem well. Suppose the bandwidth between vehicles A and B is less than 2M, the image compress is launched, and the compressing ratio is 50 %. The configuration script of the application is as follows: \Components[ \component[ \name[ GRAB \/name[ … \/component[ \component[ \name [ COMPRESS \/name[ component[

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Fig. 3 CARMVE context-aware model

Fig. 4 An example for vehicular applications based on CARMVE

…

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\/component[ \component[ \name[ DECOMPRESS \/name[ … \/component[ \component[ \name[ DISPLAY \/name[ … \/component[ \/Components[ \Rules[ \rule[ \Trigger[ \event[ \Operator[ LT \/Operator[ \LC[ \Output[ networkawareness.bw \/Output[ \/LC[ \RC[ \const[ 2 \/const[ \/RC[ \/event[ \/Trigger[ \Componentchain type=‘‘Send_Chain’’[ \SetParam[ COMPRESS.CompressQuality=50 \/SetParam[ \SetChain[ GRAB.PtrOutput -[ COMPRESS.PtrInput; COMPRESS.StreamOutput -[ SEND; Grab.Start \/SetChain[ \/Componentchain[ \Componentchain type=‘‘Re_Chain’’[ \SetChain[ RECEIVE -[ DECOMPRESS.StreamInput; DECOMPRESS.StreamOutput -[ DISPLAY.Input; \/SetChain[ \/Componentchain[ \/rule[ … \/Rules[

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We declare four components in the script: WebCam video collector, JPEGCompress compress component, JPEGDeCompress decompress component, and display component.

4 Conclusion CARMVE decreases developing cost and complexity of vehicular applications, and improves flexibility, adaptability, and portability. It brings theory and practical effects on vehicular applications.

References 1. Wong KD, Tepe K, Chen W et al. (2006) Inter-vehicular communications. IEEE Wirel Commun 13(5): 6–7 2. Yang DR, Luan J, Jun-zhong GU (2005) Comparative research on middleware techniques. Appl Comput Syst (3):27–30 3. Ye F, Adams M, Roy S (2008) V2V wireless communication protocol for rear-end collision avoidance on highways. In: Proceedings of the IEEE communications workshops. Piscataway, IEEE, pp 375–379 4. Huan Z, Shou-zhi X, Cheng-xia L (2009) A V2V broadcast protocol for chain collision avoidance on highways. In: Proceedings of 2009 IEEE International Conference on Communication Technology and Applications, pp 2062–2067 5. Tang A, Yip A (2010) Collision avoidance timing analysis of DSRC-based vehicles. Accid Anal Prev 42(1):182–195

Analysis of the Adaptation of a New Method for Four-Wheel-Hub Electric Vehicle Online-Mass Estimation Jin Zhang, Zhuoping Yu, Lu Xiong and Yuan Feng

Abstract An accurate estimation of vehicle mass is important in automation of vehicle, vehicle following manoeuvres and traditional power train control schemes. It is easy for four-wheel-hub electric motor to get accurate speed signals and torque signals. Based on this feature we introduce a new algorithm for electric vehicle online-mass estimation by decoupling vehicle mass and road grade. In the Matlab/ Simulink simulation environment we establish the new estimation algorithm model and an 18 degree; of freedom vehicle model. We analyze the accuracy of this online-mass estimation method by changing the value of different parameters respectively, for example, different masses, different rolling resistances… This new mass estimation method is fast and reaches a high accuracy without extra sensors. Keywords Mass estimation Simulation

 Longitudinal dynamic  Decoupling  Algorithm 

1 Introduction Analysis of road accidents statistics show that the number of people killed decreases for the last 6 years [1]. This is the consequence of several factors. One of them is the improvement of driver assistance systems (ABS, ESP…). In vehicle control, many control decisions can be improved if the unknown parameter of the

F2012-D03-014 J. Zhang (&)  Z. Yu  L. Xiong  Y. Feng Tongji University, Shanghai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_41,  Springer-Verlag Berlin Heidelberg 2013

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vehicle can be estimated. The more accurate the estimation is, the better performs the driver assistance system. Moreover, accurate estimation is essential to the engine control unit (ECU) for reduced emission. The proposed schemes for direct estimation of vehicle parameters, particularly vehicle mass and road grade can in general be classified into two categories: sensor-based and model-based methods. Due to the high cost for extra sensors, the model-based methods provide a cheap alternative in estimation. Model-based methods use a model of the vehicle and data like engine torque, vehicle speed, engine speed and gear ratio which are available through the Can Bus to estimate the unknown parameters. Different model-based approaches in this area have been proposed in the past few years: Since sharp longitudinal accelerations and decelerations excite a vehicle’s mass significantly, in [2] a method based on this idea is proposed. Similarly, one approach [3] which has been patented and has been used in industry is estimation of mass based on the velocity drop during a gearshift. However, based on a fair amount of trial, the velocity drop is normally minor during a gearshift and this limits the accuracy of the method due to the small signal-to-noise ratio [4]. Bae proposes an averaging recursive least square estimator that utilizes longitudinal force, acceleration and GPS-based road grade measurements to determine vehicle mass. But this method is not accurate because it regards the road grade as a constant parameter, which actually is also a time varying parameter. For estimating multiple parameters that vary with different rates, recursive least square algorithm with multiple forgetting is proposed by Vahidi and Stefanopoulou in [5], whose error for mass and grade estimation are both below 4 %. However this method takes too long for estimation. By analyzing and deforming the longitudinal function, we find that we could decouple mass and grade these two time varying parameters under some conditions. This provides us a new way for an accurate estimation method within a very short of time. The following paper introduces the fundamental theory for decoupling estimation and shows the performance of the new method based on this theory.

2 Vehicle Model 2.1 Vehicle Longitudinal Dynamics The longitudinal dynamics can be presented in the following simple form: Fa ¼ Ft  Ff  Fb  Fw 4 P 1 M v_ x ¼ ½Ti RJw x_ i   lr Mg cos b  Mg sin b  1:63 Cd Av2x

ð1Þ

i¼1

In this equation Fa is the force of acceleration, Ft is the force of each wheel, Ff is the rolling resistance, Fb is the climbing resistance, Fw is the aerodynamic resistance, b is the road grade, Cd is the drag coefficient, A is the frontal area of the vehicle, Ti is

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the torque of each wheel, Te is total torque, Jw is the power train inertia of each wheel, x_ i is the rotational engine speed, R is the radius of the wheel and lr is the rolling resistance coefficient. Equation (1) can be rearranged into: Mð_vx þ g sin bÞ ¼

4 X Ti  Jw x_ i 1 Cd Av2x   lr Mg cos b  ½ 1:63 R i¼1

ð2Þ

where asensor;x ¼ v_ þ g sin b

ð3Þ

Accelerometer can measure the component of gravity acceleration along the measurement axis. FR ¼ lr Mgcosb þ

1 Cd Av2x 1:63

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Equation (1) can be deformed into: 4 X Ti  Jw x_  ¼ Masensor;x þ FR ½ R i¼1

ð5Þ

2.2 Estimation Method Based on Decoupling Theory  and F  R are the true values, M ^ and F ^ R are the values to be estimated. When we M regard the value of mass as constant, then the equation can be translated into: 4 X Ti  Jw x_ i  sensor;x þ F ^R  ¼ Ma ½ R i¼1

ð6Þ

4 X Ti  Jw x_ i ^ sensor;x þ F R ½  ¼ Ma R i¼1

ð7Þ

Similarly when we regard the value of force as constant, then the equation is written as:

According to recursive least square algorithm the minimum of the following two functions need to be satisfied: S1 ¼

m X j¼1

 sensor;x:j  F ^ R Þ2 ðFt;j  Ma

ð8Þ

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S2 ¼

n X j¼1

^ sensor;x:j  F  R Þ2 ðFt;j  Ma

ð9Þ

In order to get the minimum of the functions, these are the derivative of the functions: 8 m P dS1 >  sensor;x:j  F ^R Þ ¼ 0 > < dF^R ¼ 2  ðFt;j  Ma j¼1 ð10Þ n P > 2 > ^  ¼ 2  ½ðF  Ma  F Þ  a  ¼ 0 : dS t;j sensor;x:j R sensor;x:j ^ dM j¼1

The solutions are as followings: 8 m m P  P > > FR ¼ m1 Ft;j  M asensor;x:j > m > > j¼1 j¼1 < n n P P R asensor;x:j F Ft;j asensor;x:j > j¼1 j¼1 >M ^ > ¼  n n > P P > : ðasensor;x:j Þ2 ðasensor;x:j Þ2

ð11Þ

j¼1

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There must be a difference value between the true value and the estimated value. For this little difference we use eM and eFR to stand for it.  ~ ¼M  þ eM M ð12Þ ~R ¼ F  R þ eFR F Then equations can be rearranged as followings: m m m X X eM X M ^ R0 ¼ 1 asensor;x:j Ft;j  asensor;x:j  F m j¼1 m j¼1 m j¼1

^0

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n P

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P n

j¼1

n P

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^ R0 with F ^R; M ^ 0 with M: ^ There is an error term respectively: Compare F e0FR ¼  e0M

¼ eFR

n X j¼1

m eM X asensor;x:j m j¼1

asensor;x:j

, n X j¼1

ðasensor;x:j Þ2

ð15Þ ð16Þ

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These two error terms stands for the difference between true value and estimated value. The smaller these two terms are, the more accurate are the estimations. Parameter asensor;x:j appears in both equations above. From the equations above we can draw a conclusion: When the value of asensor;x is small, then the value of e0FR is small. So it is more accurate for the force estimation. When the value of asensor;x is big, then the value of e0M is small. So it is accurate for the mass estimation. According to this conclusion we find a new method for mass and force estimation, which is: when the acceleration is small, we estimate only force. When the acceleration is big, then we estimate only mass. Since longitudinal dynamics of the vehicle depends on both mass and force, for the first step (force estimation) we use half load vehicle mass as the true value of mass. For the second step (mass estimation) we use the estimated force from first step as the true value of force, to estimate the final mass. That is the theory behind our method.

2.3 Suitable Working Condition for this Method In order to apply this method into practical we need both small acceleration and big acceleration. As we know during the vehicle starting the acceleration value is usually increasing gradually from zero. Only few drivers step on the gas very hard from the very beginning of starting driving. So this phase provides the ideal working condition for our methods. In order to estimate the value of mass and force we also need to define the boundary value of acceleration. After many experiments and simulations we set 0.5 m/s2 as our boundary, which reaches the highest accuracy. Under this value we estimate force. Above this value we estimate vehicle mass.

2.4 Introduction of the 18 degree of Freedom Model for Simulation We have built an 18 DOF (degree of freedom) vehicle model with Simulink in Matlab, which consists of four models. They are wheel dynamic model, vehicle body dynamic model, suspension dynamic model and aero dynamic model. This 18 DOF vehicle model has been validated with the experimental results of Smart (Fig. 1).

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Fig. 1 18 degree of freedom vehicle model

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Fig. 2 Influence of different vehicle masses on the accuracy of mass estimation

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3 Analysis of the Decoupling Estimation Method 3.1 Influence of Different Vehicle Masses on the Accuracy of the Method Figure 2 shows the absolute values of error in mass estimation of different masses are all below 2 percent, which is quite satisfied. Moreover, the estimation process lasts no more than 10 s.

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Fig. 3 Influence of different rolling resistance coefficients on the accuracy of mass estimation

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3.2 Influence of Different Rolling Resistance Coefficients on the Accuracy of the Method In Fig. 3 different rolling resistance coefficients f0 from 0.015 to 0.04 are simulated. The influence of rolling resistance on the accuracy of mass estimation is small.

3.3 Influence of Different Acceleration Noises on the Accuracy of the Method It is not easy to get a stable acceleration data from the acceleration sensor, especially during the starting. We simulate the acceleration noises from 0 to 4 %. From Fig. 4 we can see the error is getting bigger as the noises increase. The absolute values of error in estimation are below 5 %. Therefore a stable acceleration data from the sensor is important for this method.

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Fig. 5 Influence of different road class on the accuracy of mass estimation

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3.4 Influence of Different Road Class on the Accuracy of the Method Figure 5 shows that the influence of different road classes on the accuracy of estimation is not significant. The absolute value of errors is below 3 %.

3.5 Influence of Different Torque Noises on the Accuracy of the Method We simulate the torque noises from 0 to 4 % in Fig. 6. The influence of torque noises is similar as acceleration noises. In order to minimize the error of estimation a stable torque sensor data is necessary.

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Fig. 7 Influence of different wheel velocity noises on the accuracy of mass estimation

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3.6 Influence of Different Wheel Velocity Noises on the Accuracy of the Method From Fig. 7 we can see the influence of wheel velocity noises is small.

3.7 Influence of Different Positions of Pedal on the Accuracy of the Method Figure 8 simulates the influence of different pedal positions on the mass estimation, from 60 to 100 % of pedal position. The errors of mass estimation are below 6 %.

3.8 Influence of Different Wheel Radiuses on the Accuracy of the Method Different wheel radiuses are simulated in Fig. 9. The influence shows in Fig. 9 seems to be significant. But actually the wheel radius cannot change much by the starting of the vehicle. So the error of estimation can be definitely controlled within 4 %.

3.9 Influence of Different Starting Ways on the Accuracy of the Method Different staring ways are presented in Fig. 10. Each signal represents a way of starting the auto. For example signal 1 shows a common way of starting a vehicle

Fig. 8 Influence of different positions of pedal on the accuracy of mass estimation

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by a new driver, who is lack of experience, with a little acceleration vibration. Signal 2 represents the normal way of starting the vehicle for our estimation method. It means the drivers step on the pedal gradually and meanwhile the acceleration signal increases gradually from 0 to 1 m/s2. According to theory of our method we can use the acceleration phase between 0 and 0.5 m/s2 for the force estimation. Then we use the acceleration phase between 0.5 and 1 m/s2 together with the estimated force for our mass estimation. Signal 3 stands for the most improper way of starting. The drive steps hard on the pedal from the very beginning, with the acceleration signal jumping from 0 to 1 m/s2 immediately. Without the acceleration phase between 0 and 0.5 m/s2 we cannot collect enough data for the force estimation. Therefore the accuracy of mass estimation will also be low. Signal 4 shows the most suitable way for our estimation method. At the beginning the acceleration is very small and of course under our boundary 0.5 m/ s2. Then the acceleration jumps immediately to 1 m/s2. From the introduction of our decoupling method we know: the smaller the acceleration is in the first step, the more accurate is our force estimation; the bigger the acceleration is in the second step, the more accurate is our mass estimation. So with the signal 4 we can get the most accurate estimation theoretically.

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Fig. 10 Different acceleration signals stand for different starting ways respectively 14

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Fig. 11 Influence of different starting ways on the accuracy of mass estimation

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As we predicted, in Fig. 11 signal 4 gets the most accurate and stable estimation, better than signal 2. Also as we predicted, signal 3 gets the least accurate estimation, worse than signal 1.

4 Conclusion From the simulations we can see our decoupling mass estimation method can reach a good accuracy, with estimation errors lower than 3 % in the most cases. This method is also fast and don’t need extra sensors. But as we simulated there are still some extreme situations that the accuracy of our estimation is not that satisfied, for example, the starting way like signal 3 from the different starting way simulation. Improvement will be done accordingly in the future.

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Acknowledgments This work was supported by National Basic Research Program of China. (No.2011CB711200)

References 1. ONISR, Les grandes donn’ees de laccidentologie (2005) In rapport ONISR. www.securiteroutiere.gouv.fr/infosref/observatoire/accidentologie/ 2. Breen MT (1996) System and method for determining relative vehicle mass. No. 5,482,359 3. Genise T (1994) Control method system including determination of an updated value indicative of gross combination weight of vehicles. No. 5,490,063 4. Bae HS, Ryu J, Gerdes JC (2001) Road grade and vehicle parameter estimation for longitudinal control using GPS. In: Proceedings of the IEEE intelligent transportation systems conference 5. Vahidi A, Stefanopoulou A, Peng H (2005) Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Veh Syst Dyn 43:1, 31–55

Design and Implementation of Bootloader for Vehicle Control Unit Based on Can Bus Tingqing Tan, Hanhan Tang and Yaling Zhou

Abstract Considering the need for constantly updating user code during developing Vehicle Control Unit (VCU), the special code update process (Bootloader) which consults embedded system bootloader technology is designed in this chapter. The chapter first introduces the basic principles and work process of Bootloader, and then described the specific design and implementation of Bootloader software of the VCU-side and host-side. Experimental results show that the Bootloader boots the application accurately and implements application’s downloading and upgrading exactly and easily.



Keywords Bootloader Vehicle control unit (VCU) module (BAM) S-record format



 CAN bus  Boot assist

Communications between the electronic control units in the pure electric vehicle mainly rely on the CAN bus, as the nerve center of the entire electronic control system, the vehicle control unit (VCU) is responsible for network communications and information exchange of the electronic control units. With the increase in automotive electronic control units, vehicle control strategies become more complex, and thus a higher demand on the processing power of the vehicle controller. 32-bit microcontrollers in computing speed and processing power is much better than the 16-bit microcontrollers to meet the performance requirements of VCU. VCU control strategy in the development process is continuously adjusted,

F2012-D03-016 T. Tan (&)  H. Tang  Y. Zhou EV Research and Development Center, China Automotive Engineering Research Institute Co., Ltd, Shanghai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_42,  Springer-Verlag Berlin Heidelberg 2013

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so you need to repeatedly update the VCU application. For Freescale 32-bit microcontroller, you can usually use the P&E and other special download tools to update the application, this need to VCU from the original car or bench to uninstall and removed from the shell out, resulting in tedious work, seriously affecting the efficiency and cycle of product development. This difficulty is overcome by using Bootloader way, it downloads new applications using CAN bus which connected to the VCU, no need to demolish VCU which has been packaged and installed. CAN bus has a high-speed, real time, with error diagnostics and other advantages, to meet the needs of communication between VCU and host in the Bootloader process. CAN Bootloader used in Freescale’s 32-bit microcontroller based on a custom communication protocol is designed in this chapter, it successfully achieved the online update of the application.

1 The Basic Principle of Bootloader Bootloader is called boot loader in embedded systems. The boot is a small program to run after power up or reset the system, this program leads the hardware environment of the system to an appropriate state to prepare a good fit for the final calling to the application environment. The loader copied the software components in non-volatile memory (NVM) to RAM, and ultimately guides the running of application. Bootloader in automotive ECU is the code update process: ECU was in a special mode, connected by some kind of bus communication interface with the host, the host downloaded the new object code to the ECU, the ECU’s code was updated, new applications would be running after the next power up or reset [1]. Taking into account the convenience and reliability of the CAN bus, it is often used as communication interfaces of the Bootloader. In order to distinguish between the entire code update system and the implementation program of microcontroller, Bootloader refers to the entire system, and bootloader refers to program of microcontroller in this chapter. Implementation loaded from the flash is a common way of the CAN Bootloader, bootloader and the application (object code) were stored in different flash memory location of the microcontroller, updating the application through the implementation of bootloader. Defects of this way is external interference (such as power failure) may destroy the original bootloader program in the application writing process, the result is that users can only re-programming bootloader program through a special download tool. Another way is loading bootloader directly from the communication interface to the ram of microcontroller and then running it. The biggest benefit of this way is not afraid of power outages in the implementation of the flash programming process. As the bootloader itself without taking the microcontroller flash memory, there is no need to change the underlying configuration file of application code. However, because loading bootloader from ram needs to use the unique BAM module, which is usually only owned by 32-bit microcontroller, the 16-bit microcontroller is generally not used in this way. This chapter explains how to achieve this Bootloader [2].

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Bootloader’s hardware is mainly composed by the host (PC) and VCU. The host itself without the support of the CAN bus interface, and therefore required to the USB-CAN adapter as a bridge of communication between the host computer and the VCU. Work process of Bootloader is divided into two main stages: In the first stage, the host firstly initializes the USB-CAN adapter to establish connection with the CAN communication between host and VCU, and then downloads bootloader to the SRAM of microcontroller; In the second stage, bootloader begins to run, bootloader first preparations for the implementation of flash programming based on host instructions, including the initialization of the flash, the corresponding flash address space protection and erasing, then loop executes the target code receiving, calibration and flash programming, until the object code writing is completed.

2 The Implementation of Downloading Bootloader Through BAM The BAM is a nonvolatile memory based software program. The BAM’s primary function is to perform essential system initialization and to locate and execute the application code. The BAM also supports serial download of user code. The execution of the BAM is affected by the censorship status. BAM is executed when reset is negated. BAM provides four kinds of boot mode, serial boot mode is used for downloading bootloader.

2.1 The Entry of Serial Boot Mode The BAM is accessed by the device core after the negation of RSTOUT, before user code starts. First, the BAM program configures the core memory management unit (MMU) to allow access to all device internal resources, This MMU setup remains the same for internal flash boot mode. After the MMU configuration, the BAM program checks the BOOTCFG field of the reset status registers (SIU_RSR) and the appropriate boot sequence is started. The BOOTCFG pin needs to be driven high through an external switch, selecting serial boot mode. Internal boot mode is the normal selection if the BOOTCFG pin is not asserted. Serial boot mode supports the SCI bus way and CAN bus way, depending on the first data frame type received by microcontroller. CAN bus way is used in this chapter, a message with 0 9 11 ID, containing 8 bytes, is received by CAN controller first. The serial boot mode can run in either of two modes of operation: Standard serial boot mode using fixed baud rates derived from the crystal oscillator used; Baud Rate Detection serial boot mode, which allows communication with adaptable speed, based on measured input signal. The design uses the fixed baud rates mode, on the hardware configuration requires EVTO pin is set low, 8 MHz crystal corresponding to the CAN baud rates to be determined as 200 kHz.

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2.2 Work Flow of Downloading Bootloader When the BAM switches to serial boot mode the watchdog is always enabled. The watchdog is refreshed only after the correct flash/public password is accepted and subsequently after each write to the internal SRAM. When BAM has initialized the CAN module, host can follow the download protocol to send the appropriate data and code. The download protocol follows four steps: 1. 2. 3. 4.

Host sends 64-bit password. Host sends start address, size of download code in bytes, and VLE bit. Host sends the application code data. The device switches to the loaded code at the start address.

The communication is done in half-duplex manner, any transmission from host is followed by the device transmission. The host computer should not send data until it receives echo from the device. All multi byte data structures have to be sent most significant byte (MSB) first. When the CAN is used for serial download, the data is packed into standard CAN messages in the following manner: 1. A message with 0 9 11 ID and 8-byte length is used to send the password. The device transmits the same data, but the message ID is set to 0 9 1. 2. A message with 0 9 12 ID and 8-byte length is used to send the start address, length, and the VLE mode bit. The device transmits back the same data, but with ID set to 0 9 2. 3. Messages with 0 9 13 ID are used to send the downloaded data. The device transmits back received data with message ID of 0 9 3. Figure 1 shows the simplified program flow of the serial boot mode.

3 Design of Bootloader of VCU-Side The bootloader of VCU-side is written in Freescale codewarrior software environment, the generated code files downloaded to SRAM of microcontroller through serial boot way. Compared to traditional load from flash way, this way don’t need to make any changes to the underlying configuration of the application, but more on the bootloader itself.

3.1 Underlying Configuration of Bootloader 3.1.1 The Division of SRAM Memory Space In serial boot mode bootloader is downloaded to the SRAM of microcontroller, SRAM size of different types of microcontroller is often different, so the rational

Design and Implementation of Bootloader Fig. 1 Serial boot mode flow diagram

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no STOP Wait for reset or watch dog time out

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allocation of the storage address of the code to be based on the chip manual is necessary. This chapter is optional Freescale’s 32-bit microcontroller MPC5634M, its SRAM memory space is 94 KB, and the address range is from 0 9 4000_0000 to 0 9 4001_77FF. BAM protocol stipulated the starting address of code in SRAM, program will jump to the start address to perform after the code has been download, which requires the entry address of the program code stored in the starting address. Works created by Codewarrior default program entry address is in the init code segment, this address is usually not the starting address of the code space. In this chapter, the way is to increase the ‘prestart’ segment in the Link File and set the segment start address to be 0 9 4000_0000, and then put the ‘_start’ procedure into the ‘prestart’ segment, so that we achieve the entry address of the program is the start address of code. Figure 2 shows the division of SRAM Memory Space.

3.1.2 The Initialization of Variable Space When loading code to internal RAM (using the BAM serial download or otherwise) the user must be aware that error correction coding (ECC) is implemented for all SRAM (SRAM). It is essential that the ECC parity bits are initialized after power on. A 64-bit cache inhibited write to each location in SRAM must be used to initialize the SRAM array. Code downloaded to SRAM by the serial download mode of the BAM is loaded in 64-bit writes, initializing the SRAM as the code is

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Fig. 2 Division of SRAM memory space

downloaded. However care must be taken to ensure that SRAM areas allocated to variables (Located in ‘internal_ram’ segment), heap and stack are also initialized. There are several ways to handle this: 1. Assign all stack and variables to non-SRAM address space. 2. Develop code in assembler, which does not rely on a stack or any variables. 3. Initialize additional SRAM array space to accommodate variables/stack. The third way is selected in this chapter. By placing SRAM initialization code at the start of the download code (before stack or heap is established), the entire SRAM array can be initialized before executing any code that could make use of uninitialized SRAM. Consider the case where 13,568 bytes of code is downloaded via CAN. This could occupy address range 0 9 4000_0000 to 0 9 4000_3500. The remaining 82,688 bytes of RAM from address 0 9 4000_3500 to 0 9 4001_7800 can be initialized by adding the following code to the start of the download code. Figure 3 shows the SRAM initialization code [3, 4].

3.2 Work Flow of Bootloader Bootloader jumps to the main function to perform after underlying configuration had been finished. As mentioned earlier, the watchdog will be opened automatically when enter the BAM serial boot mode, BAM fed the watchdog until the end of the download process. To prevent the watchdog timer overflow, the user must either service the watchdog or disable it after serial download is complete. The watchdog can be serviced in several ways. The simplest method is to increase the timeout period to ensure that there is sufficient time for the downloaded code to

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Fig. 3 SRAM initialization code

run. After that the main function initializes the system clock (set for 64 MHz), and then initializes CAN module to complete the preparation of communication with the host. VCU firstly sends a connection request command to the host, after receiving the instruction the host starts to implement the update process of the application code in accordance with a custom protocol. CAN bus baud rate is 500 Kbps, standard frame format, the ID of the host is 0 9 22a, and the ID of the VCU is 0 9 22b. The max data length of a CAN frame is 8 byte, in addition to the data length of sending application code for the 8-byte, the reset of the data are one byte. The communication protocol between the host and the VCU is shown in Table 1. During the data transfer process, the host based on VCU feedback response to commands and data sent successfully or not, when a command fails, or data validation fails, the host just resends the instruction or data in order to ensure the correctness of data transmission. Figure 4 shows the work flow of flash programming.

3.2.1 Object Code Interpretation Format of the application code is Motorola S-Record file format, it is generated by Freescale Codewarrior compiler and can be executed in the microcontroller, and its suffix is ‘.S19’. To achieve the correct reception of the application code and programming, the application code must be converted to address and data needed for programming, this process is called object code interpretation. Object code is explained either in the host or VCU, there is not much difference in practical applications; this chapter uses the way of explaining object code in the VCU [5]. The S-record format is an ASCII hexadecimal (‘‘hex’’) text encoding for binary data. It is also known as the SREC or S19 format. Each record contains a

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Table 1 Communication protocol of download application code Frame ID Direction Command Meaning 0 0 0 0 0 0

9 9 9 9 9 9

22B 22A 22B 22B 22B 22B

VCU to Host Host to VCU VCU to Host VCU to Host VCU to Host VCU to Host

0 0 0 0 0 0

9 9 9 9 9 9

00 01 02 03 aa 04

Request a connection Initialize flash Complete flash initialization Successfully received a record Receive failed Receive complete

Fig. 4 Flash programming flow diagram

start

Receive one line s-record

no

yes Check successfully?

no

yes no

Accumulated 64 bytes data ? yes Flash programming

no

Has written the last line of the record ? yes End

checksum to detect data that has been corrupted during transmission. The first record (S0) may include arbitrary comments such as a program name or version number. The last (termination) record (S7, S8, or S9) may include a starting address. The records have the following structure: 1. Start code, one character, an S. 2. Record type, one digit, 0–9, defining the type of the data field. 3. Byte count, two hex digits, indicating the number of bytes (hex digit pairs) that follow in the rest of the record (in the address, data and checksum fields).

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4. Address, four, six, or eight hex digits as determined by the record type for the memory location of the first data byte. The address bytes are arranged in big endian format. 5. Data, a sequence of 2n hex digits, for n bytes of the data. 6. Checksum, two hex digits—the least significant byte of ones’ complement of the sum of the values represented by the two hex digit pairs for the byte count, address and data fields [6]. Microcontroller will verify each row record received from host. When the checksum calculated is equal to the checksum comes in the S-record, the program will follow-up flash programming work, or else abandon the record, and return a receive-error frame.

3.2.2 Flash Programming of Application The flash programming of application is an important part of the whole bootloader. Program of this part uses flash driver library C90LC_JDP provided by Freescale, it contains all operation functions of flash. Sequence of operations is the first call the initialization the function ‘flshinit’, then check the latch state of the concrete blocks by using function ‘pGetLock’ and use function ‘pSetLock’ to set the latch state; erase the programming blocks, and use ‘pBlankCheck’ function to verify the erased blocks; finally, write the data to the blocks using function ‘pFlashProgram’, and verify the written data using function ‘pProgramVerify’. User must be in strict accordance with the above order to ensure that the code written correctly. It is noteworthy that flash erasing is a block as a unit; the flash writing is 64 bytes as a unit [7].

4 Design of Bootloader of Host-Side The software of host-side is built in Visual C++ 6.0 development platform, the interface is shown in Fig. 5. The software is generally no longer be modified after successful commissioning, so make is as simple as possible for using. The software interface contains four buttons: ‘Connect CAN’ button controls the connection between the host and the USB-CAN adapter; ‘Start CAN’ button controls the start of the USB-CAN transceiver module; ‘Read File’ button is used to specify the application file; ‘Start Sending’ button controls the start of code update process. User can complete the whole process of code updates, just follow the screen prompts Click the four buttons. Code update process firstly downloads bootloader to SRAM of microcontroller according to BAM protocol, since the bootloader is a fixed file, so the host software will automatically get the file path; users only need to specify the application file path.

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Fig. 5 Interface of host-side software

Sending and receiving procedures of CAN message are provided in the application routine of the USB-CAN adapter, simply call these functions in the preparation of software can achieve sending and receiving CAN message. It is noteworthy that the dynamic link library file (ControlCAN.dll and USBCAN.dll) must be contained in software folder; otherwise the error will be occurred [8]. The main function uses a timer interrupt mechanism: program turns on the timer after the execution of each movement (such as initializing instructions), timer time length depends on the specific tasks; Interrupts are generated after the timer out, relevant variables in the interrupt service routine, and then call the relevant function to perform the next task. To ensure the real-time nature of the software, the task update interval is set to 1 ms. In addition, the software also includes the s-record file reading function, CAN sending and receiving functions, ASCII to HEX function, progress tracking displaying functions, communication handshaking function, not described in detail here [9].

5 Conclusions This chapter discusses the implementation method of CAN bus-based Bootloader with Freescale’s 32-bit microcontroller MPC5634M, the methods are equally applicable to other microcontroller with BAM module. The entrance of Bootloader is controlled through an external switch connected to the microcontroller’s BOOTCFG pin. The host firstly downloads the bootloader program to the microcontroller’s SRAM with serial boot mode of BAM. After the bootloader running, the host communicates with VCU through the custom handshake protocol to achieve the goals of download of application code in S-record format. Under this method, bootloader code itself does not take space of flash, eliminating the need to do any modifications to the underlying configuration of the application, while also saving monolithic integrated circuit flash storage resources; because BAM module is a microcontroller comes with a driver module, even if the brush to

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write down and other failures will not affect the next application update. The Bootloader has been successfully applied to the VCU code update, the operation results show that: it is easy to operate and operating stably, effectively overcome the tedious demolition process of application update with site download tools and improve the efficiency of research and development of VCU.

References 1. Yan Z, Kejin B (2011) Design and implementation of BootLoader of vehicle control unit. Comput Eng 37(12):232–235 2. Chu L, Feng L (2010) Implementation and application of the CAN Bootloader based on freescale’s microcontroller. J Suzhou University (Engineer in Science Edition) 30(2):57–61 3. Freescale Semiconductor (2011) MPC563XMRM: MPC563XM Microcontroller Reference Manual [DB/OL]. http://www.freescale.com/webapp/sps/site/prod_summary.jsp?code=MPC 563xM 4. Freescale Semiconductor (2008) AN2831: MPC5500 Boot Assist Module [DB/OL]. http:// cachefreescale.com/files/32bit/doc/app_note/AN2831.pdf?fsrch=1&sr=1 5. Aiyun Z, Xiaoming Z, Chen Y (2010) Development and implementation of Bootloader of the diesel engine electronic control unit. Modern Veh Power 140:17–19 6. Wikipedia. SREC (file format) [DB/OL]. http://en.wikipedia.org/wiki/SREC_(file_format) 7. Freescale Semiconductor (2010) Standard Software Driver for C90LC Flash in xPC56xx [DB/ O L]. http://cache.freescale.com/files/soft_dev_tools/software/device_drivers/MPC56XX_v 8. ZhiYuan Electronics Co., Ltd (2007) USBCAN Data Handbook V2.0 [DB/OL]. http:// www.embedcontrol.com/products/PCI/USBCAN/USBCAN.pdf 9. Yongguo Y (2007) Visual C++ 6.0 practical course, 2nd edn. Tsinghua University Press, Beijing

Automated Code Generation for Development of Electric Vehicle Controller Peng Geng, Minggao Ouyang, Jianqiu Li and Liangfei Xu

Abstract Code generation for Simulink model is widely used in the development of vehicle controller. In traditional way, only the code of vehicle control model can be generated automatically by Real-Time Workshop. So programmers have to handwrite code for peripheral device drivers. The objective of this study was to develop a custom driver blocksets to support I/O devices on high performance 32-bit MPC5644A microcontroller. Furthermore, to fulfill modeling, code generation, compilation and downloading all accomplished ‘‘at the touch of a button’’. This method is used and tested in the development of electric vehicle controller, which saves time and money greatly.



Keywords Automated code generation Embedded target MPC5644A Electric vehicle controller



 Matlab/Simulink 

1 Introduction In order to save time and cost, software engineers always focus on the length of code but do not pay attention to the correctness, which makes code difficult to maintain and transplant. Moreover, most software projects are completed in collaboration. If programmers handwrite code separately and combine them together at last, it is almost impossible to put into application directly because of F2012-D03-019 P. Geng (&)  M. Ouyang  J. Li  L. Xu State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_43,  Springer-Verlag Berlin Heidelberg 2013

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all kinds of handwritten errors. Matlab/Simulink is a powerful tool which is widely used in the ‘‘V-Model’’ development of Vehicle Control Unit (VCU). ‘‘V-Model’’ includes function design, Rapid Control Prototyping (RCP), code generation for embedded target, Hardware-In-the-Loop Simulation (HILS) and test/calibration. Thanks to the powerful functions in modelling, simulating and analyzing, Matlab/ Simulink becomes a basic tool in modelling the VCU algorithm as well as in code generation. Although this ‘‘half’’ automated code generation method unifies code style and increases efficiency, it still has weaknesses. In the development of VCU, code generation includes model algorithm code and microchip driver code. The later needs to be done through handwriting for the reason that Matlab/Simulink provides very limited toolboxes which only include Motorola MPC555, Infineon C166, TI’s C2000, C5000 and C6000 [1]. To solve the problem, Microchip develops RCP toolbox, which supports dsPIC33 Controller [2]. D.Hercog develops a self-made, DSP-based RCP system, which is used for motor control [3]. S.Rebeschiess launches an embedded toolbox, named ‘‘MIRCOS’’, for graphical programming of 16-bit processor 80C166 [4]. R.Duma presents a RCP toolbox for Renesas M32C87 microcontroller [5], and C.RUSU invented a toolbox for F24X DSK Digital system in the purpose of education and industrial application [6]. R.Bartosinski gives a method of integrating Processor Expert Tool to Matlab environment [7]. Luo devotes himself to the method of Real Time Workshop (RTW) and developing a custom embedded target—MC9S08DZ60 [8]. This paper firstly introduces a method of developing device drivers for embedded target—32-bit MPC5644A, using Matlab Real-Time Workshop Embedded Coder (RTWEC). Then it applies this method to the development of electric vehicle controller, focus on vehicle control algorithm, real-time scheduler and generated code analysis. Verification and conclusion is given at last.

2 Developing Embedded Targets 2.1 Real-Time Workshop Embedded Coder As a powerful tool of Matlab/Simulink, RTWEC is able to convert graphic model into high level language code, which frees software engineers from all kinds of datasheets and redundant handwriting code. Besides, this compact and fast code is very important for MCU used in production and real-time embedded systems. With RTWEC, Simulink model is translated into an intermediate RTW file first, which contains all the model-specific information used in code generation. Target Language Compiler (TLC) generates code according to this file using system control files, block target files and so on. Though RTWEC increases efficiency greatly, limitations exist. First, Matlab only supports several embedded target platforms such as MPC5xx series. In addition, these toolboxs can only realize simple hardware functions which hardly satisfy users’ need. So, it is necessary to develop custom embedded target.

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Fig. 1 Automated C code generation platform for MPC5644A

Figure 1 introduces how to develop an embedded target MPC5644A used in next-generation fuel cell vehicle control. Building a custom MPC5644A driver library is the first step, using the method mentioned later. This library provides an easy-to-use graphical user interface for device driver in the Simulink environment. Then, RTWEC can generate high quality code according to vehicle control model automatically. This method also integrates RTWEC with cross-development environments—CodeWarrior IDE which makes code generation, compilation and downloading all seamlessly accomplished ‘‘at the touch of a button’’.

2.2 Developing Target Directory Structure Baseline target files include system target files, template make files, target block library files, target block files and so on. These target files are readable and easy to understand so the developer can customize the target for application needs. Initial tasks in developing an embedded target are creating a series of organized directories in Matlab path, and locating required target files in specific directories, see Fig. 2.

2.3 Modifying System Control Files System control files includes system target files (STF), template make files (TMF), hook files and so on. STF controls the presentation of target to end user. Developer

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Fig. 2 MPC5644A directory structure

Fig. 3 STF mpc5644a.tlc in system target file browser

can modify this file to display custom target information in the STF browser. Moreover, STF also provides the main entry point to the top-level TLC, the definition of target-specific code generation, inheriting options from another target, and so on. ‘‘mpc5644a.tlc’’ is an ERT-based STF created for processor MPC5644A, see Fig. 3. Hook file can customize building process and invoke target-specific actions at specified points. In order to compile, link, download the generated code automatically, hook file needs to interface with development tool. Take MPC5644A for example, solutions are given to support the complete build process which interfaces with the Freescale CodeWarrior IDE. To implement this function, developer should create an eXtensible Markup Language (XML) file, which indicates CodeWarrior project to add the generated source/header files. Build process automation depends

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Fig. 4 Dialog box for eQADC driver block

on Matlab COM automation functions. A series of CodeWarrior API functions such as ‘‘CreateCWComObject’’, ‘‘OpenCW’’, ‘‘OpenMCP’’, and ‘‘BuildCW’’ are invoked in hook file, after the code generation process is completed. Then Codewarrior starts automatically, compiles and downloads the code into controller through BDM. This example helps developers to deal with similar interfacing problems with particular integrated development environment.

2.4 Developing Device Drivers for Embedded Target To create device drivers for MPC5644A, a C MEX S-function is built primarily for use in simulation, and a driver TLC is created for use in code generation. C MEX SFunction is a C code file that implements specific functions, such as ‘‘mdlInitializeSizes’’, ‘‘mdlOutputs’’, ‘‘mdlRTW’’, to initialize driver block structure, validate block parameter data input by end users and pass these data to rtw file. Command ‘‘mex’’ is used to build a MEX-file from custom driver source code and designer can add icons, dialog boxes, initialization commands to an S-Function block by masking it, see Fig. 4. Driver TLC is used to create hardware support files including header files, macro definitions and driver code libraries. For example, the purpose of the ‘‘start’’ function in eQADC driver TLC ‘‘eqadc.tlc’’ is to generate code that initializes 32bit control registers of MPC5644A Analog-to-Digital driver; while the ‘‘output’’

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function generates code that repeats the ‘‘convert’’ operations for all selected AD channels. Code related to hardware drivers are all generated to a source file named ‘‘MPC5644A_drivers.c’’

3 Simulink Fixed Point and Custom Storage Classes 3.1 Writing Fixed-Point S-Functions Within microprocessor like MPC5644A, numbers are represented as either floatingpoint or fixed-point data types. Floating-point data types contain three parts: sign bit, fraction field, and exponent field; while fixed-point numbers are characterized by word size in bits and binary point, whether signed or unsigned. If a MCU support fixed-point, it always means the chip size can be smaller with less power consumption. Furthermore, fixed-point calculation needs less time, memory and cost compared with floating-point. So, when write C S-functions, how to support fixedpoint data types is an inevitable question. User-written S-functions supports many kinds of fixed-point data types, such as ‘‘sfix16_En13’’, ‘‘ufix32_En11’’, and ‘‘sfix35_S3_B4’’, provided that ‘‘fixedpoint.h’’ and ‘‘fixedpoint.c’’ have been included at appropriate places in the Sfunction. Each data type used in S-function has a unique data type ID, which programmers use to get and set information about data types. The assignment of data type IDs follow ‘‘first come first served’’ rules. To set fixed-point data types, developer should register a data type first using data type registration functions. And then use the returned data type ID for input and output ports, run-time parameters.

3.2 Creating and Using Custom Storage Classes Storage classes are widely used in the code generation process. They decide how signals, parameters are declared and stored in generated files. There are four builtin types that can satisfy users demand most of the time. For example, storage class ‘‘Auto’’ put all data into one single structure while ‘‘ExportedGlobal’’ variables are declared and stored in unstructured way. In the development of embedded system, programmers often need flexible control over the representation of data. Thus, custom storage classes (CSC) are provided as complement of built-in storage class to control the structure of generated data. Another advantage is that developer can create and edit memory section definition using CSC. Before creating CSC, a data object package should be built to support CSC definition using data class designer, see Fig. 5. CSC designer is a tool for creating and managing custom storage classes and memory sections. Developer can choose

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Fig. 5 Data class designer

either inheriting properties from existing CSC or creating a new CSC. The former choice configure CSC designer step by step, while the later choice requires knowledge of TLC programming. To make it east, template TLC files are provided to help developer writing custom TLC file for CSC. After having created the TLC file, register this file CSC designer and apply CSCs to parameters and signals, so it can generate data structures as the TLC file describes.

4 Code Analysis and Verification 4.1 Designing Real-Time Scheduler Matlab code generation structure can be divided into three main parts—core algorithm, hardware support and real-time scheduler. The core algorithm is composed of self-carried Simulink blocks and TLC controls how to translate user’s configuration into core algorithm code. Hardware support works in the similar way but using custom GUI blocks and TLC files. Having developed driver blocks in Simulink library, designer can build vehicle control algorithm and configure peripheral device drivers all in one model without any handwritten work. The middle block in Fig. 6 is a subsystem describing electric vehicle controller core algorithm and the sideward blocks consist of MPC5644A CAN, AD, I/O blocks. The eQADC blocks are used to collect analog signals, such as accelerator pedal position, brake pedal position. I/O blocks gather digital signals like gear state, warning level and so on. The CAN Receive block receives can messages from Auxiliary Power Unit (APU) controller, battery controller, motor controller. These data are calculated in vehicle control algorithm and new control commands are sent back to each controller through CAN Transmit block. Time Trigger Controller Area Network (TTCAN) protocol plays an important role in this distributed

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Fig. 6 Model of electric vehicle control unit

communication network. TTCAN improves real-time and accuracy, but the design of the network are always complicated projects, let alone automated code generation process. In the application of electric vehicle controller, in order to guarantee TTCAN transmission delay less than 100 ls, two Periodic Interrupt Timer (PIT) channels are used in this model to fulfil this scheduling mechanism. The PIT with lower priority is attached to model’s base sample time step function ‘‘rt_OneStep’’. ADs or other drivers’ sample times are integral multiple of base sample time so they run at each sub-rate. To do this, a function called ‘‘rate_scheduler’’ is created to compute which sub-rates should run during the next base time step. Sub-rates are an integer multiple of the base rate counter. Therefore, the subtask counter is reset when it reaches its limit and subtask starts running. Sample time offsets are handled by priming the counter with the appropriate non-zero value in the model’s initialization function. For example, the electric vehicle algorithm here is executed every 0.01 s, eQADC channel one samples every 0.04 s, and eQADC channel two samples every 0.08 s after 0.3 s time delay. The PIT with higher is assigned to CAN transmission blocks. It can interrupt model algorithm to transmit CAN messages at once which guarantee network real-time. In this model, three messages ‘‘VCU_C_1’’, ‘‘VCU_D_1’’, ‘‘VCU_S_1’’ are transmitted at different rates following TTCAN protocol, with transmission delay less than 10 ls.

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Fig. 7 Model of electric vehicle control unit

4.2 Analyzing Generated Code Click ‘‘generate code’’ button, RTWEC transforms electric vehicle model into intermediate rtw file first and then generates several source files. [Model].c file is closely related to the control algorithm and hardware drivers. Every segment code is related to a block and controlled by block TLC. This modularity characteristic makes the code easy to read and modify. In Simulink model, blocks are connected through signal lines. Each block has input and output ports, so there are corresponding block I/O variables’ definitions in the generated code. A local block I/O variable can be an input to one block but an output to next block following the signal line. Initialization function in this file initializes what the model needs at the beginning and hardware registers as well. Step functions describe electric vehicle control algorithm block by block and are generated according to block sample times. Besides, there are special functions such as interpolation function because this model supports fixed-point operation. [Model]_main.c file contains model main function called ‘‘rt_OneStep’’. This key function runs base rate task and then call ‘‘rate scheduler’’ function to judge which step function should be executed. Generally, step zero function is attached to base rate sample time, while block TLCs generate code into other step functions if their sample times are integral multiple of base rate. Rate scheduler function is called by PIT interrupt every base sample time to decide which sub-rate task should run. [Model]_data.c file is used to store all blocks signals and parameters. How these data declared and stored are controlled by custom storage classes mentioned above. MPC5644A_drivers.c file includes hardware related functions such as ‘‘AD_convert’’, ‘‘CAN_transmit’’, ‘‘CAN_receive’’.

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5 Conclusion This paper presents a technical route to develop an embedded target—MPC5644A and use it in the development of electric vehicle controller, see Fig. 7. Automated code generation frees programmers from handwriting driver code and lets them focus on the design of vehicle control algorithm. Experimental results show that the R&D time and cost can be drastically reduced. Besides, automated generated codes are easy to maintain and transplant, but the memory space it takes is a little bit bigger. The fuel economy and the vehicle dynamic performance of electric vehicle are kept in the similar level compared to traditional methods because the control algorithm does not change. Acknowledgments Supported by the NSFC (National Natural Science Foundation) of China under the contract of No. 61004075, the MOST (Ministry of Science and Technology) of China under the contract of No. 2010DFA72760 and No. 2011AA11A269, and the Tsinghua University Initiative Scientific Research Program (Grand No. 2010THZ08) is greatly acknowledged.

References 1. The Mathworks, Inc., Supported hardware, http://www.mathworks.org 2. Microchip Technology, Inc., MATLAB device blocks for MATLAB IDE, http:// www.microchip.com 3. Hercog D, Curkovic M, Jezernik K (2006) DSP based rapid control prototyping systems for engineering education and research. In: Proceedings of IEEE international symposium on computer aided control systems design, Munich, Germany 4. Rebeschiess S (1999) MIRCOS—microcontroller-based real time control system toolbox for use with Matlab/Simulink. In: Proceedings of IEEE international symposium on computer aided control system design, pp 267–272 5. Duma R, Petreus D, Sita VI, Dobra P, Rusu A (2010) Rapid control prototyping toolbox for Renesas M32C87 microcontroller. In: Proceedings of IEEE international symposium on automation quality and testing robotics (AQTR), pp 1–6 6. Rusu C, Radulesvu M, Balan H (2007) Embedded toolbox for F24X DSK. International Aegean conference on electrical machines and power electronics, Aegean, Greek, pp 556–559 7. Bartosinski R, Hanzalek Z, Struzka P, Waszniowski L (2007) Integrated environment for embedded control systems design. In: Proceedings of IEEE international symposium on parallel and distributed processing (IPDPS), pp 1–8 8. Feng L, Zhihui H (2010) Embedded C code generation and embedded target development based on RTW-EC. In: Proceedings of IEEE international symposium on computer science and information technology (ICCSIT), vol 5, pp 532–536

Research on the Development Process for the ECU Control Software of Vehicle Powertrain Xiaoyan Dai, Changlu Zhao, Ying Huang, Huan Li and Gang Li

Abstract The research and development status and the facing problems for Powertrain ECU control software are analyzed in this chapter. By the study of the characteristics of today’s mainstream software development process, a suitable software development process has been proposed which fits the specific characteristics of powertrain ECU control software. In this process, model-driven development method, off-line simulation, rapid control prototype and hardware in the loop simulation are integrated. The various stages in the process, the requirements of software documentation and related activities are described in details. And the chapter is also discussed the characteristics of this process from the point of view: to improve controllability, maintainability and quality of software products during the process of research and development.



Keywords Software development process Model-driven development control prototype Off-line simulation Hardware in the loop





 Rapid

1 Introduction Today, automotive application software becomes more and more complicated. With the continuously improved requirements about product quality, safety and the expansion of the system scale, the automotive software development is facing a

F2012-D03-020 X. Dai (&)  C. Zhao  Y. Huang  H. Li  G. Li Beijing Institute of Technology, Beijing 100081, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_44,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 Traditional software development process Requirement

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huge challenge [1]. On one hand, more and more stringent requirements requires less development time and lower cost for automotive electronics product. And due to continuing advances in hardware technology, the electronic hardware life cycle must be shortened; the standardization of software architecture has become an inevitable trend. On the other hand, the function of automotive electronic control, task management and the system scale is expanded and complicated. How to easily integrate more features in an automotive system and the requirements of software reusability and portability becomes an important issue faced by automotive enterprises. The traditional software development methods are not suitable to meet development needs any longer. To adopt a new system design methodology and development process is an urgent need due to the above problem. This needs a study on today’s powertrain ECU control software features, using software engineering as a guide, to explore the software development process for the specific application requirements.

2 Backgrounds 2.1 Traditional Software Development Method Traditional automotive software usually adopts ‘V’ model development method, shown in Fig. 1. The left side of the ‘V’-process is software analysis and design; this can be called ‘implementation process’, the right side is a test and validation process against the left side to determine whether it meets the needs of users [2].

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Most of the tools in the ‘V’-process lack of model reusability and compatibility and can not achieve the results of the various stages automatically generated and used in the next stage [3]. It constrains the efficiency of software development. Meanwhile, the process lacks of feedback mechanism in the early stages of development, making the error or change in the early stages identified only at last. This has a serious impact on the quality and development efficiency of embedded software, increases the software development cycle, cost and reduces the available maintenance.

2.2 AUTOSAR Software Development Methodology AUTOSAR [3] standard is a summary of the international advanced development of automotive industry and proposes open, standardized software architecture for the development of automotive electronics. Based on the standard, automotive application software has good portability and scalability, and can achieve the reuse of existing software effectively. It is conducive to the exchange and updating of software. Using this standard can standardize interface characteristics in ECU with different structures, reduce the software development cycle effectively, and improve software quality. In recent years, it is gradually popularized in the automotive industry. And a number of research results have been acquired. AUTOSAR defines a methodology [4] for the automotive electronics software development process, shown in Fig. 2. The first stage is the system configuration phase: obtain the input of software components description, ECU resource description and system constraints description, and then generate the system configuration description file. The second stage is the ECU configuration: extract each ECU system configuration description information from the system configuration description file, then configure ECU. Finally, according to the ECU configuration description file the basic software configuration and integration with application software components based on AUTOSAR has been completed, and ultimately the executable code of the ECU has been generated. In order to achieve optimal results, this process may take several iterations. In recent years, model-driven development (MDD) [5] has been commonly used in software development, mainly to solve the consistency problem of the requirements and code, and can identify and solve problems at an early stage. MDD has been widely used in the field of aerospace. Automotive electronics, such as aerospace, belongs to the complex systems. The introduction of MDD in the development process will be the trend of development in the future. In this chapter, the method of MDD, ‘V’ model software development methods and AUTOSAR software development methodology have been combined together and a new software development process suitable for powertrain has been proposed.

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Fig. 2 AUTOSAR software development process

3 The Software Development Process of Vehicle Powertrain ECU 3.1 The Overview of the Development Process Based on the characteristics of the powertrain ECU software, referred to the traditional software development method and AUTOSAR software development process and combined with model-driven development approach and system engineering theory [6], a new software development process has been proposed, as shown in Fig. 3.This process rises software development to the level of system development.

3.2 Details of Software Development Process 1. System requirements analysis and goals setting In the new development process, requirements are described as models in the form of use case diagrams, which represent the function that the powertrain ECU control software should implement. As shown in Fig. 4, it includes several functions, such as: acquisition, output drive and communication. The acquainted data has been transmitted into the database, the operating modes judgment, calculation,

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Fig. 3 The software development process of Vehicle powertrain ECU

communication and diagnosis use case reads the data from database and writes new data to database, while actuator driver use case reads data from database and drive the actuator. The software requirements specification document, UML use case diagram can be formed at this stage. 2. System logical structure analysis and system physical structure definition Widely used in the system logical structure analysis process is the gradually decomposition of the system functions, which not only determine the system components but also their interfaces and functionality. System logical structure is a detailed definition of function network, functional interface and communication between various functions in the entire powertrain system. The system logical structure is the basis of the system’s physical structure. System logical structural analysis and functional simulation has been implemented by object modeling via offline simulation. And the result was supported by series of theory and methods of interdisciplinary engineering. System physical structure includes the definition of all the functions and sub-functions, which is also called as the software requirements, to assign different functions to different ECU. Documentation of system function, class diagram, relationship of class diagram can be formed at this stage. In series of cars, engine and drive system are often reused as different powertrain system, which can be distinguished according to the program and data versions. The

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reuse of components is better than any other decomposition by functions. The same components can be assigned to different ECU by different program version, such as: for the powertrain control programs, gear box control software component can be placed on the same ECU with engine control software component, or can be used alone in another ECU and communication with other ECU via the bus. 3. Software requirements analysis Design the detailed use case model and analyze the software requirements. The use case diagram and software requirements specification will be formed at this stage. 4. Establishment of the application software architecture and application software components Software components’ interfaces and properties, the operation mode and status of the software is important to determine the software architecture. Referred to the AUTOSAR standard, the software component description and software layers have been formed, and software components have been detailed defined. The detailed software architecture diagrams, class diagrams, and evaluation comment documents should be formed at this stage. The control software of powertrain has been divided into 8 software components, shown in Fig. 5: calculation, data acquisition, output driver, communication, status judgment, database, fault diagnosis, control.

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5. The design, implementation and test of software components The software component design phase is divided into the application software design and the basic software design. For the application software, the software components data, dynamic and real-time model had been implemented in detail. And code has been generated automatically. For the basic software, drivers and the operating system have been programmed. Rapid control prototype is used to verify the application software in this stage. Software component model (class diagram, state diagram, sequence diagram, activity diagram, component diagram), the code, software version, software debugging report, error log should be formed at this stage. Take data acquisition component for example, this chapter gives out it’s class diagram and sequence diagram (Figs. 6, 7). For status judgment component, detailed state diagram has been given out, shown in Fig. 8. 6. Integration of software components and software test Once the software components have been developed and passed the test, all components can be integrated into a software system for the final integration testing. Test includes the interface specification or variable name check, as well as memory allocation scheme which belonging to the static test. In this stage, the final version of the software, data version, production and service description file and the test report should be formed. 7. System integration and system test After the testing of software components, software should be integrated with the hardware as ECU. Then the ECU and other electronic components integrated, such as set point generator, sensors and actuators. In the next, the interaction of all system equipment should be tested. System test use the hardware in the loop simulation tools to evaluate the behavior of ECU functions. In this stage, the program version, data version should be updated and the review report should be formed. 8. Calibration The calibration of the ECU software functions always means the calibration of the parameters which are eigenvalues, characteristic curves and the characteristic diagrams. And these parameters have been set by calibration tools. The final version of the data, calibration records should be formed in this stage.

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9. System test and acceptance check Finally, according to the system logical architecture, the system test and acceptance check could be launched. The system test and acceptance report can be formed in this stage.

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In the software development process, varieties of software development documents and models have been formed, which improve the progress of work and product quality; ensure the controllability, sustainability and maintainability of software.

4 Applications Based on this development process, the ECU control system for a unit pump diesel engine has been developed. The software component interfaces, ports, and data types are configured in the UML model by using Rhapsody. Then the UML model has been designed in Rhapsody according to AUTOSAR standard and automatically generated to arxml flie (XML language file) and can be imported into Simulink [7, 8] to generate a skeleton model automatically. The whole process is shown in Fig. 9. The skeleton model is shown in Fig. 10. The model contains the input, output interfaces of the software components. This figure shows the structure of inject timing control component, which is a part of the control component. The component requires engine speed, fuel quantity calculated by calculation component as input. The injection timing can be obtained through the detailed internal behavior. The next step is to model the internal behavior in Simulink, refereed to the requirements model and the dynamic models in UML, shown in Fig. 11. The function of the injection timing model is to look up basic injection timing in a map by using calculated fuel injection quantity (Fuelquantity) and the engine speed (EngineSpeed). There is a certain delay angel (DelayAngle) between the fuel supply signal is giving out and the injection is beginning. Therefore, the final fuel supply advanced angle equals basic fuel inject timing add delay angle. Other application software components of the whole ECU can be obtained by using the same development process, shown in Fig. 12. Due to the limitations of time and transmission tools, the model transformation process is only carried out at a preliminary stage. The automatically conversion of whole ECU software system structure and the internal specific behavioral models will be realized in the future.

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Fig. 10 Skelton model of inject timing control component

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Fig. 12 Application software components in Simulink

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Fig. 13 RCP and HIL

Fig. 14 Real-time monitoring interface

Off-line simulation for verification the correctness of the full-range speed governor control has been developed for powertrain ECU. The rapid control prototype for unit pump diesel engine has been designed and directly connected to HIL simulation system using the diesel mean value engine model through signal interfaces, shown in Fig. 13. Diesel engine mean value model was downloaded to the hardware platform and monitoring interface was built by using ControlDesk. Figure 14 shows ECU control software monitor interface, which includes diagram of the engine speed, air charge pressure, air charge temperature, fuel injection timing, injection angle, the circulating fuel quantity, the actual speed and the target speed. The dynamic control process was shown by adjusting pedal and the PID parameters. Red curve in the curve window in Fig. 14 represents the target speed, the green curve represents the actual speed when drag the pedal bar (shown in the rectangle) to change pedal continuously. The result shows that the actual speed followed the target speed in timely.

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5 Conclusion Based on the model-driven development method, traditional software development method and rapid control prototyping technology, a new software development process for powertrain has been proposed in this paper. Using this process, software functions and software architecture can be verified in the early stage by simulation and experiment through HIL. Complied with the AUTOSAR standard, this process can improve system flexibility, configurability, code reusability and the efficiency of the powertrain ECU development.

References 1. Broy M (2006) Challenges in automotive software engineering. In: Keynote, Proceedings of the 28th international conference on software engineering (ICSE 2006), ACM Press 2. Schöuffeld J, Zurawka T (2005) Automotive software engineering principles, processes, methods, and tools 3. AUTOSAR Partnership (2008) Technical overview V2.2.1 R3.0 Rev 0001. http://www. autosar.org/ 4. AUTOSAR Partnership (2008) AUTOSAR methodology V1.2.1 R3.0 Rev0001. http://www. autosar.org/ 5. Model Driven Solutions and Data Access Techonologies, Model Driven Architectrue. http:// www.modeldriven.com/MDA.shtml,2010 6. Broy M (2005) Automotive software and systems engineering (IEEE 2005) 7. Sandmann G, Thompson R (2008) Development of AUTOSAR software components within model-based design. SAE 2008-01-0383 8. MathWorks, Key products for developing AUTOSAR applications, http://www.mathworks.cn/ automotive/standards/autosar.html

MCON: Automation Tool for MATLAB Modeling Development Based on V-Model Mingshi Xie and Wanrong Wang

Abstract The develop method of combining modeling together with the V-model process has been the main stream of automotive electronics software development internationally. This method is more effective and better for the complicated software. In the practice of modeling development based on V–model process, for some nonlogic design process, such as the drawing offramework model, the connection to upper layer, the build of simulation environment and integrating the model code will occupy a lot of working hours. All these steps seem discrete but they are interdependent and interact to each other. Analysis shows that the variable and software architecture are the main thread running through the entire modeling process. So a unified database is established to manage the main thread, and more, a tool named MCON is developed for modeling process automation. Practice has proved that the MCON can improve development efficiency a lot, and it can fully support the modeling process automation.









Keywords Matlab Modeling V-Model Automation MCON Model Framework

1 Introduction With the rapid development of automotive electronics technology, more and more advanced electronics technology and high-performance electronics systems are applied to vehicles. The software development of automotive electronics

F2012-D03-021 M. Xie  W. Wang (&) Pan Asia Technical Automotive Center Co. Ltd, Shanghai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_45,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 V-model software lifecycle

components is becoming more complicated rapidly. The traditional waterfall software lifecycle model, and manual coding approach has been unable to meet the development needs of the modern automotive electronics software quality and efficiency. As a result, V-model and object-oriented modeling method are introduced and they are becoming the mainstream method of the automotive electronics software development internationally [1–5]. As the V-model showed in Fig. 1, design phase and the testing phase is associated in parallel. This parallel has a lot of advantages such as rapid development, quality issues found in advance, low cost of reworking and so on. As the main tool for object-oriented modeling, Matlab also has a powerful data analysis capability, ample and powerful simulation testing environment, multilangue support for code generation advantage. Above advantages gives the strong support for the implementation of the V-model development procedure, especially for following phases: architecture design, module development, coding integration, unit testing, integration test. The combination of V-model and Matlab modeling will bring software engineer a lot of benefit without doubt. However, in the exact process of development, software engineering are found to spend many efforts on drawing the framework of the model, parameter configuration, connection and models integration etc. jobs, besides necessary software architecture design and logic development. And these activities are occupying a large proportion of the working hours during the modeling process. The analysis of these activities seems discrete but they are

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interdependent and interactive essentially. So, the possibility of developing a tool for extraction and use of common core elements in the modeling process, and for automate modeling is considered. Based on this goal, MCON is created. This tool manages a core of common elements by the establishment of a unified database for variables and software architecture. So, this tool can connect different phase of V-Model and it also has the automatic test function. The use of MCON can increase the modeling efficiency obviously in practice.

2 MCON Solution 2.1 Modeling Scope and Basic Concept The automotive electronics embedded software is divided into three lays in Autosar: application, the runtime environment and basic layer for software. In practice, Matlab modeling is mainly adopted for Application layer development. As to phases of V-model, architecture design, unit development, code integration, unit testing and integration test are mainly implemented by Matlab. SW-C: In AUTOSAR, application layer of software is consisted of SW-Cs (software-components), and SW-C is the smallest software module unit. SW-C Model: model for SW-C. Subsystem: Function group of specific category in a system, such as body control functions can be classified as exterior lighting, interior lighting, door locking etc. subsystems. Each subsystem application is formed by a number of the SW-Cs. Subsystem Model: Models created for subsystem. Framework of Model: Model with architecture and interface info, but not contains detailed logic and algorithm.

2.2 Establishment of the Model Database In specific practicing process, the main task of modeling process (see Fig. 2) contains designing and monitoring the behavior change of the variable in environment. Variables in each stage are organized based on the architecture design, for example in the Module Design stage, the modeling and the synthesis of SW-Cs is based on the software architecture. In some sense, it can be said that the modeling process is to complete organizing and monitoring the variables in accordance with the architecture design. Variables and architecture design are the common elements and main thread of the modeling process. If the architecture design and variables can be managed in accordance with certain rules, with the help of the tool chain, the discrete modeling steps can be automatically connected with each other by this main thread. The establishment of model framework,

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Fig. 2 Matlab modeling process and the corresponding modeling activity

model synthesis, and test environment setup can be completed automatically. In specific implementation, the establishment of database is in the architecture design phase. The task of this database is to manage the architecture design and variable attributes. The SW-Cs that define and use the variable as well as the division of SW-Cs should be explicit in the database.

2.3 Implementation Plan The building of MCON is based on the database analysis and the entire modeling process. Figure 2 shows Matlab modeling process and the corresponding modeling activity. The specific implementation plan of the modeling process will be introduced step-by-step bellow: 1. Architecture Design stage: database creation and management. MCON creates a unified database template. Programmers fill in each SW-C according to the template (i.e. the variable information and SW-C scheduling approach). SW-Cs are synthesized and create some information that contains: Subsystem path, which SW-C defines the variable, which SW-Cs use the variable, etc. According to the information above, MCON could analyze and summarize the synthesized model of SW-Cs, the related information of Subsystem Model, and the variable interface information which is used by model when automatically generating the codes.

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2. Module Design stage: creating and executing the matlab M Scripts, MCON can generate SW-Cs Model framework and SW-Cs synthesized model or the Subsystem Model framework on the basis of database information. 3. Unit Testing stage: base on the database information and matlab M script, MCON builds the white box test simulation model automatically for the SW-Cs Model, synthesized model or Subsystem Model which is built in Module Design stage. The simulation environment is need to contain the corresponding input and output variable controls, and some common testing programs which are summed up through modeling practice. 4. Integration Testing stage: on the basis of database information and matlab, for each Subsystem Model, MCON automatically builds dSPACE simulation test model. It is need to set up the corresponding input and output variables for the simulation environment. It can also introduce some common testing programs which are summed up through modeling practice, and configure the model in accordance with the dSPACE requirements. 5. Coding stage: on the basis of database information and matlab, MCON configures the SW-C model or Subsystem Model to generate application code for embedded code integration. It mainly uses the variable interface information of the database to configure the variable interface of SW-C model automatically, and set the model configuration. After this, compiling the model would generate the embedded code. Furthermore, through reading the variable attributes of the database and variables information in SW-Cs, MCON supports to generate the RTE layer software automatically for code integration. Model API can be generated with the interface of AutoSar or using manufacturer’s custom method.

3 MCON Realization MCON realizes standardization management of the database tables, automatic generation of the model framework and the simulation environment. It also realizes the automatic model testing. MCON uses EXCEL VBA to develop the main program, and makes use of the Matlab M file to access the model objects. Moreover, the EXCEL is used as the human machine interface. The realization of the MCON is listed below:

3.1 Standardization Management of the Database Tables MCON can automatically create the standard EXCEL template of SW-C. The programmer fills in the corresponding SW-C and variable attributes in the template sheet. The information to which subsystem or upper layer model the SW-C belong

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Fig. 3 The completed SW-C sheet

should be also filled in. Figure 3 shows the completed SW-C sheet. Each SW-C corresponds to a unique sheet. This sheet constitutes the core element of the database. Then MCON does a series of operation to input the data information during the whole modeling process. Through analyzing each SW-C Sheet filled by the programmer, the MCON integrates them into a data information set of the subsystem. It generates a subsystem called Global Signal Sheet, a subsystem called Calibration Sheet and the related.m Model Attachment, such as the *.m file contains the calibration variable and the *.m file defines the enumerated variables, for compiling and simulation in each modeling stage. Besides, according to the variable attributes of SW-C sheet filled by the programmer, MCON could generate the variable interface information for the SW-C Sheet automatically to support the variable port configuration during Matlab Model Coding Generating. The variable interface information includes Storage Class, Get Function(Function name corresponds to variable read), and Set Function (Function name corresponds to variable write). The variable interface information can be generated with the interface of AutoSar or using manufacturer’s custom method.

3.2 Model Framework and Simulation Environment Generation The following content states how to generate the framework of model. Firstly, it needs to establish the Model Template Document and finish the Matlab M Script Document. Model Template is the prototype, and further target framework of model is generated based on modification of this prototype. With the database information, MCON could call the Model Template and Matlab script to generate the calibration variable model, SW-Cs model framework and subsystem model framework in the module design phase. It would also

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Fig. 4 The generation process from the framework of SW-C model to framework of simulation model

generate framework of packaged subsystem model, and framework of simulation model for the unit testing as well as the integration testing. The variable attributes and related configuration have been set. User just needs to use the State Flow interface of the SW-C model framework to do the logic algorithm development. The simulation environment model framework consists of testing controls and the common testing program. This feature not only saves lots of manual operation, but also provides a good foundation for the automatic testing. Figure 4 shows the generation process from the framework of SW-C model to framework of simulation model.

3.3 Automatic Testing MCON supports automatic testing for the unit testing: The ‘‘Framework of Simulation Model for Unit Testing’’ is generated automatically. It contains an input control called ‘‘Signal Builder’’ and an output control called ‘‘Scope’’ (see Fig. 5). With the tools of MCON, the signal line of ‘‘Signal Builder’’ can be exported to *.MAT file, the output of ‘‘Scope’’ can also be exported to *.MAT file. Besides, MCON supports to import the *.MAT file into ‘‘Signal Builder’’. Thus, the signals of ‘‘Signal Builder’’ can be exchanged among multiple Simulation Models. The output data can also be imported into ‘‘Signal Builder’’ as the input data. It makes inputting testing signals easier. ‘‘Signal Builder’’ is selected as the testing interface, because of its graphic user interface and its characteristics that it can save the test case signals which is based on the time axis. Besides, MCON realizes automatic comparison between the new and old testing results (or between the new testing results and the expected testing results): MCON can read both the new and old testing results from the.MAT file and show them in the same graphical interface, see Fig. 6. Through calibrating the tolerance range, it could also determine the correctness of the testing results, then generate the testing report in HTML format.

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Fig. 5 The I/O testing control introduced by MCON

The previous contents introduced that MCON uses Signal Builder to save the Test Case signal. Then it presents that the MCON can determine the testing results automatically. These contents constitute a complete automatic testing process. Moreover, if filling in the test case in EXCEL with a certain format, MCON could import the test case signals into the Matlab Signal Builder, and compare the testing result with the expected value of test case. Then it determines the correctness, and generates the testing report.

3.4 Model Configuration for Coding Generating MCON supports to configure the model for codes generation: After the model attributes and I/O attributes being configured by MCON, the model can be easily compiled to generate the embedded codes. The variable interface information automatically generated by the database should be used in setting the I/O attributes.

3.5 Integration Model Based on the analysis of the variables and software architecture in database, MCON supports to generate the RTE layer software automatically for integration. Thus saves lots of works for manual coding and the code is generated according to the unified standard form.

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Fig. 6 Comparison between new and old testing results

The application of MCON makes the machine operation to replace a large number of manual operation, and greatly improves the efficiency of modeling. If a subsystem applies the full manual operation to build a Matlab simulation environment by Module Design, Coding, Uint Testing and Integration Test, it usually needs four weeks. However, Using MCON to assist in the development can save about one week. As well as improving the efficiency of modeling, MCON also helps to improve standardization of models and the modeling process.

4 Conclusion and Prospect MCON tools have been formally put into use for more than one year, and have completed modeling works for several projects. Facts have proved that: it effectively supports V-model process. It improves the modeling efficiency, reliability and team cooperation. Nowadays the Matlab model development is popular. And this MCON thought does have a better popularization significance.

References 1. Li X-l, Liu H (2006) Software test analysis of the V model and the CMMI model. Comput Eng Sci 12:107–109 2. XIAO L, XIE J (2006) On the testing method with automated tools based on V-model. J Chongqing Inst Technol 08:83–86 3. Qiu B, Zuo W, Wang F (2011) Research of automotive electronic control system based on Simulink/RTW. Comput Meas Control 2011(05): 1086–1088

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4. Li Y, Zhao P (2009) Software’s development theory and model by stages. J Weifang Educational College 4:85–86 5. Wang y-x, Qi X-j, Zhang J-z, SUN Y-t (2012) Development on automobile ABS controller based on dSPACE. J Heilongjiang Inst Technol 2012(01): 28–31

A Model-Based Design for Electronic Control Unit of Electric Motorcycle Sung-Suk Jnug, Jin-Ho Kim and Jea-Wook Jeon

Abstract A model-based design for the electronic control unit of an electric motorcycle is proposed. In recent years, in-vehicle systems have been distributed, and embedded software for controlling these systems has been steadily increasing in complexity and size [1]. Furthermore, because environmental pollution regulations have become stricter, research on green cars like HEV(Hybrid Electronic Vehicle) or EV(Electric Vehicle) are progressed actively in the automotive field. For these reasons, it is essential to prepare for the increased use of electric motorcycles by analyzing HW(Hardware) or SW(Software) platforms applied to vehicles, and developing appropriate integrated ECUs (Electric Control Units) for electric motorcycles. We have developed a system using MBD(Model-Based Design) in MATLAB/Simulink on an integrated ECU. Each function is designed as graphic blocks. Developers can easily design systems using modeling. Eventually, preverification will be achieved through rapid prototyping and simulation, which will have a decisive effect on reducing the time and cost of development.

 



Keywords Model-based design Electric motorcycle ECU (electronic control unit) SW (software) platform HW (hardware) platform keyword



F2012-D03-022 S.-S. Jnug (&)  J.-H. Kim  J.-W. Jeon School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_46,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction In the face of environment crisis and the exhaustion of natural resources, various benefits and charge infrastructures are being forged for the growing supply of electric motorcycles based on support by governments in various countries. Electric motorcycles receive much attention as typical green transportation, and appropriate electronic control techniques are necessary for high performance and high reliability and for controlling the various electronic systems in such as motors, batteries, etc. [2]. For these reasons, in a previous paper, we designed a HW and SW platform and developed an integrated ECU for performance evaluation through efficient and methodical development and simulation motorcycles. The importance of embedded software is magnified due to increased use of sensors, actuators and electronic control devices in motorcycles [3]. It is necessary to have an easy way to check for errors and to recover systems using diagnosis and network management functions. Opportunities for improving performance, safety, and maintenance through the use of sophisticated, on-board, software-based electronic controls can be provided by MBD in the automotive field [4]. We developed basic software using MBD. If a new system is developed for commercial purposes, various applications can be designed readily using the software. Consequentially, significant time and cost is saved because we can implement, pre-verify and test several times before a whole system is developed completely. Figure 1 show the role of integrated ECU. The Integrated ECU applies to monitoring system, simulation, BMS(batter management system) and motor control and so on.

2 Platforms for Electronic Control Units 2.1 SW Platform The SW platform for motorcycles is designed using the above SW modules. If a developed SW platform is applied to motorcycle development, it development time and cost can be reduced due to outstanding portability and reusability such as OSEK and AUTOSAR [5, 6]. Figure 2 shows that a SW platform for electric motorcycle consists of five layers: the device driver, abstraction, firmware, library and application. • Device driver – Various interfaces for Digital IO, analog IO, communication channel and motor control – This section connects directly to HW structure

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Fig. 1 Integrated development environment

Fig. 2 SW platform for electric motorcycle

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Table 1 Information for HW platform MCU Infineon XC2000 family Serial CAN ADC DAC PWM Digital Input Digital Output

2 2 5 4 4 3 3 3 3

Ch Ch Ch Ch Ch Ch Ch Ch Ch

144pin, 100pin RS-232 CAN 2.0A/B 0*5V 0 * 10 V 0*5V 0*5V 0 * 10 V 0*5V 0 * 10 V

• Hardware abstraction layer – Division between dependent and independent source code – Connecting device driver layer and library layer • Firmware based task management – Real time multiprocessing – Multitasking – Task management based on interrupt and timer • Library – Proven library to connect application layer and low layer – Application reuse based on standard • Application – Supporting Control algorithm and application program using library layer. The SW platform developed for motorcycles connects high task management and library layers with the low device driver layer. It is possible to reuse applications by mapping the HW abstraction layer on the device driver layer [7, 8]. Firmware based tasking management is done by the task-scheduling layer. As mentioned previously, vehicles have quite a larger number of ECUs performing unique functions and SW complexity. Therefore, tasks are managed by task priority. However, because motorcycles have fewer ECUs and tasks than other vehicles, the system can be controlled by using a multi-timer to manage tasks rather than task priority.

2.2 HW Platform Table 1 and Fig. 3 show the overall HW platform composition [9, 10]. XC2265 N was chosen as the most efficient MUC. RS-232 standardizes the interface connecting the DTE (Data Terminal Equipment) and DCE (Data Communication Equipment) that transmits serial binary data. RS-232 communication in this

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Fig. 3 HW platform for electric motorcycle

embedded system plays a role in monitoring and debugging whether or not work is complete for each function. CAN communication is the most frequently used for in-vehicle network. It is expected to be faster and a more reliable proven network than other networks, and is needed for the growing field of electronic devices in electric motorcycles. All modules in this embedded system control the driving section, which plays a role in generating driving force, use CAN communication to transmit the data. Various sensors, including radar sensors, laser sensors, lidar sensors, vision sensors, and ultrasonic sensors, pressure sensors, temperature sensors and steering angle sensors will be used for smart vehicles in the near future. The ADC/DAC module is designed for sensors applied to various applications for motorcycles. The ADC converts analog sensors output signals to digital signals of 0*5 or 0*10 V using amplification and filters. The DAC is applied to devices using an analog input signal, such as an audio system. The ADC/DAC in this embedded system is used to control conductivity sensors, illuminance sensors, speed sensors, etc. The Motor control module to control the motor of the driving section in electric motorcycles is configured to PWM mode. PWM can control the motor using pulse width and regular cycles. Finally, the Digital and Analog input/ output plays a role in the control of the motor, as well as switching, for various applications which require 0*5 or 0*10 V, including brakes, lights, turning signals, selecting mode, controlling hill hold valve and precharge relay, etc.

3 Model-Based Design Figure 4 shows the general software development process applied to an embedded system. The first step is requirement analysis. Next is the development of the prototype for implementation and verification of whether or not the system operates properly [11].

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Fig. 4 Development process

MBD uses graphic blocks for software algorithms. It enables developers without knowledge of the hardware to design, simulate, test and verify various models. MBD can often be verified from the beginning step to the last step before developing MBD completely. The errors and faults in the system are detected at the beginning step. Auto-code generation can be used to reduce the amount of errors since hand-coding is not needed, and time and cost to develop the system is saved. With Model-Based Design, engineers improve efficiency by [12]: • • • • • • • •

Using a common design environment across among project teams Linking designs directly to requirements Integrating testing with design to continuously identify and correct errors Refining algorithms through multidomain simulation Automatically generating embedded software code and synthesizable HDL code Developing and reusing test suites Automatically generating documentation Reusing designs to deploy systems across multiple processors and hardware targets.

Figure 5 shows that MBD processes the several steps in the yellow box at one time. It is possible to integrate design, implementation and simulation simultaneously. For these reasons, the cost and time of development are saved, and the probability of errors can be reduced [13]. MBD in vehicles is used in various fields, such as safety systems, powertrain and body controls. MBD is even applied to the development of entertainment and multimedia systems. Recently, since it is complicated designing with standard software like AUTOSAR and OSEK OS, and embedded systems in the automotive field, developers that design embedded systems implement functions using graphic blocks in MBD. It is more readable and reusable system than manual programming. The graphical models

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Fig. 5 V-Model

provide graphical results as well as simulation function that enable developers to be understood readily. In addition, as the control logic comprises a combination of graphical blocks, it is easy to understand the flow of the algorithm [14]. MBD can generate auto-code and executable files that can be used in embedded systems without any manual programming or modifications necessary [13].

4 Model-Based Design for Basic Software After the system model is designed by MBD In MATLAB/Simulink, embedded code can be generated automatically. The MATLAB/Simulink supports an autocode generator in Real-Time workshop [14]. The MATLAB/Simulink provides support for the following MCUs used to design embedded systems to develop functions easily. • Freescale: MPC55xx, MPC74xx, MPC5xx • Infineon: C166, TriCore • Texas Instruments: C2000, C5000, C6000. The developer can set one of the above MUCs as the target processor. Then, embedded code and a built project are automatically generated. As mentioned, because developers can automatically generate embedded code for prototyping targets, implementation errors and target system inconsistencies may be avoided [14, 15].

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Fig. 6 Digital IO, communication device drivers

We developed integrated ECU with an Infineon XC2265 as the target system. In order to compile auto-code generated automatically, the target compiler is set to TASKING Classic through IDE Link Target Preferences. Then, because MATLAB/Simulink does not support the XC22xx family, we have to configure an appropriate driver for XC2265. We designed various function models related to general purpose platform using MATLAB/Simulink. We designed these blocks for Digital IO, serial communication, and CAN communication in Fig. 6. The CAN is based on the communication protocol in this system. Since it is necessary to analyse CAN messages, the graphic blocks of CAN pack and CAN unpack were designed. The blocks of CAN Transmit and CAN Receive are for CAN communication. The blocks of ASC Transmit and ASC Receive are for RS-232 serial communication. The blocks of Digital In and Digital Out are designed for various modules in the integrated ECU. Because the XC2200 family is not supported in the Real-Time Workshop Embedded Coder, XC2265 Resource Configuration was designed for target configuration. Thus, a developer can build a system with graphic blocks by designing the modules comprising an integrated ECU for electric motorcycles without hand-coding. Figure 7 is an example model using the developed graphic blocks. The example model consists of three tasks. The first is the CAN communication control model. The second task is serial communication. This task prints out ‘‘Hello World’’ to a serial communication terminal. The last task controls LEDs using Digital Out block.

5 Experiment The Embedded system for electric motorcycles is developed in Fig. 8. Two layers were designed because the MCU can be changed variously depending on electric motorcycle models. The high layer is for MCU platform. And the low layer

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Fig. 7 Example modeling

Fig. 8 The experiment configuration

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supports IO, communication interface, ADC/DAC, etc. This embedded system can reuse proven IO, communication interface through mapping between high layer and low layer. Figure 8 shows an experiment configuration. The model was designed using developed graphic blocks in MATLAB/Simulink. The embedded code is generated automatically by building designed model. Since MATLAB/Simulink is interlocked with TASKING Classic, the generated embedded code can be ascertained in TASKING compiler. The auto-code is compiled for making an executable file by TASKING compiler. The executable file is downloaded to the integrated ECU. We use an example model in Fig. 7. We checked digital out signal using LEDs in the integrated ECU. The CAN communication was checked by the Kvaser canking tool that monitors CAN messages and we checked ‘‘Hello world’’ on serial communication by the terminal program.

6 Conclusion The various functionalities in an electric motorcycle can be optimized quickly by controlling the ECU with various methods. Program errors will be reduced using model-based programming. And it enables to implement and verify the system without hardware. For these reasons, if we develop the new model using MBD, it is expected to save development time and cost [16]. Acknowledgments This work was supported by the Ministry of Knowledge Economy and the Korea Institute for Advancement in Technology through the A Development of High Efficiency Electric Motorcycle and Construction of Integrated Development Environment.

References 1. Sung-Suk J (2012) Design and implementation of HW/SW platform for electric motorcycle. Industrial technology (ICIT), IEEE international conference on March 2012, pp 760–765 2. Suk-Hyun S, Jin-Ho K, Sungho H, Key Ho K, Jae Wook J (2009) A model-based design for electronic control units based on OSEK/VDX. Industrial electronics, ISIE 2009. IEEE international symposium on July 2009, pp 681–685 3. Tom E, Michael B, Michael C, Wensi J (2008) Applying model-based design to commercial vehicle electronics systems. The MathWorks, Inc 4. Smith PF, Prabhu SM, Friedman J (2007) Best practices for establishing a model-based design culture. The MathWorks, Inc 5. The MathWorks, Inc. [Online] Available: http://www.mathworks.com 6. Oh WH, Lee JH, Kwon HG, Yoon HJ (2005) Model-based development of automotive embedded systems: a case of continuously variable transmission (CVT). Embedded and realtime computing systems and applications. Proceedings of the 11th IEEE international conference on, pp 201–204 7. Sangiovanni-vincentelli A (2003) Electronic-system design in the automobile industry. Micro IEEE 23(3):8–18

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8. Simonds C (2003) Software for the next-generation automobile. IT professional 5(6):7–11 9. Ping X, Peicheng S (2011) Research on the driving system of hybrid electric motorcycle. Electric information and control engineering (ICEICE), International conference on April 2011, pp 2532–2536 10. XC2000 Brochure XC226xN Data Sheet, XC228x Data sheet, Infineon, Infineon Technologies AG [Online] Available: http://www.infineon.com 11. OSEK/VDX [Online] Available: http://portal.osek-vdx.org 12. AUTOSAR, AUTomotive Open System Architecture, [Online] Available: http://www. autosar.org 13. Erkkinen T (2007) Automatic code generation—technology adoption lessons learned from commercial vehicle case studies. The MathWorks, Inc 14. Vijayagopal R, Shidore N, Halbach S, Michaels L, Rousseau A (2010) Automated model based design process to evaluate advanced component technologies. SAE International 15. Di Natale M (2008) Design and development of component-based embedded systems for automotive applications. Reliable software technologies lecture notes in computer science, pp 15–29 16. Friedman J (2006) MATLAB/Simulink for automotive systems design. Design, automation and test in Europe, pp 1–2

Model Based Nonlinear Controller Design for Fuel Rail System of GDI Engine Pengyuan Sun, Baiyu Xin, H. Chen and J. Li

Abstract The precise control of rail pressure in GDI engines is an important issue. To reduce the workload of calibration and enhance the robustness in automotive product development process, a model-based controller design method is presented in this paper. A control-oriented fuel rail system nonlinear dynamics model, involving the high pressure pump, the fuel rail and the injectors, is established. The backstepping technique is used to derive a nonlinear rail pressure controller for the simplified model. The simulation results with MATLAB/Simulink demonstrate the effectiveness of the proposed control scheme, and control precise and response satisfy the design requirements.





Keywords GDI engine Fuel rail high pressure control Backstepping algorithm

1 Introduction The gasoline direct injection (GDI) technology has been widely used in the internal combustion engine to meet the increased environmental requirements and demands on decreased fuel consumption. The unique working mode of GDI engine that the fuel injection happens in the cylinder directly, determines its economy and emissions [1]. The fuel injection system based on the Fuel Rail architecture is a

F2012-D03-026 P. Sun (&)  J. Li China FAW Co., Ltd R&D Center, Mainland, China B. Xin  H. Chen Jilin University, Changchun, China SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_47,  Springer-Verlag Berlin Heidelberg 2013

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key device in GDI engine. The stable rail pressure makes GDI engine easy to control precisely the fuel injection. So the fuel rail pressure control becomes one of main tasks in GDI engine. Many research studies have been carried out on the rail pressure control topic. For instance, a model reference adaptive control algorithm based on a common rail (CR) mean value model is proposed to reduce the residual pressure in the rail [2], and the results are satisfactory. The author of the paper [3] proposed an injection pressure regulation to stabilize the fuel pressure in the CR fuel line. The experimental results with the closed loop performance confirm the effectiveness of the control algorithm in the mean value rail pressure model. In the paper [4], the author identifies the second-order CR system model through the experiments data, and a CR pressure robust controller is designed and analyzed under the QFT control theory. A feedforward based fuzzy PID controller is developed for the CR pressure control in the paper [5]. In the paper [6], a CR fuel system is modeled by system identification theory, and the model validation is then carried out with experimental data. A rule modelling method is provided for the diesel engine, and a slide controller is derived for the rail pressure control [7]. However, the choice of the sliding surface depends on experience, and there are some differences between GDI engine and diesel engine in the fuel system. GDI engine works with unique features. In the automotive product development process, model-based controller design methods have been widely accepted to reduce the workload in engineering calibration and improve the control performance. In this paper, a mathematical model of fuel rail system based on the structure of GDI engine is established. There are some nonlinear characteristics in the model. A nonlinear rail pressure controller is derived by backstepping technology with the simple model. The simulation results with Simulink validate the controller performances. The paper is organized as follows. The math model of the GDI engine fuel rail system is briefly described in Sect. 2, including the general description of the fuel rail system’s operational principle. And a nonlinear controller is derived by backstepping technology in detail in Sect. 3. Then simulation results are presented to validate the backstepping controller performances in Sect. 4. Section 5 gives the conclusions.

2 The Math Model of the Fuel Rail System As one of the most important parts in GDI engine fuel system, the fuel rail system’s structure and characteristics make the injection pressure up to 150*200 bar. The pressure is independent of the engine speed to ensure a good spray atomization in a low engine speed. The basic structure of the GDI fuel rail system includes a pressure control valve, a high pressure pump, a fuel rail, the injectors, a rail pressure sensor and the Electronic Control Unit (ECU). Take CA4GA1T1 engine, made by FAW, as an instance, the structure diagram of the GDI fuel system is shown in Fig. 1.

Model Based Nonlinear Controller Design

505 Fuel rail

Rail pressure sensor

High pressure pump High pressure injectors

Fuel pump Fuel tank

ECU

Fig. 1 The diagram of the fuel rail system

The low pressure pump generates about 3*5 kg/cm2 fuel pressure. The fuel flows into the high pressure pump passed by the pressure control valve. The fuel pressure is raised up to 50*120 kg/cm2 by the high pressure pump. The fuel rail is a fuel container made by aluminum alloy to absorb the pulse of the high pressure fuel. The injectors are connected with the rail and get a high injection pressure. The ECU gets the real-time rail pressure from the rail pressure sensor as a feedback signal and sends the order to the pressure control valve for rail pressure control. The other function of the ECU is to issue the fuel injection pulse width commands for injection. The high pressure pump, a pressure sensor, a pressure limiting valve and injectors are installed next to the fuel rail. The pressure limiting valve prevents the fuel rail from the damage by excessive pressure. The high pressure pump is lubricated by gasoline. The outlet check valve of the high pressure pump ensures the system to work. Considering compressibility of the fuel, the basic principle of modelling is shown in the following expression [7]. Kf ¼ 

dp dp ¼ dv=v dq=q

ð1Þ

where Kf is the bulk modulus of elasticity defined as the relationship between the density and pressure, q is the fuel density, p is the pressure of the fuel and the volume is defined as v. The relationship between the volume change and the pressure change can be got from Eq. (1), dp Kf dv ¼  dt v dt

ð2Þ

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Fig. 2 The principle of the high pressure pump in GDI engine

And dv dvm ¼  qin þ qout dt dt

ð3Þ

Where dv=v takes into account the intake and the outtake flows qin and qout ; and the volume changes dvm =dt due to the motion of mechanical parts. According to the energy conservation law, the fuel flows can be calculated as the following expression. sffiffiffiffiffiffiffiffiffiffi 2jdpj q ¼ sgnðdpÞ  cd  A0  ð4Þ q Where A0 is the interested orifice section, cd is discharge coefficient defined as the ratio of actual and ideal flows, which is decided by the shape of the cross section. dp is the pressure difference of the cross section on both sides. Based on the above principle, the mathematical model of the GDI fuel rail system is established. To achieve the goals that the model can catch the fundamental physical aspects and the model is simple enough for control, a controloriented model of a GDI fuel rail system is deduced.

2.1 The High Pump Model There are many types of the high pump structure in fuel rail system. One of the types is cam-driven structure. The principle is shown in Fig. 2.

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The cam is driven by the crankshaft of the engine in normal operation. The piston moves downward, when the cam moves to the pump bottom dead centre from the pump top dead centre. Due to the pressure difference, the fuel flows into the high pressure pump from its intake. As the check valve exists, there is no backflow at the outlet of the high pressure pump. The fuel flows to the fuel rail from the high pump, when the pressure control valve keeps close and the cam runs to the pump top dead centre from the pump bottom dead centre. On the contrary, the fuel flows back to the low pressure circuit, if the pressure control valve is open. The pumped fuel volume is controlled by the control valve at the inlet of the high pressure pump. The fuel flow pressure equation in the high pressure pump is shown as follows:   Kf ðpp Þ dvmp p_ p ¼ þ qu  qpr  q0 ð5Þ vp ðhÞ dt Where qu is the volume flow at the inlet of the high pressure pump, qpr is the intake volume flow of the fuel rail, and q0 is the leakage fuel. The fuel volume change due to piston motion is dvmp =dt: dvmp dhp dhp ¼ Ap  ¼ Ap  xrpm dt dt dh

ð6Þ

And qu and qpr can be written in the form: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   2 Pt  p p  qu ¼ sgnðPt  pp Þ  ctp  ðU  Atp Þ  q qpr ¼ sgnðpp  pr Þ  cpr  Apr 

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   2 pp  pr  q

ð7Þ ð8Þ

where U is the state of the pressure control valve. U ¼ 0 when the valve is closed contrarily, U ¼ 1 when the valve is open. ctp and cpr are the discharge coefficient of the inlet of the high pressure pump and the discharge coefficient of the rail inlet respectively. Atp and Apr are the interested orifice section of the high pressure pump inlet and the interested orifice section of the rail inlet. Ignore the change of the fuel density caused by the pump pressure variety. The pump pressure state can be rewritten as Eq. (9), and vp ðhÞ ¼ Vp0  Ap  hp ðhÞ: rffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2jPt pp j Kf ðpp Þ dhp p_ p ¼ vp ðhÞ ðAp  xrpm dh  sgnðPt  pp Þ  ctp  ðU  Atp Þ  q rffiffiffiffiffiffiffiffiffiffiffiffiffi ð9Þ 2jpp pr j  q Þ sgnðpp  pr Þ  cpr  Apr  0 q

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2.2 The Fuel Rail In short, the fuel rail is a fuel container with a certain volume. As a storage component, the main effect of the fuel rail is to make the hydraulic pressure stability, reduce the pressure fluctuation, and hold the fuel pressure. The rail pressure sensor and pressure limiting valve are beside the rail. To simplify the model, the volume of fuel injected qri is considered as a disturbance known. Ignore the tiny variety of the volume flow which is caused by pressure change. Then the model of fuel rail can be written in the following form. p_ r ¼

Kf ðpp Þ ðqpr  qri Þ Vr

According Eq. (4), the rail model representation is 0 1 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi     2 p  p Kf ðpr Þ @ p r  qri A p_ r ¼ sgnðpp  pr Þ  cpr  Apr  Vr q

ð10Þ

ð11Þ

Where Vr is the volume of the fuel rail.

3 Design of a Backstepping Controller for the Fuel Rail System According to the structure and the operation principle of the GDI fuel rail system, the block diagram of the rail pressure control is shown in Fig. 3. The reference rail pressure, which is decided by the engine working condition, and the real-time rail pressure are the inputs of the controller. The output of the controller affects the GDI fuel rail injection system. The system model should be simplified in order to design an appropriate control law. The injection quantity and the leakage of the high pressure pump are considered as the disturbance with uncertainty, and the impact of the fuel pressure on the volume bulk modulus is neglected here. Then the system model is stated as Eq. (12). 8 rffiffiffiffiffiffiffiffiffiffiffiffiffiffi > 2jPt pp j > Kf dhp > p_ p ¼ vp ðhÞ ðAp  xrpm dh þ sgnðPt  pp Þ  ctp  ðu  Atp Þ  > > q > > r ffiffiffiffiffiffiffiffiffiffiffiffiffi < 2jpp pr j ð12Þ  q0 Þ sgnðpp  pr Þ  cpr  Apt  q > > r ffiffiffiffiffiffiffiffiffiffiffiffiffi > > > > 2jpp pr j > : p_ ¼ Kf ðsgnðp  p Þ  A  q Þ r

Vr

p

r pr

pt

q

ri

Define x1 ¼ pp ; x2 ¼ pr and u ¼ qu : Taking the actual application into consideration, the ECU can control the pump only when x1 [ x2 ; and when x1  x2 ; the rail pressure will be reduced as engine runs. So the state-space representation is

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Fig. 3 The block diagram of the rail pressure control in the fuel rail injection system

8 pffiffiffiffiffiffiffiffiffiffiffiffiffiffi Kf dh ðAp  xrpm dhp þ u  a12  x1  x2  q0 Þ < x_ 1 ¼ vp ðhÞ :

K

x_ 2 ¼ Vrf ða12 

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi x1  x2  qri Þ

ð13Þ

Where a11 and a12 are shown as a11

sffiffiffi sffiffiffi 2 2 ; a12 ¼ cpr  Apr  ¼ ctp  Atp  q q

ð14Þ

Because there are coupling between x1 and x2 ; a new variable z is introduced as pffiffiffiffiffiffiffiffiffiffiffiffiffiffi z ¼ x1  x2 : Then equations of state system can be written with the new variable z as 8 h i < z_ ¼ 1 Kf  A  x dhp þ Kf  u  ðKf þ Kf Þ  a  z  Kf  q þ Kf  q ri p rpm 12 0 2z vp ðhÞ dh vp ðhÞ Vr vp ðhÞ vp ðhÞ Vr : x_ 2 ¼ Kf  ða12  z  qri Þ Vr

ð15Þ

The whole system is divided into several sub-systems in backstepping technique. By building the state error, the virtual control input is designed for each sub-system. The stability of the system is ensured by Lyapunov theory. The derived process of the controller is simple. There are many success cases in the aerospace and process control areas [8]. So the backstepping technique is used to derive the nonlinear controller with a feed-forward controller for the problem stated earlier. According to the form of the system state equation, the fuel rail tracking error variance e2 is defined to be e2 ¼ x2  x2 : e_ 2 ¼ x_ 2  x_ 2 ¼ x_ 2 

Kf ða12  z  qri Þ Vr

ð16Þ

As the first Lyapunov function, V2 is defined as V2 ¼ 12 e22 : If the virtual control z is selected as z ¼ KfVar 12  x_ 2 þ Kkf1aV12r  x2  Kkf1aV12r  x2 þ aq12ri ; and when z is equal to z ; there is Eq. (17). Kf V_ 2 ¼ e2  ½_x2  ða12  z  qri Þ ¼ k1  e22  0 Vr

ð17Þ

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And the system meets the Lyapunov stability condition. A new error variable e1 is selected as e1 ¼ z  z. The error e1 is rewritten in the form: e1 ¼

Vr k 1  V r  k 1  Vr qri  x_  þ x   x2 þ z Kf  a12 2 Kf  a12 2 Kf  a12 a12

ð18Þ

The system Lyapunov function is selected as the form V1 ¼ 12 e21 þ 12 e22 ; and if the derivative of V1 is negative, the fuel rail system is stable.   Kf Kf  qri V_ 1 ¼e1  e_ 1 þ e2  e_ 2 ¼ e2 x_ 2   a12  z þ þ e1  e_ 1 Vr Vr   ð19Þ Kf  a12 ¼  k1  e2x þ e1  e2 þ e_ 1 Vr K a

When the control law is selected as (20), the condition e1 ð fVr 12  e2 þ e_ 1 Þ ¼ k2  e21 is met, and there is an equation likes Eq. (21) to make the system stable. u¼

vp ðhÞ Kf dhp Kf Kf Kf  ½ vp ðhÞ  Ap  xrpm  dh  Vr  qri þ vp ðhÞ  q0 K Kf K a Þ  a12  z þ 2z  fVr 12  ðx2  x2 Þ þ 2z  KfVar 12 þðVrf þ vp ðhÞ þ2z  Kkf1aV12r  x_ 2  2z  Kkf1aV12r  x_ 2 þ 2z  ðaq12ri Þ0 þ2z  k2 ðKfVar 12  x_ 2 þ Kkf1aV12r  x2  Kkf1aV12r  x2 þ aq12ri  zÞ

V_ 1 ¼ k1  e21  k2  e22  0

 €x2

ð20Þ

ð21Þ

In order to make the controller applied in the actual fuel rail system, the final control law is collated as (22). u ¼ AðzÞ þ Kp ðx2  x2 Þ þ Kd ð_x2  x_ 2 Þ

ð22Þ

The parameters of PD feedback control law are Kp and Kd : AðzÞ is the feedforward. AðzÞ ¼ Ap  xrpm 

dhp vp ðhÞ vp ðhÞ  Vr  þ q0 þ a12  z þ  €pr  ða12  z  qri Þ þ 2z  2 Vr dh Kf  a12 ð23Þ Kp ¼ 2z 

  vp ðhÞ Kf  a12 k1  k2  Vr þ  Kf Vr Kf  a12

ð24Þ

vp ðhÞ ðk1  k2 Þ  Vr  Kf Kf  a12

ð25Þ

Kd ¼ 2z 

Considering the form of the system state equations, the dynamic fuel characteristics of the high pressure pump and the rail are included in the feed-forward.

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Table 1 The values table of the parameters in GDI fuel rail system The volume of rail (m3) The The The The The The

2

section of the inlet in high pressure pump (m ) section of the inlet in fuel rail (m2) section of the pump piston (m2) max volume of high pressure pump (m3) leakage of the high pressure pump (m3/s) pressure supplied by low pressure pump (bar)

65:824  106

8:553  106  0:75 12:566  106  0:65 78:5398  106 0:27  106 0:0005 5:7

Fig. 4 The block diagram of the close-loop fuel rail system with backstepping controller

4 The Controller Performances To test the rail pressure, a fuel rail plant model is established with Simulink software. And the parameters of the fuel rail system are shown in Table 1. With the parameters, the Simulink model works well and can reflect the actual fuel rail system characteristics to a certain extent. Embed the backstepping rail pressure controller into Simulink system, as shown in Fig. 4. Two different operating conditions by using Simulink software are simulated to test the control law.

4.1 Condition 1: Constant Pressure Tracking Test Set the referenced rail pressure as 150 bar. The stable engine speed is 5000 rpm The fuel injection pulse width is 2.2 ms, and 66. The start angle of injection is

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Fig. 5 The simulation result for the constant pressure tracking test 1

Fig. 6 The simulation result for the constant pressure tracking test 2

270and the density of the gasoline is 0.73 kg/L. The controller parameters are set as k1 ¼ 5:1; k2 ¼ 3:03: Within the 0.5 s simulation, the rail pressure can be stable at the set-point pressure by the control action. The results are shown in Figs. 5 and 6. The max error is less than 0.4 bar.

4.2 Condition 2: Sine Function Referenced Pressure Tracking Test To verify the dynamic tracking performance of the nonlinear controller, a sine pressure tracking test is implemented. The signal sine has amplitude of 10, bias of 140 bar and frequency of 4.5 rad/s. In Figs. 7 and 8, the rail pressure is properly taken close to the set-point with a small fluctuation. The max error is less than 2

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Fig. 7 The simulation result for the sine function referenced pressure tracking test 1

Fig. 8 The simulation result for the sine function referenced pressure tracking test 2

bar. The backstepping controller is still able to maintain the rail pressure close to the reference value. From the results, the controller designed before has the performance to stabilize the rail pressure. The real-time bench test will be implemented at the next step for validating the controller real-time performance.

5 Conclusions To meet the rail pressure control requirements in GDI fuel rail system, this paper presents a mathematical model (in the control-oriented) of a fuel rail system for GDI engine. The model is obtained by the main fluid dynamic. The parameters are

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obtained from the real geometrical data of a real GDI engine. Then the rail pressure controller is derived by backstepping technology based on the simple model. The controller performance is validated preliminarily by the simulation. This method-based on model reduces the calibration work and the development cost of ECU. For the application of this nonlinear rail pressure controller, the fuel pressure in high pressure pump is needed to be known. So an estimator for the pump pressure may be the next work in the future. Then the hardware in loop experiment will be finished for testing the on-line performances of the fuel rail controller with a real GDI fuel rail test bench. Acknowledgments Thank to the sustentation of the National Nature Science Foundation of China and Program for Chang jiang Scholars and Innovative Research Team in University.

References 1. Myung C, Park S (2011) Exhaust nanoparticle emissions from internal combustion engines: a review. Int J Automot Technol 13(1):9–22 2. Montanaro U, Gaeta A and Giglio V (2011) An MRAC approach for tracking and ripple attenuation of the common rail pressure for GDI engines, preprints of the 18th IFAC world congress, Milano, Italy, 28 Aug–2 Sept 2011 3. Corno M, Savaresi S, Scattolini R, Comignaghi E, Sofia M, Palma A and Sepe E (2008) Modelling, parameter identification and dynamics analysis of a common rail injection system for gasoline engines, proceedings of the 17th World Congress The International Federation of Automatic Control, Seoul, Korea, 6–11 July 2008 4. Chatlatanagulchai W, Aroonsrisopon T and Wannatong K (2009) Robust common-rail pressure control for a diesel-dual-fuel engine using qft-based controller. SAE paper 2009-011799 5. Su H, Hao G, Li P, Luo X (2010) Feed forward fuzzy PID controller for common-rail pressure control of diesel engine, proceedings of 2010 international conference on measuring technology and mechatronics automation. Changsha, China, 13–14 March 2010 6. Gaeta A, Fiengo G, Palladino A and Giglio V (2011) Design and experimental validation of a model-based injection pressure controller in a common rail system for GDI engine, 2011 American control conference on o’farrell street, San Francisco. CA, USA, 29 June–01 July 2011 7. Lino P, Maione B, Rizzo A (2007) Nonlinear modelling and control of a common rail injection system for diesel engines. Appl Math Model 31:1770–1784 8. Khalil H (2007) Nonlinear systems, 3rd edn. House of Electronics Industry Press, pp 589–603

Research on OBD Performance of T-GDI Engine Song Yan, Pengyuan Sun and Tonghao Song

Abstract In order to fulfill requirement of Euro V regulation and meet the high reliability and performance requirements, the On-board Diagnostics (OBD) software architecture of turbo-charged GDI engine is redesigned, the analysis and improvement of OBD reliability and performance based on software structure are described in this chapter, the main factors have been taken into consideration. Keywords GDI

 EURO V  OBD

1 Introduction According to the inspection of EPA (Environmental Protection Agency), above 60 % air pollution from passenger car was caused by the fault of the emission control system. In recent years, many techniques have been applied to reduce the pollutant emission. On-board Diagnostics technique had been introduced to vehicle for this purpose in 1989 when the first OBD regulation released by CARB (California Air Resources Board), and the similar legislation appeared in European Union [1], which is called EOBD regulation. The Euro V stage, including EOBD for SI-engine on passenger vehicles, separated into two sub-stages, named as EU5a and EU5b, or as EU5 and EU5 plus, has been implemented since 2009 (Fig. 1). F2012-D03-027 S. Yan (&)  P. Sun  T. Song FAW R&D Center, Changchun, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_48,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 Schedule of OBD implementing regulation

According to the clauses from EOBD regulation, all components or systems which might lead emissions exceed the standard limits should be monitored, and monitors should give some indications on finding any fault or deterioration of components or systems. In addition to the illumination of the MIL, the faults storage and a standardized environment datasets are required. All information should be sent to an external scan-tool by a standardized communication protocol and interface. In recent years, the OBD software development has encountered challenges because it is experiencing a continuous increase of functionality extent and complexity. Today, in some high-end SI-engine based on Euro V regulation, the OBD related executable software codes in engine control unit is above 40 %, and the number of OBD calibration parameters exceed 15,000 because of the stricter legislation and costs saving demand. The cost reduction is mainly focused on minimized components cost. Take misfire detection strategy for example, it could be done by using cylinder pressure sensors to measure the cylinder pressure, which could be easier for the software implementation but higher cost because the cylinder pressure sensors is so expensive. It could also be done by using engine speed sensor to measure the fluctuation of engine speed, which could be more complicated for the software implementation but much cheaper because the engine speed sensor has already existed in system. In order to fulfill the Euro V regulation and meet the high reliability and performance requirements, the OBD software of a new passenger car with turbocharged GDI engine is redesigned. There are some researches related with OBD performance topics. But they are mainly focused on hardware design and test field. For example, in terms of hardware design, smart driver ICs are used by various loads like injector, fuel pump, O2 heater and lamps, and fed back diagnostic information to a micro chip via SPI [1], it can detect power-stage fault much reliably and decrease main chip load. In terms of test, to confirm the performance of the OBD system and to avoid

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any kind of misdetection in the OBD system, several field tests such as the fleet test and the environmental test have been described in the chapter [3, 4], and from the experimental results, it indicates that significant potential exists in the optimization of the real-world OBD system performance [5].

2 Reliability and Performance Analysis The Euro V EOBD is inherited from Euro IV, most of components and systems monitors requirements are all similar except for some of special demands. For example, catalyst NOx conversion monitoring strategy is needed for Euro V EOBD. The reliability mainly refers to regulation fulfillment and fault reports, and the performance is mainly referring to efficiency of functions execution. Some factors, except for cost, which affect the reliability and performance are considered. In this chapter, the three factors are discussed, including optimization of OBD system management, condition interlock and efficiency of function execution.

2.1 Factor 1: Optimization of OBD System Management In a short trip, some monitoring functions perhaps have no chance of execution, and with software continuous increase of complexity, it is possible that some functions would have no chance to run. It would not fulfill the regulation of Euro V plus. So a dynamic priority of functions and in-use performance ratio (IUPR) concept are introduced to solve the problem. The dynamic priority is conceived from operation system. Scheduler can determine the status of a function depending on the dynamic priority, and the status is comprised of sleep, ready, activated, and etc. When a function is activated, it means the function has got the priority to run. The IUPR is from EU5b stage regulation demand, it was legislated in Euro V plus, and used to monitor the diagnostic frequency of functionality by drive cycles. The dynamic priority is designed and is determined by the significance of the function and its history of IUPR records. As a monitoring function running frequency is so low in recent drive cycles that IUPR ratio value below a certain limit, the dynamic priority of the function should be increased. It can assure that all monitoring functions have chance to execute recently. All the work is implemented by function scheduler to arrange the monitoring function execution order (Fig. 2).

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Monitoring function (1) Scheduler Monitoring function (2)

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2.2 Factor 2: Condition Interlock To ensure the correct implementation of the functionality and to avoid incorrect results report, functions physical conditions are introduced, which are used to prevent monitoring functions from running with error. Too strict conditions would always lead monitoring functions to be locked, and too loose conditions may lead to incorrectly execution on the contrary. Both cases are not reliable. From the perspective of software development, the correct result is needed to be considered based on legal demand. It means running under correct conditions should be guaranteed from functionality consideration and should test the conditions under the NEDC cycle provided by European emission regulation. Beside the physical conditions, there is another important condition called ‘‘derivative faults’’. Figure 3 shows an example of the ‘‘derivative faults’’. The three-way catalyst ageing monitoring strategies is based on dual lambda sensors (also called oxygen sensors), they are located in front and rear of catalyst respectively. If no consideration about ‘‘derivative faults’’ is taken, any defect of lambda sensors may lead to a false report of the catalyst, so both faults of lambda sensor and catalyst would be reported although catalyst has no fault actually. Therefore, the result should be regarded as incredible. To solve the ‘‘derivative faults’’ problems, the primary fault and secondary fault are defined to distinguish the root cause of faults. In this case, the lambda sensor fault is considered to be primary, while the catalyst fault is considered to be secondary. If the primary fault is reported, the function to report the secondary fault will be locked and no secondary fault will be reported.

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An even more complexity of ‘‘derivative faults’’ problem arises with cycles of fault primary-secondary relations (Fig. 4). ‘‘A system’’ fault will cause ‘‘B system’’ error, and ‘‘B system’’ error also can cause ‘‘A system’’ fault report. So both faults are primary faults and can not be distinguished for each other. In this example, the ‘‘C system’’ is secondary error to ‘‘B system’’, and its status will distinguish the root cause fault: If ‘‘C system’’ has a fault, then ‘‘B system’’ is validated to be the root cause but not ‘‘A system’’. Vice versa if ‘‘C system’’ has no fault, ‘‘A system’’ is the root cause. There is a simple case constituted by front lambda sensor, fuel trim system and rear lambda sensor respectively. The additional logic will make the diagnostic results more credible. The existence of function lock is not only up to primary-secondary ‘‘derivative fault’’ relationship, but also up to the cross-influence of the functions. An example is introduced here in Fig. 5, the function which is pointed by a arrow will be effected by the other function, which is on the other end of the arrow. There are four monitoring functions around the lambda monitoring system. In case of rear lambda sensor monitoring function is not running, the other three functions would be affected. To prevent the influence, a test result is introduced. When a function is executed completely, its test-over flag will be reported. The flag is reported not only to other three functions which have influence on this function, but also to standardized external communication interface, like scan-tool. Only when the rear lambda sensor has been tested ok and test-over flag is reported, the other three monitoring functions will have the permission to be activated. Otherwise, they will be locked.

2.3 Factor 3: Efficiency of Function Execution The efficiency here is not the code efficiency but the architecture of the program emphasized in this chapter. All functions come from regulation requirements. But when the functions are executed in CPU, the hardware load has to be taken into consideration. Most of cases, one functionality is able to be separated into several tasks to run in control unit (Fig. 6), and each task could be regarded as a sub- functionality, while the function integrity does not be affected. They could be running in deferent time slice on the the operating system. The basic principle of separation is according to sample rates of the output variables.

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catalyst monitoring

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The task could be carried out in a certain time interval repeatedly, for instance, 10, 50 ms, and etc., or every ignition interrupt event, which is similar to OSEK operating system. In order to add the tasks into the operating system, it is necessary to analyze the relationship among tasks and to test CPU load under the most unfavorable case repeatedly. If a task could run in either 50 or 10 ms time interval, the 50 ms period is preferred for chip load consideration. If the task could run in either 10 ms time interval or ignition event, 10 ms period is preferred based on the same consideration as above. Although the separation could decrease the CPU load, there is still a disadvantage that is the increasing of switch-over time between tasks as the number of tasks growing, and sometimes the total switch-over time may be considerable. To reduce the time increase, merging the tasks which have the same time interval is very useful. Another issue around the functionality running efficiency is distinguishing between inline and offline running mode. The off-line are generally referring to non real-time running mode, like the functions activated by external tester request, while the in-line referring to real-time running mode. Take the tooth error learning function for example, the function is used to correct the crankshaft tooth wheel error, the error may have a significant impact on misfire diagnosis especially when the engine speed is too high. The tooth error learning function can running in either on-line or off-line mode while it has little significant impact on reliability. When the function is running in in-line mode, the tooth error learning function will run every time the engine is under overrun fuel cut-off condition. If the off-line mode is selected, the tooth error learning function will be activated only by the external

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tester request when the engine is under overrun fuel cut-off condition. Generally speaking, offline mode is priority to online mode from the viewpoint of efficiency, but it is more reliable if the online mode is selected.

3 Conclusion The main goals of the research are improving the reliability and performance of OBD system to fulfill regulation of Euro V and meeting continuous increasing of software complexity. To improve the reliability and performance of software, three factors are taken care specially: • Optimization of OBD system management • Conditions interlock • Efficiency of function execution. Optimization of OBD system management has introduced the dynamic priority and in-use performance ratio (IUPR) concept; they are used by OBD monitoring function scheduler to improve OBD reliability and performance. Conditions interlock is focused on ‘‘derivative faults’’ and the analysis of functions cross-influence. Efficiency of function execution contains the analysis of task divisions and the differences between online function and offline function. Acknowledgments The authors would like to thank Jilin University who supplies engine test bench to us for validating the functionalities.

References 1. Official Journal of the European Union (2007) Regulation (EC) no 715/2007 of the European parliament and of the council[S] 2. Xie H, Hu C, Nenghui Z, Hao M (2006) A Micro-controller based control unit for motorcycle engines to meet emission and OBD requirements[J]. SAE paper no. 2006-01-0402 3. Park S, Chung Y, Park J (1998) The OBD-II system in the hyundai accent[J]. SAE paper no. 982551 4. Unger A, Smith K (1993) The OBD-II system in the volvo 850 turbo[J]. SAE paper no. 932665 5. Tsinoglou DN, Samaras ZC (2009) Malfunctions in selected emissions-related components of Euro 4 passenger cars: emissions increase and OBD system response[J]. SAE paper no. 200901-0731

Part IV

Electromagnetic Compatibility (EMC)

Resonance Mechanism in Power Electronic Products for Automobiles and its Relationship to EMC Performance Masato Izumichi

Abstract Research Objective. This chapter explains the resonance mechanism in automotive power electronic products and how resonance affects EMC performance. Power electronic products consist of a printed circuit board (PCB) and some connecting parts, such as bus bars, all enclosed in a metal case. The PCB pattern and connection parts have self-inductance (L). Stray capacitance (C) is formed between the PCB and metal case. These L and C form an unintended resonance circuit that lowers EMC performance. This chapter describes the resonance mechanism in motor controllers, and how it affects both immunity and emission tests. Methodology. A product controlling motor rotation speed was tested at 1–400 MHz based on bulk current injection (BCI) test standards. The frequency at which the abnormality occurred and the motor rotation fluctuation were recorded. Next, the resonance frequency was measured at the connector using a network analyzer. Comparing the BCI test result with the measured resonance frequency showed a relationship. Then, by measuring the magnetic field of the PCB surface, the resonance location was identified. It was assumed that the PCB pattern, bus bar, and metal case formed an unintended resonance circuit. Therefore, we modified the PCB pattern shape to change the stray capacitance between the PCB and case, confirming changes in the resonance frequency and BCI result. Finally, a dumping resister was added to suppress the resonance and its effect was confirmed. In addition to BCI, we investigated conducted emissions and confirmed the relationship between resonance and emitted noise from a product. Results. The investigated product was abnormal at 50–58 MHz on the BCI test, and the measured resonance frequency matched at 56 MHz. Meanwhile, the resonance F2012-D04-002 M. Izumichi (&) Denso Corporation, Kariya, Japan e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_49,  Springer-Verlag Berlin Heidelberg 2013

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location measured magnetically was around the PCB ground pattern and bus bars. The parts’ self-inductance was estimated at 70 nH. Similarly, the stray capacitance between the PCB and case was estimated at 100 pF, indicating a resonance of around 60 MHz. Supporting this was an unintended resonance circuit, which affected the BCI result. By adding a dumping resister between the ground pattern and case, the resonance was dissolved and the BCI abnormality disappeared. In addition to BCI, we tested conducted emission using another product. Even though the switching devices had no ringing waveform, a resonance of 120 MHz was observed at the connector. This worsened the emission level. We improved the emission level by adding a dumping resister. Conclusion: The self-inductance of the pattern of PCB, bus bars and stray capacitance between the PCB and metal case generates unintended resonance, creating a close relationship to the EMC test results. In BCI, the ground impedance increased in resonance and caused instability in the products. With conducted emission, the harmonic noise from the switching device was amplified by the resonance. As mentioned above, the unintended resonance circuit lowers EMC performance. Diminishing the resonance with a dumping resister improves the EMC performance of the products. It is important to strive for a design that creates no resonance. Keywords EMC

 Resonance  BCI  Conducted emission

1 Introduction Several studies have shown that there is harness resonance in the bulk current injection (BCI) method [1]. Some studies have analyzed the resonance relationship between the parasitic capacitance of power supply line on the printed circuit board (PCB) and equivalent series inductances (ESL) of decoupling capacitors [2]. However, little research has been done not only on the resonance caused by the products’ structure but also on the influence of both immunity and emission tests. This chapter shows the resonance mechanism in automotive power electronic products and how resonance affects EMC performance, i.e. both immunity and emission. The power electronic products consist of a (PCB) and some connecting parts such as bus bars. These are enclosed in a metal case. The PCB pattern and connecting parts have self-inductance (L). Stray capacitance (C) is formed between the PCB and metal case. These L and C form an unintended resonance circuit that lowers EMC performance. The present study is undertaken to clarify the resonance mechanism using two motor controllers, and how it affects both immunity and emission tests. This chapter has two sections. In the first section, we discuss BCI as the immunity test, and in the second section we discuss the conducted emissions (CE) test.

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2 Relationship: Between Resonance and Immunity Test (BCI) 2.1 Observation 2.1.1 Tested Product A product that controls motor rotation speed was tested. Figure 1 shows the tested products. The product is a three-phase inverter, consisting of a (PCB) and some connecting parts such as bus bars. These are attached to a motor whose outer case is made of metal (aluminum). On the PCB, are an integrated circuit (IC) and eight power MOS devices. These control the motor rotation speed by PWM signals, which are input from the connector.

2.1.2 BCI Test Method The BCI test is standardized in ISO 11452-4 [3] and a substitution method was used. Figure 2 shows the test setup for the BCI test. A 1 m long wiring harness was set on the isolation block, which was set on a ground plane made of copper. The product was connected to a control box through the wiring harness. The control box, which simulated the ECU, generated PWM signals, which were applied to the product.

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2.1.3 Resonance Frequency Measurement The resonance frequency was measured at the connector of the product using a network analyzer. The resonance appeared at 56 MHz. (‘Original’ in Fig. 6). The frequency agreed with the frequency, which the abnormality was observed in the BCI test.

2.1.4 Measurement of the Magnetic Field Above PCB Surface To identify the location where the resonance occurred, we conducted a magnetic field measurement. A probe with the capability of picking up the magnetic field was used. The probe scanned the PCB surface, while RF energy was injected to each connector terminal (Terminals A, B, C and D). Figure 4 shows distribution maps of

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the magnetic field. It should be noted that at the frequency range of 50–60 MHz, the magnetic field concentrates on the GND pattern on the PCB, regardless of the injected terminals. In contrast to this result, at other frequency ranges, such as 100–180 MHz, the magnetic field concentrates on each injected terminal.

2.2 Assumption of the Mechanism We made the following assumption based on the above observation (Sect. 1.1). The PCB pattern and connection parts (bus bars) have (L). Stray capacitance (C) is formed between the PCB and metal case. The PCB pattern, bus bar, and metal case form an unintended resonance circuit (Fig. 5). Because the product develops the resonance circuit around the GND pattern, it is possible the magnetic field could concentrate on the GND pattern. Also, the resonance changing the GND impedance could result in IC instability, affecting the BCI test result.

2.3 Method of Proof Assumption We modified the PCB pattern’s shape to change the stray capacitance between the PCB and case. The impedance of the four cases of GND pattern were measured and compared with BCI results. The GND area size was ordered as ‘‘Original [

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Case (1) [ Case (2) [ Case (3)’’. As an additional investigation, we tried two methods to suppress the resonance. The first method was to add a capacitor to the resonance circuit. A 1000 pF capacitor was added between the GND pattern on the PCB and the metal case. The second method was to add a resister to the circuit; in this case a 10-ohm resister was added between the GND pattern on the PCB and the metal case. The screw is included in the resonance circuit, so in the future tests can be done with this screw removed. However, it is necessary to fix the PCB on the motor case.

2.4 Results ‘‘Original’’, Cases (1), (2) and (3) in Fig. 6 show the relationship between the modified GND impedance and BCI test results. (Please note that since a wire probe was used to measure the impedance, this could have had some effect on the impedance increasing as the frequency increased.) The smaller the GND area, the higher the resonance frequency. Additionally, it is confirmed that BCI test results follows each resonance frequency. In the original case (before modifying the product), the self-inductance of the parts is estimated at 70 nH. The stray capacitance between the PCB and case is estimate at 100 pF, meaning the resonance occurred around 60 MHz, given by Eq. (1). If we reduce the GND area, the stray capacitance is reduced, indicating that the resonance frequency moves to a higher frequency. This supports the assumption that there is an unintended resonance circuit and that it affects the BCI test result. f¼

1 pffiffiffiffiffiffiffi 2p LC

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In case of adding a capacitor: The resonance frequency decreased from 58 to 30 MHz. The abnormality was no longer observed in the BCI test. See Fig. 7b. In case of adding a resister: The resonance frequency disappeared. The abnormality was no longer observed in the BCI test. See Fig. 7c. We adopted the method of adding a resister rather than a capacitor to improve immunity (Fig. 8). This is because when adding a capacitor, the resonance frequency simply shifted to a lower frequency. There is also the possibility that an abnormality would appear at the lower frequency if something is changed such as the increasing the length of the wire harness in an actual vehicle.

3 Relationship Between Resonance and Conduct Emissions Test (CE) 3.1 Observation 3.1.1 The Tested Product Another power electronics product was used for investigating the relationship between the resonance and conducted emissions test. Figure 9 shows the tested product. The test sample structure is similar to the one used for the BCI analysis, a DC-DC converter used as a motor controller in a vehicle. It consists of a PCB and

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some connecting parts, such as bus bars. These are attached to a cooler made of metal (aluminum). On the PCB are an IC and two power MOS devices. The power MOS devices are switched by inputting a PWM signal to drive a motor through the wire harness. An LC filter and Pi-filter are also equipped to reduce noise emitted from the power MOS devices.

3.1.2 Conducted Emissions Test Method The setup test is standardized in CISPR 25 [4]; however, the following was changed from the standard. In the conducted emissions test (voltage test method) under CISPR 25, the measurement is performed at an LISN (line impedance

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stabilized network) terminal. However, we measured the noise at the product terminal to clarify the influence of the resonance of the product. If we had measured at the LISN terminals, the influence of the LISNs and wire harness would have been strong. It would make the analysis complicated. The reference point of the measurement was the ground plane. Figure 10 shows the test setup. The wiring harness was set on an isolation block, which was on the ground plane made of copper. The product was connected to the LISNs, a control box, and a load, whose impedance was equivalent to an actual motor, by a wiring harness. A control box, which simulated the ECU, generated a PWM signal, which was applied to the product. The measurement was conducted from 1 to 300 MHz with a spectrum analyzer. The conductive emitted noise from the product was recorded (upper graph in Fig. 11). The noise can be seen peaking around 60 and 120 MHz. In this chapter, we focus on the 120 MHz resonance.

3.1.3 Resonance Frequency Measurement The resonance frequency was measured at the connector using a network analyzer. The resonance appeared around 120 MHz. (As with the BCI case, please note that since a wire probe was used to measure the impedance, this could have had some

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effect on the impedance increasing as the frequency increased). The frequency agreed with the recorded frequency in the emissions test. It can be presumed that the resonance worsens the emission level. We also measured with an oscilloscope to obtain the time domain waveforms. Even though the switching device had no ringing on the waveform because of the CR snubber, the 120 MHz ringing waveform was observed at the connector (Fig. 12).

3.2 Assumption if the Mechanism As with the BCI case, the PCB pattern and connection parts have (L). Stray capacitance (C) is formed between the PCB and metal case. It is believed that the PCB pattern, bus bar, and metal case form an unintended resonance circuit. When the power MOS device is switched by PWM (100 kHz), harmonic noise with an interval of 100 kHz appears. This 100 kHz harmonic noise is caught by this resonance circuit and is amplified at the resonance frequency. Since this amplified noise affected other signals, it is regarded as common mode noise. Figure 13 shows the resonance loop that is assumed. It is very similar to the BCI case; however, the noise direction is completely different such that noise flows from inside the product to the resonance circuit.

Resonance Mechanism in Power Electronic Products for Automobiles

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3.3 Method for Proof of Assumption A resister, 10-ohm, was added between the power ground and cooler to absorb the resonance energy and its effectiveness was confirmed. (See Fig. 13)

3.4 Results By adding a resister, 10-ohm, the resonance energy is absorbed and the emitted noise becomes lower at 120 MHz. Figure 14 shows the result.

4 Conclusions of Sections 1 and 2 The self-inductance of the pattern on the PCB, bus bars, and stray capacitance between the PCB and metal case generates unintended resonance, creating a close relationship to EMC test results. In the case of BCI, the ground impedance became higher due to resonance, causing product instability. In conducted emissions, the harmonic noise of the switching device was amplified by the resonance. As mentioned above, the unintended resonance circuit lowers EMC performance. For both immunity and emissions, by dissolving the resonance with a dumping resister, the products’ EMC performance could be improved. It is important to strive for a design that creates no resonance. Therefore, it is necessary to identify the location where resonance occurs. The magnetic field measurement (mentioned in Sect. 1) or 3D full-wave electromagnetic simulation is an effective tool to detect resonance circuits.

References 1. Shigenobu T, Kazuhisa Y, Koh T (1995) A study on bulk current Injection method,’’ technical report of IEICE, EMCJ 95–33: 33–38 (in Japanese) 2. Yusuke Y, Kengo I, Yoshitaka T (2011) Insertion of dumping resister RF IC-oower-current peak caused by resonance due to parasitic impedance. IEICE technical report, EMCJ 2011-84: 29–34 (in Japanese) 3. ISO 11452-4 (2005) Road vehicles: Component test methods for electrical disturbances from narrowband radiated electromagnetic energy, Part 4: Bulk current injection (BCI) 4. CISPR 25 (2008) Vehicles, boats and internal combustion engines—radio disturbance characteristics—limits and methods of measurement for the protection of on-board receivers

Application of the MOS Tube on Power Window Switch Yihai Wang, Xia Li and Rongxia Zhang

Abstract We use MOS tube as a control element, which decides on–off of current flow, to solve the problem of contact ablation in 24 V power window switch,the relay enclosed in switch is only to shift the direction of current flow, MOS tube is precedent in controlling on–off of current flow than the relay, which itself doesn’t participate in the control process, Without electric spark in the contact, the durability of relay is improved, Switch without contact is realized. Keywords Power window switch

 MOS tube  Relay  Heavy truck

1 Introduction With the development of society, the truck window operation convenience and comfort are put on the agenda, hand-operated window system gradually quit the stage of history, electric window in truck applications become more and more popular. Electric window system by the electric window switch, power windows, power window motor side switch, electric window switch by controlling the window motor to realize the rise or fall of window glass. Power window switch for power and signal model two kinds, power to switch to directly drive the window motor, signal type for vehicle body controller provides a signal switch to BCM, powered by BCM window motor. Signal type switch must be and BCM use BCM,

F2012-D04-004 Y. Wang (&)  X. Li  R. Zhang Anhui Jianghuai Automobile Co., Ltd, Hefei, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_50,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 Motor process current curve

because of higher prices, the switch is used less. Power type electric window switch for convenient use, lower prices in the heavy truck, light truck, commercial vehicle on popularization [1, 2].

2 Jianghuai Heavy Truck Power Window Switch Principle 2.1 Cause Analysis of Relay Contact Jianghuai heavy truck power window switch is the power switch, prior to the introduction of passenger car of the switch, the 12 V system is changed to a 24 V system, according to the 24 V system requirements to design of switching the internal PCB and matching the 24 V relay. But in the practical use of the relay contact ablation failure rate. The contact problem is easy to be ablated arc sparks temperature exceeds the contacts can stand, arcing is the cause of motor in the up/down movement ended, and the formation of blocking, the switch current detection circuit detects the motor, will release relay, shutting machine. In the motor locked rotor condition, current is about 8–12 A, current curve in Fig. 1, the cut-off relay, will form a strong reverse electromotive force (EMF), the contact of the relay is disconnected from the moment to form an arc, arc spark temperature up to several thousands of degrees, the contact forming ablation, long-term and such finally, burning of relay contact.

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Fig. 2 A diode and a capacitor inhibits reverse EMF diagram

2.2 Reverse Electromotive Force Suppression Method Reverse electromotive voltage is a reverse of the high voltage pulse, so in return relay contact before, be protection diodes to form a short circuit, absorb part of peak voltage, so as to achieve the purpose of protection relay contacts. Actual tests show that, this method is invalid. The main reason is the switch time is too long, can effectively absorb the peak voltage. Absorption capacitor is a widely used method for eliminating electric spark, so be sure to be effective, because the diode absorption pulse voltage failure, increase the capacity of the capacitor, to 47 uF, because the motor positive inversion, the output line polarity is variable, so the use of non-polar electrolytic capacitor. Motor peak inverse voltage through a capacitor, the capacitor will charge the capacitor, similar to a short circuit, so can the end forming protection. Protection time depends on the charging time of the capacitor, once the capacitor is filled, and the peak inverse voltage exists, protect the function disappeared, a diode and a capacitor inhibits reverse EMF principle is shown in Fig. 2. Laboratory proven: capacitance added to the 47 uF, contact spark is greatly reduced. Proof of actual use has greatly improved, but there are still 1–3 % burning of contact problems.

2.3 The Application of MOS Tube Burning of contact reason is arc, so need to solve the electric arc generated by reason. Arcing is the cause of relay in switching process contact exists between the potential difference of voltage. Reverse EMF is simply the higher voltage. If the

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Fig. 3 Power window switch application MOS control schematics

switch relay in the closure and release process, there is no potential difference is zero voltage, so there is no reverse EMF. When the contact only to overcome static wear can maintain better life. To make the contacts and no voltage difference, must let the relay in the circuit opened under the circumstances, namely the need in the switch circuit with electronic switch can do. In the loop gain a big power MOS switch tube, MOS tube control circuit on/off, and other circuit stops working, then operate relay, relay contact without current switching. The working principle is shown in Fig. 2: starting switch is open, MOS switch is in the off state, the first relay contact closure, the contacts and no potential difference between contacts, so no current, starting relay without contact spark. Relay starting after closure, and then open the MOS switch, motor, glass upward or downward movement. Motor off, shut off the MOS tube, and the motor stops rotating, the contacts are in the operating state, the motor locked rotor current, reverse electromotive voltage of contact without effect. When the MOS tube is disconnected, the motor stops working, then disconnect relay contact, when the motor is in a stop state, the contact has no potential difference, no current. Therefore, no electric spark generated (arc) (Fig. 3). Based on the MOS tube power control module function 1. Manual mode to achieve the current protection The original manual mode and no current protection function, which uses the manual rise/decline, even if the motor, as long as the hand is not loosened, the circuit

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Table 1 P55N06 MOS tube parameter Model: P55N06 Performance

Parameter

Unit

Rated working voltage Rated working current of 25 C Rated working current of 100 C Pulse voltage Ambient temperature

60 55 34.8 B400 -55*150

V A A V C

has been working, on the motor is great test. Improved after joining the manual mode protection function, as long as the plug is off, MOS tube and release the relay, protection of motor. 2. Join the under abnormal condition, the automatic shutdown function When entering the automatic mode, 10 s after the still not detected motor stall current, automatic shutdown. Prevent when motor damage/harness off in abnormal condition, the switch has been in a state of abnormal problems caused by burn.

2.4 MOS Tube Parameters Matching Using MOS tube as a current switch, MOS tube performance requirements are relatively high, but also in the circuit is disconnected, motor peak inverse voltage full on MOS tube ends, so the pressure has certain requirements, motor locked rotor current maximum working voltage of 12 A, the largest 32 V, peak inverse voltage of about 200 V, design 3 times left margin, using P55N06 high power tube (parameters see Table 1), rated current 55 A, rated voltage 60 V, under 400 V following pulse interference. MOS tube has a protection diode, without the need for an external protection.

2.5 Application Effect of MOS Tube Through in the Jianghuai heavy truck on batch application, market performance is good, the power window switch failure rate plummeted, from 1–3 to 0.1 %, confirmation of MOS tube in the electric window switch application can fundamentally solve the 24 volt power window switch ablation problem.

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3 Conclusion Power window switch adopts MOS technology, in order to solve the traditional switch relay contacts easy ablation problem, using the MOS as the current control switching element, realize contactless switch, high reliability, long service life, is the development direction of automobile power switch.

References 1. Dai H (2010) Bora car electric window failure analysis. Automobile repair 03:36–37 2. Li Z (2005) MOS tube model. Electron world 02:36–38

Simulation Analysis of Electromagnetic Compatibility in Vehicle Ignition Control System Ya’nan Li, Wenqiang Chen, Xingmin Wei and Fuquan Zhao

Abstract The ignition control system is the critical Electromagnetic interference source in automobile, and the transient voltage in primary circuit loop attacks the accumulator and interferes with the ECU or other electronic devices by power cord, and simultaneously the high frequency spark noise caused by spark plug may produce radiated noise which affects the electromagnetic environment inside and outside the automobile. Lots of vehicle manufactory are faced with the problem that the EMI testing value exceeds the threshold value. It is an urgent need to research the ignition system electromagnetic compatibility and seek the ignition system EMI suppressing method for enhancing the vehicle EMC. The objective of this study is to analyze the ignition system EMI formation mechanism and its effect for electromagnetism environment inside the vehicle, then taking effective suppressing measures to optimize ignition system EMC design. This paper simulates the ignition primary circuit transient current and voltage in EMC simulating software by analysing the working principle of gasoline engine ignition system and the formation mechanism of ignition system EMI. This study establishes secondary coil circuit loop spark plug discharge modle, the high voltage wire is equivalent to antenna, the electric field EMI distribution inside vehicle has been worked out, and then it researched the electromagnetic environmental effect of high-voltage wire position, length, working frequency and load impedance. The radiated electric field distribution is acquired by establishing vehicle body, antenna model in the Simulation Analysis. The longer length the high-voltage wire is, the stronger the radiated noise is. The height of wire from ground has little effect on electric field intensity, working frequency is higher and the radiated noise is F2012-D04-008 Y. Li (&)  W. Chen  X. Wei  F. Zhao Zhejiang Geely Automobile Research Institute CO. LTD, Hangzhou, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_51,  Springer-Verlag Berlin Heidelberg 2013

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stronger. These studies provide judgement basis for the optimization of electronic equipment installation position. This paper analyses the reason of ignition system EMI and electromagnetic environmental effect, and gives suggestion for susceptive electronic equipment installation. An important limitation of the current study is our study can’t include all ignition system problems, and some problems need to be discussed further; more convenient vehicle body modeling methods and spark plug noise suppressing methods need to be researched further. It is a new study of analysing ignition system EMI, establishing math model and analysing the effect of susceptivity equipment for electromagnetic environment. At present, most of car manufactories mainly focus on electromagnetic noise testing, so the simulating researching of ignition system disturbance characteristic can improve automobile overall EMC design. The research result indicates: making use of damp highvoltage wire, resistance type spark plug and shielding method for ignition coil and high-voltage wire can reduce radialization disturbance, but sometimes various suppressing measures will be taken.



Keywords Automobile Ignition control system High-voltage wire Noise suppression



 Ignition coil  EMC  EMI 

1 Introduction Along with the progress of modern electronic technology and automotive electronics technology development, Automobiles are equipped with more and more electronic products, Electronic products increase the car’s economy, security and comfort, at the same time, the automotive electromagnetic compatibility problems become more complex. The ignition system is the most important source of electromagnetic interference in the vehicles. As early in 1906, people found that a car engine running on the road can cause interference on surrounding radios, so the restrictions was proposed on the electromagnetic interference generated by the vehicle ignition system. However, this work is one of the difficulties which include the automotive electromagnetic interference suppression and achieving electromagnetic compatibility. The ignition system related to the performance of the ignition circuit, the ignition energy controlling ignition time controlling, cylinder conditions and many other technical issues. Large amounts of electromagnetic disturbance will be produced during ignition process, which mainly involve conducted interference caused by the ignition coil primary circuit transient voltage, high voltage wires and spark discharge high frequency radiation interference. Conducted interference is not only an impact on the battery voltage but spreads other electronic equipment of the car through the power cable. High-frequency electromagnetic radiation may also have a greater impact on the vehicle electronic control unit (ECU) and serious harm to the vehicle monitoring and security. The ignition system on

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Fig. 1 Electronic device in Vehicle

electromagnetic compatibility is the focus of attention of many car manufacturers and research institutes. Many countries have carried out testing of the system for ignition noise, and begin to electromagnetic compatibility prediction. This research started late in China, lots of researches was focused on the electromagnetic noise test at present. So the simulation research for interference characteristics of the ignition system can effectively improve the overall vehicle electromagnetic compatibility design.

2 Vehicle Ignition System Components and Working Principle 2.1 Ignition System Structure The modern automotive ignition system is composed of battery, ignition switch, ignition coil, high voltage ignition wires, distributor and spark plugs and other composition. Simplified model diagram is shown as Fig. 1: 1. Ignition coil. Every two cylinders share a common ignition coil, the ignition appears in the exhaust stroke of the cylinder and in the compression stroke cylinder at the same time. The four-cylinder engine requires two ignition coils. The primary winding of the ignition coil is controlled by the ECU power transistor; Secondary winding is connected directly to the spark plug by the high voltage wire. Normally, when the engine is in the compression stroke, the

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Cam position sensor

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spark plug 2 spark plug 3 Ignition coil 2

Constant current control

Fig. 2 Electronic ignition system block diagram

cylinder internal pressure is very high, compressed mixture gas resistance is larger between spark plug poles; when the engine is in the exhaust stroke and the cylinder internal pressure gets close to atmospheric pressure, the air resistance is relatively smaller between spark plug poles. Most of the high pressure applies on the spark plug in the compression stroke, forcing the breakdown of spark plug electrode gap to conduction. 2. Ignition components. Constant current control integrated in the closed angle ignition components, and has a cylinder discrimination circuit in the ignition components, the ignition signal is sent to the appropriate ignition driver circuit by cylinder sequence discrimination signal to control the ignition coil primary winding off and on. 3. Cam position sensor. There are three signal coil sensor which are used to identify the number of cylinders and detect crank angle to determine the ignition time.

2.2 Vehicle Ignition System Working Principle When the vehicle starts, the distributor rotates together with the cam in the engine camshaft driven; rotating cam alternately makes the breaker contacts open and close. In the case that the ignition switch is connected, when the contacts are closed, current flow through the primary winding of the ignition coil, which produces a magnetic field in the core. When the breaker contacts are open, the primary circuit

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Fig. 3 Vehicle ignition system schematic diagram

is turned off, the primary winding current quickly drops to zero, causing the magnetic flux dump, and self-inductance electrical potential generated in a winding up to 200*300 V amplitude U1max, Secondary winding products as high as 15–20 kV high-voltage electrical potential U2max in mutual inductance which is proportional to turns ratio. The spark plug gap have a breakdown under the electrical potential, spark ignites the gas mixture in the engine cylinder (Fig. 2).

3 The Formation Mechanism of the Ignition System Conducted Interference Ignition system schematic diagram and equivalent diagram as shown below: (Figs. 3 and 4) In ignition process, the coil transient voltage on the impact of the car battery is the source of the interference caused by conducted interference, when the battery voltage is higher, current and voltage waveform rise faster, oscillation intensified after the switch is closed. Coil voltage waveform is impacted by capacitance C1 repeatedly charging and discharging, it is an oscillating decay curve, the ripple voltage will be directly coupled to the ECU, car entertainment systems, ABS and other electronic devices interfere by the wires. To avoid this effect, the IGBT (insulated gate bipolar transistors) is applied on modern vehicles to control the ignition timing, and TVS (transient voltage suppression diodes) is used to protect the control circuit to ensure that the ignition timing is more accurate, the connection of the battery Ground and capacitors should be reliable in order to reduce the contact discharge electromagnetic interference. According to the conclusions of the study of the Ford Motor Company, the formation mechanism of electromagnetic interference and coupling path in the course of car ignition systems working can be represented in Fig. 5. On the Spark plug breakdown moment, rapid voltage changes between the center electrode and side electrode enable to form a transient noise current source on the spark plug

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Fig. 4 Ignition system primary circuit diagrams

R i1 U

L K

C1

Fig. 5 Primary ignition current and voltage

inside the cylinder and its connection conductor, the transient noise mainly enters into the ignition coil by conduction, the electromagnetic energy is coupled into the car and its surrounding space by high-voltage wire and primary cable ‘‘antenna’’ effect. In view of the above, in order to reduce the electromagnetic interference of the ignition system, the following measures can be taken: (1) Reducing the noise current source formed in the spark plug breakdown process. (2) Reducing the noise current from the ignition coil coupled to high voltage wires and the primary cable. (3) Shorten the length of the primary cable and high-voltage wire.

4 The formation of Radiation Electromagnetic Disturbance in Vehicle Ignition System 4.1 Spark Plug Electromagnetic Interference The radiated electromagnetic interference of ignition system is caused by a high frequency pulse ignition current, so this paper first studies the ignition current on breakdown of the spark plug gap.

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Fig. 6 The formation mechanism of the vehicle ignition system electromagnetic interference and coupling path

The spark plug is considered as an implanted resistance of the coaxial capacitor, the physical model structure and size as shown in Fig. 6. When the spark plug electrode gap has a breakdown, according to the secondary loop equivalent circuit model as shown in Fig. 7. Cq, Cp is the spark plug coaxial distributed capacitance, Cr is the spark plug damping impedance distributed capacitance to ground; Rr is built-in resistor for spark plugs; rg is spark resistance; Rw, Cw is respectively high-voltage ignition resistance and distributed capacitance; RL, L2, and CL is respectively the resistance of the secondary ignition coil, inductance and parasitic capacitance (Fig. 8). The following equation can be obtained through the formula derivation: I ðsÞ ¼ 

1 þ sZp Cr þ Cp

I ðsÞ  
g 1 þ sZw Cq þ Cr þ C2w

It can be seen from the above analysis, the ignition current i and the spark current ig can be approximately calculated to get by the spark plug size and the spark plug gap breakdown voltage. At the same time, the above equation shows that the ignition current i decreases with the impedance Zw and Zp increasing and it is proportional to spark current. Electromagnetic radiation of the spark current and high-voltage wire in spark plug breakdown is the source of radiation interference in the ignition system, spark plugs is located in a shielded cylinder, the external radiation is weaker, and the in-vehicle interference comes from the high voltage wire. Therefore, the electromagnetic radiation generated by high-voltage wire is equivalent to a monopole antenna is located in under the car hood, the numerical simulation method can figure out the electromagnetic field distribution of the vehicle radiated interference generated by the ignition system.

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Fig. 7 Spark plug model diagram ZL

Zw

Zp

i

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ig RL

C r /2 CL

CW/2

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Fig. 8 Secondary loop equivalent circuit

4.2 Connecting Cables Electromagnetic Interference In order to effectively reduce the electromagnetic interference, it is necessary to implement a comprehensive management of the ignition system. In addition to the above design measures for Spark plug, high-voltage wires and ignition coil, the effect of the connecting cables should also be considered. The noise current coupling in high-voltage wires and the primary cable can radiate electromagnetic energy to the car and its surrounding space. Differential mode and common-mode radiated field caused by the connection cable can be expressed as: ED max ¼

1:316  1014 ID f 2 Ls r

EC max ¼

1:257  106 IC f Ls r

Where, IC is the noise current on the cable; f is frequency; L is cable lengths; s is distance between the two cable conductors. Common-mode radiated caused by a single wire is ECmax/2. From the above equation, reducing the noise current on the ignition coil cables and shortening the

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Fig. 9 Ignition coil electromagnetic interference suppression mixed filter

length of the connecting cable will reduce the ignition system radiated electromagnetic interference. The high-voltage wire length and layout are subject to the design requirements of the engine, so it is generally more difficult to change. However, the arrangement position of the EMS and the battery can be easily optimized to reduce the length of the primary connection cable, thus the interference of electromagnetic radiated by the primary cable can be reduced. The following figure shows a mixed-type filter designing for suppression of noise current on the ignition coil primary power line, LC is common mode suppression inductance, CD is the differential mode suppression capacitor. The interference of electromagnetic radiated by the primary cable can be reduced through the suppression of ignition coil primary cable common mode and differential mode noise current (Fig. 9).

5 Vehicle Ignition Systems Radiated Electromagnetic Disturbance Calculation Model and Simulation Analysis The following vehicle ignition system radiated simulation analysis is done combined with specific vehicle model EMC experiment and measured data. During vehicle EMC broadband trial, the left side of the vertical position, the engine speed is 1500 rpm, the vehicle electrical devices are fully turned on, the interference peak is respectively on 38, 48, 88 MHz, over limits are respectively 1.2, 0.3, 1.9 dB, and they do not meet the regulations, and need to be improved (Fig. 10). High-voltage wire introduces the high-voltage electric potential in the secondary coil into spark plug, so that the spark plug gap has a breakdown to ignite mixture gas of oil and gas, damping wire are usually used the high-voltage wires, about 20 cm in length. In order to get the high-voltage wire radiation field distribution, in this paper the high voltage wire is equivalent to the monopole antenna to calculate electric field distribution inside the vehicle. Because the automobile is an extremely complex system, firstly a right physical model should be established, and the effective

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Fig. 10 Practical vehicle EMC broadband trial data

Fig. 11 Model of automobile

electromagnetic field calculation method should be selected before simulation analysis, at the same time necessary simplifications and assumptions should be done. When creating a numerical model, if the characteristics of field distribution can be correctly analyzed and the complex computer models can be simplified according to problem and the desired accuracy, the computational workload may be greatly reduced ensuring to achieve the target of the same requirements. For electromagnetic simulation, lights, mirrors, exhaust pipes and junctions, gaps, holes, welding bolt modeling is very complex, these components should be simplified. In addition, vehicle body is curve surface configuration, so some secondary should be ignored as much as possible in the simulation of the models.

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Fig. 12 Model fractionize gridding and any position electric field distribution

Fig. 13 In-vehicle electric field distribution

A block plane approach is used to construct the surface structure. The vehicle model as shown below (Fig. 11): Vehicle body is set up as a good conductor, the inside and outside body calculation region are in vacuum, the antenna model is set to k/2 monopole antenna [1], Antenna length is 15 cm and close to the actual voltage wire. Gaussian pulse is considered as the excitation source. When ignition signal is high level, the circuit between the primary coil and the battery is connected, the magnetic field stored in the ignition coil continues to increase. After a period of time, the ignition signal goes low, the circuit is disconnected, and the primary coil induced electromotive force. After ignition coil amplification, high-voltage generated in the secondary coil, spark plug gap has a breakdown discharge.

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Fig. 14 Electronic Field Distribution along with observe line

The figure shows that the radiation noise generated by the wire at the front part of vehicle is mainly focused within the hood, it is the main factors to cause electromagnetic disturbance for other electronic devices in the car. There are also the electric field distribution in the passenger compartment and trunk, but they are relatively weaker. The previous analysis shows that damping wire can effectively suppress the spark noise, this paper calculated electronic field distribution along with observe line in condition of the antenna terminal open circuit and terminal with resistance, the results as shown in Figs. 12, 13 and 14.

6 Conclusion In order to optimize the car ignition systems, electromagnetic compatibility, combined with EMC test measured data and calculated results of the simulation software, this paper gives a depth analysis for the mechanism of electromagnetic interference generated by the ignition system and the corresponding interference suppression measures. The following conclusions can be drawn: 1. As the vehicle electronic equipment is mainly located on the body front, so the design of high voltage conductor installation location and length is very critical in the early. The selected high voltage conductor places should be far away from the ECU as far as possible, selected the nearest ground, and shorten the length of the high voltage wire trace. Other sensitive equipment should also avoid installation in the radiation area, in order to avoid the interference of strong radiation and increase security. Increasing the spark plug resistance and lead resistance can effectively suppress ignition system radiated noise.

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2. Wire wound resistors high-voltage wire interference suppression mainly is determined by the resistance in the low frequency; the suppression effect is not obvious. Along with the frequency increasing, the enhancing of the skin effect and inductance of wire wound resistor strengthens its high-frequency interference suppression capability 3. Increasing the length of the spark plug damping resistor and ensuring ignition reliability, increasing the damping resistor can effectively reduce the ignition system radiated electromagnetic interference. 4. High-voltage wire length has a significant impact for the field strength distribution, the wire is longer, and its radiated ability is stronger, so the ignition system should shorten length of the high-voltage wire in the design process as far as possible. 5. Adding Shield between primary and secondary windings of the ignition coil and connecting reliable ground can improve the electromagnetic compatibility of the ignition system to a certain extent The radiation of ignition coil device working is difficult to be predicted due to device complexity. Therefore, the radiation characteristics and spectral distribution around engine, ignition coils and other electronic devices can be measured by the test equipment. These test data can be imported into electromagnetic simulation software, and then vehicle external electromagnetic radiation can be simulated, and then the result can be showed whether EMC certification standards can be met.

Reference 1. Fujiwara O (1982) Calculation of ignition noise level caused by plug. Gap breakdown. IEEE Trans Electromagn Compat 24(2):26–32

A Method for Improving Radiated Emission of Automotive Spark-Ignition System with Improved Micro-Genetic Algorithm Yanming Qin, Bin Li, Qingsong Liu, Xiangling Xu and JianPeng Zhai

Abstract A new method for improving radiated emissions of automobile spark-ignition system is proposed. This method realizes automatic optimization design of automotive spark-ignition system with EMI simulation models and optimization algorithm in which it takes the models’ parameters as optimized variables and radiated emission as optimized object to automatically simulate and seek better models. A new optimization algorithm called improved micro-genetic algorithm (I-MGA) is also developed to promote the optimization efficiency and speed. The results show that the performance of I-MGA is more superior to other optimization algorithms and combined application of numerical simulation technology and I-MGA can be effectively used to improve the radiated emission of automotive spark-ignition system.



Keywords Automotive spark-ignition system Electromagnetic compatibility Radiated emission Improved micro-genetic algorithm Optimization







1 Introduction Radiated emission caused by high-voltage electric spark igniting the automotive engine has serious electromagnetic interference (EMI) to electronic systems onboard and ambient electromagnetic environment. To prevent disturbing nearby F2012-D04-009 Y. Qin (&)  B. Li  Q. Liu  X. Xu  J. Zhai Chongqing Vehicle Test and Research Institute and Chongqing EMC Engineering and Technology Research Centre, Chongqing, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_52,  Springer-Verlag Berlin Heidelberg 2013

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electronic systems, stringent electromagnetic compatibility (EMC) criteria should be fulfilled, such as CISPR 12 [1]. So it is necessary to study the electromagnetic disturbance characteristic of automotive spark-ignition system. However experimental analysis method cost too much and because of the complicate structure of spark-ignition system, the theoretical analysis method can only be used for guide but not solving specific problems. By simulating EMI predicting model of automotive spark-ignition system to find out the hidden EMI problems is an effective way to improve EMC design of automobile and parts [2], which can make up the existing shortage of experimental and theoretical analysis method. However, previous study on automotive sparkignition system mainly focused on conduced EMI issues. Lots of works were done to build up equivalent electrical circuit and to carry out theoretical analysis [3–5]. Few works were done to study the radiated EMI issues and radiated simulation, much less the fast EMI optimization problem. There are still a number of works to do on how to acquire a fast and effective method to suppress the radiated emission of automotive spark-ignition system. Fast EMC optimization approaches via artificial neural networks (ANN), fuzzy optimization method and so on have been developed [6, 7]. Comparing with traditional optimization method with artificial modeling process or parameter sweep process, they take less computational time and can more effectively promote optimization efficiency. However, they require additional prior knowledge that can not be easily acquired, which limits their application. This paper is to provide a novel and efficient method which can automatically build and optimize the model of automotive spark-ignition system for predicting and suppressing its radiated emission. The parametric simulation models have been built up to estimate the radiated emission. An optimization algorithm will be used to automatically build and seek better models by taking their parameters as optimized variables and radiated emission as optimized object. A novel algorithm called improved micro-genetic algorithm (I-MGA) has been proposed to promote the optimization effectiveness and speed.

2 Improved Micro-Genetic Algorithm It is well known that the genetic algorithms (GA) falls under a special category of optimization schemes that are robust stochastic search methods based on the principles of natural selection and no special prior knowledge is needed [8]. For multi-dimensional and multi-modal optimization problems, conventional GA (CGA) needs large initial population size (typically 100–10,000) and process lots of generations to achieve or be close to the global maxima, which costs a great deal of computational resource and time. To avoid these difficulties, micro genetic algorithms (MGA) is used [9]. Although the breeding procedure of MGA is similar to that of CGA, there is a significant difference in a way of realizing the random search. The MGA starts

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with a random and small population size, typically 5–50. The population evolves in CGA fashion and converges (locally) in a few generations (about 4–5). At this point, keeping the best individual from the previously converged generations (Elitism), a new random population is chosen and the evolution process restarts. The small population size employed by the MGA makes it prone to premature convergence to local extreme, thus necessitating the population restart strategy. Because of the small population size, the mutation process is skipped. To further improve the performance of MGA, we modify the genetic operators and replace the worse individual with the optimal individual every generation in each cycle. According to the procedure of GA, two different individuals in the evolutionary population will implement crossover operation. But for a multidimension optimization problem, if each dimension (variable) of an individual undergoes the crossover operation, the weakened effect will dominate the improved effect, which may lead to approximately stochastic and blindfold searching. So for a selected individual, only a certain dimension selected randomly implements the operation and the other dimensions carry out the operation under a small probability. After that, the son-individuals will compete with the parentindividuals and the best two individuals remain. So is the mutant operator but every dimension mutates under a small probability and the best individual mutates in range of Gaussian distribution. The optimization performance of I-MGA is compared with that of MGA, GA, particle swarm optimization (PSO) and evolution strategy (ES). The testing function is Schaffer’s F6 function which has been widely used. The function’s true optimum is 1 and it is described as follows, ! !, !2 rffiffiffiffiffiffiffiffiffiffiffiffi X X max f ðxÞ ¼0:5  sin2 1 þ 0:001 x2i  0:5 x2i ð1Þ n n xi 2 ½100; 100

Where n is the number of dimensions. The parameter setting of these algorithms is settled for all testing cases and results are recorded when current optimum converges to true optimum or maximum iteration number achieves. The average results of 100 tests are shown in Table 1. It can be seen that I-MGA can always find the true optimum or near-optimum and has better performance than that of the other algorithms in almost all cases. Although MGA also converges very quickly, it is unstable and too easy to get into premature convergence, especially in high-dimension optimization problems. Additionally, other 5 common test functions with different characteristics are also used for testing, which can be seen below. And the results are all similar. ,  .p ffi  X Y 2 min f ðxÞ ¼ i xi 4000 þ 1  cos xi ð2Þ xi 2 ½600; 600 n

n

GA

14633 0.9985 0.0023 98552 0.9904 0.0011 150000 0.9903 0.0000

34140 0.9932 0.0042 100029 0.9843 0.0148 150000 0.9820 0.0126

290 0.9995 0.0003 7127 0.9986 0.0052 27158 0.9650 0.1043

Iteration number Optimum Variance Iteration number Optimum Variance Iteration number Optimum Variance

220 0.9994 0.0003 9930 0.9994 0.0018 121345 0.9924 0.0059

Table 1 Optimization result comparison of different optimization algorithms Average results I-MGA MGA PSO ES 14847 0.9989 0.0005 100000 0.9903 0.0000 150000 0.9903 0.0000

Dimensions

6

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Fig. 1 The simulation model of automotive spark-ignition system. 1 High-voltage ignition wire;2 spark plug cap;3 spark plug; 4 engine cylinder

X

max f ðxÞ ¼ min f ðxÞ ¼

X n

n

ðsinðxi Þ  sinð2xi =3ÞÞ xi 2 ½3; 13

x2i  10 cosð2pxi Þ þ 10

max f ðxÞ ¼

X n

jxi j sin

pffiffiffiffiffiffi jxi j




xi 2 ½5:12; 5:12

xi 2 ½500; 500

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , ffi1 u X  uX max f ðxÞ ¼20 exp@0:2t x2i nA þ exp cosð2pxi Þ=n 0

n

ð3Þ ð4Þ ð5Þ

ð6Þ

xi 2 ½30; 30

Because each test function represents different kind of optimization problem and the radiate emission optimization problem of automotive spark-ignition system belongs to which kind is always unknown. So I-MGA is more superior to the others, it has a wider range of application.

3 Simulation and Optimization Generally the simulation model should fulfill the requirements below: (1) the dimension and structure is close to actual physical model or within a permitted range; (2) if possible, the model should be as simple as possible to reduce the computational demands. Spark plugs and high-voltage ignition wire are main interfering sources of the automobile spark-ignition systems [4]. However because the spark plug is within the engine cylinder, its radiated emission will be weakened and high-voltage ignition wire produces main EMI interference. Therefore in this study only spark plug, high-voltage ignition wire and engine cylinder are modelled excluding other parts of automobile spark-ignition system, such as ignition coil, shown in Figs. 1 and 2. The excitement defined by user will be directly fed on the spark-ignition system. To exanimate the validity of simulation model, an example is given according to a kind of spark-ignition systems. The exciting signal is acquired from conducted

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Fig. 2 The detailed structure of spark plug. 1 Front spark electrode; 2 insulation dielectric material; 3 embedded resistance; 4 rear spark electrode; 5 metallic shell; 6 spark plug gap

Fig. 3 Comparison of simulated result and test result

Fig. 4 Comparison of result before and after optimization

circuit according to paper [4] and he results are shown in Fig. 3. It can be found that the results of simulation basically agree with that of tests. The validity of the model is verified. All the dimension and material of automotive spark-ignition system can be parameterized for easily modifying the simulation model. The parametric simulation models for full-wave transient analysis then can be used to estimate the radiated emission of automotive spark-ignition system by using CST micro wave

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studio (MWS) based on the finite integration technique (FIT). In fact, the forming of CST simulation model can be controlled from a Visual Basic (VBA) script in internal program environment or even in combination with external programs via special interface. So an optimization algorithm programmed by VBA or other program languages can be used to build a new simulation model, calculate the radiated emission and analyze the results by taking their parameters as optimized variables and radiated emission as optimized object to seek better models. This is the reason why we can improve radiated emission of automotive spark-ignition system with optimization algorithm. Theoretically the longer optimization runs, the better results can be found. In this paper, length of high-voltage ignition wire (ranging from 200 to 500 mm), relative dielectric constant of insulation dielectric material (ranging from 7 to 12) and distance of spark plug gap (ranging from 0.5 to 1.1 mm) are taken as the optimized variables. The relative magnitudes of field strength of radiated emission and that of 3 m-limit described in CISPR 12 are taken as the optimized object to reduce the radiated field strength. The exciting signal is Gauss pulse ranging from 30 to 1000 MHz to accelerate verification process. The initial population number of I-MGA is 20, small optimum-seeking population number is 5 and the maximal generation is set to 75. The optimization results are shown in Fig. 4. The before-optimization results in Fig. 4 refer to the best model of initial population generated randomly. It can be found out the radiated emission of simulation model after optimization has been effectively improved.

4 Conclusion A new method for improving radiated emissions of automobile spark-ignition system has been developed. This method realizes automatic optimization design of automotive spark-ignition system to improve its EMC performance with combined application of numerical simulation technology and optimization algorithm. Because there are no additional requirement for I-MGA, this method can also be applied to automobile or other parts of automobile according to this paper. Acknowledgments The author would like to acknowledge the support of Chongqing Natural Science Foundation (CSTC,2008BB6343) and Chongqing Bureau of Quality and Technology Supervision Research Project (2009-KY-11).

References 1. CISPR 12 (2009) Vehicles, boats and internal combustion engines—radio disturbance characteristics—limits and methods of measurement for the protection of off-board receivers 2. Canavero F (2000) Numerical simulation for early EMC design of cars. The 4th conference on electromagnetic compatibility, Brugge, Beligum (9): 32-39

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3. Gao F, Chen L, Zhai J, Wu C (2008) Modeling of vehicle ignition system and simulation on its conducted interference. Automot Eng 30(10):894–897 4. Wang Q, Lu C, Yu J, Liu Q (2007) On suppressing electromagnetic interference caused by automobile spark-ignition system. J Chongqing Univ 30(7):46–49 5. Li Y, Deng Q, Yu J, Wang Q (2008) Simulation of an automobile ignition system electromagnetic interference. J Chongqing Univ 31(10):1149–1153 6. Gao Y, Ma X, Chen R (2006) Electromagnetic compatibility prediction of automobile based on fuzzy inference. J Jilin Univ (Eng Technol Ed) 36(3):399–403 7. Li Y, Zhu Y, Li X, Yuji H (2008) Artificial neural networks-based prediction of electromagnetic compatibility problems. J Chongqing Univ 31(11):1313–1322 8. Rahmat-Samii Y, Michielessen E (1999) Electromagnetic optimization by genetic algorithms. Wiley, New York 9. Kazarlis SA, Papadakis SE, Theocharis JB (2001) Microgenetic algorithm as generalized hillclimbing operators for GA optimization. IEEE Trans Evol Comput 5(3):204–217

Simulation of Electromagnetic Characters of Vehicle Whip Antennas Based on Mom Liguo Zang, Youqun Zhao, Wei Wang, Jian Wang and Haiyan Sun

Abstract Base-driven whip antenna is negatively affected by metallic car body seriously because car body plays a role of radiating portion of the antenna. The overall goal of this study is to analyse some important electromagnetic characteristics of whip antenna mounted on the vehicle. In this paper vehicle electromagnetic modeling based antenna modeling was presented. Whip antennas installed on different positions in the modeling were analysed based on method of moments (MoM) with the purpose of showing the impact of the car body on the performance of the whip antenna. The optimal antenna location was determined by performance of antenna coupling S-parameter and radiation field gain direction figure in given frequency band. Through the analysis we can know that the optimal location of the whip antenna is the middle of the top part of car body. It can get good performance both omni-directional work and radiation power. Another area of interest is the value of coupling between two antennas. The study showed that antenna coupling degree is determined by distance of installation position and dimension of an antenna. Keywords Vehicle Coupling degree

 Whip antenna  MoM  Electromagnetic compatibility 

F2012-D04-011 L. Zang (&)  Y. Zhao  W. Wang  J. Wang Department of Automotive Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China e-mail: [email protected] L. Zang  Y. Zhao  W. Wang  J. Wang State Key Laboratory of Automotive Simulation and Control, Jilin University, Jilin, China H. Sun College of Transportation, Jilin University, Jilin, China SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_53,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction Automotive electromagnetic compatibility (EMC) has been a serious problem since more and more mobile communication equipments and electronic equipments were installed in the car, especially in electric vehicle and hybrid electric vehicle [1, 2]. Antennas of different communication equipment affect one another when they are installed in the limited space of the automotive. Short-wave antenna and ultrashort wave antenna are the most common radio transmitter among vehicle-borne antenna. The performance of automotive antenna system is impacted when antenna is installed in the automobile. This is because of diffraction and scatter of electromagnetic wave on the car body shell and coupling degree of different antenna. It is very important to analyse antenna electromagnetic characters before they are installed on complex vehicle body. In general, the number of short-wave antenna is 4–5, and the number of ultrashort wave antenna is less, not more than 2 of the most commonly [3]. Antenna electromagnetic characters are not only affected by the metal body, but also antenna performance characteristic disturbs one another [4, 5]. For base-driven whip antenna, metal car body which is good conductor plays a role of radiating arm. The antenna and metal car body constitute radiation structure. The distribution of antenna field is changed by complex metal car body. In this paper unsealed car body modeling was presented to simulate distribution of antenna field. Car body modeling was more accurate than previous ones which were simplified closed metallic surface usually.Whip antennas installed on different positions on the body modeling were simulated to get the far field gain direction figure based on MoM. The optimal antenna location was determined according to influencing rule of car body and pattern character. Moreover, the optimal location of other antennas was determined by coupling degree after the first antenna was installed. Antenna cross polarization and side lobe level were reduced on the basis of this design proposal.

2 Electromagnetic Modelling of Vehicle Usually, car body is simplified a sealed metallic surface in previous automotive antenna electromagnetic compatibility problem. Current on metallic surface is solved by magnetic field integral equation, and current on wires is calculated by electric field integral equation. The range of conductance was complicated and its current was calculated by both magnetic field integral equation and electric field integral equation which domain of integration included line segment and metallic surface [6, 7]. The Method of Moments (MoM) is a kind of effective method to solve operator equation. At present the method is generally used in problems of antenna analysis, design of microwave device and RCS calculation of complex device [8]. MoM is a

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method of electromagnetic computation based on the frequency domain mixedpotential electric field integrals combined with the entire-domain Galerkin method [9]. The advantage of this method, first of all, is that it can avoid numerical dispersion because of application of green function. Electromagnetic field radiation qualification on infinitely distant points is included in integral equation and the unknown quantities relation is determined definitely. On the other hand, the number of unknown is much less than that of differential equation [10]. MoM is applicable to solve electromagnetic scattering problem of electrically large complex object. For MoM, unknown function is expressed a group linear primary functions, and is matched operator equation. At the last, expansion coefficient is calculated by discrete linear simultaneous equations. In this paper, unknown function is f ðxÞ  a1 f1 ðxÞ þ a2 f2 ðxÞ þ    þ aN fN ðxÞ ¼

N X

an fn ðxÞ

ð1Þ

n¼1

where fi ði ¼ 1; 2;    NÞ and ai ði ¼ 1; 2;    NÞ denote, respectively, a group of primary functions and unknown expansion coefficient. Also we can express the equation in a typical operator equation equivalent to (1) given by N X

an Lfn ðxÞ  hðxÞ

ð2Þ

n¼1

We choose a group of weight functions xi ði ¼ 1; 2;    NÞ to multiply respectively. In this way, we can obtain a linear algebraic equation which an is unknown coefficient. N X

Zmn an ¼ bm ; m ¼ 1; 2; 3    ; N

ð3Þ

n¼1

The values Zmn and bm are given by Z Zmn ¼ xn ðxÞLfn dx bm ¼

Z

xn ðxÞhðxÞdx

ð4Þ ð5Þ

The function is determined by obtained coefficients, then others field quantities are obtained. In this paper the electromagnetic modelling considering electromagnetic influence factors of window gap, windshield space, and ignoring the influence of the tiny car connection gap, car seats and body small accessories is presented. The antenna modelling is ultrashort wave in given frequency range 30*88 MHz. The structure of the computer modelling of the vehicle with antenna used in the MoM simulation methods is presented in Fig. 1. Five antennas are installed on four different positions

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Fig. 1 Vehicle antenna electromagnetic modelling

of a vehicle, front, back, middle and the rear middle of the car body. The length offour antennas is 0.75 m and the remainder antenna is 0.3 m. A non-uniform meshing was provided to ensure high accuracy of the electromagnetic modelling. It is used an equivalent network for the antenna feed with pulsed excitation of the frequency domain amplitude voltage is 1 V; voltage phase is 0. Input impedance of antennas is 50 ohm, and antennas’ radius is 0.0003 m (Figs. 2, 3). Simulations of E-field from an antenna mounted on several different places on a vehicle in given frequency (f = 74, 82, 88 MHz) for Phi = 80 have been performed. It can be determined that base-driven whip antenna is negatively affected by metallic car body, especially antennas in the left front and right rear position of vehicle coordinate system. The performance omni-directional work could get bad for antennas mounted on side of car body in the low frequency band. Also the antenna at the middle of the top part of car body could get better performance of radiation power than others in given frequency. Antenna at right rear position is more seriously affected than one at left front by metallic car body. As the image shows, the performance omni-directional of antenna 1 is better than antenna 4, but the radiation power of antenna 1 is less than antenna 4 (Fig. 4). Antennas gain lines can be seen in Fig. 5. Antenna gain showed a tendency to increase from 30 to 88 MHz. The findings clearly indicate the performance of antenna 1 and antenna 4 is better than others. This is because of variation trend of antennas gain is gently to ensure the performance of antennas. It is also interesting to note that gain lines of antenna 2 and 4 change severely in given frequency band, especially at 40 and 80 MHz. The reason for this could be that there is a resonance phenomenon between antenna natural frequency and radio wave. The S21 can be calculated according to electromagnetic field theory from the following relation:      b1 S11 S12 a1 ¼ ð6Þ b2 S21 S22 a2   S11 S12 denotes normalized scattering matrix of the two port where S ¼ S21 S22 network. Scattering parameter Sij ði ¼ 1; 2; j ¼ 1; 2Þ is defined when four ports load are matched.

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Fig. 3 Total electric gain in given frequency (f = 74, 82, 88 MHz) for Phi = 80 of antenna 3 and 4

Fig. 4 Far field 3D plot for antenna 4 (f = 30 MHz, Phi = 80)

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The value of antenna coupling is defined by the following relation   Po Cd ¼ 10 log ðdBÞ Pi

ð7Þ

where Po and Pi denote, respectively, transmitting antenna output power and antenna input power. Also the value of antenna coupling can be obtained according to equivalent circuit method of microwave theory. The antenna coupling is that the amount of unwanted disturbance an antenna received from a transmitting antenna. It can be determine by antenna coupling S-parameter if the degree is accepted between antennas mounted on a vehicle [11, 12]. In order to study the coupling impedance matrix between antennas was calculated based on MoM, and coupling was obtained according to equivalent circuit method of microwave theory. The main advantage of this method is that mutual interference is parameterized to optimize antenna location. It is used an equivalent two-port network to determine admittance matrix. In this paper S21 is value of coupling between two different antennas. The new calculate S21 is illustrated in Fig. 6 (magnitude in dB) and as we can see now coupling show a tendency to increase from 30 to 88 MHz. These results suggest that the value of coupling is concerned with the distance of two antennas. Our findings show a significant, positive, and strong relationship between the value of coupling and antenna frequency except resonant frequency band. Figure 8 illustrates the value of coupling between antenna 4 and other antennas (Fig. 7). The purpose of mounting antenna 5 to is to discuss the relation between antenna dimension and value of coupling. Large-sized antenna 5 was installed in the same position with antenna 1. Coupling results are depicted in Fig. 8 and the value of task41 is less than task45. Results indicate that the value of coupling shows a positive correlation with antenna dimension.

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3 Conclusion The paper presents a vehicle antenna electromagnetic computer modelling. The electric gain pattern and coupling are analysed for different antennas mounted on a vehicle. It can be concluded that base-driven whip antenna is negatively affected by metallic car body seriously in given frequency. The performance omni-directional work could get bad, especially on side of car body mounted antenna. Our findings further suggest that the value of coupling is concerned with the distance of two antennas and antenna dimension. This paper fulfils an identified information need and offers practical help to optimize antenna location ahead of its installation into a vehicle. Acknowledgements This work is supported by National High Technology Research and Development Program of China (2011AA11A220) and Open Foundation of State Key Laboratory of Automotive Simulation and Control Program (20111109).

References 1. Kuvedu-Libla J-RK (2007) Vehicle active antennas face ‘‘EMC’’—and ‘‘RF-reception’’ challenges. In: IEEE international symposium on electromagnetic compatibility (EMC), vol 9–13, pp 1–6 2. Mutoh N, Nakanishi M et al (2005) EMI noise control methods suitable for electric vehicle drive systems. IEEE Trans Electromagn Compat 47(4):930–937 3. Guan DD (2010) Modeling and EMC analysis of angled reflector antenna in vehicular multiantenna system. XIDIAN University, vol 2010, pp 5–8 4. Ji-hui YU, Xiao-lei MA, Ya-li Z (2008) Simulation and research on electromagnetic character and coupling degree of vehicle antennas. J Syst Simul 20(6):1603–1605 5. Jobava RG, Bogdanov FG, Gheonjian AL, Frei S (2003) Analysis of influence of vehicle body shell on the characteristics of wire antennas using a new MoM-based EM/EMC solver. IEEE Int Symp Antennas Propag Soc 4(22–27):831–834 6. Newman EH, Pozar DM (1978) Considerations for efficient wire/surface modeling. IEEE Trans AP 28(1):121–125 7. Thiel W, Sabet KF, Katehi LPB (2003) A hybrid MOM/FDTD approach for an efficient modeling of complex antennas on mobile platform. In: Microwave conference 2003 33rd European, vol 2. Michigan Univ, Ann Arbor, pp 719–722 8. Yang R-G (2008) Higher electromagnetic theory. : Higher Education Press, Beijing 9. Chavka G, Sadowski M et al (2005) Structure and EMC simulation of vehicle radio Communication base station. In: IEEE 6th international symposium on electromagnetic compatibility and electromagnetic ecology, 21–24:111–115 10. Han X-L, Yu Y-H, Yang K-Y et al (2010) Car electromagnetic analysis of wire antenna moment method application. Aviat Precis Manuf Technol 46(4):60–62 11. Ankarson P, Carlsson J (2002) FDTD-simulation the electrical environment for vehicles by using CAD-Data. In: 3rd international symposium on electromagnetic compatibility, vol 21–24, pp 272–275, May 2002 12. Scogna AC, Wang J (2008) Study of a conformal hidden wire antenna used for the detection of stolen cars. In: IEEE international symposium on electromagnetic compatibility EMC 2008, Aug. 2008(18–22): 1–6

Study on Conducted Interference and Radiated Interference of Buck-Boost Converter in Electric Automobile Jian Wang, Youqun Zhao, Liguo Zang and Wei Wang

Abstract Buck-boost converter is an important component of electric automobile, it is an important interference source in electric automobile, the study of the interference source is very important to restrain interference. The buck-boost converter in continuous conduction mode (CCM) is established by using circuit simulation software PSPICE. According to the request of GB18655-2002, the simulation study on common-mode conducted interference,differential-mode conducted interference and far-field radiated interference of buck-boost converter are given. The commonmode current radiation of buck-boost converter is simplified as an electric dipole radiation mode and the differential-mode current radiation is simplified as a rectangular loop antenna. In order to improve the electromagnetic compatibility of buckboost converter in electric automobile, some measures to reduce the conducted interference and the radiated interference are proposed.



Keywords Buck-boost converter Conducted interference ence PSPICE Electric automobile





 Radiated interfer-

F2012-D04-012 J. Wang (&)  Y. Zhao  L. Zang  W. Wang College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China e-mail: [email protected] J. Wang  Y. Zhao  L. Zang  W. Wang State Key Laboratory of Automotive Simulation and Control, Changchun 130025, China SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_54,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction The output voltage of buck-boost converter can be regulated above and/or below its input voltage with high efficiency [1]. Buck-boost converter has been widely used in power electronics and communication field. The battery pack of Chery electric vehicle consists of 100 lithium cells in series, one cell voltage is 3.6 V. Some low voltage electric systems such as dashboard system, lighting system, electric drive control system and car stereo system need 12 V voltage supply, the voltage can be regulated from 360 to 12 V by using buck-boost converter. In this paper, the switching frequency of buck-boost converter is 20 kHz. The switching operation generates high du/dt and di/dt, and, consequently, wide disturbance bandwidths [2]. In such condition with high frequency, serious conducted interference and radiated interference are generated. According to the request of GB18655-2002, in the frequency range of interest (150–30 MHz), the conducted interference of buck-boost converter has been studied using Line Impedance Stabilization Network (LISN). The common-mode current radiation of buck-boost converter is simplified as an electric dipole radiation mode and the differentialmode current radiation is simplified as a rectangular loop antenna. At present, some electromagnetic simulation softwares such as FEKO, HFSS, CST and EMC Studio are used to design and analyze antenna widely [3]. In this paper, EMC Studio is used to simulate the radiated interference of buck-boost converter, which is based on numerical calculation. Some measures to reduce the conducted interference and radiated interference are proposed in the following paper.

2 Working Principle of Buck-Boost Converter The output voltage polarity is opposite to the input voltage in buck-boost converter. Buck-boost converter can shift from continuous conduction mode (CCM) to discontinuous conduction mode (DCM) with the variation of converter circuit parameters such as the input voltage and inductance value [4]. In this paper, buckboost converter in CCM is studied. Figure 1 shows the main circuit of buck-boost converter, it can be seen that the main circuit of buck-boost converter is composed of switching transistor, diode, inductor, capacitor and resistor. Figure 2 shows the current path when the switching transistor Z works in switch-on. In this condition, the inductor Lf is charged by dc source and the load energy of R is given by the discharge of the capacitor Cf. Figure 3 shows the current path when the switching transistor Z works in switch-off. In the first stage, the inductor discharge energy to the capacitor and the load R. In the second stage, the load energy of R is given by the inductor Lf and the capacitor Cf. The output voltage is given by

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Fig. 1 Main circuit of buckboost converter

Fig. 2 Current path in switch-on

Fig. 3 Current path in switch-off

D Vin 1D

ð1Þ

Rð1  DÞ2 RVin2 ¼ 2f 2f ðVin þ V0 Þ2

ð2Þ

V0 ¼

where D is the duty cycle of buck-boost converter, Vin is the input voltage, Vo is the output voltage. The output voltage can be regulated by changing the duty cycle D, when D \ 0.5, the output voltage is lower than the input voltage, when D = 0.5, the output voltage is equal to the input voltage, when D [ 0.5, the output voltage is higher than the input voltage. The critical inductance for the occurrence of transition from CCM to DCM can be obtained as follows: Lf ¼

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Fig. 4 Buck-boost converter

where f is the switching frequency, R is the load resistor. If the inductance is less than Lf, the buck-boost converter will operate in DCM, otherwise the buck-boost converter will operate in CCM.

3 Simulation Study of Buck-Boost Converter In this section, buck-boost converter is built by using circuit simulation software PSPICE. The value of input voltage is 360 V, the output voltage is 12 V, the switching frequency is 20 kHz and the output power is 600 W. Figure 4 shows the simulation model of buck-boost converter, which is performed by using PSPICE software platform with the following parameters: Vin = 360 V, Lf = 20 uH, Cf = 667 uF, R = 0.24 X, R1 = 10 X. With: Z: power switching tube APT50GF100BN, D: diode MUR100, R1: current-limiting resistor. The transient analysis results of buck-boost converter circuit are obtained by using software PSPICE and the simulation time is 2 ms. From Fig. 5, it can be seen that the inductor current is continuous. Figure 6 shows the output voltage, it can be seen that the output voltage is -12 V. Figure 7 shows the output current which value is -50 A. From above results, it can be obtained that the output power of buck-boost converter is 600 W.

4 Study on Conducted Interference of Buck-Boost Converter The drain-source voltage of switching tube can change in a short time, which produces high du/dt. The high du/dt spreads through the parasitic capacitance between the switching tube and ground, so the common-mode interference (CMI) is formed. High reverse recovery current is generated by diode when diode works from on state to off state. Because distributed capacitance and inductance are existed in the input and output wires, when high frequency surge current flow through the wires, the differential-mode interference (DMI) is formed. According to the request of GB18655-2002, in order to study the conducted interference,

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Fig. 5 Inductor current

Fig. 6 Output voltage

Fig. 7 Output current

Line Impedance Stabilization Network (LISN) installed between power supply and Equipment Under Test (EUT) is used. Figure 8 shows the circuit of LISN, all the parameters are listed in the following: L2 = L3 = 50 uH, C2 = C3 = 1 uF, C4 = C5 = 0.1 uF, R3 = R4 = 50 X. Figures 9 and 10 shows the conduction path of CMI and DMI. The common-mode interference voltage and differential-mode interference voltage can be obtained on R3 or R4. Figure 11 shows the relationship between the conducted interference voltage and frequency. According to the request of national standard GB18655-2002, the test results are compared with the standard limits given by the national standard. From Fig. 12, it can be seen that the test results of the conducted interference exceed the national standard limits. Two methods are used to reduce the conducted interference. Firstly, an RCD snubber circuit is added in parallel with switching tube IGBT. Secondly, an RC snubber circuit is added in parallel with diode D. Figure 13 shows measures for improving the circuit. After taking these measures, the test results of conducted interference are shown in Fig. 14.

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Fig. 8 Line impedance stabilization network

Fig. 9 Conduction path of CMI

Fig. 10 Conduction path of DMI

5 Study on Radiated Interference of Buck-Boost Converter Radiation field consists of far field and near field. High du/dt generates radiated electric field, high di/dt generates radiated magnetic field. In this paper, far-field radiation of common-mode interference and far-field radiation of differential-mode interference are studied. The relationship between conducted interference current and frequency is shown in Fig. 15.

Study on Conducted Interference and Radiated Interference Fig. 11 The conducted interference voltage

Fig. 12 Comparison results between test results and standard limits

Fig. 13 Measures for improving the circuit

Fig. 14 Comparison between test results and standard limits

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Fig. 15 Conducted interference current

Fig. 16 Electric dipole mode

At the frequency from 150 kHz to 0.5 MHz, differential-mode conducted interference takes the main part. At the frequency from 5 to 30 MHz, commonmode conducted interference takes the main part. In order to study the common-mode current far-field radiated interference, electromagnetic simulation software, EMC Studio, is used. The electric dipole radiation mode is built in EMC Studio (Fig. 16). L is the half length of the dipole, which is made of conductor. Where 2d = 1 cm. According to the request of GB18655-2002, the standard measurements of farfield radiated interference are done at a distance of 10 meters from the EUT and the measure frequency range vary from 30 to 500 MHz. Table 1 sums up the simulation parameters for electric dipole. The electric field strength of common-mode current far-field radiation is given by 1 E ¼ 1:26  106 ðfILÞð Þ r

ð3Þ

where f is the frequency of common mode current, I is the value of common mode current, L is the path length of common mode current, r is the distance between test point and the interference source. In order to study the far-field radiation strength, different common mode current sizes and different path lengths are used to study the effects. It can be seen that when the path length (common mode) L is under the same condition, the larger the common mode current, the stronger the electric field strength of far-field radiation will be (Fig. 17). From Figure 18, it can be seen that when the common mode current I is under the same condition, the longer the path length (common mode), the stronger the electric field strength of far-field radiation is. In order to reduce the common mode current far-field radiation, reducing the common mode current size and the path length (common mode) are needed.

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Table 1 Parameters for electric dipole Common mode current (uA)

L1 (cm)

L2 (cm)

L3 (cm)

800 600 400

5 5 5

10 10 10

15 15 15

Fig. 17 The electric field magnitude

The differential-mode current far-field radiation is simplified as a rectangular loop antenna. The rectangular loop antenna is designed in electromagnetic simulation software EMC Studio (Fig. 19). Figure 19 shows the rectangular loop antenna built in EMC Studio, where M is the width of the rectangular, L is the length of the rectangular. The electric field strength of differential mode current far-field radiation can be obtained as follows: 1 E ¼ 1:316  1014 ðf 2 SIÞð Þ r

ð4Þ

where f is the frequency of differential mode current, I is the value of differential mode current, S is the rectangular area, r is the distance between test point and interference source. In order to study the effect of the rectangular area on the electric field strength, some parameters for rectangular loop antenna are shown in Table 2. From Fig. 20, it can be seen that the larger the rectangular loop area, the stronger the electric field strength of far-field radiation is.

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Fig. 18 The electric field magnitude Fig. 19 Rectangular loop antenna

Table 2 Parameters for rectangular loop antenna L (cm) Loop area (cm2)

M (cm)

9 16 25 9 16 25

3 4 5 4.5 8 12.5

3 4 5 2 2 2

Figure 21 shows when the rectangular loop area is equal to the square loop area, the electric field strength of rectangular loop is less than square loop. In order to reduce the differential mode current far-field radiation, reducing the rectangular loop area is needed. Using narrow rectangular loop instead of square loop can reduce the electric field strength of far-field radiation.

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Fig. 20 Electric field magnitude

Fig. 21 Electric field strength comparison between rectangular loop and square loop

6 Conclusion According to the request of GB18655-2002, the conducted interference and radiated interference have been studied. In order to reduce the conducted interference, RCD buffer circuit and RC buffer circuit are used. The methods of reducing the common mode current and shortening the path length of common mode are used to reduce the common mode current far-field radiation. The method of reducing the rectangular loop area and using narrow rectangular loop instead of square loop are adopted to decrease the differential mode current far-field radiation. Acknowledgments This work is supported by National High Technology Research and Development Program of China (2011AA11A220) and the Open Foundation State Key Laboratory of Automotive Simulation and Control Program (20111109).

References 1. Restrepo C, Calvente J, Romero A et al (2012) Current-mode control of a coupled-inductor buck-boost DC–DC switching converter. IEEE Trans Power Electron 27(5):2536–2549 2. Franc Mihalic, Dejan Kos (2006) Reduced conductive EMI in switched-mode DC–DC power converters without EMI filters: PWM versus randomized PWM. IEEE Trans Power Electron 21(6):1783–1794

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3. Haupt RL (2008) Using MATLAB to control commercial computational electromagnetics software. Appl Comput Electromagn Soc J 23(1):98–103 4. Bao B, Zhou G, Jianping X et al (2011) Unified classification of operation-state regions for switching converters with ramp compensation. IEEE Trans Power Electron 26(7):1968–1975

Study on Electromagnetic Interference Restraining of Motor Control System Li Zhai, Runze Gao and Qiannan Wang

Abstract A study on electromagnetic interference restraining of electrical vehicle motor control system is presented. The reason why the motor control system would generate electromagnetic interference, the propagation ways and the hazard of electromagnetic interference are presented in this paper, through analysis of the structure and control strategy of motor control system. And it takes corresponding technologies to restrain electromagnetic interference, such as the selection of power devices, system grounding, electromagnetic shielding technology, filter isolation technology, mechanism design, and cables reasonable distributing. The results of electromagnetic emission test of electrical vehicle motor control system show that the motor control system designed by electromagnetic compatibility (EMC) technology has good electromagnetic interference restraining performance.





Keywords Electromagnetic Interference (EMI) Electrical vehicle (EV) Motor control system Electromagnetic compatibility (EMC)



1 Introduction Motor control system is not only the important basic supporting system for electric vehicle, but also the significant link for the process of commercialization and industrialization of electric vehicles. The electromagnetic compatibility of motor F2012-D04-013 L. Zhai (&)  R. Gao  Q. Wang National Engineering Laboratory for Electric Vehicle, Beijing Institute of Technology, Beijing, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_55,  Springer-Verlag Berlin Heidelberg 2013

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Battery

Motor

A

C0

B

+

Drive circuit PWM signal Vehicle Rotate speed signal control unit Torque signal

TMS320 LF2812

Control circuit

Current feedback signal Voltage feedback signal Torque feedback signal

Fig. 1 Working principle of motor control system

control system have direct effects on the reliability of the motor control system as well as the safety of the electrical vehicle, so it is especially important to research on electromagnetic interference restraining for motor control system. There are many references on the whole electric vehicle or charging system in previous researches. However, there is little research on electromagnetic compatibility of motor control system for electric vehicle [1, 2]. The reason why the motor control system generates electromagnetic interference (EMI), the transmission paths, the hazard of EMI and the corresponding technologies to restraining EMI are presented through analysing the structure and control strategy of motor control system in this paper. The results of electromagnetic emission test of electric vehicle motor control system show that the motor control system designed by electromagnetic compatibility technology has good EMI restraining performance.

2 Working Principle of Motor Control System 2.1 Topological Structure of Motor Control System The motor control system of electric vehicle consists of two parts, as shown in Fig. 1, which are the power circuit and the control circuit. The main power circuit uses three-phase voltage mode inverter circuit which consists of six IGBT-modules. The master control chip DSPTMS320LF2812 is used to perform real-time digital control in the control circuit which consists of current feedback, voltage feedback, torque feedback, rotate speed feedback, PWM signal output circuit etc.

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2.2 Working Principle According to the signals analysed by the vehicle control unit, the torque signal and speed signal are sent to motor controller control circuit by CAN bus. According to the designed program and the current feedback signals, the voltage feedback signals, rotate speed feedback signals, the PWM signals are generated by the master control chip TMS320LF2812. The PWM signals are imported to the driving circuit after optoelectronic isolation to control the switch states (on or off) of six IGBTs. The PWM wave signal is a kind of positive and negative half-cycle symmetrical high frequency pulse. Thus the power circuit can complete the DC/ AC conversion and provide expected voltage to the motor. Therefore the required rotate speed and torque of the motor are finally generated to satisfy the driver’s command.

3 Electromagnetic Interference Existing in Motor Control System 3.1 Electromagnetic Interference Source Electric vehicle motor control system contains system interference source and out system interference source. System interference source mainly refers to the EMI generated by voltage and current sudden change in short time during the switching process of the six IGBTs in the power circuit. And in this process, high frequency pulse signal would be generated, which has big du/dt and di/dt. Due to the inductance and capacitance devices existing in circuit, lead inductance existing in IGBT itself and other devices, and routing stray inductance and capacitance, noise voltage and noise current are generated. These noise voltage and noise current would not only affect the power module, but also harm the control performance by flowing into control circuit via the power supply and ground. Also, a current loop with big di/dt is a radiated source. It would radiate electromagnetic wave in space to form the very strong EMI, and affect other parts in vehicle. In addition, besides the useful fundamental wave, there is large amount of high harmonics in PWM wave produced by inverter switches. The radiated interference would be produced to influence other equipment in electric vehicle. Out system interference sources include lightning, electrostatics, charging system and other power switching devices and so on.

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i dm1 idm2 Battery

Motor i1

Fig. 2 Differential mode interference

3.2 Coupling Path In electric vehicle motor control system, there are two electromagnetic disturbance coupling paths which include radiated coupling way which transmit in space and conducted coupling way which transmit in circuit. The radiated coupling way mainly refers to the near-field coupling way, otherwise the conducted coupling way is an important path for interference transmission. According to the difference of interfering ways, EMI source include differential mode (DM) interference and common mode (CM) interference [3]. The DM interference as shown in Fig. 2, DM voltage would be generated from the output side of the inverter when IGBT switch on or off, and DM interference current idm1 and idm2 are formed on the output side of the inverter, so there are two interference transmission paths. Loop i1 produced by the DM current, shown in Fig. 2, can be seen as a small loop antenna, which radiate disturbance signal to space. As can be seen in Fig. 3, there is one transmission path of CM interference current. The CM current iCM flows from the output side of the inverter, along the motor shell and the chassis (ground), finally flows back to the inverter. The inverter and the motor shell cannot completely insulate from the reference ground, therefore there is stray capacitance exist between them. The du/dt would be generated large CM interference current on the output side of the inverter.

3.3 Sensitive Equipment Sensitive equipment of the electric vehicle motor control system mainly include DSP digital control circuit, the PWM inverter, the signal driving circuit, signal amplifying circuit, current sensors and voltage sensors, CAN bus, ABS, EDS, lightning equipment, audio equipment, in-car entertainment system, GPS, parking sensors, semiconductor device, signal cables and so on.

Study on Electromagnetic Interference Restraining Fig. 3 Common mode interference

589 PWM Inverter Motor Axis

Battery CM Current

Chassis (ground)

Interference to any of the above components may affect safety and stability of the entire system. Take charging system for example, the signal distortion of the sensors may lead to overcharge and damage the battery. These sensitive devices also can be interference sources, which may interfere on other on-board equipment with high sensitivity.

4 EMI Suppression Measures 4.1 EMI Suppression of Power Device In general, such methods, like changing the topology and structure of circuit, improving control strategy and optimizing driving circuit, would be adopted in order to decrease the strength of interference generated by switching of power devices. Increase the turn-on and turn-off time of power devices; Reduce the electric field coupling between IGBTs and radiator [4]; Every IGBT’s gate driving circuit adopt independent insulated power, and should not share current branch with the main circuit’s current; To avoid noise interference, snubber circuit should be adopted between power device’s collector and emitter, as shown in Fig. 4. These measures are taken to decrease the change rate of voltage and current greatly when IGBTs switch on or switch off, thus, the strength of interference source is decreased.

4.2 PCB EMC Design Electromagnetic compatibility design for DSP digital control PCB of electric vehicle motor control system control circuit includes PCB layout and routing. Grouping layout of components is used here. Space on PCB is divided according to the groups, and those components, which are in the same group, are placed together so that interference in space would be avoided among groups.

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Fig. 4 Inverter EMI suppression circuit

Battery

Motor

C0

Snubber circuit

Radiator Chassis

Fig. 5 Layout with reduced interference

Voltage feedback circuit

DSP power supply circuit

Reset circuit

External RAM Current feedback circuit

Over-temperature & analog signal input circuit Rotate speed feedback signal circuit

Switch Input & output circuit

DSP2812 PWM Output circuit JTAG interface circuit

CAN communication circuit

Serial ports Communication circuit

Classification of group layout includes high-speed and low-speed category, high current and low current category, analog circuit and digital circuit category. In this main control board, the classification is done according to high-speed and lowspeed, shown in Fig. 5. The high-speed CAN bus transceiver circuit is arranged on one side of the circuit board, and the low-speed circuits including digital circuits and analog circuits (PWM inversion circuit) are arranged on the other side of the board [5]. The width and clearance of PCB routing are according to density of routing and high frequency characteristics of wires. During the distribution of the power line, the decoupling capacitors should be properly used at the power input side to reduce

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Fig. 6 Multi-point grounding

the coupling noise that gets into the printed circuit board and ground noise voltage between power and earth wire. To avoid the common ground wire impedance coupling interference, the incompatible ground wire, such as large current and small current, high frequency and low frequency, analog and digital, different supply voltage ground wire, should be set separately. As showed in the Fig. 6, By divide the ground plane, the ground connections of the master control board can be divided into 4 parts, which is AGND, DGND, 5VGND, 15VGND and CAN5VGND. Therefore the grounding wire inductance is minimum to reduce the grounding impedance, then the coupling of CM impedance is restrained. When comes to double panel, the ground network should be designed at first in order to effectively reduce the area of signal loop. The DM radiation and CM radiation are proportional to the area, so the DM radiation and CM radiation are decreased.

4.3 Grounding Design The EMI is restrained by the method of filter isolation for the electric vehicle motor control system. The ground loop generates ground loop interference which is a usual EMI. Filter isolation technology is applied to restrain ground loop interference. The isolation measurements are used to isolate the motor control system circuit and common ground or common conductor which can generate loop current. The isolation measurements usually adopt transformer or optocoupler. Take CAN bus interface circuit for example, as shown in Fig. 7. There are two optocouplers 6N137, and they isolate the process of sending and receiving data respectively.

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Fig. 7 The principle drawing for CAN bus interface circuit

4.4 Shielding Design Electromagnetic shielding technology is used to restrain the transmission of disturbance in the space, which cuts off the transmission channel of the radiated coupling. Shielding is one of the most basic methods of EMI restrain control. Electromagnetic radiated disturbance transmits in a way of electromagnetic wave by monopole antenna and small loop antenna in near-field and far-field. Use high conductivity materials and grounding to cut off the electric field disturbance in near-field, and use high conductivity materials and grounding to cut off the electric field disturbance in far-field. The electromagnetic shielding design of the electric vehicle motor control system includes the shielding design of system chassis and cable. The overall shielding effectiveness of shielding chassis is determined by the shielding body’s weakest link which refers to various gaps and holes. In fact, there are many leaking sources on the chassis, such as gaps of the junction of different parts, vents, display windows, buttons, indicator lights, cables, power lines. Usually electromagnetic sealing gasket is used at apertures, cut off waveguide filters is used at ventilated places, and conductive glass is used at the display apparatus, etc., to improve the shielding function.

4.5 Filtering Design Conducted disturbance can transmit through wires such as power lines, signal lines, interconnect lines, and conductors like shielding bodies, grounding conductors and so on. Usually signal line filters and power line filters are used to cut off cable coupling paths [6]. The motor driving control circuit of the motor control

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Common -mode choke L2

VDCin

L0

Motor

C0

i1

Fig. 8 Filtering design for power source module

system in Electric Vehicle is low source impedance and high load impedance, so LC type of filter structure is chose, shown in Fig. 8. Because there is DM interference at the input port of power module, a CM choke should be added, seen it in Fig. 8.

4.6 Transient Pulse Suppression Design The transient disturbance pulse and surge voltage that generated by lightning, electrostatic, switches, motor drive system, and other power switching devices, etc., would interfere the motor control system by near-field coupling through CAN bus and other cables. When design the CAN bus circuit, Transient Voltage Suppressors (TVS) should be chose to absorb the surge voltage (shown in Fig. 7) and protect components, then the reliability of system would be enhanced greatly.

5 Test Results Firstly, the motor control system power on to preheat, and achieve stable working state, make sure the input signal, output signal of motor control system and the communication signal of CAN bus perform normally; According to the band width and minimum measuring time provided by national standard, the measuring receiver can scan at a applicable frequency from 10 kHz to 10 MHz. The test results of power line in control module of charging system is shown in Fig. 9, it can be seen that the conducted disturbance generated on the power source port in control module of charging system meet the limits requirements provided by standard.

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Fig. 9 EMI test result

6 Conclusion The electromagnetic radiated interference and conducted interference are generated by DM current and CM current through analysing the structure and working principles of motor control system. In order to restrain the EMI, power module EMC design, PCB EMC design, grounding design, shielding design, filtering design and transient pulse suppression design are adopted. The results of electromagnetic emission test of electrical vehicle motor control system shows that the motor control system designed by EMC technology has good Emi restraining performance.

References 1. Dou R-Z, Wang H-B, GOU Y-T, CHENG P, Yang Y, WU J-Y (2011) Research of electromagnetic compatibility of motor drive system applied in electric vehicle. J Tianjin Polytechnic Univ 30:6 2. Niu L, Tang Y, Jin B (2009) Electromagnetic compatibility and disturbance restraining of electrical vehicle charging systems. East China Electric Power 37:10 3. Pei X (2004) Research on conduction electromagnetic interference of PWM Inverter. Ph.D. thesis, Huazhong University of Science and Technology, China 4. Jiang Baojun (2007) Research on the suppression methods of conduction common mode EMI for PWM motor drive system. Ph.D. thesis, Harbin Institute of Technology, China 5. Cao J, Men R, Zhang A (2006) EMC and immunity-measures of engineering control panel based on TMS320LF2407A. Electric Drive Autom 28:6 6. Zhai L, Dong S, Zhang C, Wang Z (2011) Study on electromagnetic interference restraining of electric vehicle charging system. In: 4th international conference on power electronics systems and applications (PESA)

Part V

Vehicle Sensor and Actuator

GPS Based Estimation of Vehicle Sideslip Angle Using Multi-Rate Kalman Filter with Prediction of Course Angle Measurement Residual B. M. Nguyen, Yafei Wang, Sehoon Oh, Hiroshi Fujimoto and Yoichi Hori

Abstract In this paper, a new vehicle sideslip angle estimation based on GPS is proposed. Course angle obtained from GPS receiver can be utilized as one measurement for estimation design, together with the yaw rate from gyroscope. While yaw rate is sampled every 1 ms, the sampling time of course angle is much longer (200 ms). During inter-samples (between two updates of course angle), the conventional estimation method relies upon only yaw rate measurement. In order to enhance the estimation accuracy, multi-rate Kalman filter with the prediction of course angle measurement residual during inter-samples is designed. Experiments are conducted to verify the effectiveness of the proposed algorithm. Keywords Sideslip angle residual

 GPS  Multi-rate  Kalman filter  Measurement

1 Introduction Sideslip angle estimation technique plays an importance role in vehicle stability control (VSC). In VSC system, sideslip angle must be controlled to prevent the vehicle accidents which may happen in critical driving situations, such as vehicle cornering into slippery road at high speed [1]. In fact, current vehicles are not F2012-D05-001 B. M. Nguyen (&)  H. Fujimoto  Y. Hori Department of Advanced Energy, The University of Tokyo, Bunkyo, Japan e-mail: [email protected] Y. Wang  S. Oh Department of Electrical Engineering, The University of Tokyo, Bunkyo, Japan SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_56,  Springer-Verlag Berlin Heidelberg 2013

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equipped with an ability of measuring sideslip angle directly. Corrsys–Datron provides the non-contact optical sensor for sideslip angle calculation based on lateral and longitudinal velocity measurement [2]. Because of the high cost, Corrsys–Datron sensor cannot be a practical solution. For both cost reducing and safety purpose, sideslip angle estimation has been a big issue in motion control of vehicle. In conventional sideslip angle estimation method, lateral accelerometer is used as output measurement [3]. Therefore, cornering stiffness appears in the measurement equations. In fact, the variation of road friction introduces uncertainties into the estimation model. In order to improve the sideslip angle estimation, nonconventional sensors have been utilized, such as visual information using camera image processing [4], attitude information from GPS receiver [5, 6], and the measurement of tire lateral force sensor [7]. However, the poor update rate of image processing is the main disadvantage of this approach. Camera visibility may also be unavailable when road makers are covered with leaves, snow, water, or dirt. Like visual based estimation, the main problem of GPS based estimation is the update rate of GPS receiver (from 1 to 10 Hz) which is not fast enough for motion control of vehicle. The high cost of tire force sensors is a question for the application of this method in commercial vehicles. Thanks to Japan’s own GPS system which has been constructed as national projects, high accuracy of vehicle motion measurement based on GPS is achieved. In this chapter, sideslip angle estimation based on multi-rate Kalman filter is designed using yaw rate (sampling time of 1 ms) and course angle obtained from GPS receiver (sampling time of 200 ms in this study). Using course angle measurement, cornering stiffness disappears in the measurement equations. The estimation steps between two continuous updates of course angle is called inter-samples in this chapter. During inter-samples, conventional multi-rate Kalman filter relies upon yaw rate measurement only. In this study, prediction of course angle measurement residual during inter-samples is proposed. Therefore, sideslip angle is estimated every 1 ms with high accuracy, even under model uncertainties, such as the variation of cornering stiffness. The proposed method is implemented in the control system of in-wheel motored electric vehicle COMS prototyped by Toyota Auto Body Co., Ltd. Two in-wheel motors are equipped in the rear wheels to generate the yaw moment. A RT-Linux operating system computer is used as the controller of COMS with the control period of 1 ms. A Corrsys-Datron optical sensor installed in the front of vehicle can be used to calculate the sideslip angle at the center of gravity. GPS receiver CCA-600 is supported by Japan Radio Co. Ltd. It can provide the measurement of vehicle course angle with the accuracy of 0.14 RMS every 200 ms. This is more accurate than the one used at Stanford University (course angle accuracy of 0.25 RMS) for the research in [5]. Experimental vehicle and GPS receiver are shown in Fig. 1.

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Fig. 1 Electric vehicle COMS

2 Modeling of Vehicle Dynamics The planar bicycle model of vehicle is shown in Fig. 2. This model is constructed under the following assumptions: (1) Tire slip angle is small such that lateral tire force is at linear region. (2) Vehicle is symmetric about the fore-and-aft center line. (3) Load transfer is neglected. (4) Vehicle velocity is approximately constant. Table 1 shows the list of nomenclatures. Sideslip angle is defined as the angle between velocity, vector and longitudinal direction. Course angle of a moving vehicle is the angle between vehicle’s direction and geodetic North. Using this definition, course angle can be represented as the summary of yaw angle and sideslip angle: c¼wþb

ð1Þ

The lateral force equation and yaw moment equation can be expressed as follows:   Fyf þ Fyr ¼ Mvx b_ þ c ð2Þ Fyf lf  Fyr lr þ Nz ¼ Iz c_

ð3Þ

where   lf Fyf ¼ 2Cf af ¼ 2Cf df  c  b vx   lr cb Fyr ¼ 2Cr ar ¼ 2Cr vx

ð4Þ ð5Þ

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Fig. 2 Planar bicycle model of vehicle

North f

vx

c

V

CG

lf

vy

lr

Table 1 Nomenclatures

Distances from front (rear) axle to the center of gravity Front (rear) cornering stiffness Yaw moment of inertia Front steering angle Yaw moment generated by in-wheel motors Vehicle mass Sideslip angle Yaw rate Yaw angle Course angle obtained from GPS Longitudinal, lateral, and velocity vector

l f ; lr Cf ; Cr Iz df Nz M b c w c vx ; vy ; V

From (1)–(5), the state space equation of vehicle dynamics is constructed as (6)–(10). Front steering angle and yaw moment are selected as input vector. x_ ¼ Ax þ Bu x ¼ ½b u ¼ ½ df

c

ð6Þ

w T

ð7Þ

Nz T

ð8Þ

2ðCf lf Cr lr Þ 2ðCf þCr Þ 0 1  Mvx Mv2x 7 6 7 6 7 6 2ðCf lf Cr lr Þ 2 2 2ðCf lf þCr lr Þ A¼6 7 0 7 6 I z vx Iz 5 4

3

2

0

1

0

ð9Þ

GPS Based Estimation of Vehicle Sideslip Angle

2

2Cf Mvx

6 6 2C l f f B¼6 6 Iz 4 0

601

0 1 Iz

0

3 7 7 7 7 5

ð10Þ

3 Prediction of Inter-Sample Measurement Residuals 3.1 Dynamics of Single-Rate Kalman Filter For the sake of simplicity, steady state Kalman filter is used to derive the dynamics of measurement residual. Assume that the output measurement’s sampling time is the same as the control period Tc. The discrete model under process noise wk-1 and measurement noise vk is expressed as follows:  xk ¼ Ad xk1 þ Bd uk1 þ wk1 ð11Þ yk ¼ Cd xk þ vk The Kalman filter has two states as follows where Ld is the Kalman gain matrix. • Prediction:

xk ¼ Ad ^xk1 þ Bd uk1

ð12Þ

^xk ¼ xk þ Ld ek ¼ xk þ Ld ðyk  Cd xk Þ

ð13Þ

• Correction:

where ek is the measurement residual which is updated every Tc in this case. From (11–13), the measurement residual is derived as follows: ek ¼ Cd Ad ek1 þ Cd wk1 þ vk

ð14Þ

From (11–14), the dynamics of estimation error is obtained as: ek ¼ ðI  Ld Cd ÞAd ek1 þ ðI  Ld Cd Þwk1  Ld vk

ð15Þ

The measurement residual in the next estimation step can be derived as: ekþ1 ¼ Cd Ad ek þ Cd wk þ vkþ1

ð16Þ

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Fig. 3 Dual-rate system jr

...

(j+1)r

...

(j+2)r

Time

u

Tc TS

y

From (15) and (16), under zero-noise condition, the relation between measurement residual at step k ? 1 and measurement residual at step k is derived as:
1 ekþ1 ¼ Qd ek ¼ Cd Ad ðI  Ld Cd ÞCdT Cd CdT ek ð17Þ The estimation error in the next n step can be derived as:

ekþn ¼ ½ðI  Ld Cd ÞAd n ek1 þ Wsr;n ðwk1 ; wk ; . . .wkþn1 Þ þ Vsr;n ðvk ; vkþ1 ; . . .; vkþn Þ Wsr;n ðwk1 ; wk ; . . .wkþn1 Þ ¼

i¼n X i¼0

½ðI  Ld Cd ÞAd ni ðI  Ld Cd Þwk1þi

Vsr;n ðvk ; vkþ1 ; . . .; vkþn Þ ¼ 

i¼n X i¼0

½ðI  Ld Cd ÞAd ni Ld vkþi

ð18Þ ð19Þ ð20Þ

3.2 Dynamics of Conventional Multi-Rate Kalman Filter Assume that the measurement output’s sampling time Ts is longer than the control period Tc. Define r = Ts/Tc is the multi-rate ratio and assume that r is an integer. The steps between two measurement update are named inter-samples. The dualrate system is shown in Fig. 3. If measurement output is updated (at step k = jr), the prediction and estimation equation are the same as the single-rate case. During inter-samples (at step k þ n; k ¼ jr; n 2 ½1; r  1), because no new measurement is updated, the correction term Ld ek is not accounted in the correction stage. Dynamics of estimation error during inter-samples is derived as follows: ekþn ¼ And ½ðI  Ld Cd ÞAd ek1 þ Wmr;n ðwk1 ; wk ; . . .wkþn1 Þ þ Vmr;n ðvk Þ

ð21Þ

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Fig. 4 Idea of inter-sample residual prediction

Wmr;n ðwk1 ; wk ; . . .wkþn1 Þ ¼ And ðI  Ld Cd Þwk1 þ

i¼n1 X

Adni1 wkþi

i¼0

Vmr;n ðvk Þ ¼ And Ld vk

ð22Þ ð23Þ

Equation (21) shows that under model uncertainties with the influence of noises and disturbances, estimation performance may be degraded due to the lost of correction term Ldek. The situation will become serious if the undesirable poles happen to the matrix Ad, due to model parameter variation.

3.3 Proposal of Inter-sample Measurement Residual Prediction The key idea can be explained using Fig. 4. If the measurement update is available, the real measurement residual is used to correct the estimated state. During inter-samples, the predictive measurement residuals are utilized to enhance the dynamics of the multi-rate estimation. The formulation of predictive residual is proposed as follows: ~ekþn ¼ Qnd ek ; k ¼ jr; n 2 ½1; r  1

ð24Þ

By applying (24) for r – 1 times, we can prove the general formulation of estimation error with prediction of inter-samples: ekþn ¼ ½ðI  Ld Cd ÞAd n ek1 þ Wehmr;n ðwk1 ; wk ; . . .wkþn1 Þ þ Vehmr;n ðvk Þ ! n n X X i Ani Ani Wehmr;n ðwk1 ; wk ; . . .wkþn1 Þ ¼ d Ld Qd wk1 d wk1þi  i¼0

Vehmr;n ðvk Þ ¼ 

n X i¼0

i Ani d Ld Qd

ð25Þ

i¼0

ð26Þ

!

ð27Þ

vk

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From (25), the dynamics of estimation error during inter-samples is enhanced in comparison with the case of conventional multi-rate as expressed in ((21). However, the accuracy of inter-samples relies upon the past measurement at step k = jr. In case of single-rate estimation, as represented in (18), estimation error at any step is driven by the current and the past measurement noise. Thus, if the system is zero-noise, the proposed method has the same estimation error dynamics as the single-rate estimation. If a measurement error happens at step k = jr, the proposed estimation cannot be as good as the single-rate case. This is because the error at step k = jr is transferred to every step during inter-samples. Even though, dynamics of estimation error of the proposed method is better than the conventional multi-rate estimation.

4 Sideslip Angle Estimation Design 4.1 Output Measurements Yaw rate and course angle are selected as output measurements for Kalman filter design. The sampling time of yaw rate is the same as the control period Tc = 1 ms. Course angle is obtained from GPS receiver every Ts = 200 ms. Inter-samples are defined as the estimation steps between two continuous updates of course angle. The measurement equation is constructed as follows: yk ¼ C d xk þ vk

ð28Þ

where the measurement matrix is: Cd ¼



0 1 1 0

0 1



ð29Þ

4.2 Discrete Model The continuous model in (6) is transformed into discrete model (30) by using the transformation (31) and (32). Tc = 1 ms is the fundamental sampling time. xk ¼ Ad xk1 þ Bd uk1 þ wk1

ð30Þ

Ad ¼ eATc  AðTc I Þ þ I

ð31Þ

Bd ¼

ZTc 0

eAs dsB  BðTc I Þ

ð32Þ

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Correction Prediction



Fig. 5 Algorithm of sideslip angle estimation using multi-rate Kalman filter with prediction of course angle residual

4.3 Multi-rate Kalman Filter Algorithm Kalman filter is designed based on the dynamics model (29) and the measurement equation (27). Qv and Qw are the process noise and measurement noise covariance matrices, as expressed in (32) and (33), respectively. They are tuning parameters of the Kalman filter algorithm shown in Fig. 5.  2  0 rc gyro Qv ¼ ð33Þ 0 r2c GPS 3 2 2 0 0 0 q11 0 6 0 q2 0 0 0 7 22 7 6 2 6 ð34Þ Qw ¼ 6 0 0 q33 0 0 7 7 4 0 0 0 q244 0 5 0 0 0 0 q255 If Qv is too large, the Kalman gain will decrease, thus, the estimation fails to update the propagated disturbance based on measurement. In (33), rc_gyro and rc_GPS denote the variance of yaw rate noise and course angle noise, respectively. They are chosen based on the idea that measurement of course angle is more reliable than measurement of yaw rate. Small Qw results in unstable estimation. On the other hand, large Qw forces the estimation to completely rely upon the measurements. Therefore, the noise associated with the measurement is directly transmitted into the estimated values. At step k = jr, both yaw rate and course angle are update, thus, estimated states are corrected with real course angle residual and real yaw rate residual: ^xk ¼ xk þ Lck eck þ Lck eck

ð35Þ

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Fig. 6 GPS interface software

At step k = jr ? 1, only yaw rate is updated, thus, estimated states are corrected with real yaw rate residual and predictive course angle residual, using the prediction formula (20). ^xk ¼ xk þ Lck eck þ Lck~eck

ð36Þ

5 GPS Interface Design GPS receiver CCA-600 outputs the information in NMEA-0183 protocol. In order to transfer data from CCA-600 to the experimental vehicle, GPS interface software is designed in a laptop (Fig. 6). It receives the NMEA messages from CCA-600 through serial port. Then, it decodes the messages for required data, such as vehicle position, course angle, and velocity. The decoded data are sent to the controller of experimental vehicle through LAN cable using user datagram protocol (UDP/IP). Measurements of course angle and velocity using the GPS interface are shown in Fig. 7.

6 Experiment Results In order to demonstrate the effectiveness of the proposed method, other three sideslip angle estimation methods are performed. The proposed method is named ‘‘Enhanced Three-State MRKF’’ in this study. The name and description of each method are listed as follows:

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• Two-State KF: The single-rate Kalman filter using only yaw rate measurement. • Three-State MRKF: Conventional multi-rate Kalman filter using yaw rate and course angle measurement. During inter-samples, sideslip angle is corrected based on yaw rate residual only. • Three-State MROb: From literature review, the enhancement of inter-sample estimation was proposed by Hara et al. [8]. The key idea of this method is to hold the real residual to correct the estimated state during inter-samples. The observer gain is redesigned to confirm the convergence and stability of estimation. This method was applied in hard disk drive system. We re-apply this method for sideslip angle estimation by holding the real course angle residual during inter-samples. It is important to notice that, the multi-rate ratio in case of hard-disk drive (r \ 10) is smaller than the multi-rate ratio of vehicle system, due to the limitation of GPS receiver (r = 200 in this study). Moreover, vehicle control system is a time varying system due to the change of road friction coefficient and velocity. The unknown external disturbance may be introduced into the system. Therefore, in case of vehicle system, Kalman filter is applied because it is the optimal linear estimator in sense that no other linear filter can gives a smaller variance of estimation error.

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Fig. 8 Lane change test experiment results. a Two-State KF. b Three-State MRKF. c ThreeState MROb. d Enhanced three-state MRKF. e Vehicle attitude. f Inter-sample performance

Figure 8 shows the results of lane-change test on the asphalt road surface. The real cornering stiffness are Cf = 10,000 [N/rad] and Cr = 10,000 [N/rad]. However, the cornering stiffness of the estimation model are set as Cfm = 10,000 [N/rad] and Crm = 10,000 [N/rad]. This makes the model error condition for experiment. Two-State KF shows the poorest estimation performance. Three-State MRKF shows the better estimation result. The estimation error is reduced when course angle is updated. However, during inter-samples, sideslip angle is corrected by only yaw rate measurement, the estimation performance of Three-State MRKF is degraded. From Fig. 8f, thanks to the prediction of course angle residual, Enhanced Three-State MRKF shows the best estimation performance. Besides sideslip angle estimation, yaw angle is estimated at 1 kHz in comparison with

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Table 2 Rmsd of sideslip angle estimation Estimation method

RMSD [rad]

Two-state KF Three-state MRKF Three-state MROb Enhanced three-state MRKF

1.02 0.86 0.48 0.36

9 9 9 9

10-2 10-2 10-2 10-2

5 Hz course angle, as shown in Fig. 8e. Root-mean-square-deviation (RMSD) from measured sideslip angle is calculated for comparison. The results is shown in Table 2 in which, the proposed method has the smallest RMSD value.

7 Conclusions From the view of control theory, this paper proposes a new method for enhancing the multi-rate estimation. During inter-samples, estimated state is corrected with the predictive measurement residual. The proposed method is applied in sideslip angle using GPS and multi-rate Kalman filter for vehicle control system. Experiments are conduct to evaluate the effectiveness of the proposal in comparison with the previous estimation methods. Even under model error, accurate sideslip angle estimation is achieved. In future works, auto-tuning of process noise and measurement noise covariance matrix will be examined. Acknowledgments The authors would like to thank Japan Radio Company (JRC) for their supports of GPS receiver for experiments in this study.

References 1. Wong JY (2001) Theory of ground vehicles, 3rd edn. Wiley, Hoboken 2. Corrsys-Datron http://www.corrsys-datron.com/optical_sensor.htm 3. Geng C, Mostefai L, Denai M, Hori Y (2009) Direct yaw moment control of an in wheel motored electric vehicle based on body slip angle fuzzy observer. IEEE Trans Industr Electron 56:1411–1419 4. Wang Y, Nguyen BM, Kotchapansompote P, Fujimoto H, Hori Y (2012) Vision-based vehicle body slip angle estimation with multi-rate kalman filter considering time delay. In: 21st IEEE international symposium on industrial electronics 5. Bevly DM, Ryu J, Gerdes JC (2006) Integrating INS sensors with GPS measurements for continuous estimation of vehicle sideslip, roll, and tire cornering stiffness. IEEE Trans Intell Transp Syst 7(4):483–493 6. Anderson R, Bevly DM (2004) Estimation of slip angles using a model based estimator and GPS. Am Control Conf 2004:2122–2127 7. Nam K, Oh S, Fujimoto H, Hori Y (2011) Vehicle state estimation for advanced vehicle motion control using novel lateral tire force sensors. Am Control Conf 2011:4853–4858 8. Hara T, Tomizuka M (1999) Performance enhancement of multi-rate controller for hard disk drives. IEEE Trans Magn 35(2):898–903

Multi-Gas Sensor by Infrared Spectrometer Tetsuya Enomoto, Tomoki Tanemura, Shuichi Yamashita, Hiroyuki Wado, Yukihiro Takeuchi and Yutaka Hattori

Abstract In order to detect many types of gases (CO2, NOx, SOx, C2H5OH) in the automotive cabin by infrared absorption sensor, we developed a novel micro electro mechanical systems (MEMS) based Fabry–Perot spectrometer with an ultra wide wavelength range (3.20–8.40 lm) compared to previously reported spectrometers (typically 2.80–5.80 lm). The wavelength range of a Fabry–Perot spectrometer is known to increase by increasing the ratio of the refractive indices of the multilayer mirrors. Thus, a novel mirror structure was proposed replacing the low refractive index layer of SiO2 (nL = 1.44) with ‘‘air (nL = 1.00)’’ for a wider wavelength range. To fabricate the proposed structure, the internal stress of the four ultra-thin polycrystalline silicon films (ca. 320 nm) was controlled tensile by the deposition temperature. A gas sensor was fabricated using our developed spectrometer. It was found that the sensor detected CO2 and C2H5OH successfully. Keywords Infrared absorption sensor MEMS

 Gas sensor  Fabry–Perot spectrometer 

1 Introduction Recently, there is increasing demand for the detection of many types of gases (CO2, NOx, SOx, C2H5OH, and so on) in the automotive cabin. There are two types of gas sensors; chemical sensors and physical sensors. Chemical sensors such as F2012-D05-003 T. Enomoto (&)  T. Tanemura  S. Yamashita  H. Wado  Y. Takeuchi  Y. Hattori DENSO Corporation, Kariya, Japan e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_57,  Springer-Verlag Berlin Heidelberg 2013

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612 Fig. 1 Schematic diagram ofinfrared absorption gas sensor

T. Enomoto et al.

Infrared source

Gas absorption cell

Gas molecule

Fabry-Perot spectrometer

Infrared sensor

Can package

metal oxide gas sensors cannot identify and quantify more than two gases with a single sensor because of their lower selectivity for many types of gases [1]. On the other hand, physical sensors such as infrared absorption sensors detect many types of gases with a single sensor because of their excellent selectivity for many types of gases due to the different absorption spectra of the target gases. The infrared absorption sensor was developed to detect many types of gases [2]. The infrared absorption gas sensor is composed of an infrared source, a spectrometer, and an infrared detector as shown in Fig. 1 [3]. In this kind of sensor, various types of gases are identified using the absorption spectra of the detected gases and their concentrations are determined by their absorbance. The key device for this kind of sensor is a spectrometer with an ultra wide wavelength range and a high spectral resolution in order to be able to successfully detect many types of gases. There are various spectrometers, such as the Michelson interferometer, the diffraction grating, and the Fabry–Perot spectrometer (FPS), and so on. The micro electro mechanical systems (MEMS) based FPS is suitable as a gas sensor because it has a high spectral resolution and is compact [3–6]. However, the conventional FPS has a relatively narrow wavelength range, typically 2.80-5.80 lm, which does not cover the absorption peaks of gases in the automotive cabin as shown in Fig. 2. In this paper, a novel MEMS based FPS with an ultra-wide wavelength range (3.20–8.40 lm) is presented. The proposed FPS has multilayer mirrors, which consist of an air layer embedded in the polycrystalline silicon (poly-Si) films. A gas sensor was fabricated using our developed FPS. The detection of CO2 and C2H5OH will be described.

2 Design of the Spectrometer 2.1 Optical Design The FPS is an optical resonator consisting of two parallel mirrors facing each other. The resonance condition of a FPS is k = 2d/m, where d is the distance of the two mirrors, k is the wavelength and m is an arbitrary integer. The FPS transmits only the resonant wavelengths of the incoming light. Therefore, the transmittion spectrum is tuned by the distance of the mirrors by controlling the electrostatic

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Fig. 2 Absorption spectra of gases in automotive cabin 1

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force between them. The mirrors of the FPS are composed of dielectric multilayer films. The thickness of these films is represented by kmid/4n, where kmid is the central wavelength of the wavelength range, and n is the refractive index of each film. The wavelength range is extended by increasing the ratio of the refractive indices of the multilayer nH/nL, where suffixes H and L are for the high and low refractive index of the layers, respectively. To obtain a wavelength range that includes the absorption peaks of the gases in the automotive cabin ranging from 3.20 to 7.60 lm, a refractive index ratio (nH/nL) of more than 3.00 is required. The dielectric mirror of the conventional FPS [3 - 6] was composed of Si (nH = 3.45)/SiO2 (nL = 1.44)/Si (nH = 3.45) layers. The refractive index ratio of Si/SiO2 was 2.40, and this ratio was less than 3.00. The mirror structure was proposed replacing the low refractive index layer of SiO2 (nL = 1.44) with air (nL = 1.00) for a wider wavelength range. The mirror structure of the proposed FPS was composed of Si (nH = 3.45)/air (nL = 1.00)/Si (nH = 3.45) layers. The refractive index ratio of the multilayer nH/nL was increased from 2.40 to 3.45, and this ratio was more than 3.00. The kmid was determined as 4.5 lm which corresponds to the central wavelength of the wavelength range from 3.20 to 7.60 lm. The thickness of the Si and air layers, which make up the multilayers of the mirror, was 0.32 and 1.15 lm respectively.

2.2 Structure The structure of the FPS with an ultra wide wavelength range is shown schematically in Fig. 3. The lower mirror was formed on an Si-substrate. The upper mirror was formed in the shape of the membrane on the spacer which was formed on the lower mirror. The upper mirror was moved to the lower mirror by electrostatic force between them. The membrane consists of the mirror region and the outer region which surrounds the mirror region. The diameter of the membrane and the mirror region was 1800 and 800 lm, respectively. The mirror region was divided into many small honeycomb structures to increase its rigidity. All Si layers were composed of poly-Si.

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electrodeA

poly-Si SiO2 poly-Si SiO2 poly-Si Thermal Oxide

upper mirror electrodeC Lower mirror electrodeB

Si-sub 500µm

800µm

500µm

Outer region Mirror region Outer region

Fig. 3 Schemetic diagram of Fabry–Perot spectrometer with ultra-wide wavelength

Fig. 4 Deformation of FPS by applied voltage between upper and lower electrodes

Incident light

dmin

dmax

Interfering light

The Si/air/Si mirrors of the proposed FPS can deform more easily than the conventional Si/SiO2/Si mirrors of the FPS as shown inFig. 4. The full width at half the maximum (FWHM) of the transmission spectra from the proposed FPS can increase, because the distance of the two mirrors can be varied at every point. In order to prevent the increase of the FWHM, the electrostatic force must be generated only in the outer region surrounding the mirror region. A three electrode configuration was used to overcome this issue. Three electrodes were formed in the lower mirror region (electrode-A), in the lower outer region (electrode-B), and in the upper mirror region (electrode-C), respectively, as shown in Fig. 3. The same positive voltage was applied to electrode-A and electrode-C as was applied to electrode-B to generate an electrostatic force only in the outer region. These electrodes were formed by an ion implantation process of Si. Electrode-A was made of n-type Si by implanting phosphorus (1016 cm-3). Electrode-B and electrode-C were made of p-type Si by implanting boron (1019 cm-3).

Fig. 5 Deposition temperature dependence of the internal stress of the polySi

615 Internal stress (MPa)

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3 Experiment 3.1 FPS Experiment The internal stress of the poly-Si must be tensile to fabricate this ultra-thin (320 nm) poly-Si structure without buckling or deformation. However the stress of the poly-Si is compressive when it is deposited using low pressure chemical vapour deposition (LP-CVD) at 620 degrees C which is the standard deposition temperature. In order to make the poly-Si layers tensile, after amorphous deposition of poly-Si at low temperature, it was crystallized by annealing in N2 for 3 h at 950 C [7]. The affect of the deposition temperature on the stress of the poly-Si is shown in Fig. 5. As the deposition temperature becomes lower, the tensile stress of the polySi becomes higher. If the deposition temperature is 540 C, the stress of n-type poly-Si by implanted phosphorus (1016 cm-3) and p-type poly-Si by implanted boron (1019 cm-3) is 74.9 and 61.2 MPa, respectively. The FPS was fabricated using a standard MEMS process as shown in Fig. 6. Four poly-Si layers were deposited by LP-CVD at 540 C, as mentioned above. Three SiO2 layers which have the role of the sacrificial layer to form the mirrors and the movable membrane were also deposited by plasma enhanced chemical vapour deposition. After all layers were deposited, the wafer was annealed for 3 h at 950 C to control the stress of the four poly-Si layers. The sacrificial layers made from SiO2 were released by HF wet etching, and then the wafer was dried by a supercritical drying process [8, 9] to successfully form the narrow gap between the The fabricated FPS was observed by optical microscope and scanning electron microscope. The transmission spectra of the fabricated FPS were measured by fourier transform infrared Spectroscopy (FTIR).

3.2 Gas Sensor Experiment A gas sensor was fabricated using the developed FPS with an ultra-wide wavelength range, and then the gas-sensing performance was characterized. The operation principle used was Single-Beam Dual-Wavelength measurement. An IR

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annealing process The sacrificial etching (wet HF) Drying process with supercritical fluid

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Fig. 6 The process flow of proposed FPS

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Bandpass filter CAN package FPS Spacer Infrared detector STEM

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detector and the FPS were mounted on a stem as shown in Fig. 7a. The stem was hermetically sealed by a CAN with a band pass filter, which attenuated the light except for the wavelength range of the FPS, to prevent the infiltration of the detected gas. The infrared source, a gas absorption cell with a length of 0.05 m, the CAN package, and the detection circuitry were assembled as shown in Fig. 7b. The detecting scheme of the proposed sensor was as follows. The infrared source was electrically modulated, i.e. turned on and off continuously. Light then entered the gas absorption cell, where part of the photons at a certain wavelength were absorbed by the target gas. The FPS was tuned so that the transmission spectrum coincided with the absorption wavelength of the target gases. The signal amplitude was defined as the signal difference at the on and off of the infrared source and was recorded by the infrared detector.

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The signal amplitude when 100 % N2 (VN2) was detected was recorded as a reference value, because N2 has a much lower absorption coefficient. The sensor signal (Abs) by the target gas was defined as Eq. (1). Abs ¼ 1 

Vgas VN2

ð1Þ

where Vgas is the signal amplitude of detecting the target gases. The gas concentration dependence of the sensor signal can be represented by the Lambert–Beer law; Abs ¼ 1  10aLc

ð2Þ

where a is absorption coefficient of the target gas, L is the absorption length (0.05 m), and c is the gas concentration. The performance of the sensor was examined by CO2 gas and C2H5OH gas. The concentration of the supplied CO2 gas was controlled by diluting the mix with N2 gas using a mass flow controller. The range of the concentration was from 1 to 10 %. C2H5OH gas was made from the liquid phase using N2 bubbling. The concentration of C2H5OH gas was 8.7 % at 27 C, which was the measurement temperature because it corresponded to the saturated vapour pressure.

4 Results and Discussions 4.1 FPS Figure 8a, b show optical and SEM pictures of the fabricated FPS. No buckling or deformation and no sticking of the poly-Si layers were observed, as shown in Fig. 8a, b.

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The experimental transmission spectrum and calculated transmission spectrum by the matrix method [10] are shown in Fig. 9. The calculated spectrum coincided closely with the experimental one. The wavelength range of the FPS with Si/air/Si mirrors was from 3.20 to 8.40 lm, which is much wider than the conventional FPS with Si/SiO2/Si mirrors (typically 2.80–5.80 lm). Therefore, the wavelength range of our FPS includes the absorption peaks of gases in the automotive cabin (CO2, NOx, SOx and C2H5OH) ranging from 3.20 to 7.60 lm. Figure 10 shows the transmission spectra of the FPS when it was driven by an electrostatic force. The peak wavelength was shifted from 4.50 to 3.20 lm by the electrostatic force. Its shift range included the absorption spectra of CO2 and C2H5OH. However, the peak wavelength shift by the electrostatic force was limited by the pull-in phenomenon which was a unique problem associated with the electrostatic device. The peak shift needs to be widened in the future. The FWMH of the FPS was less than 60 nm in the wavelength region from 4.5 to 3.25 lm as shown in Fig. 10. The FWHM was almost independent of the electrostatic force. This indicates that the novel electrode structures avoided the deforming mirror, as mentioned above. This FPS had high spectral resolution to identify multiple gases.

4.2 Multi Gas Sensor Figure 11 shows the dependence of the sensor signal defined as Eq. (1) on the transmission wavelength of the FPS with dots when the sensor is exposed to 10 % CO2 and 8.70 % C2H5OH. Figure 11 also shows the absorption coefficients of the gases with a continuous line for comparison. The sensor signals had peak wavelengths around 4.25 and 3.35 lm, respectively. These peak wavelengths corresponded to the absorption peaks of CO2 and C2H5OH, respectively. This indicates that the fabricated sensor successfully detects CO2 and C2H5OH. However, a sensor signal detecting C2H5OH was increased at a wavelength of around 4.00 lm which corresponded to the low absorption of C2H5OH. These remaining sensor signals may have originated from the scattering of C2H5OH droplets on the gas absorption cell. On the other hand, the spectra of the sensor signal were wider than the absorptance spectra

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of these gases. This difference could have been caused by an uncollimated beam into the FPS. The sensor signal dependence on CO2 concentration fitted according to the Lambert–Beer law as shown in Fig. 12. The experimental value had good agreement with the theoretical value of the Lambert–Beer law. This means that the sensor determines the concentration of gases. These results indicate that the gas sensor using an FPS with a wide wavelength range is capable of detecting multiple gases.

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5 Conclusion To achieve an infrared multi-gas sensor, an FPS with an ultra wide wavelength range (3.20–8.40 lm) was developed. A novel mirror structure of the FPS was proposed replacing the low refractive index layer of SiO2 (n = 1.44) with air (n = 1.00) for a wider wavelength range. To fabricate the proposed structure, the internal stress of the poly-Si was controlled by deposition temperature. Moreover, a three electrode configuration was used to avoid an increase of the FWHM of the transmission spectra from the FPS. The wavelength range of the FPS with Si/air/Si mirrors was from 3.20 to 8.40 lm, which is much wider than the conventional FPS with Si/SiO2/Si mirrors. The FWHM of the transmission spectra from the FPS was less than 60 nm when the FPS was driven by the electrostatic force in a wavelength range from 4.5 to 3.25 lm An infrared absorption sensor using our FPS was fabricated to identify CO2 and C2H5OH, which had an absorption peak around 4.25 and 3.35 lm respectively. These absorption peak wavelengths corresponded to the peak wavelengths of the sensor signals. Thus this sensor successfully identified multiple gases (CO2, C2H5OH). The CO2 concentration dependence of the sensor signals had good agreement with the theoretical value of the Lambert–Beer law. Thus this sensor determined the concentration of CO2. These results indicate that a gas sensor using an FPS with a wide wavelength range is capable of detecting multiple gases with a high degree of accuracy.

References 1. Chang SC (1979) Thin-film semiconductor NOx senor. IEEE Tran Elect 26(12):1875 2. Meléndez J, de Castro AJ, López F, Meneses J (1995) Spectrally selective gas cell for electrooptical infrared compact multigas sensor. Sens Actuators A 47(1–3):417–421 3. Norbert N, et al. (2005) Micromachined mid-infrared tunable Fabry-Porot filter. Transducers’05 2E4: 139 4. Kentaro S et al (2003) Wide wavelength range tunable Fabry-Perot filter for gas measurement. IEEJ Trans Sens Micromach 123(10):392–397 5. Blomberg M et al (1997) Electrically tunable micromachined fabry-perot infermeter in gas analysis. Physica Scripta T69:119–121 6. Norbert N, et al. (2003) Tunable Fabry-Perot-interferometer for 3–5 lm wavelength with bulk micromachined reflector carrier. In: MOEMS and miniaturized systems III, proceedings, Vol 4983. p 215–226 7. Elwenspoek M, Jansen HV (2004). In: Elwenspoek M, Jansen HV (eds) Silicon micromachining. Cambridge University Press, Cambridge, pp 419. ISBN 0521607671 8. Jafri I, Busta H, Walsh S (1999) Critical point drying and cleaning for MEMS technology. In: Conference on MEMS reliability for critical and space applications, Sep 21–22, p 51–58 9. Lee Y et al (1997) Dry release for surface micromachining with HF vapor-phase etching. J Microelectromech Syst 6:226–233 10. Heavens OS (1955) Optical properties of thin solid films. Butterworth Scientific Publications, London

Dynamic Characteristics Analysis and Experimental Study of Multilayered Piezoelectric Actuator for Automotive Applications Chuanliang Shen, Xuewei Song, Jingshi Dong and Shuming Chen

Abstract The dynamic characteristics of multilayered piezoelectric actuator (MPA) are analyzed by using the Finite element method (FEM) and experimental methods. The results of FEM show that the resonant frequency of expending mode is 38.5 kHz. The result of impedance analysis is 68.75 kHz and the result of sine sweep method is 7 kHz. The Comparative analysis of FEM and experimental results shows: the Resin encapsulation structure which is not considered in finite element model affect the accuracy of the analysis result; the impedance analysis method has a high accuracy for the test of; the power limit of voltage source and the capacitive characteristic of multilayered piezoelectric actuators lead to a fast decrease of displacement amplitude.



Keywords Multilayered piezoelectric actuators Finite element method Dynamic characteristics Experimental study Impedance analysis







1 Introduction Multilayered piezoelectric actuator (MPA) is an Electrical—Mechanical converter which is co-fired by many thin layers of piezoelectric ceramics. The MPA features high resolution, high response, low power consumption and no electromagnetic F2012-D05-004 C. Shen (&)  X. Song  S. Chen State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, China e-mail: [email protected] J. Dong School of Mechanical Science and Engineering, Jilin University, Changchun, 130025, China SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_58,  Springer-Verlag Berlin Heidelberg 2013

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622 Fig. 1 The structure of multilayered piezoelectric actuator (MPA)

C. Shen et al. insulant Internal electrode External electrode Piezoelectric ceramic

noise and it is widely used in the areas of fuel injection, vibration and noise control, energy harvesting, active engine mount and active damper [1–5]. Compared to the piezoelectric bimorph actuator, the MPA has the advantages of high energy convert efficiency, high output force, high response and stable output displacement. Compared to the stacked piezoelectric actuator, the MPA characterized small volume, low drive voltage and convenient usage. The characteristics of high response and high precision make MPA a superior choice to replace the traditional piezoelectric actuator and the electromagnetic motor. The current research of MPA is focusing the applying technology [1–5], and the rare research is about the dynamic analysis of the MPA. The dynamic characteristics of MPA are studied by the theoretical and FEM analysis together with experimental study based on actual structure of MPA. The parameters of dynamic characteristic provide a basis to the establishment of the entire system model.

2 Theoretical Modeling of MPA 2.1 Structure of MPA MPA is formed by many layers of piezoelectric ceramics which are co-fired mechanically in series and electrically in parallel as shown in Fig. 1. Because the polarization direction follows the axle of MPA, the total displacement of MPA is the sum of every single layer. There is isolating glass instead of adhesive material between the ceramic layers and the external electrodes are silver-palladium alloy. The distance between two electrodes is about 100 lm and the drive effect can be achieved by a certain low voltage.

2.2 The Equivalent Model of MPA The MPA is equivalent to a circuit [6] electrically as shown in Fig. 2. R0 is the internal resistance of the power supply, Rp is the equivalent resistance of MPA, Cp is the equivalent capacity of MPA, ui is the input voltage and uo is the actual working voltage.

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Fig. 2 The equivalent circuit model of MPA

Ro RP

ui(t)

CP

u0(t)

It can be derived from the current law of Kirchhoff iR0 ¼ iRp þ iCp

ð1Þ

The parameters of Eq. (1) can be derived based on the Ohm’s law iR0 ¼

ui ðtÞ  uo ðtÞ Ro

ð2Þ

uo ðtÞ Rp

ð3Þ

iRp ¼

iCp ¼ Cp

duo ðtÞ dt

ð4Þ

By substituting Eqs. (2, 3) and Eq. (4) into Eq. (1), it can be obtained Ro Cp

duo ðtÞ Ro þ Rp þ uo ðtÞ ¼ ui ðtÞ dt Rp

ð5Þ

Make a Laplace transformation on the both sides of Eq. (5) Ro Cp sUo ðsÞ þ

Ro þ R p Uo ðsÞ ¼ U i ðsÞ Rp

ð6Þ

The transfer function of MPA is obtained Gpie ðsÞ ¼

Rp Ro þRp Ro Rp C p Ro þRp s þ

1

ð7Þ

Because of R0 \\ Rp, Eq.(7) can be simplified Gpie ðsÞ ¼

1 Ro C p s þ 1

ð8Þ

The MPA is a first order inertia element in the form of electricity from Eq. (8) and the time constant is RoCp. In order to enhance the response speed of the MPA, reducing the internal resistance and the equivalent capacity is the effective way to reduce the time constant, and a faster response can be achieved.

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3 The Dynamic Simulation of MPA 3.1 FEM Modeling Firstly, the solid model of MPA is established. The thickness of thin layer piezoelectric ceramic which is used to form the MPA is about 20–100 lm. Hundreds of layers with rectangle cross section adopt the Ag-Pb electrodes and the thickness of an electrode is about 6–10 lm. The type of MPA is AE0505D16 and it is dissected for structure analysis. Peel off the epoxy resin coat, the number of piezoelectric ceramic layers and the structural parameters can be measured. The main parameters are the thickness T, the length of a side B for the piezoelectric ceramic layer and the number of layers n. The multilayered structure of piezoelectric ceramic is modeled in the FEM software. The material properties of the piezoelectric ceramic are defined such as the density, Poisson’s ratio and the piezoelectric stiffness matrix. The size of MPA is 5 9 5 9 20 mm, and number of ceramics layers n = 400. The combination of piezoelectric ceramic layer and electrode is realized by the command of VGLUE. Then the element type of ceramic layers is defined and the solid model of MPA is meshed. The FEM model of MPA is established.

3.2 The Modal Analysis of MPA The results of modal analysis included the natural frequency and vibration mode shows the vibrating characteristic of designed structure. The goal of modal analysis for the MPA is to obtain the natural frequency and corresponding vibration mode. And it is also to check whether the resonance-frequency is at the range of working frequency. The natural frequency is higher than the working frequency means that the system has a good dynamic characteristic and this component can not influence the bandwidth of the whole system. The natural frequency is in the range of working frequency means that this component is the weakness and the stiffness of the component should be enhanced to a higher frequency compared with the working frequency. Another way is to increase the structure damping factor to suppress the vibration amplitude corresponding to the frequency. That is to reduce the damage of the resonance. The established FEM model is analyzed. The vibration status of the MPA is freedom. The orders of modal analysis and frequency range are defined. The modals of the first 8 orders are solved by using the FEM software. The natural frequencies are shown in Table 1 and the modes are shown in Fig. 3. Table 1 and Fig. 3 show that the 1st mode and the 2nd mode of MPA have the same frequency and both of them are bending mode. The 4th mode and the 5th mode also have the same frequency and both of them are bending mode. These results are determined by the symmetric structure. The MPA is a cube with the size of 5 9 5 9 20 mm.

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Table 1 The FEM results of natural frequency for MPA Order 1 2

3

4

Natural Frequency/kHz Order Natural Frequency/kHz

15.9 7 47.8

26.3 8 58.9

5.4 5 26.3

5.4 6 35.8

The length direction follows the axle of Z and the MPA is an axis-symmetry structure in the directions of x and y. So the vibration characteristics of these two directions are the same and the mode frequencies are equal. The first order frequency of the MPA is about 5.4 kHz means that the MPA features high natural frequency and high dynamic characteristic. The 6th mode which follows the displacement direction of the MPA is stretching vibration with the frequency of 35.8 kHz. The actual working mode is stretching deformation along the length direction (the direction of layer’s thickness). So the dynamic characteristic of MPA will not restrain the response of the whole system and it will benefit the enhancement of the dynamic characteristic in mechanical structures.

4 The Dynamic Characteristics Test of the MPA 4.1 The Impedance Test and Analysis of the MPA The dynamic characteristics of the MPA refer to the frequency response which means the response speed corresponding to the input. It is an important index which is to evaluate the high speed characteristic of a system. In the resonance status of piezoelectric vibrator, the maximum current is appeared at the value of minimum impedance. Therefore, the impedance of piezoelectric vibrator can be tested instead of displacement amplitude to evaluate the dynamic characteristic. The frequency of minimum impedance is the resonance frequency. The Agilent 4294A impedance analyzer is adopted to test the impedance characteristic of the MPA with the type of AE0505D16. The open circuit and short circuit compensations are carried on firstly. Then the two electrode wires are connected to the terminals of the impedance analyzer. The start frequency and stop frequency are set for the test of impedance characteristic curve which is tested as shown in Fig. 4. The sweep range is 1–150 kHz. Figure 4 shows that the impedance decreases sharply in the range of 10 kHz. There is a peak in the frequency of 80 kHz and other part has a even shape. The impedance angle in the range of 60–90 kHz change obviously. It is also in the range of ±90. The above change shows that the MPA produces resonance in the range of frequency. It is known from the resonance characteristic of non-loss piezoelectric vibrator that the resonance frequency of piezoelectric vibrator is the frequency corresponding to the minimum impedance. The influence of the loss is ignored because of the small value of the loss.

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Fig. 3 The vibration modes of MPA

Fig. 4 The impedance characteristics of MPA(1 kHz*150 kHz)

So the frequency corresponding to the minimum impedance can be recognized to the resonance frequency. The impedance angle changes little in the frequency range of 1 kHz-60 kHz. It just changes in the region of -90 to -80. It can be proved that the MPA has a characteristic of high capacity. The further analysis of impedance characteristic for the MPA by changing the width of the frequency window which is from 50 to 100 kHz shows clear impedance characteristic.

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Fig. 5 The impedance characteristics of MPA (50 kHz–150 kHz)

Figure 5a shows that the value of impedance |z| for the frequency of 68.75 kHz reaches the minimum value of 146.71 mX and the phase angle is -3.70. Because the impedance reaches a minimum value while the current reaches a maximum value the MPA is in the status of resonance. This also means that the MPA has maximum output displacement amplitude and has maximum power consumption. Figure 5b shows that the value of impedance |z| for the frequency of 80.5 kHz reaches the peak value of 38.18 X and the phase angle is -15.58. The impedance value to the curser is the maximum and the impedance angle is -15.5803. This frequency is the inverse resonance frequency according to the resonance characteristic of piezoelectric vibrator [7]. The results of impedance analysis show that the resonance frequency of the MPA is 68.75 kHz which is in agreement with the frequency value from the producer. The impedance method for obtaining the frequency characteristic of MPA is proved to be an effective way with high precision. The result of FEM is 35.8 kHz which is different from the result of impedance analysis because the epoxy resin coat is ignored in the procedure of FEM modeling. While the epoxy resin coat has a high stiffness which will increase the stiffness of the MPA. Therefore, a lower result than the actual MPA is obtained. On the other hand, the function of epoxy resin coat is the reason of constraining the first 5 orders vibration mode except protecting the internal piezoelectric ceramic layers. Then the stretching mode could be the first vibration mode of the MPA.

4.2 The Frequency Characteristic Test of the MPA The direct way to test the frequency characteristic is utilizing the sweep method. The vibration amplitude according to the different frequency is recorded. The test diagram is shown in Fig. 6. The test result of magnitude-frequency characteristic for the MPA is shown in Fig. 7. The bandwidth (the frequency corresponding to -3 dB) is about 7 kHz.

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AG1200 Signal Generator

LV1610 Doppler Vibrometer

CF5220 Fourier Analyzer

MPA

7058 Amplifier

Data output

Fig. 6 The test diagram of dynamic response

1 0

G /dB

Fig. 7 The amplitudefrequency characteristic of MPA

-1 -2 -3 -4 -5 1

10

100

1000

10000

100000

F /Hz

The magnitude-frequency characteristic shows that the MPA has no obvious resonance in the range of 1–10 kHz. So there is no resonance occurs in this frequency range. That is to say that the resonance frequency is higher than 10 kHz. The curve in Fig. 7 shows that the amplitude decreases sharply after the frequency of 7 kHz; nevertheless, the impedance test result is 68.75 kHz. There is a big difference between 7 and 68.75 kHz. Because the MPA has a high capacity which is in the order of micro-farad the load impedance decreases along with the increasing of power driving frequency. The output current of the power amplifier goes up rapidly while the power of the amplifier is limited. So the voltage acted on the MPA declines dramatically and then reduces the output amplitude of displacement.

5 Conclusion The dynamic characteristics of MPA are analyzed by using the Finite element method (FEM) and experimental methods. The MPA is modeled according to the actual detailed structure and the modal analysis is done by FEM software. The frequency response characteristics are tested by impedance analysis method and sine wave sweep method. The first 8 order natural frequencies are obtained by using FEM. The FEM results show that the resonant frequency of expending mode is 38.5 kHz. The result of impedance analysis is 68.75 kHz. The result of sine

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sweep method is 7 kHz. The Comparative analysis of FEM and experimental results shows: the resin encapsulation structure which is not considered in finite element model affects the accuracy of the analysis result and gets a higher frequency result; the impedance analysis method has a high accuracy for the test of natural frequency; the power limit of voltage source and the capacitive characteristic of multilayered piezoelectric actuators lead to a fast decrease of displacement amplitude. The low capacitance and high power of supply can enhance the amplitude-frequency characteristic. All the results show that the MPA has fast frequency response. And the MPA has good potentials in many applications of automotive engineering. Acknowledgments This work is supported by the National Nature Science Foundation of China (51005092, 51175218) and Research Fund for the Doctoral Program of Higher Education of China (20090061120092). Any opinions, findings, and conclusions or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the sponsoring institution. Song Xuewei is the corresponding author of this work.

References 1. Senousy MS, Rajapakse RKND, Mumford D, Gadala MS (2009) Self-heat generation in piezoelectric stack actuators used in fuel injectors. Smart Mater Struct 18(4):1–11 2. Satkoski Chris A, Shaver Gregory M, More R, Meckl P, Memering D, Venkataraman S, Syed J, Carmona-Valdes J (2011) Dynamic modeling of a piezoelectric actuated fuel injector. J Dyn Syst, Measur Control, Trans ASME 133(5):1–8 3. Vasques CMA, Rodriques JD (2008) Dias. Numerical and experimental comparison of the adaptive feedforward control of vibration of a beam with hybrid active-passive damping treatments. J Intell Mater Syst Struct 19(7):805–813 4. Feenstra J, Granstrom J, Sodano H (2008) Energy harvesting through a backpack employing a mechanically amplified piezoelectric stack. Mech Syst Sign Process 22(3):721–734 5. Sui Li, Xiong Xin, Shi Gengchen (2012) Piezoelectric actuator design and application on active vibration control. Phys Procedia 25:1388–1396 6. Cui Y (2003) Study on piezoelectric ceramic self-sensing actuators and the control method of micro-motion worktable driven by it. Dalian university of technology, Dalian 7. Li Y, Qin Z, Zhou Z (1984) Piezoelectric and ferroelectric materials measurement. Science Press, Beijing

Intelligent Sensor Bearing for Torque Ripple Reduction Yi Yuan, Mathieu Hubert, Stephane Moisy, Francois Auger and Luc Loron

Abstract Permanent magnet synchronous machines (PMSM) are widely used in the automobile industry (E.g. EV&HEV, EPS). However, an important problem of PMSMs is that its parasitic torques may degrade the performances of the drive system. In the vehicle, they bring uncomfortable feelings to passengers. These torque ripples generally vary periodically with the rotor position and lead to speed ripple. To suppress these speed ripples, an iterative learning control (ILC) is used, because it is a good candidate for dealing with periodical errors. In this paper, a new technique called ‘‘intelligent sensor bearing’’ is proposed and analyzed. Compared to the existed torque ripple reduction approaches which implement the current compensation calculation in the controller, this technique is realized by modifying the feedback speed information of sensor. ILC is integrated into this technique for computing the modified speed information. Simulation and experiment are used to check the effectiveness of this approach. Results prove this intelligent sensor technique has a good performance. Keywords Sensor bearing ripples

 PMSM  EPS  Iterative learning control  Torque

F2012-D05-009 Y. Yuan (&)  M. Hubert  S. Moisy ADC-SI, SKF France, Paris, France e-mail: [email protected] Y. Yuan  F. Auger  L. Loron IREENA laboratory, University of Nantes, Nantes, France SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_59,  Springer-Verlag Berlin Heidelberg 2013

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1 Introducion PMSMs are appealing candidates for many high performance applications in the automotive industry, because of their attractive characteristic, such as high torque density, high efficiency and high reliability. However, inherent torque ripples of PMSM are considered as a serious problem in many industrial applications, particularly in the low speed and high torque situation. These torque ripples usually lead to a degradation of PMSM drive system performance and may bring vibrations and noise, which strongly influence the vehicle comfortability. Hence, PMSM torque ripple reduction is a valuable and popular topic in both the automobile industry and the academic research. Currently two kinds of methods are used to reduce the torque ripple [1]. The first one focuses on the machine itself, optimizing the machine structure. However, special designs increase the complexity of the machine, hence it is a kind of fixed and expensive method. The other one is based on the use of advanced control methods. Since the controller is a necessary part of the PMSM drive system, this kind of solution does not add any extra cost for torque ripple reduction. Moreover, active control algorithms can be easily matched to any kind of machine. Several sources of torque ripples such as cogging torque, harmonic torque, current scaling error, inverter defect and mechanical unbalance may occur in a PMSM. Most of them are depending on the rotor position only. Hence, they result in periodical oscillations of the machine speed and torque. Since the torque ripple reduction can be considered as a kind of periodic disturbance rejection problem, the ILC technique, as an iterative control method, naturally fits this goal, as already reported in [2–5]. As other advanced control methods, the ILC technique is usually implemented inside the controller also, which means that in the automobile industry, to reduce the torque ripple by the ILC technique, engineers have to redesign the system controller. Indeed, sensor, as a fundamental part of the PMSM drive system, can take place of controller for embedding the ILC technique. The sensor including advanced method is called intelligent sensor. This paper reports on the possibility of using this intelligent sensor to reduce the torque ripple and presents the implement details. This paper is organized as follows: Firstly, the PMSM mathematic equations and a conventional PMSM drive system are presented, and the different kinds of torque ripples are discussed. Secondly, the nature of ILC technique is introduced, and its several control principles are studied. Moreover, the possibility of the intelligent sensor technique is analyzed and the use of intelligent sensor to reduce torque ripples is investigated. Finally, both simulation and experiment are used to verify the correction and effectiveness of this new technique under several different situations.

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2 PMSM Model and Drive System Under the assumptions that the PMSM is unsaturated and that eddy currents and hysteresis losses are negligible, the stator d-q axis voltage equations of the PMSM in the synchronous reference frame are given by [6]: ud ¼ Rs id þ

dwq dwd þ xe wd  xe wq uq ¼ Rs iq þ dt dt

ð1Þ

where wd ¼ Ld id þ W, wq ¼ Lq iq , xe is the electric angular speed, ud and uq are the d-q axis voltages, id and iq are the d-q axis currents, Ld and Lq are the d-q axis inductances, Rs is the stator resistance, wd and wq are the d-q axis flux and W is the rotor flux. In the synchronous rotating reference frame, the rotor flux is a periodic function of he , the order of its harmonics being integer multiples of 6 [6]: W ¼ W0 þ W cosð6h6 Þ þ W cosð12h12 Þ  

ð2Þ

where he is the electric angle, W0 is the fundamental component of the rotor flux, W6 and W12 are the coefficients of 6 and 12 harmonic components. The resulting electromagnetic torque Te is 3 3 Te ¼ np ðwd iq  wq id Þ ¼ np ðWiq þ ðLd  Lq Þid iq Þ 2 2

ð3Þ

where np is the number of pole pairs. Equation 4 shows that the PMSM electromagnetic torque consists of two parts: the main and the reaction torque. With a field oriented control of the PMSM, the stator current will usually be controlled to obtain a right angle between stator current and rotor flux (id ¼ 0AÞ and therefore not to contribute to magnetization, but only to torque production: 3 Te ¼ np Wiq 2

ð4Þ

Finally, the mechanical angular speed is related to the torque ripple through [6] Eq. 5. J

dxm ¼ ðTe  Tl  Tfric Þ dt

dh ¼ xe ¼ np xm dt

ð5Þ

where J is the rotor inertia, Te is the load torque, Tl is the friction torque, Tfric is the electrical speed xm is the mechanical speed. Figure 1 shows a PMSM drive system scheme, where two closed-loops are used. The external one is the speed loop and the inner one is the current loop. One speed controller and one current controller are used to control this system and both of them are usually implemented inside a digital controller. In practice, if we want

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Fig. 1 The PMSM drive system scheme

to improve the system performance by adding a new control method, the algorithm of this method is also realized in this controller and influences the whole system by modifying the current information.

3 Torque Ripples Overview Several kinds of parasitic torque ripples [4] such as cogging torque, harmonic torque, offset torque and mechanical bias torque exist in PMSM. In many commercially available machines, cogging torque has a nominal value of 5–10 % of the rated torque [6], therefore it is the main target of the torque ripple reduction. On the other hand, compared to other torque ripples, the harmonic torque is relatively more important. Their nature and model are briefly presented in this section.

3.1 Cogging Torque Cogging torque [6] manifests itself by the tendency of the rotor to align in a number of stable positions, even when the machine is unexcited. It is caused by the interaction between the magnet flux and the stator slots [7]. An approximation expression of Tcog is Tcog ðhm Þ ¼ K1 sinðn1 zhm Þ þ K2 sinðn3 zhm Þ þ K3 sinðn3 zhm Þ   

ð6Þ

where z is the stator slot number and ni is the number which can make nz=2np an integer, Ki is the coefficient which is determined by the machine structure. Equation 6 indicates that cogging torque is a function of the mechanical angular position.

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3.2 Harmonic Torque Harmonic torque is caused by the interaction between the stator current and the rotor magnetic field [8]. Substituting Eq. 2 into Eq. 4 leads to: 3 3 Te ¼ T0 þ ðT6 þ T12 þ . . .Þ ¼ np iq W0 þ np iq ðW6 cosðhe Þ þ W12 cosðhe Þ. . .Þ 2 2 ð7Þ This equation shows that the electric torque can be separated into two parts: one is the product of the flux fundamental component and current, the other one is the product of the flux harmonic components and current, which is called harmonic torque and depend on the electrical angle. Since he ¼ np hm , the harmonic torque can also be considered as a function of the mechanical angle. According to this, the cogging torque, the harmonic torque and their sum are all functions of the mechanical angle [9].

4 ILC Technique Introduction Iterative Learning Control (ILC) is based on the notion that the performance of a system that executes the same task several times can be improved by learning from the previous executions (trials, iterations, passes) [10]. Compared to other kinds of active control methods, it has the following advantages: it changes the control signal, which means that it does not need to change the structure of the previous control system, and it is not sensitive to the parameter variation of the controlled object. In fact, a successful implementation of ILC can even be done without knowing the model of the controlled process. If a discrete-time, linear time-invariant (LTI) dynamic system is considered here, defined as yi ½k ¼ PðzÞusi ½k þ d½k

ð8Þ

where i is the iteration index, k is the relative time index and k 2 ð0; . . .; N  1Þ, N is the length of an iteration. Then the absolute time index is j ¼ ni þ k, n is the number of iteration, z is the forward time-shift operator, usi is the control signal, d is an exogenous signal that repeats at each iteration, y is the system output and P represents the system transfer function. A widely used ILC learning algorithm is ui ½k þ 1 ¼ QðqÞui1 ½k þ LðqÞei1 ½k þ 1

ð9Þ

where the LTI dynamic QðqÞ and LðqÞ are called the Q-filter and learning function respectively, u is the output signal of the ILC algorithm and the ei ¼ yref  ym is the control error yref is the reference input and ym the measured output.

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As an iterative method, ILC has a good performance when tracking a periodic reference or rejecting a repetitive disturbance. A PMSM with periodic torque ripple could be regarded as a process with a periodic disturbance. Therefore, it is reasonable to choose ILC for achieving torque ripple reduction. For the ILC to be effective, the following basic postulates are required [11]: (1) Each iteration (pass, cycle, batch, repetition) ends in a fixed time of duration. (2) Invariance of the system dynamics is ensured throughout the repetition. (3) The output yi ½k is measured in a deterministic way. The stability of this controller can be assured by the following criterion [10]: qðQ  LPÞ\1

ð10Þ

where qðAÞ is the spectral radius of the matrix A. However, this condition can only guarantee the stability of the ILC control system, in practice, we hope the system is stable with its error converging into a small value. This kind of stability is called monotonic stability and is assured by the following criterion rðQ  LPÞ\1

ð11Þ

where rðAÞ is the maximum singular value matrix A. On the other side, the steady state performance of ILC is derived from the amplitude of the asymptotic error. The ILC asymptotic error e1 is given by [10] e1 ¼ ðI þ PðI  QÞ1 LÞÞ1 ðyref  dÞ

ð12Þ

In Publications [2–4], the ILC controller and the PI speed controller are used in parallel. ILC input is the speed error information between the reference speed and the measured speed and ILC output is a current signal [12, 13].

5 Intelligent Sensor According to the basic PMSM control knowledge, we know that when the sensor is able to provide the true speed information to the controller, the controlling signal based on this correct information makes the PMSM generate a torque with unwanted ripples. Since the speed-loop controls current though a PI controller, the feedback speed information can affect the current signal which is the usual controlled object for torque ripple minimization. Therefore, in order to decrease the torque ripple, we can modify the speed information. Since the sensor is responsible for providing the speed and position information, the active control method which is used to control the speed information can be accomplished in the sensor. It means that the sensor will provide a modified speed signal which contains not only the true information but also the additional information which is calculated by the active control method and is used to minimize the torque ripple. In this paper, such

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Fig. 2 The PMSM drive system with the intelligent sensor scheme

Fig. 3 The experimental bench and the detail of the SKF intelligent sensor bearing

torque ripple reduction strategy is called intelligent sensor technique. Consider SKF sensor bearing, where sensor is integrated in the bearing of PMSM, therefore, the intelligent sensor can enable the bearing be capable of reducing torque ripples. This kind of bearing is called intelligent sensor bearing. In this paper, ILC technique is chosen to take the task of the torque ripple reduction. Figure 2 shows a scheme of a PMSM drive system using an intelligent sensor basing on the ILC technique. In this case, ILC algorithm is embedded inside the sensor, its input is speed error e which is provided by a high-pass filter and its output xl is calculated by Eq. 9. Finally, a new feedback speed information xc which can reduce the PMSM torque ripple is obtained by xm þ xl . Note that there is no any modification of the conventional PMSM drive system. Therefore this technique may be used to improve a previously designed controller thanks to a replacement of the position sensor. The advantage of this technique is that it can decrease the complexity of the torque ripple reduction application for the PMSM

638 Table 1 The PMSM parameters Rated power 1.4 kW Stator resistance 0.2 X Flux linkage 0.17 Wb Inetia 0.047 kg.m

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Rated speed Stator inductance Number of poles Number of slots

2,000 rpm 4 mH 8 12

Fig. 4 Simulation results

Fig. 5 dSPACE based experimental bench results

drive system designer. Since the torque ripple reduction algorithm is integrated in the sensor bearing, the previously designed PMSM control system does not need to be modified.

6 Simulation and Experiment Firstly, Matlab/Simulink as a simulation platform is used to verify the effectiveness of the proposed approach. To testify the method practical performance, a dSPACE based experimental bench with SKF intelligent sensor bearing is chosen,

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as shown in Fig. 3. This experimental system includes following components, HP mobile work station, Dspace DS1005 Controller, dSPACE Power Unit, PMSM used for Renault Mégane II EPS, SKF sensors. The machine parameters are illustrated in Table 1. In the simulation, PI current parameters, to avoid the current overshoot, the proportional component is chosen as 0.2 and the integral component is chosen as 10. PI speed controller according to Ziegler-Nichols tuning principle, its proportional component and integral component are chosen as 0.1 and 0.5 respectively. The ILC parameters Q and L are chosen as 0.9 and 0.015 separately. Depend on these parameters, a simulation with a reference speed 120 rpm and a reference torque 10 N.m has been done, and both speed and torque results are shown in Fig. 4, which shows after using of the ILC at 1s, the amplitude of speed peak-to-peak decreases from 0.43 to 0.05 rpm and the amplitude of torque peakto-peak decreases from 7.52 to 1.91 N.m. Therefore in the simulation, this intelligent sensor is capable of eliminating 89 % speed ripples and 75 % torque ripple. An experiment with three different speeds 50, 60 and 70 rpm was realized in our experimental bench. Its results are shown in Fig. 5, where two figures show the speed ripple situations without and with the intelligent sensor respectively. The speed peak-to-peak without the intelligent is around 12.2 rpm, and the speed peakto-peak with the intelligent is nearly 5.5 rpm, therefore 55 % speed ripple was reduced by the intelligent sensor. Meanwhile, we notice that the intelligent sensor can keep its efficient in various speeds.

7 Conclusion Utilization of the SKF intelligent sensor bearing to solve PMSM torque ripple problem is proposed in this paper. The possibility of an original idea of embedding the intelligent control method in the sensor bearing, replacing of traditional approaches which implemented in the controller, is carefully analyzed. The first laboratory simulations and experimental measurements have shown a good efficiency of this intelligent sensor bearing. There is no denying that the arrival of this new technology can alleviate the burden of the automobile engineer who needs to handle the PMSM torque ripple reduction problem and its simply realization is able to accelerate the project progress, winning the time and the profit.

References 1. Jahns TM, Soong WL (1996) Pulsating torque minimization techniques for permanent magnet AC motor drives-a review. IEEE Trans Industr Electron 43(2):321–330 2. Lam BH, Panda SK, Xu JX (1999) Torque ripple minimization in PM synchronous motors using an iterative learning control approach. In: Proceeding IEEE international conference on power electronics and drive systems (PEDS’99), vol 1, Hong Kong, pp 144–149, July 1999

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3. Sahoo SK, Panda SK, Xu SK (2007) Application of spatial iterative learning control for direct torque control of switched reluctance motor drive. In: Proceedings IEEE power engineering society general meeting, 01–07 July 2007 4. Qian WZ, Panda SK, Xu JX (2004) Torque ripple minimization in PM synchronous motors using iterative learning control. IEEE Trans Power Electron 19(2):272–279 5. Yuan Y, Auger F, Loron L, Debrailly F, Hubert M (2011) Design of a lying sensor for permanent magnet synchronous machine torque ripple reduction using the iterative learning control technique. In: Proceedings IEEE international conference on power electronics and drive systems, Singapore, December 2011 6. Quang NP, Dittrich JA (2008) Vector control of three-phase AC machines. Springer, Germany 7. Krishnan R (2009) Permanent magnet synchronous and brushless DC motor drives. CRC Press, USA 8. Zhu ZQ, Ruangsinchaiwanich S, Schofield N, Howe D (2003) Reduction of cogging torque in interior-magnet brushless machines. IEEE Trans Magnetics 39(5): ER–04 9. Ashabani M, Kaviani AK, Milimonfared J, Abdi B (2008) Minimization of commutation torque ripple in brushless DC motors with optimized input voltage control. In: Proceedings IEEE international symposium on power electronics, Electrical drives, Automation and motion, 2008 10. Bristow DA, Tharayil M, Alleyne AG (2006) A survey of iterative learning control. IEEE Cont Syst Mag 26(3):96–114 11. Ahn HS, Chen YQ, Moore KL (2007) Iterative learning control: brief survey and categorization. IEEE Trans System Man Cybern 37(6):1099–1121 12. Ahn HS, Moore KL, Chen YQ (2007) Iterative learning control—robustness and monotonic convergence for interval systems. Springer, Germany 13. Verwoerd MHA (2005) Iterative learning control-a critical review. Wohrmann Print Service, Netherland

Part VI

In-Vehicle Network

Study on Diagnostic Methods of Lin Slaves Jitai Li, Ted Huang, Lifang Huang and Liguo Wang

Abstract LIN specification V2.1 defines a diagnostic method of LIN slaves. According to this method, the diagnostic messages are communicated between diagnostic tester and LIN slaves by using LIN master as a gateway. However, this method is not suitable for the transmission of mass data because it will cause high network load of the backbone bus and reduce the transmission efficiency. In this paper, two new diagnostic methods of LIN slaves are introduced. The gateway function of LIN master is eliminated in both methods to realize the direct communication between diagnostic tester and LIN slaves. As a result, the impact to the backbone bus is minimized. Moreover, two simulation diagnostic systems are built in CANoe environment to prove that both methods are realizable. Keywords LIN

 Diagnosis  Slave  Simulation  In-vehicle network

1 Introduction As the LIN bus is applied more widely on vehicles, it is necessary to design a diagnostic method of LIN slaves. A method is defined in LIN specification V2.1 by using LIN master as the gateway to transfer the diagnostic messages between diagnostic tester and LIN slaves [1]. However, this method is not suitable for the F2012-D06-001 J. Li (&)  T. Huang  L. Huang  L. Wang Automotive Engineering Institute, Guangzhou Automobile Group Co., Ltd, Guangzhou, china e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_60,  Springer-Verlag Berlin Heidelberg 2013

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transmission of mass data. The limitation of this method is that the data is transferred through the gateway. This will cause high network load of the backbone bus, and reduce the transmission efficiency. In this chapter, two new diagnostic methods of LIN slaves are introduced, which eliminate the gateway to realize the direct communication between diagnostic tester and LIN slaves. As a result, they can solve the problem of high network load of the backbone bus and improve the transmission efficiency. Moreover, two simulation diagnostic systems are built in CANoe environment which can prove that both methods are realizable and effective [2].

2 The First Diagnostic Method of LIN Slaves Diagnostic tester is connected to the original LIN network as a LIN slave, and to the CAN network which contains the LIN master. Figure 1 shows the diagnostic architecture of this method. At first, the diagnostic tester sends a request to LIN master to ask for the diagnosis of LIN slaves via CAN bus. And then, the LIN master sends a positive response to the tester via CAN bus and switches to the diagnostic schedule table, so that the tester can communicate with the LIN slaves via LIN bus. Figure 2 shows the data flow sequence. 1. The diagnostic tester sends a request to LIN master to ask for diagnosis of LIN slaves via CAN bus. 2. The LIN master sends a positive response to the tester via CAN bus if possible, and switches to the diagnostic schedule table in LIN bus. 3. The LIN master sends a header of diagnostic request frame via LIN bus, and then the diagnostic tester sends the response of this frame so that the whole diagnostic request is sent to the slave. 4. The LIN master sends a header of diagnostic response frame via LIN bus, and then the LIN slave sends the response of this frame so that the whole diagnostic response is sent to the tester. As a result, the diagnostic communication between the tester and the slave is accomplished.

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Fig. 3 Topology of CAN and LIN network

After the diagnostic communication is finished, the LIN master switches to the normal schedule table in LIN bus and the diagnostic tester is disconnected. According to this method, most of the diagnostic information is communicated via LIN bus, and there is no gateway in the diagnostic procedure. As a result, there is no impact to CAN bus and it will not cause the reduction of transmission efficiency.

3 Simulation Diagnostic System of the First Method In order to prove the feasibility of this method, a simulation system is designed in CANoe. It assumes that there are three nodes in the LIN network, Body Control Module (BCM), Front Door Module (FDM), and Roof Module (RM). BCM is the master and also the gateway between CAN and LIN network. Figure 3 shows the topology of the CAN and LIN network. There are three schedule tables in LIN bus, application schedule table, diagnostic schedule table for FDM, and diagnostic schedule table for RM. The relative codes are showed below.

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Schedule_tables { Application_Table { //application table BCM_LIN_1 delay 20 ms ; //application frame sent by BCM FDM_1 delay 20 ms ; //application frame sent by FDM RM_1 delay 20 ms ; //application frame sent by RM BCM_WaitFor200ms delay 140 ms ; //free time } Diagnostic_Table1 { //diagnostic table for FDM BCM_LIN_1 delay 20 ms ; FDM_1 delay 20 ms ; RM_1 delay 20 ms ; DiagReq delay 10 ms ; //diagnostic request FDMDiagRsp delay 10 ms ; //diagnostic response from FDM BCM_WaitFor200ms delay 120 ms ; } Diagnostic_Table2 { //diagnostic table for RM BCM_LIN_1 delay 20 ms ; FDM_1 delay 20 ms ; RM_1 delay 20 ms ; DiagReq delay 10 ms ; RMDiagRsp delay 10 ms ; //diagnostic response from RM BCM_WaitFor200ms delay 120 ms ; } } When BCM receives the diagnostic request for LIN slaves sent by tester via CAN network, it switches to the diagnostic schedule table in LIN bus, and then the tester can communicate with the slaves via LIN network. The relative codes are showed below. on message 0 9 720//CANID 0 9 720 is for diagnostic request of BCM { if((this.byte(0)==0x02)&&(this.byte(1)==0x10)&&(this.byte(2)==0x41)) //ask for diagnosis of RM { linChangeSchedTable(2); //switch to RM diagnostic schedule table } }

Figure 4 shows the communication traces. It proves the feasibility of this method.

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Fig. 4 Communication traces Fig. 5 Diagnostic architecture of the second method

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Fig. 6 Diagnostic data flow

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4 The Second Diagnostic Method of LIN Slaves Diagnostic tester is connected to the specific LIN diagnostic network as a LIN master instead of the original LIN network. As a result, it requires each LIN slave has two LIN channels. One is for the original LIN network, and the other is for the specific LIN diagnostic network. Figure 5 shows the diagnostic architecture of this method. At first, the diagnostic tester is connected to the diagnostic LIN bus and wakes it up. And then, the tester can communicate with the slaves. Figure 6 shows the data flow sequence. 1. The diagnostic tester wakes up the specific LIN diagnostic network. 2. The diagnostic tester sends the whole diagnostic request frame to the slave. 3. The diagnostic tester sends a header of diagnostic response frame, and then, the slave sends the response of this frame, so that the diagnostic tester can receive the diagnostic response. As a result, the diagnostic communication between the tester and the slave is accomplished. After the diagnostic communication is finished, the diagnostic tester is disconnected and the specific LIN diagnostic network falls asleep. According to this method, all the diagnostic information is communicated via a specific LIN diagnostic network. As a result, there is no impact to CAN bus or the original LIN bus, but it demands that the LIN slaves have an additional LIN channel.

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Fig. 7 Topology of LIN networks

5 Simulation Diagnostic System of the Second Method In order to prove the feasibility of this method, a simulation system is also designed in CANoe. It assumes that there are three nodes in the original LIN network, Body Control Module (BCM), Front Door Module (FDM), and Roof Module (RM). BCM is the master. There is another specific LIN diagnostic network which has three nodes, FDM, RM and tester. The tester is the master. FDM and RM both have two LIN channels. Figure 7 shows the topology of these two LIN networks. There is an application schedule table in the original LIN network, and there is a diagnostic schedule table in the specific LIN diagnostic network. The relative codes are showed below.

Schedule_tables { //for original LIN network Application_Table { BCM_LIN_1 delay 20 ms ; // application frame sent by BCM FDM_1 delay 20 ms ; // application frame sent by FDM RM_1 delay 20 ms ; // application frame sent by RM BCM_WaitFor200ms delay 140 ms ; //free time } } Schedule_tables { //for specific LIN diagnostic network Diagnostic_Table { MasterReq delay 10 ms ; //diagnostic request SlaveResp delay 10 ms ; //diagnostic response TESTER_WaitFor200ms delay 180 ms ; //free time } }

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Fig. 8 Communication traces

When the diagnostic tester connects to the diagnostic LIN bus, it wakes up the LIN diagnostic network. And then, the tester can communicate with the slaves according to the diagnostic schedule table. There is no impact to the original LIN network. Figure 8 shows the communication traces. It proves the feasibility of this method.

6 Conclusions This paper introduces two effective diagnostic methods of LIN slaves. Both methods realize the direct communication between tester and LIN slaves so that they can solve the problem of high network load of the CAN bus and improve the transmission efficiency. However, both methods increase the cost of the diagnostic system. The first method requires the tester has both CAN and LIN channels. The second method requires each LIN slave has an additional specific LIN diagnostic channel. In a word, this paper provides two new options for the engineers to design and improve their diagnostic systems.

References 1. LIN Consortium. LIN specification package revision 2.1 [M]. 2006 2. Vector Informatik GmbH. CANoe Help V7.2.5 [M]. 2009

The Research of Vehicle Network Control System Model Kai Li, Juan Wan, Jie Bai, Jianxian Chen, Gan Chen, Fanwu Zhang and Jianguang Zhou

Abstract With the development of vehicle electronics, the problem of strong coupling and poor generality of vehicle electronic control products become more significant due to the closed-loop control mode of the vehicle electronic control systems. According to the trend analysis of the future development of Vehicle Electrical and electronic by using s-curve and nine-screen method of the TRIZ, vehicle network control system, also referred as VNCS is not only an important way to solve the above mentioned problem, but also an important trend for Vehicle Electrical and Electronic development. Since the basic theory and model of VNCS have not fully developed yet, the methodology of research and design of VNCS still need to be further studied. In this paper, according to vehicle electrical and electronic features, the theory of industrial network control system and communication system model is introduced to build up the vehicle network control system model, furthermore, vehicle network control system model based on CAN and FlexRay are simulated and analyzed. The research results indicated that, real-time performance of vehicle network control system model based on FlexRay is better than which based on CAN. At the same time, with the separation f vehicle control system sensors, actuators and controllers, the diversification of the system functions is increased, which is an effective solution for the problem of strong coupling and poor generality of vehicle electronic control products. This research provided guidance of basic theory and design for VNCS, and also forecasted the future of vehicle network control system development prospect.

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Keywords CAN FlexRay Vehicle network control system model simulation Functional analysis



 Real-time

1 Introduction With the widely use of CAN, LIN bus technology in vehicles, the traditional method of vehicle control has developed from point-to-point control to distributed control, which enables the exchange of information between various control systems by the communication network. Compared with the traditional point-to-point control system, network communication technology is introduced to realize the information and resource sharing with the advantages of strong diagnostic capability, reduced system wiring, increased system flexibility, reliability and interactive etc. Network communication technology has brought many advantages, while due to its own closed-loop control mode, there is exists the problems of poor generality and strong coupling of vehicle electronic control products. The existence of these problems have caused great distress to vehicle control system design and production of car companies, and increased the design and research cost. In order to solve these problems, new requirement of vehicle network development has been raised.

2 The Development Process and Future Forecast of Vehicle Electrical and Electronic System Vehicle electrical and electronic systems have gone through the following three stages. 1. The first stage from the 1960s to the late 1970s In 1955,GE (General Electric Company) installed the transistor radio in vehicles. In 1964, Toyota used Automatic Electrical door locks for the first time. In the late 1970s, vehicle electrical control turned to use digital technology, and electronic control devices gradually completed the transition from discrete components to integrate. 2. The second stage from the late 1970s to the early 1990s With the development of large scale integrated circuit and control technology, many electrical control systems for some special control functions achieved rapid development taking MCU as the core of the control systems. In the late 1990s, there has been a comprehensive, integrated electronic control system. 3. The third stage from 1990s until now Vehicle electronic and electrical system has been connected to a multi-ECU and multi-node organic whole, which leads to the emergence of vehicle interior

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Fig. 1 S-curve of vehicle electronics technology development

network. Through the networked control system, information from each control module can be shared in the car, which reduces the wiring harness, and has the ability to focus on diagnostic failure. In summary, the development of vehicle electrical and electronic systems technology experienced three landmarks as the introduction of integrated circuits in 1970s, the introduction of microprocessors in the 1980s and The introduction of network control technology in the 1990s. The electronic control system sensors, controllers and actuators in today’s vehicles are integrated and articulated in the communication network, which is an integrated network control technology. According to the principle of separation from the TRIZ theory, vehicle electronics will be a centralized network control technology to a distributed network control technology direction. The development of vehicle electronics technology accords with the S-curve, shown in Fig. 1.

2.1 Vehicle Bus Technologies Since the 1980s, many well-known companies have begun to commit to the research and application of vehicle network technology [1]. The application of vehicle network has solved the problem brought by point-to-point body wiring, which makes the body wiring area normalization and standardization, reduces the vehicle costs and enhances the vehicle stability as well. At present, CAN and LIN bus technologies have been widely used in vehicle network. The developing highspeed network protocol FlexRay has been used in some high-end models.

2.2 Lin/Can/Flexray LIN is a low-cost serial communication network protocol for vehicle distributed electronic control system, its highest transmission speed is 20 kpbs, and mainly

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uses in electric window lift, seat adjustment, lighting, rear mirror and door lock control [2]. CAN is a most widely used vehicle network, the communication speed of highspeed CAN bus can reach 500 kbps or 1 Mbps, mainly used in critical real-time control, such as engine management, anti-lock braking system, chassis system [3]. The propose of FlexRay network protocol after CAN, LIN is to meet the need of future vehicle development, including faster data rates, more flexible data communication, more comprehensive topology choice and fault-tolerant computing. FlexRay provides a large number of features that traditional vehicle communication protocols don’t have, as follows [4]: 1. FlexRay supports two communication channels, and the speed of each channel reaches 10 Mbps, which is 10*20 times of CAN protocol communication band; 2. Its communication mechanism bases on time division multiple access (TDMA), which ensures the certainty of communication; 3. FlexRay supports the option of redundant message transmission, which improves the system fault-tolerance and reliability; 4. FlexRay supports a variety of topologies, provides the two options of message redundant and non- transmission as well as the combination of TDMA and FTDMA communication mechanism, which improves the system flexibility. The features above make FlexRay very suitable for wire-controlling system of control signal transmission.

2.3 Comparison of the Three Technologies The result of the comparison of the three bus technologies including LIN, CAN and FlexRay is shown in Table 1.

3 Vehicle Network Control System Model 3.1 The Current Status of Vehicle Network Control System Model At present, the typical vehicle communication network is based on CAN bus technology. The point-to-point physical model of CAN bus is shown in Fig. 2 according to the combination of communication system physical model [5] and CAN bus communication mechanism. In this typical vehicle network design, a typical vehicle network control structure is formed according to the research of CAN bus physical model, as

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Table 1 The comparison of LIN, CAN and FlexRay LIN CAN Comparison content Media access control Wiring Max

FlexRay

Single host

Muiti-host

Muiti-host

Single line communication speed

Twisted paired 20kbit/s

Twisted paired 1Mbit/s

TDMA ? FTDMA Bipolar NRZ w/bit stuffing

10-20Mbit/s Access method TDMA Encoding mode NRZ 8NI(UART) Transmission distance

40 m

CSMA Uni-polar NRZ w/bit stuffing 40 m

Data field length of each frame Network nodes

8 byte

8 byte

10 nodes

Channel Topology

Single channel Bus

16 nodes(HS-CAN) 32 nodes(MS-CAN and LS-CAN) Single channel Bus, passive star

Error detection

8 bit check sum

Max time-delay –

Scope of application

Aimed at low-speed network of sensors and actuator

24 m (bus and passive star) 48 m (one active star) 72 m (two active star) 254 byte

22 nodes

Dual-channel Bus, active star, passive star, hybrid 15 bit CRC check 11bit initial CRC check ? 24bit frame CRC check 0.5 ls 0.44 ls (bus and passive star) 0.93 ls (one active star) 1.42 ls (two active star) Now widely used in Suitable for wiring control power, chassis and system, use FlexRay to body system transmit control signals

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shown in Fig. 3. The current vehicle network control system model is formed by the point-to-point connection of this typical structure, which is aimed at network information sharing, that is sharing signals transmitted by vehicle network and its control function realized by closed-loop control system Fig. 4.

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The integration of existence sensors, controllers and actuators by this kind of vehicle network control system model leads to the problem of poor generality and strong coupling of vehicle electronic control system products, which is not conducive to the modular production of electronic control products.

3.2 The Development Trends of Vehicle Network Control System To solve the problem of strong coupling of vehicle electronic control system, a new vehicle network control system model is formed according to the combination of the prediction that future development trend of vehicle electronic is distributed network technology and the traditional industry network control system model [6]. The new model is able to realize that vehicle bus connects sensors, actuators and control units together as a network system to complete control tasks, transmits control instructions through a sharing network channel, and then forms a closedloop feedback control system Fig. 5. This kind of vehicle network control system uses network transmission control instruction, which requires high reliability, real-time and bandwidth of vehicle bus communication technology. According to the comparison of CAN, LIN and FlexRay bus technology, FlexRay is suitable for this kind of future vehicle network control system.

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Fig. 6 The constitutes of Communication time-delay

4 The Analysis of Vehicle Control System Model 4.1 Real-Time Analysis 4.1.1 Constitutes of the Communication Time-Delay [7] The Communication time-delay ‘Td’ is divided into four parts: the generate timedelay ‘T1’, the queue time-delay ‘T2’, the transmission time-delay ‘T3’, and the receive time-delay ‘T4’, as shown in Fig. 6. The generate time-delay means the time period from the moment of the sending node processor receives the request to the moment of the node writes the data to

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buffer queue. The queue time-delay means the time period from the moment of the message frame enters into the sending buffer to the moment of the message frame controls the bus. The transmission time-delay means the time period from the moment of the message frame controls the bus to the moment of releases the bus. The receive time-delay means the time period from the moment of the message frame releases the bus to the moment of the valid data is provided to the receiving node processor. The generate time-delay and the receive time-delay are not considered in the analysis of time-delay, because they are not related to the communication network, but related to the processor, and they are relatively short. So the network time-delay is composed of the queue time-delay and the transmission time-delay, which means Td = T2 ? T3.

4.1.2 Real-Time Simulation Analysis 1. Introduction of the simulation tool CANoe is not only provides network monitoring and analysis function, but also has a powerful system simulation capabilities. At present CANoe is widely used in the simulation of vehicle network based on CAN/LIN bus. With the development and application of FlexRay bus technology, CANoe.FlexRay software is accordingly developed by the Vector Company for the simulation of the FlexRay network, which also monitors the FlexRay network in the underlying hardware support. 2. The simulation of vehicle network control system model based on CAN The communication simulation model of a pure electric vehicle is built in CANoe, as shown in Fig. 7. In this model, CAN bus speed is configured to 500 kbps, and has six nodes on the CAN bus, as shown in Table 2. Each node sends different message numbers, for example, VCU sends three messages with the identifier of 0 9 E5,0 9 F0 and 0 9 F2, and MCU sends two messages with the identifier of 0 9 C2 and 0 9 C3. Change the network load of the above communication system by 20 or 30 %, ‘‘the waiting time of the sending node send message’’, which means the queue time-delay is monitored by CANoe, and the results is as shown in Table 3. From the simulation results described in Table 3, it concluded that with the increase of network load, the queue time-delay of the message increases. The minimum queue time-delay is 0.533 ms (533 ls) and the maximum queue timedelay is 5.012 ms (5012 ls) when network load reaches 30 % in this simulation, the network time-delay is certainly increased if the transmission time-delay added. 3. The simulation of vehicle network control system model based on FlexRay The communication simulation model is still as shown in the above Fig. 7, the message is set to the static frames or the dynamic frames according to the communication requirement. Each node sends time to trigger the message frames in the

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Fig. 7 The simulation model of network communication system Table 2 Communication nodes and messages Communication node Abbreviation

Identifier

Vehicle control unit Motor control unit Battery management system On-board charger DC-DC Anti-lock brake system

0 0 0 0 0 0

VCU MCU BMS ONC DC ABS

9 9 9 9 9 9

E5, 0 9 F0, 0 9 F2 C2, 0 9 C3 B9, 0 9 BC 480 345 A0, 0 9 A3

Table 3 The simulation results of the time-delay Message(ID) The waiting time of the node sending message (the queue time-delay) VCU1 (0 9 E5) VCU2 (0 9 F0) VCU3 (0 9 F2) MCU1 (0 9 C2) MCU2 (0 9 C3) BMS1 (0 9 B9) BMS2 (0 9 BC) ONC (0 9 480) DC (0 9 345) ABS1 (0 9 A0) ABS2 (0 9 A3)

20 % (ms)

30 % (ms)

1.597 2.129 2.661 1.331 1.863 0.533 1.065 4.523 2.927 0.267 0.799

2.011 2.623 3.124 1.922 2.343 1.022 1.567 5.012 3.455 0.533 1.212

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Table 4 FlexRay network parameters Communication speed Communication cycle 10 Mb/s

5 ms

Static segment

Dynamic segement

60 ls//slot; 65 slot/cycle

40 ls//minslot; 20 minislots/cycle

Table 5 the simulation nodes and the identifier of the static frames Communication node Abbreviation The ID of the static frames Vehicle control unit Motor control unit Battery management system On-board charger DC–DC Anti-lock brake system

VCU MCU BMS ONC DC ABS

31,32,33 21,22 11,12

1,2

static segment, or sends events to trigger the message frames in the dynamic segment. The network parameters are as shown in Table 4. The sending time of the FlexRay static frames is fixed as it is time-triggered, so the queue time-delay of which is negligible, that means the network time-delay of the FlexRay static frame is equal to the transmission time-delay. The simulation of the FlexRay static frames is only executed in this chapter. The transmission time-delay of the FlexRay static frames can be calculated when the receiving time of which is monitored by CANoe, because the sending time of the FlexRay static frames is fixed. Table 5 describes the nodes of simulation model and the identifier of the static frames. Each FlexRay static frame is transmitted for 50 times, and the receiving time of frames that monitored by CANoe and the transmission time-delay calculated are recorded in Table 6. From the simulation results described in Table 6, the sending time and the receiving time of all static frames are the same for 50 times, and the network timedelay of the static frames is stable and low latency that maintained on 13 ls. 4. The summary of simulation analysis The simulation results show that vehicle network control system model based on FlexRay has lower latency and better real-time than vehicle network control system model based on CAN. Therefore, in the system application of strict realtime requirement, FlexRay has a great advantage compared with CAN, That is to say, the FlexRay bus technology will be more suitable for those vehicle network control systems with strict real-time requirements.

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Table 6 The transmission time-delay of the static frames Time of reception Time of sending The ID of the (ls) (ls) frame

The transmission time-delay (ls)

1 2 11 12 21 22 31 32 33

13 13 13 13 13 13 13 13 13

0 60 600 660 1,200 1,260 1,800 1,860 1,920

13 73 613 673 1,213 1,273 1,813 1,873 1,933

Table 7 Examples of system function Night vision system

Lane identification system

Sensor

D

Camera Radar Speed sensor Rain light sensor Controller Lane assist control unit Lane identification system control unit Fatigue driving warning system control unit Night Vision processing unit Actuator Warning lighting Buzzer Display screen

D D D

D

Lane assist system

Fatigue driving warning system D

D D D D

D

D D D D D

D D

D D

D

4.2 Functional Analysis The sensors, controllers and actuators are separated in the Vehicle network control system model, which can reduce the strong coupling of vehicle electronic control products. Moreover, with the separation of vehicle control system sensors, actuators and controllers, the diversification of the system functions is increased. As shown in Table 7, ‘‘D’’ indicates the sensors, controllers or actuators of certain control system, For example, Night Vision system is composed of camera, speed sensor, rain light sensor, night vision processing unit and display screen, in which the camera can be used as the component of lane identification system and fatigue driving warning system, and the speed sensor as the general component for lane identification system, Lane assist system and fatigue driving warning system

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as well. That is to say, vehicle network control system model can implement more functions by combing smaller set of components.

4.3 Summary From the above real-time and functional analysis of vehicle network control system model, it is concluded that vehicle network control system model based on FlexRay is not only meet the real-time requirement, but also implement more functions and expand easier because of the separation of the sensors, controllers and actuators.

5 Conclusions This paper compares the widely used vehicle bus technology including CAN and LIN, and the developing FlexRay. And based on the analysis of the current situation and existing problems of the existing vehicle network control system, the network control system of traditional industrial control area is introduced to build vehicle network control system model. According to the real-time and functional analysis, it can be concluded that with the separation of with the separation of vehicle control system sensors, actuators and controllers, the diversification of the system functions is increased, and vehicle network control system based on FlexRay bus technology can meet real-time requirement of control system, which is also the trend of vehicle network control system.

References 1. Tong H, Chen P (2006) Vehicle network technology. Auto Appl 11 2. Zhang Q-L, Zhang X-F (2007) Survey and forecast on networked control systems. Information and control, 2007(03) 3. Zhao M, Shi Xiao-lin, Duan H (2009) TRIZ Introduction and practice, Science Press 2009(03) 4. Long Y (2008) Application status and development trend of the modern vehicle electronic technology; Mechanical Management and Development 2008(08) 5. Ji G-J, Wan M-S (2006) New platform of vehicle ECU communication: flexRay (V2.1) protocol criterion. Auto Electric Parts, 2006(10) 6. Fan C (2009) Modern communication principles. Posts and Telecom Press, 2009(10) 7. LI J, Tian G, Niu X, Chen Q (2007) Response time for flexray communications. J Tsinghua Univ, 2007(47)

Research on Reformation Method of Vehicle Intelligent Electric/Electronic System Weiwei Kong, Diange Yang, Tao Zhang, Bing Li and Xiaomin Lian

Abstract With development of vehicle electric/electronic system, vehicle harness is rapidly increasing, and connection between them is more complex and disordered. Therefore, it becomes badly essential to globally monitor electronic/electrical devices’ statues and fault diagnosis in real time, to ensure vehicle’s usage safety. One sample vehicle, with few intelligent devices, cannot gain the real-time state information of the overall electronic/electrical devices, let alone realise the whole vehicle’s state monitoring and fault diagnosis, which brings great security risks. Arming at the sample vehicle’s problems above, the writer reforms it into an intelligent electric/electronic system, and presents a reformation method based on modularization. According to characteristics and function requirements, all the traditional electronic/electrical devices are classified and developed into smart ones respectively, unified and standardized. With this proposed method, all 63 electronic/electrical assemblies are intelligentized, and achieve communication and information sharing. With every electronic/electrical device’s real-time statue and fault diagnosis information, the whole vehicle system is in total supervisory control, and the sample automotive safety is greatly improved. The feasibility of the method is verified by tests.



Keywords Intelligent electric/electronic system Vehicle intelligent electronic/ electrical devices Intellectualization design Modularization





F2012-D06-003 W. Kong (&)  D. Yang  T. Zhang  B. Li  X. Lian Department of Automotive Engineering, State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_62,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction Along with the rapid development of vehicle electronic and electric technologies, the vehicle electric/electronic devices (EEDs) are booming [1, 2], and the wirings connecting them are becoming more and more complex [3], which brings great difficulties to the EEDs’ controlling and power supplies. Therefore, the hidden failure of vehicles grows; the faults’ location and diagnosis are more difficult to determined, and the electrical using safety is increasingly difficult to be guaranteed [4]. With the advancement of automobile electronic technology, in-vehicle EEDs are gradually moving into intellectualization, and possess intelligent functions, such as digital communication, automatic control, and self-diagnosis etc. [5]. Intelligent electric/electronic devices (IEEDs) have become a key solution to solve the above-mentioned problems in the development of vehicles [6]. One sample vehicle, with few intelligent devices, cannot gain the real-time state information of the overall electronic/electrical devices, let alone realise the whole vehicle’s state monitoring and fault diagnosis, which brings great security risks. According to these problems, an intelligent electric/electronic system (IEES) is presented, and all the traditional EDDs are converted into IEEDs, with a reformation method based on modularization, proposed in this paper.

2 Intelligent Electric/Electronic System Arming at the sample vehicle, an IEES is designed, whose structure is shown in Fig. 1. As shown in Fig. 1, IEES is a globally distributed electric/electronic system, based on the in-vehicle bus. After intellectualization transformation, the entire EEDs are converted to IEEDs, which interconnect into the vehicle network as separated network nodes, realising the entire vehicle networking and communicating. According to importance and communication requirements, the original EEDs are divided into backbone EEDs (c1*ci) and local EEDs (l1*ln), access to backbone network (C) and local network (L), respectively. C and L communicate with each other via gateways ð~c1 ~ci Þ: Each IEED collects information of state and fault diagnosis, and sends to the central coordinator (CC), which implements the vehicle’s overall coordination control and fault processing. As a backbone network node, intelligent power (IP) achieves electrical power supply control and overcurrent protection, to ensure the whole vehicle’s electrical using security.

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Central Computer

Central Coordinator

C

Gateway

c∼1

L1

l1

Gateway

∼

ci

Li

li

ln

Local IEEDs

l1

li

ln

c1

ci

Backbone IEED

Backbone IEED

Intelligent Power

Local IEEDs

Fig. 1 Structure of IEES

From the above, the design and development of IEEDs is a critical step to achieve IEES.

3 Design and Development of IEEDs IEED is developed by adding a controller to the original EED. With different sorts of EEDs, their functions, control modes, and characteristics are various, so their controllers are different. Because of the huge scale and complex controlling logic of controllers, it is much needed to improve the reusability and generality of hardware and software design, that is, to realise standardized design. In the paper, functional classification is established for original EEDs, and each type of EED is intelligently designed respectively. With modularization method, controllers’ hardware and software are composed of standard circuit modules and software algorithm modules, which will be introduced in detail as following.

3.1 Modularization Design of Controllers Modularization design of controllers contains that of hardware circuits and software algorithms.

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input of load power;

Load electric protection module Controller power module Network information conversion

Calculation module

input of controller power;

Execution module Execution module

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Fig. 2 Function planning of controllers, 1 input of load power; 2 input of controller power; 3 network communication signals Table 1 Functional classification and corresponding execution modules No. Type of devices EM No. Type of devices

EM

1 2 3 4 5 6

H G V U N I

Resistor-type Motor-type Switch-type Resistor-type sensor Voltage-type sensor Frequency-type sensor

D M S A1 A2 A3

7 8 9 10 11 12

Electric-type interface Ground-type interface Analog voltage interface Serial communication interface (SCI) Gateway Relay

3.1.1 Modularization Design of Hardware Circuits According to functions the IEEDs need to implement, the planning of controllers is shown in Fig. 2. As is shown in Fig. 2, controllers consist of several function modules: load electric protection module, controller power module, backbone/local network information conversion module, calculation module, and execution module. Among them, backbone network information and calculation module (C), local network information and calculation module (L), controller power module (P), and load electric protection module (F) are general modules. Execution modules, which implement functions, such as electric driving, fault diagnosis, status collection etc., need to be schemed arming at different types of EEDs. Functional classification and corresponding execution modules (EM) are shown in Table 1.

3.1.2 Modularization Design of Software, Command Control Algorithm In IEES, CC is designed to implement whole-vehicle-level coordination control and fault processing of all the devices, and each device only needs to execute control instructions for its own. The characteristic of command control algorithm is that every IEED only receives some fixed instructions of a fixed information frame from CC, no need to concern or judge other devices’ working states.

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downlink algorithm

instruction receiving

I/O output

uplink algorithm

instruction implementation

D/A output

SCI output

message sending

state acquisition

I/O input

A/D input

I/C input

SCI input

Fig. 3 Algorithm modules of command control

Therefore, devices realise decoupling, and become more independent, resulting in simpler control logic for each controller. From the introduction above, it is logical to known that command control algorithm contains two parts: uplink algorithm, which is used for instruction receiving and implementation; downlink algorithm, for state acquisition and message sending. They can be decomposed, as is shown in Fig. 3.

3.2 Intellectualization Design of EEDs According to functions, all the original devices can be classified into seven categories: 1. Resistor-type devices: the most common devices in automobiles, only possess two states: open and closed. The corresponding execution module is D module, which can drive devices and detect failures. According to execution channels, resistor-type devices can be further divided into single resistance devices, double resistance devices, and four resistance devices; 2. Motor-type devices: should realize primarily timed accurate turn, turn over, and brake control, and detect failures such as over-current, short circuit etc. The corresponding execution module is M module; 3. Switch-type devices: need to measure the on–off signal, and convert it to signal that calculation module can identify. The corresponding execution module is S module; 4. Resistance-typed sensors: make use of variable resistance principle, the corresponding execution module is A1 module; 5. Voltage-typed sensors: make use of variable voltage principle, the corresponding execution module is A2 module;

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F

P

D

zb C

S zJ

Fig. 4 Hardware structure of generator controller

Fig. 5 Software algorithm structure of generator controller

Iq AR

B1

B2

BT

T

6. Frequency-typed sensors: make use of variable frequency principle, the corresponding execution module is A3 module; 7. Combined devices: functions are more complicated, and controllers demand several kinds of execution modules. Controllers of these seven kinds of devices need to be designed respectively, with modularization method described in Sect. 3.1 Take one combined device— generator for example, its intellectualization design is as follows:

3.2.1 Hardware Design of Generator Controller One characteristic of modularization is that controller’s hardware is developed by splicing modules together seamlessly, with no other components. Therefore, generator controller’s hardware structure is shown in Fig. 4. As is revealed in Fig. 4, there are 2 execution modules, D module and S module. D module controls the electric power supply of generator’s excitation coil, and diagnoses short circuit and break circuit. S module acquires generator’s neutral point, for charging indication. In the figure, Zb is network interface, which is standard, unified interface. ZJ is interface connecting to device.

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Fig. 6 Real controllers for IEEDs a gateways, b controllers for motor-type devices

Fig. 7 Monitoring interface of CANoe

3.2.2 Software Algorithm Design of Generator Controller Because of command control mode, controller’s software structure is quite simple, that is, front and background embedded system structure. Generator controller’s software algorithm structure is shown in Fig. 5. As Fig. 5 shows, AR is instruction receiving module, A1 is instruction implementation module, B1 is state acquisition module, and BT is message sending module. Iq is interrupt from bus information, and interrupt occurs once one information frame reaches. With front and background embedded system, AR accomplishes in background interrupts. A1 and B1 carry out in foreground. For generator, A1 implements instructions: power generation or closed, and B1 acquires generator’s working state information and fault diagnosis information, which are sent through BT. T ensures the periodic cycle operation of programs.

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Table 2 Operation conditions for road tests No. Operation Number of tested No. Operation condition devices condition

Number of tested devices

1 2 3 4 5 6

Driving in daytime Driving in night Driving in rainy day Driving in snowy day Driving in summer Driving in winter

26

7

Driving in foggy day

31

28 28

8 9

Park with OFF gear Park with ON gear

14 15

31

10

Night ? winter

32

27

11

Night ? rainy

30

30

12

Night ? snowy ? foggy 38

4 Demonstration and Verification According to the method described above, intelligent electric/electronic system (IEES) for the sample vehicle is designed, and all 63 electronic/electrical assemblies are intelligentized. Some controllers are shown in Fig. 6.

4.1 Monitoring and Fault Diagnosis Test CAN bus is used for communication in backbone network. Therefore, CANoe can be used as the test tool, to monitor the real-time status messages and analysis, as Fig. 7 shows. As is shown in Fig. 7, the CAN bus state is fully monitored and recorded with CANoe, and all the devices’ working condition and fault information can be obtained in real time.

4.2 System Reliability Test A large number of road tests are implemented on the transformed sample vehicle. Tests’ road condition include test field of Geely Research Institute, urban road in Beijing, and test environment contains daytime, night, rainy, snowy, and foggy. Operation conditions for the road tests are designed as Table 2 shows. Result of tests shows that the whole system can operate perfect, and controllers are able to work stably and reliably. The feasibility and reliability of proposed method is verified.

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5 Conclusion An intelligent electric/electronic system is designed arming at the sample vehicle’s problems, and this paper proposes a reformation method based on modularization to convert the original traditional devices into intelligent electronic/electrical devices. According to characteristics and function requirements, all the traditional EEDs are classified and each type of devices is intellectualized. Based on modular method, controllers are decomposed into standard and unified circuit modules and software algorithm modules. After each module’s design, controller is achieved by reasonably combining these modules. With the proposed method, all 63 electronic/electrical assemblies are intellectualized. Tests show that all the controllers can work effectively and reliably. All IEEDs with controllers are able to monitor working states and self-detect, and send the messages to the communication network in real time. As a result, the converted sample vehicle is wholly in real-time monitoring state, and able to send warning for diagnosis, which greatly improve the vehicle’s use safety. Therefore, it is verified that the proposed method is effective and feasible.

References 1. Kassakian JG (2000) Automotive electrical systems-the power electronics market of the future. In: Proceedings of the applied power electronics conference and exposition, pp 3–9 2. Kassakian JG, Wolf HC, Miller JM, Hurton CJ (1996) Automotive electrical systems circa 2005. IEEE Spectr 33:22–27 3. Leen G, Heffernan D, Dunne A (1999) Digital networks in the automotive vehicle. Comput Control Eng J 10(6):257–266 4. Liang L (2010) Intelligentization for vehicle electrical power supply system. Master dissertation, Tsinghua University, Department of Automotive Engineering, Beijing 5. Gu ZM, Yang DG, Zhang XF et al (2009) Distributed vehicle body electric/electronic system architecture with central coordination control. In: Proceedings of the institution of mechanical engineers, Part D journal of automobile engineering, vol 224(2), pp 189–199 6. Zhang X (2009) Automotive intelligent electrical and electronic device system. Doctor dissertation, Tsinghua University, Department of Automotive Engineering, Beijing

Ethernet-Based Integrated Network for Active Safety Sensors Jin Ho Kim and Jae Wook Jeon

Abstract The number of active safety sensors in an automotive system is increasing exponentially. However, there is no international standard or de facto standard for active safety sensor networks. Therefore, each sensor manufacturer uses a different network protocol and makes vendor-specific command protocols for their sensors. This makes active safety sensors difficult to use and increases the development costs for intelligent automotive systems using active safety sensors. Thus, we propose an Ethernet-based integrated network protocol for solving the above problems. The proposed network protocol includes a physical layer, a data link layer, a communication layer, a service layer and an application layer. To verify this proposal, we implement an embedded system that supports the proposed network and measures the synchronization performance and cycle time.



Keywords Ethernet In-vehicle sensor network In-vehicle network ADAS





Active safety sensor



1 Introduction The advanced driver assistance system (ADAS) provides intelligent service in automotive systems in such capacities as lane keeping assistance system (LKAS) or the collision avoidance systems (CAS). The number of active safety sensors for ADAS, such as radar, laser scanners, vision and ultrasonic sensors, is increasing F2012-D06-005 J. H. Kim (&)  J. W. Jeon Sungkyunkwan University, Seoul, Korea e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_63,  Springer-Verlag Berlin Heidelberg 2013

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exponentially. However, there is no international standard or de facto standard for active safety sensor networks, and each sensor manufacturer uses a different network protocol and makes vendor-specific command protocols for their sensors. This makes active safety sensors difficult to use and increases the development costs for intelligent automotive systems using active safety sensors. Moreover, when using sensor fusion techniques, the different network protocols must be connected using gateways. This results in increased costs, network delays, development complexity, and higher maintenance costs. The Local Interconnection Network (LIN), Controller Area Network (CAN) and Flex Ray are the most well-known in-vehicle networks [1], but they cannot support sufficient bandwidth to connect many active safety sensors in one network [2]. Media Oriented Systems Transport (MOST) currently supports sufficient bandwidth, but the required bandwidth is increasing exponentially, so that even this will not be suitable for integrated networks in the future [2]. In order to meet the requirements of an in-vehicle sensor network, we propose an Ethernet-based network protocol. Ethernet has several benefits for in-vehicle sensor networks, such as low cost, verified reliability in industrial network areas, sufficient bandwidth (100 Mbps, 1 Gbps), well-known technology, and the ability to reuse tools and techniques from the Internet [3]. Ethernet has several merits for automotive systems, and is expected to become one of the major in-vehicle network technologies in the future. Continental expects Ethernet to become one of the major in-vehicle network technologies in 2015 [2]. BMW has plans to use Ethernet for automotive systems in 2013 [4]. Ethernet AVB [5] and TTEthernet [6] are Ethernet protocols being considered for automotive systems. Ethernet AVB is designed for streaming data communication such as video and audio data. TTEthernet is designed for real-time communication using TDMA mechanism and is applied to airplanes [7]. Ethernet AVB is suitable for the entertainment systems and TTEthernet is suitable for the real-time control systems [8]. Research on Ethernet for automotive systems is also being conducted [9–14]. These previous studies did not consider active safety sensor networks for ADAS. Therefore, an Ethernet-based integrated network protocol for an in-vehicle sensor network for ADAS is proposed. The main idea is adding a profile layer between the application layer and the communication stack. The profile layer role for abstraction of sensor type and manufacture is similar to CANopen profile [15] and Profidrive [16]. The proposed network protocol includes a physical layer, a data link layer, a communication layer, a service layer and an application layer. To verify this proposal, we implement an embedded system that supports the proposed network and measure the synchronization performance and cycle time.

2 Profile Technology This section describes the profile technology in factory automation. The profile technologies are well known in the factory automation area. There are several profiles that are used in this area. This section describes the profile for power drive

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Fig. 1 Motion profile referenced from IEC 61800-7 [17]

systems standardized in IEC 61800-7 [17]. Four types of profiles are defined: CiA 402, CIP Motion, PROFIdrive, and SERCOS. Only CiA 402 profile is described for convenient understanding of the proposed idea (Fig. 1).

2.1 Profile for Power Drive Systems The motion profile defines a standard method and interface to control motor drive. The application control program can use several vendors’ motor drives using the same method and interface independently of network protocol and motor drive manufacturer. Figure 2 shows the main purpose of the profile for the power drive system. The application control program in Fig. 2 can control motor drive using the same method and interface, although the manufactures of motor drive are different. The CiA 402 specifies the operation mode and generic interface for motor drive. In order to control motor drive, CiA 402 uses an object dictionary, which is a group of objects. An object is a set of attributes to control motor drive, such as

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Fig. 2 Purpose of motion profile technology

desired position, actual position, and maximum velocity of the motor drive. CiA 402 defines mandatory, optional and vendor-specific objects to control the motor. In order to control motor drive, the application control program must change the value of an object of the motor drive using the network. CiA 402 can support several network protocols, such as CANopen, EtherCAT and Ethernet Powerlink. The motor drive that supports the CiA 402 profile supports the configuration file that specifies the supported object dictionary.

2.2 Database for Automotive System The automotive system uses a CAN database [18] for the CAN, and FIBEX [19] for the FlexRay for the application layer. CAN databases and FIBEX are wellknown technology in automotive systems. The databases define messages, signals, and attributes for automotive systems. The manufacturer of an automotive system defines the database. As the communication database is different between manufacturers, standard methods like CiA 402 cannot be supported. Moreover, there is no standard or de facto standard database for the active safety sensor networks. Therefore, the database mechanism is not suitable to support standard methods and interfaces for active safety sensors.

3 Protocol Architecture The proposed Ethernet-based integrated network consists of a physical layer, a data link layer, a communication layer, a service layer and an application layer. As the OSI 7 layer aspect, the proposed protocol consists of a physical layer, a data

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Fig. 3 The proposed protocol architecture

link layer and an application layer. The application layer in the OSI 7 layer is divided into the communication layer, service layer and application layer, as in Fig. 3. The physical layer of the proposed protocol is the same as the Ethernet standard. Although the most efficient topology in automotive systems has already been addressed [20], redundancy mechanisms were not considered. In this paper, bi-directional ring topology is used for redundancy because active safety sensors used in active safety systems such as CAS are very important to assure driver safety. The data link layer of the proposed protocol supports three types of data. The realtime message box supports real-time messages that are sent periodically, like with static slots in FlexRay. The real-time messages must satisfy deadlines. Non-real-time message boxes support non-real-time messages, which are event-triggered, like with CAN or dynamic slots in FlexRay. Non-real-time messages use the best effort mechanism and are used for configuration, diagnosis, and re-programming. The streaming buffer supports streaming data like high-resolution camera data. The Synchronization module supports a time synchronization algorithm. The communication layer supports IP, TCP and UDP for the Diagnostic over Internet Protocol (DoIP) [21], COM and NM modules. The COM module abstracts communication protocols like OSEK/VDX COM. The NM module monitors the network and node status (e.g. present, absent) and controls each node’s state (e.g. sleep, asleep). The service layer supports a diagnostic module and sensor profile module. The diagnostic module supports DoIP for diagnosis service. The sensor profile module supports sensor profiles for abstract sensor manufacture and HW/SW platforms. The sensor profile defines an object for each sensor, interface profile, and network protocol. The same type of sensor uses the same profile independently of the manufacturer and HW/SW implementation of the sensor. For example, there are two LIDAR sensors developed from different manufactures that use different

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Fig. 4 The embedded system for experiments

Fig. 5 The test environment

network protocols (one uses CAN, the other uses Ethernet). In this case, despite different manufacturers and network protocols, the application uses the two sensors in the same way.

4 Experiment and Results In this section, an experiment and the results of the proposed protocol are described. Real-time and synchronization performance were measured. The method was implemented as an embedded system shown in Fig. 4. This embedded system uses Montavista Linux [22] and supports two Ethernet ports and a General Purpose Input Output (GPIO) port for measurement.

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Fig. 6 Experiment environment of synchronization Fig. 7 Synchronization performance of proposed protocol

For performance measurement, test environments were implemented as in Fig. 5. The test environment uses 20 embedded systems and transmits sensor data.

4.1 Measurement of Synchronization Performance In order to measure the synchronization performance, the IEEE 1588 synchronization algorithm was used for the synchronization module in the data link layer. The slaves 1 and 19 set the GPIO pin to high and then set it low at the same time

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Fig. 8 Experiment environment of cycle time

Fig. 9 Cycle time performance of proposed protocol

using synchronized time. The GPIO pins of slave 1 and slave 19 are measured using an oscilloscope for two hours Fig. 6. Figure 7 shows the result of synchronization performance. It is lower than 1 ms. The synchronization error can be reduced in hardware implementation.

4.2 Measurement of Cycle Time In order to measure the cycle time, real-time data using real-time message boxes was sent as 64-byte Ethernet messages. The master sets the GPIO to high when it starts to send real-time messages, and sets it to low when it receives real-time messages that round 20 nodes. Figure 8 shows the cycle time experimental environment for real-time messages.

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Figure 9 shows the result of cycle time performance of real-time messages. It is lower than 1 ms. We expect to reduce the cycle time when using hardware implementation.

5 Conclusion An Ethernet-based integrated network protocol was proposed for addressing the lack of international standard or de facto standard for active safety sensor networks for intelligent automotive systems using active safety sensors. The proposed protocol architecture includes sensor profiles. The sensor profile abstracts each sensor with regard to manufacturer and HW/SW platform. The synchronization performance and cycle time of real-time messages were measured. The synchronization performance using 20 embedded systems was less than 1 ms. The cycle time of real-time messages is also less than 1 ms. Both performance metrics are expected to improve in HW implementation.

References 1. Navet N, Song Y, Simonot-Lion F, Wilwert C (2005) Trends in automotive communication systems. Proc IEEE 93:1204–1223 2. Toulouse (2011) Is ethernet the rising star for in-vehicle networks? ETFA, 23 Sept 2012 3. Rahmani M (2009) A resource-efficient IP-based network architecture for in-vehicle communication. Thesis on Technische University Munchen, Mar 2009 4. Bruckmeier R (2010) Ethernet for automotive applications. FTF Orlando Robert Bruckmeier BMW Group, June 23 2010 5. Kreifeldt R. AVB for automotive use. In: AVnu Alliance White paper, Jul 20 6. Kopetz H, Ademaj A, Grillinger P, Steinhammer K (2005) The time-triggered ethernet (TTE) design. In: IEEE international symposium on object-oriented real-time distributed computing (ISORC) 7. TTTech (2009) TTEthernet—A powerful network solution for all purposes. Available at http://www.ttagroup.org/ttethernet/doc/TTEthernet_Article.pdf 8. Lo Bello L (2011) The case for ethernet in automotive communications. In: ACM special issue on the 10th international workshop on real-time networks, vol 8, issue 4, pp 7–15 9. Muller-Rathgeber B, Eichhorn M, Michel HU (2008) A unified car-IT communicationarchitecture: design guidelines and prototypical Implementation. In: IEEE Intelligent Vehicle Symposium, June 2008 10. Lim HT, Volker L, Herrscher D (2011) Challenges in a future IP/ethernet in-car network for real-time applications, DAC 2011, June 2011 11. Steffen R, Bogenberger R, Hillebrand J, Hintermaier W, Rahmani M (2008) Design and realization of an IP-based in-car network architecture. ISVCS 2008, July 2008 12. Muller-Rathgeber B, Eichhorn M, Michel HU (2008) A unified car-IT communicationarchitecture: network switch design guidelines. In: IEEE Intelligent conference on vehicular electronic and safety, Sept 2008 13. Rahmani M, Steffen R, Tappayuthpijarn K, Steinbach E, Giordano G (2008) Performance analysis of different network topologies for in-vehicle audio and video communication. IEEE IT-NEWS 2008, Feb 2008

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14. Hillebrand J, Rahmani M, Steinbach E (2007) Coexistence of time-triggered and event-triggered traffic in switched full-duplex ethernet networks. SIES2007, July 2007 15. CANopen profile, Available at http://www.can-cia.org/ 16. PROFIDrive profile, Available at http://www.profibus.com 17. IEC 61800-7 18. CAN DB, Available at http://www.vector.com/vi_candb_en.html 19. FIBEX, Available at http://www.vector.com/vi_fibex_en.html 20. Muller-Rathgeber B, Michel HU (2009) Automotive network planning—a genetic approach. In: IEEE symposium on intelligent vehicles, June 2009 21. ISO 13400, Diagnostics over internet protocol (DoIP) 22. Montavis linux, Available at http://www.mvista.com/

Architecture for Secure Tablet Integration in Automotive Network James Joy, Anurag Raghu and Jestin Joy

Abstract The need for better technologies and features prompted vehicle manufactures to use specialized hardware devices in vehicle. Improved functionality as always comes with increased cost. Manufacturers look at giving better functionality at reduced cost. The driver information and entertainment features in the vehicle are closely in line with consumer electronics, which is growing at a very fast rate than the vehicle. It is always better to provide the connectivity to the consumer devices. One good alternative in In-vehicle infotainment is using a tablet in place of traditional inbuilt mechanism. The tablet will provide the technology advancements in the area of entertainment, connectivity and a development environment, on top of which the OEM can trademark their infotainment system. Present embedded systems on vehicles are developed to address the safety and not security requirements. But connecting third party equipment to the vehicle system causes serious security concerns. We need a full proof security mechanism for connecting tablets to our vehicle network. This integration could also pave way for a new business model in the automotive industry, something on the lines of ‘‘App Stores’’. Any application from the OEM store can be downloaded and installed in the vehicle. This paper proposes architecture for secure tablet integration in automotive network. Keywords Gateway Tablet

 Security  Infotainment  Architecture

F2012-D06-015 J. Joy (&)  A. Raghu Tata Elxsi, Bengaluru, India e-mail: [email protected] J. Joy Federal Institute of Technology, Angamaly, India SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_64,  Springer-Verlag Berlin Heidelberg 2013

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1 Introduction Automotive systems become increasingly dependent on embedded computers. The use of embedded computers provides better processing capabilities within the vehicle. The modern day luxury cars have multiplicity of ECUs on board. During the introduction of electronic control in vehicles, isolated single board solutions were used. Later the scope changes to vehicle level solutions, with the use of distributed control units in the automotive network. The future trend is traffic level solutions, which are mainly addressed by the infotainment and telematic systems. The infotainment systems are of great interest in modern automotive market. As far as a customer is concerned they need better performance, convenience, reduced cost and security for the system. Using tablets as an alternative for infotainment reduces cost and increases functionality. The tablet Connectivity will keep the technology up to date. The OEM can provide different utility applications that enhance the driving comfort. It also provides opportunity for an individual to explore the possibility of newer applications that makes the driving comfortable, informative and fun. But the downside is that an attacker could control the vehicle from ‘‘air’’ [1, 2]. Attacker, after successful intrusion could try to read or write data from the vehicle network. A wide variety of communication systems are available in today’s automotive network, for different applications, ranging from body systems, engine control, driving assistances and safety systems to a wide variety of infotainment applications. Most of the communication systems are protected against different technical interferences. But these systems are mostly unprotected against the malicious attacks where attacker tries to inject unauthorized packets into the network. The increased connectivity provided by the automotive systems especially in the infotainment area is vulnerable to the malicious attacks [3, 4]. The tablet integration shall provide a secure and reliable connectivity with the in-vehicle network. The communication buses in the automotive network are classified as listed in Table 1, based on the technical properties and the application areas [5]. None of these automotive bus technologies are providing option for secure communication in the protocol definition. Embedded security is one of the active areas within security [6]. Current automotive systems do not have inbuilt security mechanisms. Embedded security involves security against physical tampering, data security inside the device, authentication of the external devices connected and the secure communication with external authentic devices. In the case of intra-vehicle communication involving a tablet, the tablet needs to be authenticated before the connection is accepted by the vehicle. The best way of providing security is through public key cryptographic mechanism, which will address the secure communication with the tablet. Since the vehicle and the tablet are not trusted it is very difficult to introduce a reliable authentication mechanism. The approach here is certificate based authentication.

Architecture for Secure Tablet Integration Table 1 Automotive bus comparison Com. system Sub network Event triggered Buses

LIN I2C SPI K-Line UART

CAN VAN PLC

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Time triggered

Multimedia

FlexRay TTP TTCAN ByteFlight

MOST D2B GigaStar USB

The rest of the paper is organized as three sections, the first section deals with the connectivity solution and the second part address the security and last part deals with the proof of concept implementation.

2 Connectivity The fundamental technology for this revolution in the human-automotive interface is the gateway module, which act as a central access point for wired and wireless networks [7]. By using standard hardware interfaces and communication protocols, it provides a technology independent platform for building applications, which unleashes the potential that already exists in computer and cellular network. Vehicle level data exchange in automotive systems is facilitated by interconnection of different bus technologies. Gateways are used to transfer messages among each other, without taking into consideration, different physical and logical operating properties. Usually Gateways provide protocol conversion, error protection and message verification. The so called ‘Smart Gateways’ provide interconnection of all kind of buses in the automotive network that are relevant for the external communication and additionally act as an access point for other external devices to the vehicle [8]. The external devices include the tester and consumer devices. Access to external device is restricted by device authentication. Smart Gateways shall be embedded computers capable of establishing secure communication with the external devices. The proposed network topology is shown in Fig. 1. In the proposed topology, the different kind of networks inside the vehicle are connected to a central gateway module, Smart Gateway. There can be multiple CAN and LIN networks, HSCAN from the chassis network, MSCAN from the body network, LIN, K-line from the sensors and many sub nods. Additionally there will be MOST/Ethernet from the infotainment network [9, 10], FlexRay from the safety critical X-by-wire systems, are connected to the Smart Gateway. The infotainment system shall also contain some in-vehicle entities like the vehicle speakers, vehicle cameras, parking sensors and the high-end DSP audio processing modules. Those modules are also connected to the Smart Gateway. We have identified the wireless link as the best option for the external device communication which provides a lot of flexibility. WiFi is the suggested wireless

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Fig. 1 Network topology

network which can provide high bandwidth and the sufficient security for communication. Audio and video of sufficient quality can be communicated over WiFi. The necessary modules inside the smart gateway are shown below in Fig. 2. The Smart Gateway shall contain the authentication module which will provide certificate based authentication to the external connecting device. The secure communication module will provide secure data exchange between the external device and the vehicle. The Application includes a firewall and vehicle specific applications implemented in Smart Gateway. The firewall will restrict the data exchange with the external world. Its not wise to accept all data/signals from the user applications in an authenticated external device. There can be already compromised external devises. The firewall will act as secondary security mechanism.

3 Security Security is not part of the popular CAN protocol and it is difficult to introduce security in CAN as it is a proprietary protocol introduced by Robert Bosch. Here we are trying to address the security when the CAN network communicate with an external device [11, 12]. In an environment where wireless connectivity and third party tablet is used, security is a cause of great concern. Secure Socket Layer (SSL) [13] can be used to provide security to the packets. SSL protocol uses asymmetric key cryptography to exchange keys and then use symmetric key cryptography to

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Fig. 2 Smart gateway block

exchange data. So the performance overhead is less. There are versions of SSL available for embedded systems with low computation requirement. SSL is an industry standard for establishing a secure link between a server and a client. This secure link establishes integrity and security of the messages sent. This is proven and widely accepted secure communication method. It is wise to use the proven security mechanism, SSL in the vehicles. The proposed architecture for the security is based on certificate based authentication and SSL based secure communication. The different modules inside the tablet are described in Fig. 3. In the tablet side there will be a secure proxy module which will handle the authentication and secure communication between the Smart Gateway and the Tablet. OEM_App will contain the infotainment system that can be installed in the Tablet. This OEM_App will contain all standard infotainment applications, navigation applications, HVAC interface, etc. The standard audio and video applications in the tablet can communicate with the speakers and the other infotainment entities in the vehicle. The User_App is the any user application signed by the OEM and available in app store, which can be installed in the tablet. The proposed security framework involves authentication of the tablet, data integrity during the communication between the tablet and the vehicle and application authenticity for the applications downloaded and installed in the tablet.

3.1 Authentication Secure identification of the device to the vehicle is a major cause of concern. Authentication makes sure that only valid devices are attached to the Smart Gateway. Certificate based protection mechanism provides a better way of

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Fig. 3 Tablet software modules

authenticating device with Smart Gateway. SSL has features to provide client authentication. Client certificate contains information that identifies the user and it can be used to authenticate him.

3.2 Integrity Using WiFi connectivity to the automotive system enables attackers to access packets from the air and manipulate it. So the data should be exchanged securely when passed through air. Secure Socket Layer provides better security to packets sent through wireless channels. SSL provides data integrity by calculating digest of the message. At the receiver side digest is re-calculated and checked against the digest received. SSL uses HMAC for providing message authentication.

3.3 Software Security A malicious application installed in the system can certainly break the whole security functionalities provided. In Order to counter that attack, signature based methods are used. In the proposed framework all applications shall be signed by OEM. The secure proxy will allow application connection only for applications signed buy the OEM.

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Fig. 4 Eco system

4 Design Authentication for all sender applications is needed to be ensuring that only valid applications from a valid device are able to communicate with the automotive network via the Smart Gateway. The Smart Gateway will not allow connection from an unauthorised tablet. The Proxy application will not allow any unauthorised application to connect to the proxy in the tablet. The major components/parties involved in communication are: Original Equipment Manufacturer (OEM)—This is usually the vehicle manufacturer, who will issue the certificate. Application—The application available in the OEM App store. Tablet—The tablet of the customer that can be connected to the vehicle. Proxy Application—This is available in the tablet. Smart Gateway—The gateway in the vehicle which runs an OS that can handle SSL connections and control the access to vehicle network. As shown in the above Fig. 4, the Tablet connects to the vehicle through WiFi connectivity. The Smart Gateway resides in the vehicle and connects to the Tablet over WiFi. The infotainment OEM trademark application which runs in the tablet is provided by the OEM. OEM also provides a proxy which tunnels the request from different application to the Smart Gateway server over a secure channel. It is the duty of the proxy application to check the authenticity of the installed applications. It is done by verifying the signature. Every application should be signed by the OEM and Proxy application verifies the signature using the public key of the OEM. The tablet could be any modern day tablets running iOS, Android etc. The user access vehicle information through installed applications provided by the vehicle

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manufacturers. This increases trust in customer. The communication between proxy application in Tablet and the gateway server is established through SSL over WiFi. SSL is also entrusted with the job of client authentication. SSL has built in features for that. Ones a secure channel is established all the communication is done through that secure channel. The main advantage of SSL is that it is already used in internet communication and is trusted worldwide. The disadvantage is that the performance overhead is high. But there are implementations available with low performance overhead. The Smart Gateway module also maintains a list which holds information about which applications are allowed to access what vehicle information. This should be stored in a trusted platform module so that an attacked could not manipulate it. This will act as the secondary security mechanism. This is the firewall implementation in the Smart Gateway.

5 Implementation We have implemented a proof of concept in PC based environment which gives very good results. The Smart gateway is implemented in a Debian based PC and the vehicle network is simulated in another PC using the bus master and connected to the Smart Gateway using CAN-USB converter. The modules in the tablet are implemented in a third Debian based PC. The Tablet modules and the Smart Gateway modules are implemented in C language. Open SSL is used to provide the secure layer. There are a lot of SSL libraries that works in embedded environment like PolarSSL, yaSSL, MatrixSSL, SharkSSL etc. The performance may be bit different in embedded implementation [14]. But modern computing power of the embedded systems is increasing day by day and the performance will not be a limiting factor in the near future.

6 Conclusion This paper presents an architecture for secure tablet integration in intra vehicle communication network. With the cheap availability of tablets, customers can use their personal tablet itself in the vehicle instead of the present day costlier mechanisms. The use of SSL over WiFi protects the communication against eavesdropping. The possibility of installing additional applications into vehicle introduces a different concept of our vehicles.

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References 1. Wright A (2011) Hacking cars. Communications of the ACM 54(11):18–19 2. Rouf I, Miller R, Mustafa H, Taylor T, Sangho O, Wenyuan X, Gruteser M, Trappe W, Seskar I (2010) Security and privacy vulnerabilities of in-car wireless networks: a tire pressure monitoring system case study. In: Proceedings of the 19th SENIX conference on security (USENIX Security’10). USENIX Association, Berkeley, USA, p 21 3. Huaqun G, Cheng HS, Wu YD, Ang JJ, Tao F, Venkatasubramanian AK, Kwek CH, Liow LH (2009) An automotive security system for anti-theft. In: Proceedings of the 2009 8th international conference on networks (ICN ‘09). IEEE Computer Society, Washington, USA, pp 421–426 4. Lemke K, Paar C, Wolf M (2010) Embedded security in cars: securing current and future automotive IT applications, 1st edn. Springer, New York 5. Wolf M, Weimerskirch A, Paar C (2004) Security in automotive bus systems, escrypt GmbH. Springer, Germany 6. Lemke K, Paar C, Wolf M (2005) Embedded security in cars: securing current and future automotive IT applications. Springer, New York 7. Zinner H, Noebauer J, Gallner G, Seitz J, Waas T (2011) Application and realization of gateways between conventional automotive and IP/ethernet-based networks. In: Proceedings of the 48th design automation conference (DAC ‘11). ACM, New York, USA. pp 1–6 8. Park P, Jung J, Huh B (2011) Development of CAN-1394 automotive gateway system using designed modular software stack. In Proceedings of the IEEE 35th annual computer software and applications conference (COMPSAC ‘11) IEEE Computer Society, Washington, USA, pp 674–679 9. Zonghua G, Zhu W, Shijian L, Haibin C (2012) Design and implementation of an automotive telematics gateway based on virtualization. In: Proceedings of the 2012 IEEE 15th international symposium on object/component/service-oriented real-time distributed computing workshops (ISORCW ‘12), IEEE Computer Society, Washington, USA, pp 53–58 10. Ernst R, Spiegelberg G, Weber T, Kopetz H, Sangiovanni-Vincentelli A, Jersak M (2001) Automotive networks: are new busses and gateways the answer or just another challenge?. In: Proceedings of the 5th IEEE/ACM international conference on hardware/software codesign and system synthesis (CODES ? ISSS ‘07). ACM, New York, USA. p 263 11. Brooks RR, Sander S, Deng J, Taiber J (2008) Automotive system security: challenges and state-of-the-art. In: Sheldon F, Krings A, Abercrombie R, Mili A (eds) Proceedings of the 4th annual workshop on cyber security and information intelligence research: developing strategies to meet the cyber security and information intelligence challenges ahead (CSIIRW ‘08). ACM, New York, USA, Article 26, p 3 12. Hoppe T, Kiltz S, Dittmann J (2008) Security threats to automotive CAN networks— practical examples and selected short-term countermeasures. In: Michael D H, Sujan M-A (eds) Proceedings of the 27th international conference on computer safety, reliability, and security (SAFECOMP ‘08). Springer, Berlin, pp 235–248 13. Wagner D, Schneier B (1996) Analysis of the SSL 3.0 protocol. In: Proceedings of the 2nd conference on proceedings of the second USENIX workshop on electronic commerce (WOEC’96), Vol. 2. USENIX Association, Berkeley, USA, p 44 14. Gupta V, Wurm M (2008) The energy cost of SSL in deeply embedded systems. In: Technical report, sun microsystems, Inc., Mountain View, USA

Applying AUTOSAR Network Management in OSEK/VDX for Compatibility of AUTOSAR and OSEK/VDX Y. H. Lee, Jin Ho Kim and Jae Wook Jeon

Abstract AUTOSAR and OSEK/VDX are automotive software platforms; OSEK/VDX is widely used, and AUTOSAR is newly developed. Currently, the world’s leading automotive companies that are participating in the AUTOSAR consortium are executing a migration plan to AUTOSAR from OSEK/VDX. In the migration process, AUTOSAR-based devices and OSEK/VDX-based devices can be used simultaneously. However, some problems can be caused by the difference between the two platforms. One of them is incompatible network management modules. In this paper, we solve this problem by applying AUTOSAR NM on OSEK/VDX. We analysed the two network management modules, and we implemented AUTOSAR NM on OSEK OS. Finally, we applied this system on a real embedded system for verification. Keywords AUROSAR Migration

 OSEK/VDX  Network Management  Compatibility 

1 Introduction Recently, the function required in vehicles has been increasing. To meet these demands, car manufacturers use many electronic devices in cars. Automotive systems are becoming more complex according to the steady increase of these F2012-D06-017 Y. H. Lee (&)  J. H. Kim  J. W. Jeon Sungkyunkwan University, Seoul, Korea e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_65,  Springer-Verlag Berlin Heidelberg 2013

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electronic devices. Automotive electronics are a distributed and safety–critical system that must guarantee high reliability. Software platforms are required to guarantee the reliability and robustness of distributed systems. Accordingly, the OSEK/VDX software platform was developed. The term OSEK/VDX means ‘‘open systems and the corresponding interfaces for automotive electronics’’ and ‘‘vehicle distributed executive’’ [1]. OSEK/VDX started as joint project of German automotive industries to develop ‘‘an industry standard for an open-ended architecture for distributed control units in vehicles.’’ It offers a standard interface for hardware-independent software development for increased reusability. AUTOSAR software platform is now being developed in order to support development of more complex and reliable automotive systems [2]. The term AUTOSAR means ‘‘automotive open system architecture.’’ It is being developed by the AUTOSAR consortium, in which the world’s leading automotive companies are participating. It is an automotive real-time operating system that has a more widely standard range than OSEK/VDX. It standardizes the tool-based methodologies that can be used to for model-based development, and guarantees interoperability by offering standardized documents about development outcomes. Thus, new services can be developed quickly and reliably. The automotive companies participating in the AUTOSAR consortium are each executing their own migration plan [3] to allow AUTOSAR and OSEK/VDXbased devices to be used in the same network simultaneously. Problems can occur due to the difference between the two platforms, since the network management module and the diagnosis module of the two platforms are not compatible [4]. In this paper, we discuss issues with the network management module. We solved this problem by applying AUTOSAR NM on OSEK OS. First, we analysed previous studies. Next, we studied ‘‘AUTOSAR CAN NM’’ and ‘‘OSEK NM.’’ Then, we analysed the difference between two network management modules. Finally, we implemented AUTOSAR CAN NM on OSEK OS. After that, we applied this system on a real embedded system for verification.

2 Related Work There was one study to solve the compatibility problem of Network Management Modules [4] by adding a Protocol Gateway. The Protocol Gateway consists of two network management modules and a Wrapper module. The Wrapper module is a translator or an interaction layer between the network management module and the communication stack (Figs. 1, 2). There are two studies about migration to AUTOSAR. The first case does not cover the network management module in detail, but the problem caused by the network management module was mentioned [4]. They solve the problem by limiting or changing the AUTOSAR NM functions. The second case covers the

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Fig. 1 Position of protocol gateway

Fig. 2 Structure of protocol gateway

migration by the BMW automotive company. They did not cover the network management module, but they discussed how to solve the compatibility problem. They used AUTOSAR basic software as much as possible [5].

3 OSEK NM OSEK NM is one of the components of the OSEK platform. The function of NM is disabling or enabling the network [6]. It periodically monitors the network for reliability. The main concept is monitoring network messages, and it checks the deadline time of messages. OSEK NM has two modes.

3.1 Indirect NM The indirect NM monitors the application message. It is used in conditions in which every node in the network sends application messages periodically. It decides the conditions of the network depending on whether the message is received. A detailed sleep mechanism is not defined. Therefore, the developer has to define a sleep request mechanism for sleep mode of the network. The states of

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Fig. 3 Indirect NM concept

Fig. 4 Indirect NM state

NM can be divided into Normal Mode, Sleep Mode, and Limphome Mode. Normal Mode is the typical operation mode. In Sleep Mode, the bus is not used. Limphome Mode is an error mode (Figs. 3, 4).

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Fig. 5 Direct NM ring topology

3.2 Direct NM Direct NM monitors network messages. It is similar to Indirect NM, but it uses dedicated NM messages. The transfer mechanism constructs virtual ring topology, and the NM messages work like tokens in the token ring topology. Reset mode is added to Direct NM to construct the virtual token ring. If a node requires bus sleep mode, the mode has to receive agreement from every node by an NM message (Fig. 5).

4 AUTOSAR NM AUTOSAR NM is one of the modules of the AUTOSAR platform. AUTOSAR supports various networks like CAN, FlexRay, Lin, and Ethernet. AUTOSAR has a dedicated NM for each network. There is an upper layer NM for integration among different dedicated NMs [7]. In this paper, we used only the CAN NM. AUTOSAR CAN NM has three modes. Network Mode is the normal operating mode. The network is not used in Bus Sleep Mode. Prepare Bus Sleep Mode offers delay time for transition between Network mode and Bus Sleep Mode. The Network Mode has three states. The Repeat Message State starts the NM mechanism. The Normal Operation State keeps the network alive. The Ready Sleep State offers delay time to transition Prepare Sleep Mode from Network Mode. The main concept is monitoring the dedicated NM messages until deadline time. If a node receives an NM message, the network is needed. If there is no NM message until deadline time, the network moves to Sleep Mode (Fig. 6).

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Fig. 6 AUTOSAR NM concept

5 Difference of NM The main difference of the two NMs is the different means of message timeout. OSEK NM interprets message timeout as a device error, but AUTOSAR NM interprets message timeout as requesting Bus Sleep Mode.

5.1 Network Monitoring OSEK NM can detect errors of a network node, because when a node requires sleep mode, it has to send or receive request messages. A timer that expires without a sleep message indicates a node error. On the other hand, AUTOSAR NM does not define sleep requests or a notification message mechanism. If a node wants to change the network state to sleep mode, it just stops sending NM messages. Thus, if a developer wants to obtain network information, an extra protocol must be defined. AUTOSAR NM can offer network information on the initialization state, because AUTOSAR supports Bus Load Reduction function. If there is a message in the network, every node cannot enter Sleep Mode. The Bus Load Reduction function uses this rule. In this mode, only two nodes send NM messages to keep the network alive.

5.2 Bus Sleep Mode OSEK Direct NM messages have a sleep indication bit (request bit) and an acknowledge bit. If a node wants to enter sleep mode, it sets the sleep indication bit and sends an NM message. If the next node also wants to enter Sleep mode, the node forwards the sleep indication bit to the next node. But if the received node does not want to enter sleep mode, it resets the indication bit and sends an NM

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Fig. 7 OSEK direct NM bus-sleep mechanism

CAN Network

Test System

USB-CAN Converter

Fig. 8 Implemented hardware

message. When the NM message comes back to the first request node with an indication bit, the node sets the acknowledge bit and sends an NM message. When a node receives an acknowledge bit, the node prepares for sleep mode. After a predefined time, every node enters sleep mode.

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Fig. 10 Freescale code warrior

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Fig. 11 UML state chart diagram

OSEK Indirect NM is similar to Direct NM, but the detailed mechanism is not defined in a standard document. The sleep mode transition mechanism of AUTOSAR NM is simple. A node stops sending NM messages if the node wants to enter sleep mode. If there is no NM message on the network, every node enters Prepare Bus Sleep mode after a pre-defined time. After one more sleep delay, the nodes enter Bus Sleep mode (Fig. 7).

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Fig. 12 Monitoring CAN message

6 Implementation 6.1 Hardware Environment We used an MPC5668GKITJ embedded system, a development kit of the Freescale MPC5668G MCU. CAN 1ch, SCI 1ch, buttons and LEDs were used. A CAN network was made with three boards. Kvaser Leaf Lighe HS and Kvaser CanKing were used for CAN message monitoring (Fig. 8).

6.2 Software Environment We used Electorbit AUTOSAR OS 3.0 and MCAL for the CAN driver. AUTOSAR OS was made based on OSEK OS, and AUTOSAR OS and OSEK OS have backward compatibility. Therefore, if we use only OS in the AUTOSAR platform, we can have the same functions of OSEK OS [8–10]. MCAL is a device driver. OSEK/VDX does not define a data link layer detail. Only the required functions of the interaction layer are defined. Thus, we can use the MCAL CAN driver as a data link layer [11, 12]. We used EB tresos and Freescale CodeWarrior as development tools. EB tresos is a configuration tool, and Freescale CodeWarrior is a coding, compile, and debugging tool (Figs. 9, 10).

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6.3 Implementation and Verification We implement the core function only. The standard document offers a UML State Chart diagram. For implementation, we use 4 alarms and 5 tasks. The 2 buttons and 4 LEDs are used for monitoring node state and setting. Button 1 is a Request Network command, and Button 2 is a Release Network command. Each LED indicates each NM state, which can be Repeat Message State, Normal Operation State, Ready Sleep State, or a Prepare Bus Sleep Mode order. Bus Sleep Mode turns on every LED. The verification point is the transition of state on the UML State Chart diagram (Figs. 11, 12).

7 Conclusion AUTOSAR NM and OSEK NM have different basic concepts in network monitoring. Thus, we cannot have compatibility by changing parameters or correcting small parts. AUTOSAR NM does not support real-time fault detection explicitly. Thus, that function must be implemented by the developer on an application layer. On the other hand, AUTOSAR NM has simple structure for implementation. Also, AUTOSAR NM supports improvement of the bus load reduction characteristics and partial network function. Correction of AUTOSAR-based software is not recommended, because AUTOSAR’s aim is model-based development to maximize reusability. Therefore, we have to use a protocol gateway or AUTOSAR NM on existing systems. As can be seen in this paper, applying AUTOAR NM on existing systems is an alternative solution for compatibility. Acknowledgments This work was supported by the Ministry of Knowledge Economy and the Korea Institute for Advancement in Technology through the Workforce Development Program in Strategic Technology.

References OSEK VDX Portal. http://portal.osek-vdx.org AUTOSAR development partnership. http://autosar.org Kunkel B (2011) AUTOSAR-CP exploitation plans. AUTOSAR open conferencce Kum D (2008) AUTOSAR migration from existing automotive software. ICCAS, pp 558–562 Pier R (2009) Steering your design through the AUTOSAR transition. John Day’s automotive electronics news, http://johndayautomotivelectronics.com 6. Fürst S (2010) Challenges in the design of automotive software. Design, automation and test in europe conference and exhibition, pp 256–258 1. 2. 3. 4. 5.

704 7. 8. 9. 10. 11. 12.

Y. H. Lee et al. OSEK/VDX network management, V2.5.3 (2004) http://portal.osek-vdx.org/ Specification of CAN network management V3.3.0 R3.2 Rev 1 (2011) http://autosar.org OSEK/VDX operating system, V2.2.3 (2005) http://portal.osek-vdx.org/ Specification of operating system, V3.2.0 R3.2 Rev 1 (2011) http://autosar.org Behnam M (2009) Towards hierarchical scheduling in AUTOSAR. ETFA pp 1–8 OSEK/VDX communication, V3.0.3 (2004) http://portal.osek-vdx.org/

Performance Analysis of Ethernet Power link Applied to Ethernet of In-Vehicle Network Hoe Young Chung, Jin Ho Kim and Jae Wook Jeon

Abstract Recently, LIN, CAN, and Flex Ray have most often been used in In-Vehicle Networks (IVNs). Automotive manufacturers are applying active safety systems, Infotainment devices, entertainment devices, etc., and those systems communicate large amounts of data. However, CAN or Flex Ray cannot handle a large amount of data with real-time performance, so applied research on Ethernet as an alternative to the existing network of IVNs is actively underway. In recent research, many manufacturers have studied Switched Full Duplex Ethernet (SFDE). However, SFDE supports few network topologies and is expensive for implementing in networks. Ethernet Power link (EPL) is presented as an alternative for SFDE field bus network protocol based on Ethernet. The protocol stack of EPL is open source, and it supports standard Ethernet chips (not ASIC). Also, EPL guarantees high real-time performance and supports various topologies. The performance of EPL is analyzed and its suitability for Ethernet in IVNs is evaluated. Keywords Ethernet Power link Analysis

 Ethernet  In-Vehicle Network  Performance

F2012-D06-018 H. Y. Chung (&)  J. H. Kim  J. W. Jeon Sungkyunkwan University, Mokpo, Republic of Korea e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_66,  Springer-Verlag Berlin Heidelberg 2013

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Table 1 Frame of EPL Bit Offset 6

Entry defined by

Octet offset

7

0…5 6…11 12…13 14 15 16 17… n n ? 1… n ? 4

Destination MAC address Source MAC address Ether type res Destination Source Data CRC32

5

4

3

2

1

0 Ethernet II

Message type

Ethernet POWER LINK

Ethernet II

1 Introduction Low-bandwidth protocols like LIN, CAN and Flex Ray are primarily used in In-Vehicle Networks(IVNs). However, the number of ECUs and the amount of data communications are rising, and these protocols are not suitable for satisfying real-time performance requirements. Thus, applied research of Ethernet as an alternative to the existing network in IVNs is actively underway. Ethernet will be one of the primary protocols in IVNs [1]. Protocols based on Ethernet, like Time-Triggered Ethernet (TTE), can satisfy real-time performance requirements using dedicated network controllers and switch devices. Dedicated controllers and switches are very expensive, so it is not easy to construct low-cost IVN’s using this approach. Therefore, a new Ethernet solution, EPL, is suggested. EPL was originally developed for factory automation [2]. The most powerful feature of EPL is that it does not need a dedicated controller and switch, so it completely satisfies real-time performance safety by software. In addition, it is totally open source, so there is no additional cost to implement networks based on EPL. The performance of EPL applied to IVNs is analyzed. In addition we compare performance of EPL with one of other protocols based on Ethernet, we evaluate suitability with Ethernet of IVNs.

2 EPL EPL is based on Ethernet, and it is primitively developed for industrial networks. EPL is based on standard Ethernet (IEEE 802.3) and real-time capable Ethernetbased field buses (IEC 61784-2). In addition, it supports CAN open Profile. EPL provides good safety based on open SAFETY. It is certified SIL 3 according to IEC 61508 [3]. Table 1 is description of frame of EPL. The length of data is from 45 to 1499 byte [4].

Performance Analysis of Ethernet

Fig. 1

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The cycle time of EPL is divided into two phases. The first phase is isochronous phase, and the second is asynchronous phase. Isochronous phase is based on timetriggered method. An Isochronous Managing Node (MN) controls the transmission time of the Controlled Node (CN). The asynchronous phase is based on eventtriggered method. In this phase, the MN and CNs can transmit and receive asynchronous messages that are not time critical [5]. Figure 1 describes the cycle time of EPL. The most powerful character of EPL is that it is totally based on software. It means that EPL doesn’t need to add dedicated controller and switch. EPL’s software stack is open source, constructing network based on EPL is very inexpensive. One of EPL’s characteristics is topology flexibility. It supports free choice of star, tree, ring, or daisy chain [3]. Also, there is no difference in performance among topologies, so a developer can construct networks flexibly. EPL supports cross traffic function cross traffic is very useful for network distributed control systems like IVNs. Using this function, messages from CNs do not need to pass through the MN. This means CNs communicate directly with other CNs. Fig. 2 depicts the cross traffic of EPL.

3 Related Work Lim analyzed the performance of Switched Full Duplex Ethernet in IVNs in [6]. A realistic environment of an IVNs was constructed. Some scenarios were made and a simulation was executed. All of the traffic in IVNs was categorized, and priorities were given for achieving real-time performance. The end-to-end delay of all traffic was analyzed, and the real-time performance of SFDE was evaluated with priority. G. Cena analyzed the performance of EPL networks for distributed control and automation systems in [7]. A network system based on EPL was simulated, and suitable equations for an adapted network were calculated. In addition, the realtime performance of the network was analyzed.

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Cross traffic of EPL

Rainer Steffen designed and realized IP-based IVNs architecture, and an IP-based IVNs was implemented in [8]. Several categories of traffic with QoS were classified. There were three traffic categories. One is real-time control data. QoS of real-time control data was defined, and the IVNs based on IP was simulated. As a result, we refer to those papers and construct an IVNs based on EPL for simulation. Also, we analyze the real-time performance and evaluate its suitability for IVNs.

4 Performance Evaluation 4.1 Topolgy Several topologies based on EPL were constructed for simulation. There are two kinds of topologies. The first topology is based on star, and the second on daisychain. EPL recommends a hub rather than a switch [9]. Because switches based on Ethernet can add more jitter and decrease determinism. EPL also has a Master– Slave network structure [4]. The Control Unit is replaced by a Managing Node (MN of EPL). Figure 3 describes the topologies of a simulation network based on EPL.

4.2 Traffic Characteristics We define traffic characteristics for simulation. All CNs communicate only realtime control data. The maximum time of end-to-end delay between two ECUs is currently 2.5 ms, and the limitation of end-to-end delay time is mostly more than 10 ms [8]. In our opinion, EPL has to satisfy this requirement. The characteristics of real-time control data are described in Table 2.

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Fig. 3

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Network topologies for simulation

Table 2 Characteristics of real-time control data Classification

Value

Payload length End-to-end delay time

45, 64, 128, 256 … 1490 byte \10 ms

Table 3 Assumption for simulation Classification Value EPL bandwidth Wire length Cycle phase Hub delay

100 Mb/s Star topology Daisy-chain topology Isochronous phase (Refer to Fig. 1) 0.460 us [4]

29 m 22 m

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Table 4 Cycle time of EPL in star and daisy-chain topologies Classification Equation Star Topology Daisy-Chain a b

Tstar = 1.2a 9 (Tsoc ? Ncn 9 (Tsq ? Tqs) ? 2 9 0.08 9 L 9 Ncn ? 2 9 Ncn 9 Thub) ? 203.38 usb Tdaisy = 1.2a 9 (Tsoc ? Ncn 9 (Tsq ? Tqs) ? 2 9 0.08 9 L 9 Ncn ? 3 9 2 9 Ncn 9 Thub) ? 205.58 usb

Safety margin Time of asynchronous phase, one Asnd message and SoA[Payload : 1490 byte]

4.3 Assumption To simulate an IVNs based on EPL, we make some assumptions. Real-time control data is assigned in isochronous phase, because time-triggered method is safer than event-triggered [10]. In addition, MN transmits one message in isochronous phase. Table 3 depicts the assumptions of the simulation environment.

4.4 Simulation For analyzing performance of EPL in an IVNs, we define equations for end-to-end delay time and cycle time.

4.5 Cycle Time The first equation is for cycle time. There are two topologies in this simulation, so we calculate two equations. The first is for star topologies, and the second is for daisy-chain topology. The two cases have the same number of CNs, but the number of hubs is different (Refer to 3). Table 4 depicts the equation of EPL cycle time in each case. The reason why asynchronous phase is different between star topology and daisy-chain topology is that the number of hub is different. Table 5 is description about all variables.

4.6 End-to-End Delay Time End-to-end delay time is composed with three elements: propagation delay, transmission delay, and hub delay. Table 6 is a description of end-to-end delay time.

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Table 5 Description of variables Classification

Explain

Tsoc Tsq Tqs L Thub Ncn

SoC time, 45 us[4] 1 us[4] 8 us[4] Length of messages(unit : byte) Hub delay time, 0.46 us[4] The number of CNs

Tsq is that MN sends the PReq to next CN after receiving PRes from previous CN. Tqs is that CN receives PReq from MN after transmitting PRes to MN

Table 6 End-to-end delay time Classification

Value

Propagation delay time

Length of wire v propagation speed (propagation speed : 2.1 9 108 m/s) Duration until transmitting one message (Length of Message/Transmission speed) Hub latency delay time (0.46 us)

Transmission delay time Hub delay time

Table 7 Wire length of star topology

Classification

Value(m)

Ethernet HUB—[CN 1, 2, 7] Ethernet HUB—[CN 3, 4, 5, 6] MN(Control)—Ethernet HUB

2 5 3

Table 8 Wire length of daisy-chain topology

Classification

Value(m)

Ethernet HUB 1—[CN 1, 2, 7] Ethernet HUB 2—[CN 3, 6] Ethernet HUB 3—[CN 4, 5] MN—Ethernet HUB 1 Ethernet HUB 1—Ethernet HUB 2 Ethernet HUB 2—Ethernet HUB 3

2 2 3 2 2 2

We have to set the value of wire length for simulation of propagation delay. Table 7 shows the wire length of star topology. Table 8 depicts the wire length of daisy-chain topology. We define the equation of end-to-end delay time for the two topologies. Tdelay

star

= Thub + Ttransmission + Lwire / Ps

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Fig. 4

Cycle time (star topology, unit: ms)

Fig. 5

Cycle time (daisy-chain topology, unit: ms)

In star topology, all messages of CNs have to pass through Ethernet hubs. Therefore, the value of end-to-end delay time is the same for all nodes. However, in daisy-chain topology, end-to-end delay time is different among CNs. Because the number of Ethernet hubs is different from star topology, messages of some CNs like CN 5 have to pass three Ethernet hubs. We define an equation of end-to-end delay time in daisy-chain topology. Tdelay

daisy

= Nhub v Thub + Ttransmission + Lwire / Ps

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Table 9 Result of end-to-end delay time Classification Source—Destination Star topology

Daisy-chain topology

CN CN CN CN CN CN CN CN CN CN CN CN CN CN CN

1, 3, 1, 3, 1, 3, 1, 3, 4, 1, 3, 4, 1, 3, 4,

2, 7—MN 4, 5, 6—MN 2, 7—MN 4, 5, 6—MN 2, 7—MN 4, 5, 6—MN 2, 7—MN 6—MN 5—MN 2, 7—MN 6—MN 5—MN 2, 7—MN 6—MN 5—MN

Payload(bytes)

End-to-end delay(us)

1490 1490 45 45 18 18 1490 1490 1490 45 45 45 18 18 18

121.38 121.40 21.564 21.578 3.604 3.618 121.38 121.85 122.32 21.56 22.03 22.50 3.599 4.069 4.543

5 Result In the simulation environment, we obtain the get real-time performance of EPL. Cycle time is simulated first. We changed the length of the payload and checked the results of the simulation. Figure 4 shows a cycle time graph in daisy-chain topology. Figure 5 depicts a cycle time graph in star topology. In addition, using the relevant equations, we calculate the end-to-end delay time and its average. Table 8 depicts the results of end-to-end delay time of the two topologies and different payload lengths. As we compare with real-time performance of SFDE in [6]. we set payload length to 18 byte and get simulation results. As a result daisy-chain topology based on EPL in IVNs consumes more end-to-end delay. Because there are more hubs in daisy-chain topology it makes more delay time. The most important thing is that EPL’s end-to-end delay time is suitable for ethernet of IVNs. In addition EPL’s end-to-end delay time is much better than SFDE in our simulation. In our opinion delay time of hub is much better than ethernet switch, performance of EPL is better than SFDE (Table 9).

6 Conclusion and Future Work We made a simulation environment for EPL, and executed a simulation. EPL is suitable for real-time control data communication, and is a much better solution than SFDE for such purposes.

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However, structure of EPL network is based on master–slave control structure. Actually distributed network system is more reliable than master–slave control structure in. IVNs. Therefore, we will consider the way to construct reliable network based on EPL for IVNs safety. In the future, we will construct a real IVNs based on Ethernet Power link and analyze its performance. Acknowledgments This work was supported by Priority Research Centers Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology(2012–0005861) and the program for the Development of a Real-Time EthernetBased Fieldbus Gateway System for Controlling Multi-Axis Systems (C1820–1102–0020), funded by the National IT Industry Promotion Agency.

References 1. Nobauer J (2011) Is ethernet the rising star for in-vehicle networks? ETFA 2. Vaclav K (2011) Ethernet power link asynchronous phase examination, Recent Researches in Communications and IT:152–157 3. POWER LINK Basics brochure, Ethernet Power link Standard Group(EPSG), 2008, http:// www.ethernet-powerlink.org/ 4. EPSG DS 301 V1.1.0—Communication Profile’’, EPSG, http://www.ethernet-powerlink.org/ 5. Introduction to Ethernet Power link, IXXAT, http://www.ixxat.com/ 6. Hyung-Taek L (2011) Challenges in a future IP/ethernet-based In-Car Network for Real-Time Applications. In: DAC, pp 7–12 7. Cena G (2009) Performance analysis for ethernet powerlink networks for distributed control and automation systems. Comp Stand Interfaces 31(3):566–572 8. Steffen R (2008) Design and realization of an IP-based In-car network architecture. In: ISVCS 9. Paula Doyle, ‘‘Introduction to Real-Time Ethernet II’’, the EXTENSION, 2004, Volume 5 Issue 4 10. Steve CT (2009) Comparison of field bus systems, CAN, TTCAN, Flex Ray and LIN in passenger vehicles. In: ICDCSW, pp 26–31

Performance Analysis of Gateway Embedded System with Function Actively Controlling CAN Messages Hoe Young Chung and Jae Wook Jeon

Abstract Automotive manufacturers are trying to replace mechanical control systems with electronic control systems. Steer-by-wire and brake-by-wire systems are among those to be replaced. As a result, Electronic Control Unit (ECU) usage is increasing sharply. The In-Vehicle Network (IVN) is more complex now than in the past, and the amount of communication data is increasing rapidly. Many kinds of network protocols (LIN, CAN, FlexRay, MOST, Ethernet) use IVN. Therefore, a flexible gateway embedded system is both important and necessary. The gateway embedded system can improve IVN reliability and compatibility. This paper presents a CAN, FlexRay Gateway Embedded System with function actively handling CAN messages. This gateway has specific functions of automatically reducing CAN frame overhead and relieving CAN bus traffic, yield many advantages for the real-time performance of the IVN.







Keywords Gateway Embedded System CAN FlexRay In-Vehicle Network Real-Time Performance



1 Introduction Many ECUs (Electronic Control Units), sensors, and actuators are now being applied to vehicle manufacture. In addition, many mechanical control parts have been replaced with ECUs. Thus, the In-Vehicle Network (IVN) is more complex, F2012-D06-019 H. Y. Chung (&)  J. W. Jeon SungKyunKwan University, Mokpo, Republic of Korea SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_67,  Springer-Verlag Berlin Heidelberg 2013

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and the volume of communication data is increasing. Interconnection of ECUs is usually organized in a complex hierarchical network that has evolved with the rising number of ECUs [1]. Each application needs differing performance, so many kinds of network protocols are used in IVNs. Typical IVN protocols are LIN, CAN, FlexRay, and MOST. Controller Area Network (CAN) is the primary IVN protocol. This communication is ideal for Power train and Body electronic applications, such as Acceleration Skid Control (ASC), which require communications between multiple devices, including engine timing and carburetor control units, to reduce torque when drive wheel slippage occurs [2]. However, vehicle manufacturers need higher–quality performance IVN protocols. The ECU has to transmit a large amount of data due to quite a number of sensors and actuators connected and working with one ECU. CAN protocol cannot satisfy manufacturers’ needs, so requirements for new–generation IVN protocols are on the rise. Initiatives by automobile manufacturers and suppliers have led to the creation of FlexRay, an open standard for a new deterministic, fault–tolerant, and high–speed bus system [3]. FlexRay is developed for next-generation safety systems, like x-by-wire system. At the early stages when FlexRay was developed, applications and devices were very expensive. As the cost of FlexRay production drops, the functional and safety critical features of FlexRay can be used to create the next generation of automotive safety systems [4]. In the future, CAN and FlexRay will be the primary IVN protocols. The gateway embedded system is one of the important techniques for the next–generation vehicle network. Its performance also critically impacts IVN performance. Since the payload of FlexRay messages is larger than those of CAN, when a gateway converts FlexRay messages to CAN messages, the messages are overfilled. As a result, the gateway can increase CAN bus traffic, bus share, and priority delay, so the real-time performance of the CAN bus is negatively affected. A CAN, FlexRay Gateway Embedded System with CAN message active handling is suggested. The special function can improve the real-time performance of the gateway, because it reduces the conversion and transmission time of CAN messages, CAN bus occupation, and priority delay. In addition, a test bench is made and the real-time performance of gateway is analyzed.

2 Concept of the Network Protocol 2.1 CAN CAN was first developed by Robert Bosch GmbH for Mercedes vehicles that required a network system between ECUs in In-Vehicle Networks [5]. CAN is a serial communication, and it is very useful for distributed operating systems that have a high level of data integrity. CAN supports a 1 Mb/s maximum transmit

Performance Analysis of Gateway Embedded System

Fig. 1

Format of CAN message frame

Fig. 2

FlexRay communication cycle

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speed and has a multi-master network structure, so all CAN nodes can equally access the CAN bus. CAN is able to avoid message collision in the bus due to this characteristic. CSMA/CR is used so that each message has an ID. The CAN network gives priority by referring to the ID, and if message collision occurs, the CAN network node waits until there is no message in the CAN bus. Figure 1 depicts the format of a CAN message frame.

2.2 FlexRay The operational method of CAN is ‘Event-triggered’. Arbitration of bus conflicts is realized by CSMA/CR. However, overhead traffic in the CAN bus leads to deterioration in real-time performance of the CAN protocol. Manufacturers developed ‘FlexRay’ to solve this problem. FlexRay is more reliable, and has faster transmission speed and higher scalability than the CAN protocol. The FlexRay protocol will be used in next–generation safety systems, and is already used in x-by-wire systems. FlexRay has a high bandwidth of up to 10 Mb/s. It is organized with a Static Segment (SS) with TDMA operation and a dynamic segment with FTDMA [6]. Figure 2 shows a communication cycle of a FlexRay network.

3 Gateway Embedded System The MCU of the proposed gateway system is an MC9S12XF512 made by Freescale. Software to actively handle CAN messages was also implemented.

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Fig. 3 Hardware of the gateway embedded system

3.1 Hardware of Gateway Embedded System The hardware of the CAN, FlexRay Gateway Embedded System consists of the MC9S12XF512, Flex Ray transceiver, and CAN transceiver. The MCU supports the CAN, FlexRay network using the built-in CAN, FlexRay module, as well as one channel of msCAN2.0A, B and Flex Ray Dual channels [7]. The Interface of CAN networks is based on an AMIS-41682, a low-speed CAN transceiver that supports up to 125 kb Baud Rate. The FlexRay network interface is based on a TJA1080, which supports from 1 to 10 Mb/s. Figure 3 shows the hardware of the gateway embedded system.

3.2 Software of Gateway Embedded System The gateway’s software has two applications. The first application is converts message format, and the second is combines CAN messages.

3.3 Application of Converting Frame Format The application for converting frame format extracts data from messages (FlexRay and CAN) and converts the frame format to the other protocol (CAN or FlexRay). There are two cases. The first case is that the gateway converts from a FlexRay message to a CAN message, and the other is from a CAN message to a FlexRay message. The payload length of a static slot is constant and longer than one of CAN (Payload length, 1–8 bytes), so the gateway receives several CAN signals when it converts from Static Slot. The gateway sorts the signals in the received static slot. After sorting the signals, the gateway makes a number of CAN messages and

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Table 1 Advantage of application of combining CAN messages Classification Advantage Overhead

InterFrame Space

Conversion and Transmission Time Priority Delay Time Bus Occupation a

Tbus

occupation

Reduce Header and Tailer of CAN frame(Refer to Fig. 1) Loverhead = n 9 (44 bit ? 8 bit)—(44 bit ? n 9 8 bit) ; Loverhead = (n-1) 9 44 bit (n = the number of CAN messages) Reduce the number of IFS Ideal IFS length = 3 bit MC9S12XF512 * 112 us(Refer to Fig. 5) ; TIFS = (n-1) 9 112 us Converts and transmits CAN message in same FlexRay communication Cycle Shorten Priority Delay Time Decrease Bus Occupation of Gateway ; Boccupation = Tbus occupation/Taunit

= Transmission Time/Message ? 2 9 IFS/Message 9 Number of Message

copies the signals to the payload of the CAN messages. If the gateway receives a number of CAN messages, it copies the CAN signals to the payload of the static slot. After that, the gateway sends the contents of the static slot at the next communication cycle.

3.4 Application of Combining CAN Messages The application for combining CAN messages combines several CAN messages which the same destination ECU (CAN node) into one message. This is necessary because some ECUs receive several messages in normal functioning. However, if a gateway embedded system simultaneously transmits several CAN messages to one node, problems such as overhead and traffic overload can result. Therefore, if the gateway can combine several messages into one, such problems can be relieved. Table 1 shows the advantages of application of combining CAN messages. By reducing overhead, we expect that the real-time performance of the gateway will be improved, and the gateway will be able to convert and transmit converted CAN messages in the same FlexRay communication cycle. In addition, transmission time is related to the time of CAN bus occupation and the CAN bus occupation time of the gateway will be decreased. This application can also reduce the priority delay time, because the number of CAN messages and the transmission time are reduced, so the CAN messages with low priority can transmit earlier than before. Figure 4 depicts the gateway applications.

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Fig. 4

Gateway applications

Fig. 5

Test Bench of Gateway Embedded System

4 Experiment To analyze the performance of the gateway, a test bench was constructed. Figure 5 depicts the test bench for analyzing the real-time performance of the gateway. In the test bench, virtual ECUs (FlexRay nodes and CAN nodes) are generated by an In-Vehicle Network Simulator(Vector CANoe). The virtual ECUs support FlexRay communication with the gateway, and gateway converts the FlexRay messages to CAN messages. The gateway also converts CAN messages to FlexRay messages and sends them to the virtual ECUs (FlexRay nodes). In addition, an oscilloscope is used to obtain real-time data for analyzing the performance of the gateway. Table 2 shows the conditions of the experiment.

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Table 2 Conditions of experiment c Classification Condition FlexRay Baud rate 10 Mb/s Cycle Time 5 ms Payload Length(Static Slot) 32 byte CAN(without Application of Combining CAN message) Baud rate 100 kbps The number of messages Payload length(bytes) 1 2 4 CAN(with Application of Combining CAN message) Baud rate 100 kbps The number of messages Payload length 8 byte a

The number of messages 8 4 2

The number of messages 3a

14 messages combined into three messages

Table 3 Result of Measurement Classification

Performance

Overhead reduction Conversion and transmission time Priority delay reduction Bus occupation reduction

528 bit 5.49 ms(9.32 ? 3.83 ms) 6.33 ms(Minimum) 48.55 %(89.2 % ? 37.05 %, unit time : 10 ms)

Fig. 6

Conversion and transmission time

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By adapting these elements in experiment, we analyze the real-time performance of the gateway in regard to overhead reduction, conversion and transmission time, priority delay reduction and bus occupation reduction.

5 Result The proposed Gateway Embedded System was tested on the test bench. Table 3 depicts the results of simulation. The performance of the gateway with active CAN message handling has better real-time performance than the gateway without the function. As the overhead of CAN messages is highly reduced, the real-time performance is rapidly improved. Further, the conversion and transmission time are reduced, so the gateway can handle received data from FlexRay nodes in the same FlexRay communication cycle. As a result, the gateway can process a number of data in real-time. Figure 6 represents conversion and transmission time.

6 Conclusion and Future Work A CAN, FlexRay Gateway Embedded System with CAN message active handling was implemented, and the performance of the gateway was analyzed and compared to a gateway without the handling functionality. As a result, the gateway with the CAN message active handling function worked better than the one without the function, and its real-time performance satisfies the requirements of In-Vehicle Networks. The proposed Gateway Embedded System is more suitable for low-speed CAN networks. In the near future, we will test the implemented gateway with real ECUs (not virtual) and analyze the real-time performance. In addition, we will strengthen the test environment to obtain more realistic data on performance.

References 1. Sander O (2008) Reducing latency times by accelerated routing mechanisms for an FPGA gateway in the automotive domain 97–104 2. Jan T (2005) Gateway concept for automotive networks. Automotive 2005 Special Edition FlexRay, Munchen 3. Suk Hyun S development of network gateway between CAN and FlexRay protocols for ECU embedded system 2256–2261 4. Shankar D Designing FlexRay-based driver assistance system using early hardware-software architecture exploration 354–359 5. Paret D (2007) Multiplexed networks for embedded system’’, John Wiley and Sons Ltd Chichester, England

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6. Schmidt K, Schmidt G (2009) Message scheduling for the FlexRay protocol : the static segment. IEEE Trans Veh Technol 58(5):2170–2179 7. S12XF : 16-bit Automotive Microcontroller, http://www.freescale.com/

Network Architecture Design for Reliability Based on ECU Power Supply and Location Maoyuan Cui, Dongfeng Zhao, Libo Zhang, Youen Li, Boxiang Ma and Dongyang Ma

Abstract To improve the network communication reliability, the influence of ECU power supply and location on network architecture is analyzed in this chapter and some scenarios are presented. For ECU power supply, the network with KL15ECUs and the network with KL30-ECUs, and the network mixed KL15-ECUs and KL30-ECUs are evaluated separately according to network management and physical layer characteristic. The relevant requirements for the network architecture are obtained for different scenarios. For ECU location, some ECUs will result in risks for the network communication during collisions. In this study, adaptive cruise control (ACC), and active frontlight system (AFS) are analyzed. The corresponding optimization solution is given by designing separated network architecture. The results are applied to a C class passenger car successfully. Keywords Network architecture Location

 Design for reliability  ECU  Power supply 

1 Introduction The electronic and electrical (E/E) architecture of modern vehicles is getting complex as more and more E/E equipments are used in vehicles. The network architecture of a C class passenger car is shown in Fig. 1 as a typical example. F2012-D06-020 M. Cui (&)  D. Zhao  L. Zhang  Y. Li  B. Ma  D. Ma Automotive Electronic Development, China FAW Co. Ltd R&D Center, Changchun, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_68,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 Network architecture of a C class passenger car

More than 30 Electronic Control Units (ECU) communicate with each other on Controller Area Network (CAN) and Local Interconnect Network (LIN). As a result, the design difficulty of network architecture is increased [1, 2]. One of the challenges for network architecture design is how to improve the reliability of network communication. Usually communication reliability of network architecture can be improved by decreasing the busload of network or reducing the number of network ECUs or shorting the length of communication wiring harness. However, the communication reliability of network architecture also could be affected by ECU power supply mode and ECU location. The affection of ECU power supply mode and ECU location on network architecture is evaluated and relevant technical specification is defined in this chapter. Based on these research works, the network architecture of the C class passenger car mentioned above has been optimized and good result achieved.

2 The Influence of ECU Power Supply on Network Architecture 2.1 ECU Power Supply Type ECU power supply can be divided into three types. (a) KL30 is used as ECU power supply and KL15 is used to control ECU on/off as shown in Fig. 2a;

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ECU

ECU

(a)

(b)

727

ECU

(c)

Fig. 2 a Power supply type. b Power supply type. c Power supply type

(b) KL15 is used as ECU power supply and ECU on/off control as shown in Fig. 2b; (c) KL30 is used as ECU power supply and ECU on/off control as shown in Fig. 2c;

2.2 The Influence of ECU Power Supply Mode on Network Architecture Based on the ECU power supply mode, 3 types of network can be defined. (1) The network with KL15-ECUs The main feature of the network is that all ECUs in the network start communication when KL15 is ON and stop communication when KL15 is OFF. Therefore, no sleeping and wake up function is required to transceivers of all ECUs in the network and no leakage current exists in the network. For this network, KL15 is used as power supply or control ECU on/off. So the ECU power supply mode in the network can be type (a) or type (b), refer to Sect. 2.1. OSEK indirect network management [3] can be used in the network as shown in Fig. 3. (2) The network with KL30-ECUs The feature of the network is that all ECUs in the network can still keep communication when KL15 is OFF. Therefore, sleeping and wake up function is required to transceivers of all ECUs in the network. For this network, KL30 is necessary as power supply or control ECU on/off. So the ECU power supply mode in the network can be type (a) or (c), refer to Sect. 2.1. OSEK direct network management [3] can be used in the network as shown in Fig. 4. (3) The network mixed with KL15-ECUs and KL-30-ECUs The feature of the network is that some ECUs in the network can not communication and some ECUs can still keep communication when KL15 is OFF. The former ECU power supply mode is type (a) or type (b), the latter ECU power

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Fig. 3 Indirect network management

Fig. 4 Direct network management

supply mode is type (a) or (c), so it is the mixed network with KL15-ECUs and KL-30-ECUs as mentioned above. In the mixed network, no sleeping and wake up function is required to the transceivers of ECUs which stop communication when KL15 is OFF, and OSEK indirect network management is used. Sleeping and wake up function is required to the transceivers of ECUs which still keep communication when KL15 is OFF, and OSEK direct network management is used. For the mixed network, one key problem needs to be resolved is that unpowered ECUs mustn’t influence the communication of other powered ECUs. For the ECUs

Network Architecture Design for Reliability Based on ECU

KL30

Ignition Key

729

KL15

CAN leakage current

Fig. 5 Leakage current in the mixed network

Fig. 6 Voltage step caused by leakage current

which stop communication when KL15 is OFF, the leakage current is produced when the powered ECUs send signals in the network as shown in Fig. 5 because the power supply on Vcc is turned off when KL15 is OFF. If there are many unpowered ECUs in the high speed CAN bus and the leakage current of each ECU is large ([200 uA), the recessive voltage will be pulled to ground (\0.5 Vcc) and asymmetric bias of common mode signals are generated as shown in Fig. 6. This asymmetric bias will lead to a larger voltage step when powered ECUs send signals in the network, and probably cause EME problem. To improve the situation, the transceiver leakage current of unpowered ECUs must be as lower as possible, the recommended value should be less than 25 uA [4] and CAN interface with split termination should be used, as shown in Fig. 7.

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Fig. 7 CAN interface with split termination

LAFS

ACC

RAFS

Fig. 8 ECU layout

3 The Influence of ECU Location on Network Architecture Some ECUs are easy to be damaged when crash happened because of ECU location, which will probably cause the communication problem. For example, the ECU of Adaptive Cruise Control system (ACC) and Active Frontlight System (AFS) usually mounted at the forefront of vehicle as shown in Fig. 8 (LAFS is Left AFS motor control system and RAFS is Right AFS motor control system).

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Fig. 9 Original network architecture

PT CAN

EMS

TCU

Fig. 10 Optimized network architecture

ESP

ACC

ACC PT2 CAN

Gateway

PT1 CAN

EMS

TCU

ESP

3.1 Separated CAN Bus by ACC ECU To realize the ACC, the ACC ECU gets the distance information from front radar and communicates with EMS and ESP system on real time. Therefore, they are usually located in the same bus as shown in Fig. 9. But the ACC ECU could be damaged easily when the front crash happened because of it’s location, which will probably cause the communication problem, and vehicle can not be drove under serious conditions. In order to avoid the unexpected result, ACC ECU could be separated from original bus and be put in a new bus as shown in Fig. 10. In this way, the communication among EMS, TCU and ESP will not be affected even when the crash accident happened and ACC ECU damaged. As a result, the network reliability is improved.

3.2 Separated LIN Bus by AFS ECU Similar to ACC ECU, the vehicle network communication also will be affected by AFS ECU when damaged by a front crash, and vehicle can not be drove under serious conditions as shown in Fig. 11. A possible solution to the problem is using LIN network, in which the AFS ECU could be a master node and communicates with slave node LAFS and RAFS. The vehicle information such as steering angle is routed by AFS ECU to LAFS and RAFS as shown in Fig. 12. After the adaptation, AFS ECU could be mounted in the cab and protected from the crash, so the network reliability is improved.

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Fig. 11 Original network architecture

PT CAN

EMS

Fig. 12 Optimized network architecture

TCU

ESP

AFS

PT CAN

EMS

TCU

ESP

AFS LIN

LAFS

RAFS

4 Conclusion The influence of ECU power supply and location on the CAN network architecture are analyzed in this chapter. For ECU power supply, the network with KL15-ECUs and the network with KL30-ECUs, and the network mixed KL15-ECUs and KL30-ECUs are evaluated separately based on the network management and physical layer characteristics. The requirement to each network architecture is defined. For ECU location, ACC and AFS system is analyzed separately. The corresponding optimization solution is given by designing separated network architecture. The results are applied to a C class passenger car successfully.

References 1. Stolz W, Kornhaas R, Krause R, Sommer T (2010) Domain control units—the solution for future E/E architectures?. SAE Technical Paper 2010-01-0686 2. Saad A, Bauer W, Haneberg M, Schiffers J (2006) Intelligent automotive system services—an emerging design pattern for an advanced E/E-architecture. SAE Technical Paper 2006-011286 (2006 SAE World Congress Detroit, Michigan) 3. OSEK/VDX (2000) OSEK/VDX network management concept and application programming interface [M] v 2.5.1 4. ISO (2007) Road vehicles—controller area network (CAN)—Part 5: high-speed medium access unit with low-power mode. ISO 11898-5

Part VII

Multi-Media/Infotainment System

Innovative Software Architecture for Next-Generation Infotainment System Jianming Zhou, Kerun Xu, Minjie Tian, Chendong Wang and Mingshi Xie

Abstract This paper studies new innovative infotainment software architecture for the development of next-generation infotainment system. Hardware independent design rule is developed to support mainstream hardware in the market. Native Application Framework is developed for the architecture that allows native app executives to be grouped and configured to meet the different system feature requirements. Ethernet based communication methodology is designed for flexible extension of system modules. Study shows that this software architecture helps to shorten the development timing and dramatically reduce the cost.



Keywords Ethernet Infotainment Software architecture

 Application framework  Virtual network 

With the rapid evolution of information technology, more and more concepts and features have been introduced to the vehicle infotainment system to improve user experience and convenience. Connectivity, add-in applications and telematics are the most attractive innovations among the new features that may dramatically change the life style of people in the near feature, while, on the same time, bring great challenges to the OEMs. OEMs need to tightly keep pace with the trend of new technologies and applications, rapidly integrate the new software and devices into the vehicle to consolidate the leadership in this market. This paper has studied new innovative software architecture (IISA) that is about to support the development of next-generation infotainment system. F2012-D07-004 J. Zhou (&)  K. Xu  M. Tian  C. Wang  M. Xie Pan Asia Technical Automotive Center Co., Ltd, Shanghai, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_69,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 Brief system architecture of IISA

First of all, the IISA supports the hardware independent software development. All the development is done on a PC and seamlessly integrated to embedded system without any difficulty. Secondly, a Native Application Framework (NAF) is introduced to support quick system reconfiguration by add or removal of application executives. Last but not least, an Ethernet based interconnection methodology is applied to enable a smooth extension from tradition all-in-one solution to star topology based multi-module system. All these technologies play a key role to the delivery of next gen infotainment system with a relative low cost.

1 Hardware Independence The IISA is designed to be run on different mainstream hardware platform by introducing an OS abstract layer (OSAL). The OSAL acts as an adaptor [1] between the operation system and the middleware so that all upper level software modules are independent to any specific operation system. This guarantees all upper level modules be able to develop and quickly debug on a PC and easily (bug-freely) deployed to the embedded system. The OSAL contains 2 software modules, the BasicLib and the GraphicsLib. Figure 1 shows the block diagram of the system architecture. The BasicLib encapsulates most of the OS level services, including memory management, file system, process and thread synchronization, etc. to the C++ classes and interfaces. Upper modules only call the interfaces exposed by BasicLib. In doing so, the upper modules are independent to the OS and will be easy to be adapted to different OS/hardware platform without any extra effort. Only the BasicLib need to be re-implemented for particular platform. The GraphicsLib encapsulates all the 2D graphics rendering functions used for screen presentation. There is a fact that high-end hardware platform provides hardware accelerated graphic rendering features while low-end hardware provides ‘‘framebuffer’’ that only supports software level rendering implementation. To fill the gap between high-end and low-end, the GraphicsLib implements not only the software rendering, which directly ‘‘calculate’’ each pixel in system memory and flush it to the ‘‘framebuffer’’, but also support the hardware accelerated rendering by utilizing the hardware capability as well.

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Fig. 2 Reconfiguration of infotainment system

With the help of OS Abstract layer, it is possible to port the IISA to a mainstream hardware without half a week. The OEMs do not need to worry about any problem of porting IISA to any supplier’s hardware platform.

2 Native Application Framework The traditional process of developing an infotainment system is started from definition of system features, development of specifications, supplier hardware and software development, and end up with validation and approval from the OEM. It is easy to see the disadvantages in this process such as: (1) long time development without agility (2) never embrace requirements changes (3) no configurability and less reuse of software modules in final product (4) reinvent the wheel multiple times. As studied, the best way to improve configurability and scalability is to develop features as modules and construct an infotainment system by selecting and combining different software modules. Figure 2 shows the concept. There are 2 types of software modules: the software building block and the software application executives. A software building block is a black box providing particular features with well-defined exported interfaces. A building block needs to be referred by other ‘‘modules’’ to provide its features. A building block may be provided in source code form. Source code level integration may be needed for particular platform. DLLs on Windows platform and so libraries on Linux platform can be regarded as a software building block. An application executive is an OS native executive entity that can directly run on specific OS platform. An application executive is in binary form, directly providing features,

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Table 1 Difference between the building block and application executives Items Software building blocks Software application executives Executive Cannot directly run. A building block must work attribute together with other executives Published Source code level (C/C++) interfaces interface Reuse Source code level reuse. Recompile and re-link is needed for new system

Executive entity that can execute directly RPC (remote procedure call) level interfaces Binary level reuse, seamlessly integrated to new system

and working together with other application executives to provide a complete service to the end user. Table 1 shows the difference between the building block and application executives. Software executives are highly recommended to be the basic modules for configuration of infotainment system. It is necessary to define and implement a NAF to support the running, termination, inter-application communication and lifecycle management of all applications. In the NAF prospective, all the features, services are implemented as app executives. The NAF consists of basic software modules that provide interfaces for NAF applications and reserved management executive that manages all user configured applications. The basic software modules provide the following key services: (1) Application lifecycle management; (2) Update management; (3) IPC and RPC mechanism; (4) published interfaces (5) Application Executive Rapid development support. The lifecycle management service (LMS) monitors all the apps by listening and collecting the ‘‘heart beat’’ messages from each apps for every second. Each app needs to register itself to the NAF when launched and send ‘‘heart beat’’ message once per second to prove that ‘‘I’m still alive’’. The NAF will recognize that an app has stopped responding while ‘‘heart beat’’ message is no long received for more than 5 s. Countermeasures, such as killing and cleaning up the zombie of app, or restart app, shall be taken for failsoft and recovery. The service may also attempt to calculate the workset [2] of each process and kill some of which in accordance with the LRU algorithm to balance the system memory usage. The LMS service plays a key role in strengthening the app management and improving the system robustness. The Update management service is responsible for downloading, installing, updating and removal of all apps. This makes it possible for a user to update the system by downloading and installing software packages from OEM owned back office. The Update management service implements the ‘‘synchronization’’ operation, which automatically downloads the ‘‘remote app list’’ (representing the apps user ordered) of particular account from the back office to the infotainment system and compare it with the one (called local app list) stored in local system storage to determine which app need to be updated or removed. The pseudo code in Fig. 3

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Fig. 3 Mechanism of synchronization for update management service

shows how system checks each item in app list and determines what action the system shall take. The NAF also provides published interfaces for the convenience of apps. The key points is to allow the interfaces continue evolving while on the same time keep it backwards compatible. The COM [3] style interface definition has been introduced to meet this goal. All the interfaces have been defined in an abstract class form and derive from a common base IUnknown as follows: interface IUnknown { virtual LONG AddRef(void) = 0; virtual LONG Release(void) = 0; virtual HRESULT QueryInterface(GUID rid, void **ppv) = 0; };

The QueryInterface method is designed for querying all the interfaces that the component supports. A smart app can determine if the latest version of interface is supported or not by calling the QueryInterface, and then make sure which interface the app shall use. For example: interface IPerson : public IUnknown { virtual String& GetName(void) = 0; virtual int32 GetAge(void) = 0; };

interface IPersonVer2 : public IUnknown { virtual String& GetName(void) = 0; virtual int32 GetAge(void) = 0; virtual String& GetNationality(void) = 0; };

The IPersionVer2 is an extension of IPerson. The app can call: hResult = pPerson-[QueryInterface(IID_PPV_ARG(IPersonVer2, &pPersonVer2));

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and check the validity of pPersonVer2 to determine which interface shall be used. This allows coexistence of different versions of interfaces which improves the backwards compatibility. The NAF provides IPC and RPC services for application communication, which will be described in detail in Chap. 3. The NAF also implements the basic framework of an application executive in a shared library. This is used to support rapid application development. Any developer can quickly create applications with basic functionalities by deriving the ‘‘INativeApp’’ base class. The NAF itself provides the following reserved apps: • AppMgr: the executives for app lifecycle management, update management, and communication gateway • AvModules: the Audio/Video player implementation in the system • DefaultIME: the system default input method • Mpmgr: the media player manager

3 Ethernet Based Interconnection Methodology Almost all OEMs maintain several series of infotainment platform ranging from all-in-one solution for emerging markets to networked multi-module system for developed markets. How to apply similar software architecture to different hardware architecture becomes a key problem of sharing and reusing software assets to different infotainment platform. In order to solve this problem, the NAF implements an Ethernet based virtual interconnection methodology (EVIM). Basically, the EVIM implements a virtual network that supports seamlessly inter-connection among software modules regardless of their physical location. The EVIM is the key part of NAF.

3.1 Communication Patterns In the virtual Ethernet prospective, each module is regarded as a physical node with uniquely assigned IP address, while each application is regarded as a virtual node that is able to communicate with other nodes. Each virtual node contains an (physical node) IP address and a port identifier. All the nodes located in the same module share the same host IP address. Different port number will be allocated to nodes who share the same IP address. A virtual node can communicate to another by sending/receiving TCP/IP packages regardless of where the node is located at. The TCP/IP layer will handle the request from app (virtual node) and determine if it is only an inter-process communication or a remote communication, thus, automatically select a proper way to transfer the package. Figure 4 illustrates this concept.

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Fig. 4 An example of internal communication and remote communication of virtual nodes Fig. 5 Request–reply pattern

This EVIM design concept will be very helpful for the reconfiguration of a module. For example, nothing need to be changed if we attempt to move the App2 from physical module 1 to physical module 2, as shown in Fig. 4, since the TCP/IP layer will exactly understand where the App2 located at and bypass the packages to the correct ‘‘sink’’. Under this concept, a client even does not need to know where the server locates at. On the other hand, the EVIM concept even helps to accelerate the development and system integration. The developer can deploy and debug multiple software executives on one physical module (or even on a PC) while the system integrator redeploys released executives to different modules and makes the final validation. In EVIM concept, pairs of virtual nodes (apps) must conduct ‘‘handshake’’ before start of communication. The earlier launched node, as called ‘‘initiator’’, shall continuously broadcast ‘‘probe frame’’, in which the IP address and port identifier are encapsulated, to all the nodes until the later launched node, as called ‘‘client’’, responds a ‘‘handshake frame’’ to the ‘‘initiator’’ once ‘‘probe frame’’ received. After the handshake, the couple know each other well and is ready for communication. An ‘‘initiator’’ can handshake with multiple ‘‘client’’, thus provide services to multiple apps in the same time. In order to simplify the design of interconnected apps, the EVIM concept has introduced 3 types of classical communication patterns, the Request–Reply pattern, the Publish–Subscribe pattern, and the Pipeline pattern. The Request–Reply (or REQ–REP) pattern allows the ‘‘client’’ to send a request to a ‘‘server’’ and wait reply from the ‘‘server’’. Note that a reply is a must in Req– Rep pattern, which guarantees the validity of the communication. Figure 5 shows the Req–Rep pattern. The Publish–Subscribe (Pub-Sub) pattern allows the ‘‘publisher’’ to broadcast messages to all the ‘‘subscribers’’. The ‘‘subscriber’’ shall take action in

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Fig. 6 Publisher–subscriber pattern

accordance with the received messages. The ‘‘subscriber’’ does not need to reply any message to the publisher in Pub–Sub pattern. The Pub-Sub pattern is always used for notification of some system events such as ‘‘USB connected’’ or ‘‘system error’’ (Fig. 6). The pipeline, or PUSH–PULL pattern, is used to push the requests to the ‘‘workers’’ and ‘‘works’’ do the works in accordance with the requests and push the result to the ‘‘sink’’. The sink ‘‘reaps’’ all the result. The message queue system will automatically balance the network load among all the ‘‘workers’’.

3.2 Message Definition Language Tons of message types have been defined in a traditional infotainment system. This makes the system very difficult to maintain. Message definition may update and lose backwards compatibility during the update of communication protocol. Applications source code become more and more complicated because of supporting multiple versions of messages. The EVIM concept introduces a google protobuf [4] based lightweight message definition language (MDL) to define all the messages for communication protocol. To make the message definition extendable and backwards compatible, the MDL defines three keywords: required, optional, and repeat to describe the attribute of a member variable in a message. A simple message may looks like: message Persion { required string Name; required uint32 Age; };

extend to

message Person { required string Name; required uint32 Age; optional string Nationality; };

Any extension of a message definition shall be marked as ‘‘optional’’. The message parser will automatically determine if the optional field really exists during the deserialization and then construct the correct content. This is transparent to the apps so the application message handler can handle different versions of message definition without any extra effort.

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Fig. 7 Process of module development using MDL

On the other hand, the MDL is platform independent. This means all the supplier can easily understand MDL and convert MDL to specific source code (C, C++, python, java, or any other) they want with the help of MDL compiler. The MDL compiler consists of MDL syntax parser and source code generator that can support multiple programming languages. A typical development process will be: the OEM releases the MDL definition of message protocol to different suppliers and the suppliers convert the MDL definition to source code by MDL compiler. The supplier develops the software system and the OEM conducts the integration and validation. Figure 7 shows this development process.

4 Conclusions This paper describes a new innovative Infotainment software architecture that can support the rapid application development for next-generation infotainment system. A demo based on this architecture has been developed, tested and proved a 55 % cost reduction and a 35 % improvement of development timing, while on the same time, bring great flexibility. For the next steps, more efforts shall be taken on the study of making the IISA works more efficient on Ethernet based multi-module system. The Ethernet bandwidth shall be carefully allocated for not only Audio/Video streaming but also bulk data transmission. Some new technology, such as AVB, etc. may be introduced to optimize the system performance.

References 1. Gamma E, Helm R, Johnson R, Vlissides J (1995) Design patterns, elements of reusable object-oriented software [M]. Addison Wesley 2. Davis WS, Rajkumar TM (2005) Operation systems: a systematic view[M]. Addison Wesley 3. Box D (1998) Essential COM[M]. Addison Wesley 4. Protocol Buffers—Google’s data interchange format: http://code.google.com/p/protobuf/

Part VIII

Other

The Design of Invariant Wiring Harness Network in Full Electronic Automobile Shicen Zheng, Wenqiang Chen, Xingmin Wei and Fuquan Zhao

Abstract In recent years, with a growing number of intelligent electronic modules applied in automobile, the design of automotive wiring harness is becoming increasingly complicated. The Full Electronic Automobile are designed with the original design idea of the invariant wiring harness network, which bring about the design’s total universalization of the whole car wiring harness and connector. It enables that the same harness and connector perfectly be applied to all cars. More importantly, it decreases the weight and the cost of the single car wire in a large degree.





Keywords Full electronic automobile Invariant wiring harness network Power network Intelligent communication network Universalization





1 Introduction With the development of the Automobile electronic technology, an increasing number of electronic components have been used in the car. The automobile harness, used to connect these components, is getting more and more complicated, in particular after the emergence of the In-Vehicle Network. The mixed type of auto harness is formed by attaching the In-Vehicle Network harness to the traditional auto harness, which leads to the increase of the number of the harness to connect the electronic components and the complexity of the harness design.

F2012-D08-003 S. Zheng (&)  W. Chen  X. Wei  F. Zhao Zhejiang Geely Automobile Research Institute Co., Ltd, Mainland, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_70,  Springer-Verlag Berlin Heidelberg 2013

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Fig. 1 The development of auto electrical control system. a The point-to-point connection system. b The central connection system. c The local area distributed connection system. circle in-vehicle appliances, square in-vehicle electric control unit

The application of in-vehicle bus technology changes the design of auto electrical system, from the point-to-point connection system to the central connection system, then to the local area distributed connection system. The three systems’ structures are shown in Fig. 1 at a, b and c respectively. The point-to-point connection system, as shown in Fig. 1 at a, is easy to connect. However, with the updated electrical equipment, the harness is getting more and more complicated. Thus the coordination function among the electrical equipment is hard to achieve. The central connection system, as shown in Fig. 1b, can achieve the coordination function well. But in this system, the signals are still connected by wires and the coordination mechanism is finished in the central controller. The local area distributed connection system, as shown in Fig. 1c, features the multiple distributed control module in the system. The control modules transfer signals with each other through the bus. Without the central control unit, the coordination controls of the car are distributed in each module, which has a wide coupling. It does not help to the upgrades, renovation and the cooperative production. In general, the following flaws in the auto electrical equipment and the electrical system structure bring the new challenge to the car harness design. 1. The digital communication interfaces in the sensors and actuators or in-vehicle assembly are not uniform. The most intelligent electrical equipments do not have the interfaces for information exchange with other in-vehicle equipments. At present, the interface of the intelligent electrical equipments has many kinds, such as CAN bus, LIN bus, USB bus, I2C, SPI and etc. There is no uniform standard. As the design of the intelligent sensors and the actuators are not viewed in terms of the vehicle system integration, lots of the intelligent components cannot be applied in the car immediately. And the interconnects cannot be achieved directly among them. 2. The most in-vehicle electrical control systems, which based on the bus, run through the mode of control module, which still needs many wires to connect with each other. Thus it could not solve the problem of the car harness effectively. 3. There is not a formula of network design to follow up. According to the actual function requirements and the distribution of the electrical equipments, most systems adopt the different buses and the network framework, e.g. some systems use three buses, which are HS-CAN [1], LS-CAN and LIN BUS [2]; some

The Design of Invariant Wiring Harness Network Fig. 2 Intelligent electrical appliances and standard connectors

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Control Circuit Standard Connector

systems use the CAN bus and LIN bus; some other systems use M-BUS, Assembly Line Communication Link (ALDL), POWER BUS and K-BUS in terms of actual function. 4. The in-vehicle electrical devices need higher power but without the effective and safe management and monitoring. The structure of power supply in the traditional power is simple. The on–off control of the electrical devices is achieved by the switch, relay, dispenser and other components. The power is supplied to the vehicle electrical through some panels with many plug-type fuses, which leads to the wiring mess of the panels. Each electrical device is protected by the simple relay. But the electricity use and control lack the effective and safe management and monitoring.

2 The Overall Design of the Full Electronic 2.1 The Intelligent Electrical Devices in the Full Electronic Car The full electronic car design scheme is put forward by the researchers of Tsinghua University [3]. It is a fully distributed intelligent electrical control system. In this system, all the in-vehicle appliances are the standardized intelligent electrical appliances. Intelligent electrical appliance is a kind of device which can realize self control and make use of the in-vehicle bus to exchange information. Its basic structure is as shown in Fig. 2.

2.2 The Main Content of the Full Electronic Car Scheme In the full electronic car, every intelligent electrical appliance is the network node in the electrical structure, which can realize information interaction and mutual coordinative control through the main network. It realizes the whole distributed control of electrical system in the car. The new design scheme of the electrical structure in the full electronic car mainly includes the following several points:

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1. The Network Design of Automobile Electrical System The network designed is for the overall structure of intelligent electrical distributed control system. Because of the variance of the importance of electrical appliance for the electrical system, a reasonable classification is needed for these appliances, and then organize them in a networked way. At the same time, the communication network and power network need to be built to meet communication need and power supply requirements of all the intelligent electrical appliance. 2. The Design of the Invariant Harness [4] The full electronic car scheme proposed a new concept of ‘‘invariant harness nets’’ for the simplified design of automotive harness. The key of simplifying harness is how to realize the standardization of connectors, making the definition of harness have nothing to do with electrical appliances. For only independent connectors can realize the wide range of generalization and avoid mistake in operation, meanwhile, it also can provide possibility to increase and decrease the carrying load for the vehicle system with different configuration. 3. The Design of the Intelligent Power Network The in-vehicle intelligent power network needs to realize the electricity monitor and over-currency protection, so an intelligent power box need to be designed in order to realize the on/off control and over-currency protection, and other functions, thus ensuring the safety of the car. 4. The Intelligent Design of the Electrical Equipment The key of intelligent design is to add controller over the electrical appliances. Due to the differences in the amount and category of electrical appliances, the design of the controller involves a lot of design problems of different kinds of controller hardware and software. Therefore, we need to adopt modular design method to improve the reusability and reduce the workload of designing and testing.

2.3 The Invariant Harness Network of the Full Electronic Car Compared with the traditional harness design scheme, the invariant harness of the full electronic car is designed with three features, which are the assimilation of the interfaces, the standardization of connectors and the normalization of the topological. 1. The Assimilation of the Interface Universally define all kinds of the electrical interfaces of the intelligent electrical in the full electronic car as the interface of digital power, power source and

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Table 1 Similar interfaces of the CAN intelligent appliances Pin Definition

Function

1 2 3 4 5 6 7

Digital signal power Digital signal GND CAN high CAN low Power supply Power GND Constant digital power

D 12 V D GND CAN H CAN L PXV P GND D 12 V const

communication signals. And the definition of the terminally electrical interface of all the intelligent appliances depends on the quantity demand for the interface of the communication and the digital power. Table 1 is the description of the definition of the intelligent appliances assimilation through the CAN communication. 2. The Standardization of Connectors According to the definition of the similar electrical interface, all the electrical interfaces in the full electronic car are standardized as follows: CAN standard connectors, LIN standard connectors and other kinds of bus connectors, and the super-current power supply connectors. For example, according to the definition of the similar interfaces in Fig. 1, CAN standard connectors can be defined as the seven-pin standard connector. 3. The Normalization of the Topological The invariant harness network includes the two independent harness networks: the intelligent communication network and intelligent power network. Among them, intelligent communication network is divided into backbone network and local area network according to the logical relationship between the electrical parts. Therefore, based on the principle of network for harness section, the harness design of the whole car includes a smart network harness, a backbone network harness and several local area network harnesses.

3 The Design of the Invariant Harness Network in Geely Dihao EC7 According to the requirement of the design scheme of the invariant harness network in the full electronic car, Dihao EC7, produced by the car company Geely, needs an electronic design and modification. On this car, a validation of the full electronic auto system will be carried out. This car is a sedan, with 4.6 m long, 1.8 m wide and 1.5 high, and equipped with 1.8 L naturally aspirated engine and five-speed manual gearbox. As to the electric function, besides the traditional lights, wiper and electric windows, this car has the electric rearview mirror

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regulator, rearview mirror, rear windshield heater, the headlamps, whose height can be adjusted and the automatic opening and closing headlights. It has the complete electrical system and the advanced electrical configuration. This kind of the car has a certain representativeness in the China’s market of the compact family car. It is beneficial to the further promotion and application of this technology by using this car to carry the R&D and validation of the full electronic car.

3.1 The Classification of the Original Auto Electrical Assembly The first step is to classify the original auto electrical equipments, which are divided into the backbone electrical and the local area electrical in order of the importance. The network organization of the electrical equipments is realized through the electrical backbone network (hereafter referred to as backbone network) and the electrical local area network (hereafter referred to as local area network). 1. The Backbone Electrical Equipment In the charge system, the generator is the most important electrical equipment. Thus the generator is the backbone electrical equipment. In the starting system, starter is the most important electrical equipment. So it is also a backbone electrical equipment. Some other important electrical assemblies, including the engine management system, instrument system, air conditioning system, ABS control system, airbags and the combination switch for the drivers to control the electrical equipments, all should be regarded as the backbone electrical equipments. After the classification, the backbone electrical equipments of the original car are as shown in Table 2. In the Table 2, the central coordinator is used to coordinate the running of the whole auto’s electrical equipments, and the central microcomputer is the human– computer interaction platform for system and the driver. These two electrical equipments are the newly-added backbone electrical in the full electronic auto system. 2. The Local Area Electrical Equipment Besides the backbone electrical equipment, all others are the local area electrical equipment. Their distribution areas in the full electronic car are as shown in Fig. 3. As shown in Fig. 3, the local area electrical equipments are distributed across the front, the middle, the behind of the car, the engine compartment, the car door and so on. The local area electrical equipments affect the local function of the electrical system. The local area electrical equipments always spread across every place of the car’s body, which brings lots of difficulties to the arrangement, the installation and the fault check of the auto harness. The local area electrical equipments are divided into several groups by the spatial position. Based on the

The Design of Invariant Wiring Harness Network Table 2 The list of the backbone electrical in full electronic car

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No

Electrical

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Center controller1 Center controller2 Central computer Instrument Panel Combination_Switch Steering_Column Combination_Switch Engine management system Instrument ABS Airbag controller Air-condition controller Starter Generator OBD connector Immobilizer

Fig. 3 The regional division of the local area electrical equipments in the full electronic car

characteristic distributions of the full electronic auto’s local area electrical equipments, the space of the cat is divided into seven areas, as shown in the Fig. 3 from A1 to A7. Each electrical equipment in the area is as shown in Table 3.

3.2 The Standard Connectors of the Invariant Harness There are eight wires in the invariant harness system of the full electronic auto Dihao Geely. As shown in Fig. 1, the wires, from No. 1 to No. 6, including the

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Table 3 The classification of the local area electrical equipment in the full electronic car Area Description No Electrical equipments A1 A2 A3

A4

A5 A6

A7

Left-front

7 Left headlamp, daylight, front foglamp, horn, vehicle speed sensor, back-up lamp switch, brake fluid sensor, wipe motor etc. Right-front 4 Right headlamp, daylight, front foglamp, washer pump etc. Left-door 9 Left-front window lift controller&switch door latch, left-rear window lift controller&switch door latch, door lamp, left outside rear-view mirror etc. Right-door 8 Right-front window lift controller&switch door latch, left outside rearview mirror, door lamp, left-rear window lift controller&switch door latch etc. Middle 13 Reading lamp, hazard warning lamp, audible system, rear fog lamp switch, brake switch, sun sensor, interior light etc. Rear 9 Left-rear foglamp, right-rear foglamp, license lamp, left-rear rear combined lamp, right-rear rear combined lamp, rear hood lamp, stop lamp, rear window heater etc. Power 8 Relay 1–7, battery monitor sensor. supply

Fig. 4 The plug-in view of the electrical equipment

network signal lines and the controller’s electric wire, have small power. The wire 7 and wire 8, the electricity power lines for the electrical equipments, can let the large current go through. The whole car network of the intelligent electrical system in the full electronic auto includes the backbone electrical equipments and the local area electrical equipments. Among them, the backbone electrical equipments are the CAN bus network node, which need to connect seven wires, as shown in Fig. 4 at No. 1, 2, 3, 4, 5, 7, and 8. These are the invariant harness of the backbone network. The local area electrical equipments need to connect five wires, No. 3, 4, 6, 7 and 8, which are the invariant harness of local area network. In order to save the R&D expenses, all the harness connectors adopt the 14-pin connectors in this research. The definitions of the harness and the connector are as shown in Tables 4, 5, 6 and Fig. 4.

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Table 4 The general wire list of the electrical system in the full electronic car No Function Pin name 1 2 3 4 5 6 7 8

CAN high CAN low Digital signal GND Digital signal power Constant digital power LIN BUS Power GND

CAN_H CAN_L D_GND D_12V_ctrl D_12V_const LIN_Signal Power GND

Table 5 The definition of the invariant harness interfaces in backbone electrical equipment Pin Function Wire gauge (mm2) 1 2 3 4 5 6 7 8 9 10 11 12 13 14

GND Constant digital power NC CAN_H NC NC POWER GND Constant digital power NC CAN_L NC NC NC

2.5 0.5 0.5

2.5 2.5 0.5 0.5

Table 6 The definition of the invariant harness interfaces in the local area electrical equipment Pin Function Wire gauge (mm2) 1 2 3 4 5 6 7 8 9 10 11 12 13 14

GND NC NC NC NC NC POWER GND Constant digital power LIN_BUS NC NC NC NC

2.5

2.5 2.5 0.5 0.5

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Fig. 5 Controller and the connector in electrical equipment. a ECU2 and the interconnected connectors in local area electrical equipment. b ECU1 and the interconnected connectors in backbone electrical equipment

3.3 The Other Standard Connectors The original power devices in the Dihao EC7 could not meet the demand the full electronic car. Therefore, aimed at every original power device, an ECU, which can meet the full electronic auto’s demand, is specially designed. In general, this kind of ECU can be divided into two categories. The first is the ECU 1, which is aimed at the power devices of existing controllers like ABS and EMS. It takes charge in the signal exchange between the signal of the original controller and the newlydesigned network of the full electronic auto, namely realizing the function of the gateway routing. The second is ECU 2, which is aimed at the power devices without the controller like the light and the switch. It takes charge in controlling the power supply and the switch closure of the power devices, as well as the signal acquisition and transformation between the switch and the sensor. These two kinds of ECU are connected with the backbone and the local area electrical equipments of the original car by the two types of standard connectors, which are as shown in Fig. 5.

3.4 The Normalized Network Topology According to the structure of the Dihao Geely EC7, the distribution location of the electrical equipments and the network partition, the electrical system topology of the whole auto invariant harness is designed as shown in Fig. 6.

4 The Verification and Reliability Test On the platform of the Dihao Geely EC7, the scheme of the electronic auto is designed and realized, including the efficient operation of the intelligent power network and the intelligent electrical equipments. Based on the testing system of the electronic auto that is designed by ourselves, we carried out the integrated validation and the effectiveness analysis of the six parts over the electronic sample

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Fig. 6

The topological graph of the electrical system in the full electronic auto

Fig. 7

The real controller of the intelligent electrical equipment

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car Dihao Geely, including in-vehicle internet, intelligent electrical equipments and the invariant harness. Meanwhile, this sample car traveled more than 1,200 km on the testing field and the real road in Beijing, more than 1,600 km from Beijing to Hangzhou along the highway, national road and other different levels of roads. The test’s total range reaches 3,000 km. The test was carried under many different conditions, like the sunny days, the rain and snow, greasy weather and the night. The system’s reliability is sufficient proved.

4.1 The Intelligent Electrical Equipment The controller of the intelligent electrical in the electronic auto Dihao Geely is as shown in Fig. 7.

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Fig. 8

The harness of the local area network and the harness of the backbone network

Fig. 9

Invariant connectors

5 The Invariant Harness The invariant harness physical map of the electronic auto Dihao Geely is as shown in Fig. 8. The electrical-independent connector in the invariant harness is as shown in Fig. 9. The connectors of the local area network’ harness and the backbone network’s harness are standardized. The invariant harness makes the installation of the harness become easier. The guide-lines of the harness in the original car are not intuitive to identify where to go. The traces are in confusion. It does harm to the inspection and maintenance of the harness. Adopting the standard topological structure, the invariant harness is simple on the whole. What’s more, the connection relationship between the electrical equipment and the harness is very intuitive. Above all, compared with the original type of the car, the weight of the whole car’s harness is reduced by 1.6 kg, the plug-in decreased by 56 kinds. If we reach the idealized situation of the electronic auto where the electrical equipments are totally intelligent and all the plug-in standardized, the weight of the harness and the quantity of the plug-in can better achieve the effect of the lightweight and the generalization.

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6 Conclusions During the reformation of DiHao EC7, the project of the full electronic car does not accomplished totally on the system of engine wires, cabin wires, dashboard wires and so on, because the existing part controller on the original type does not redesign. For example, EMS, ABS, ACU and other relevant wire can not be reformed completely, but to add the ECU1 to achieve it. Due to the space limitation of the original part electric devices on DiHao EC7, the newly-added full electronic controller and the original electric devices can neither merge perfectly, nor be close to the electric devices without clearance, which lead to the waste of the connecting wires. Although the insufficient as above exists, this research is enough to verify the feasibility of the invariant wire harness network; the new scheme shows the significant advantages in the lightweight and the universalization over the traditional one. With the higher requirements for comfortableness, economy and security of the car, the amount of the intelligent electric devices is bound to increase and the whole car’s wire harness system will be more and more complicated. The constant wire harness design idea of the full electronic car satisfies the requirement of the universal whole vehicle wire harness and its connectors. So the design of the whole vehicle wire harness can be simplified to the difference between the wire harness scale and the amount of the connectors, which totally meet the design demand for an era of car intelligence.

References 1. Robert Bosch GmbH CAN specification. Version 2.0, Sept 1991 2. LIN Consortium. LIN Specification 2.0, 2003 3. Liao Y Yang D G, Lian XM (2006) Global electric electronic system in vehicle. In: Proceedings of 2006 IEEE international conference on vehicular electronics and safety conference, Shanghai, China, pp 265–269 4. Xinfeng Z, Dian’ge Y, Wen X, Liang L, Keqiang L, Xiaomin L (2009) Invariant harness connect system of in-vehicle electrical. Tsinghua Univ (Sci and Tech) 49 (2)

Inertia Compensation Based on Torque Signal in an Electric Power Steering System Xuewu Ji, Ning Sun, Jingguang Ge and Yahui Liu

Abstract Research and/or Engineering Questions/Objective: The dynamic performance of an electric power steering system (EPS) is deteriorated by the inertia of assistant motor. To improve the dynamic performance of EPS, it is necessary to compensate the motor inertia. Therefore, the angular acceleration of the motor rotor is needed. Generally the angular acceleration signal is derived from the differentiation of the estimated angular velocity signal in brush DC motor, which is much complex in brushless motor. Therefore this chapter attempts to obtain angular acceleration signal from the torque signal. Methodology: The angular acceleration signal of the assistant motor in an EPS can be obtained from the angle sensor or the torque sensor embodied in the system. The dynamic equation of the steering system was analyzed and the block diagram of inertia compensation was established. This chapter deduced the transfer function from the torque signal to the angular acceleration signal so as to compensate the rotor inertia. Later on, the compensation effects of the two compensation methods were compared based on the EPS bench model and the influence of the related parameters on the dynamic performance of EPS was analyzed. Results: The angular acceleration signal cannot be derived from direct differentiation of angle signal because of the amplified noise, while the angular acceleration derived from torque signal is much more reasonable. And the dynamic performance of the steering system is much improved with inertia compensation. Limitations of this study: A main limitation of the current study is the acquisition of the parameters included in the transfer function. If this method is put into practice, the related parameters of the steering system should be available.

F2012-D08-006 X. Ji  N. Sun (&)  J. Ge  Y. Liu State key laboratory of automotive safety and energy, Tsinghua University, Beijing, 100084, China e-mail: [email protected] SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 194, DOI: 10.1007/978-3-642-33829-8_71,  Springer-Verlag Berlin Heidelberg 2013

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What does the paper offer that is new in the field in comparison to other works of the author: The deduction of transfer function from torque sensor signal to angular acceleration signal so as to fulfil inertia compensation is new in this chapter. Conclusion: The influence of the rotor inertia on the steering system increases with the increase of the moment of rotor inertia and decreases with the increase of assistant ratio. The method of deriving angular acceleration signal from torque sensor included in EPS can obtain good effect, and the dynamic performance of the steering system is much improved with inertia compensation. Keywords EPS Torque signal

 Motor inertia  Angular acceleration  Inertia compensation 

1 Introduction The steering shaft inertia of an electric power steering system (EPS) is much augmented because of the assistant motor inertia, thus deteriorating the dynamic performance of the steering system. Problems such as torque shortage when starting steering and continuing rotating when stopping steering will appear, especially when the driver turns the steering wheel rapidly [1, 2]. In order to improve the dynamic performance of EPS, it is necessary to compensate the effect of motor inertia, where the angular acceleration of the motor is needed. Generally, the angular acceleration is derived from the angular velocity estimated from the motor current in an EPS with a brushed motor [3]. Even though there is an angle sensor in an EPS with a brushless motor, the angular acceleration could not be obtained by differentiating the angle signal, for the noise will be amplified. Hence, this chapter attempts to derive angular acceleration from the torque sensor included in EPS to take compensation for the motor inertia.

2 EPS Test Bench Model A column-type electric power steering (C-EPS) test bench is shown in Fig. 1, mainly consisting of steering wheel, torque sensor, steering column, assistant motor, gear reducer, EPS controller, spring load device, rack and pinion steering gear, and battery. The dynamic equations of the steering system can be given as follows according to Newton’s second law. Here the stiffness of the motor shaft and reducer shaft is assumed as infinite and the parameters used in the following equations are shown in Table 1.   xr Jsw € hsw þ Bsw h_ sw þ Ks hsw  ð1Þ ¼ Th rp

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Fig. 1 C-EPS test bench

hm  Bm h_ m Ta ¼ Tm  Jm €     xr þ GTa =rp mr €xr þ Br x_ r þ Kr xr ¼ Ks hsw  rp   xr Ts ¼ Ks hsw  rp

ð2Þ

Tm ¼ Ka Ts

ð5Þ

ð3Þ ð4Þ

xr Gxr ; hm ¼ ð6Þ rp rp The block diagram of the test bench model can be obtained from Eqs. (1–6), as 2 shown in Fig. 2. Where: Geq ðsÞ ¼ r12  meq s2 þB1 eq sþKr ; meq ¼ mr þ Gr2 Jm ; Beq ¼ Br þ hp ¼

p

G2 rp2

p

Bm

3 Compensation Control In order to compensate the effect of the assistant motor inertia on the dynamic hm should be added to the output end of performance of EPS, the inertia torque Jm € the assistant motor, where Jm can be measured through experiment [4], and the key problem is how to get the angular acceleration of the assistant motor €hm : There are two methods to get € hm ; one is to use the angle sensor signal, and the other is to use the torque sensor signal.

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Table 1 Parameter description Symbols

Meanings

Jsw Bsw Ks Jm Bm Mr Br Kr G Ka Th Ta Ts Rp hsw hm hp Xr

Moment of Inertia of steering wheel and steering shaft Damping between steering shaft and steering column Torsion bar stiffness Moment of inertia of assistant motor Assistant motor damping Rack mass Rack damping Load spring stiffness Reduction ratio Assistant gain Hand torque Motor assistant torque Torque sensor output Pinion radius Steering wheel angle Motor shaft angle Pinion angle Rack displacement

Th

θ sw

1 J sw s 2

Bsw s

Ts

Ks θp Geq ( s )

GKa

Fig. 2 Block diagram of test bench model

Fig. 3 Compensation control block diagram with angular acceleration signal derived from angle sensor

A common way to get € hm is to differentiate the angle signal directly, and the compensation control block diagram is shown in Fig. 3. The block diagram can be simplified into the form shown in Fig. 4.

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Fig. 4 Block diagram of simplified compensation control

..

Fig. 5 Transfer function from sensor torque to assistant motor angular acceleration

Considering that the angular acceleration will be polluted by the amplified noises if the angle signal is differentiated directly, it is attempted to get angular acceleration signal from the torque sensor. From Fig. 4 the transfer function from torque sensor signal Ts to motor angular acceleration can be seen clearly, which is shown in Fig. 5. From Fig. 5 the transfer function from sensor torque Ts to assistant motor angular acceleration € hm can be derived, see Eq. (7). G ðsÞ ¼

Gð1 þ GKa ÞGeq ðsÞs2 1  G2 ðJm s2 þ Bm sÞGeq ðsÞ

ð7Þ

Equation (7) shows the relationship between the torque sensor signal input and the assistant motor angular acceleration output. When the steering wheel is turned, the torque sensor will detect the steering torque and the motor rotor will rotate, and the angular acceleration signal can be derived from the torque sensor signal through Eq. (7). Thus the inertia compensation block diagram can be changed into the one shown in Fig. 6.

4 Inertia Compensation Analysis In simulation, if the angular acceleration signal is derived by differentiating the angle signal directly, the noise will be amplified so that the angular acceleration signal is not available. Figure 7 shows the angular accelerations derived from the angle sensor and the torque sensor, respectively. It can be seen clearly from Fig. 7 that the angular acceleration signal tends to be infinite as time goes if it’s derived from direct differentiation of angle signal,

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Fig. 6 Inertia compensation block diagram with angular acceleration derived from torque sensor Fig. 7 Angular accelerations derived from two sensors

therefore inertia compensation cannot be completed using the directly differentiating method; while the angular acceleration signal derived from torque sensor is much reasonable, so that the following sections will focus on the dynamic performance between with and without inertia compensation using the second method.

4.1 Compensation at Different Inertias Generally the inertia of moment of a brushless motor is about one over ten times that of a brushed motor of the same power, so here the moment of inertia of the brushless motor is set as Jm1 = 3.0 9 10-4 kg m2, that of a brushed motor is set as Jm2 = 3.0 9 10-3 kg m2, and GKa is set as 15.4. The step response of torque signal and motor angular velocity signal between with and without inertia compensation at different motor inertias are shown separately in Figs. 8 and 9. It can be seen from Figs. 8 and 9 that the torque signal is smoother and the motor starting speed is higher when inertia compensation is carried out. It can also be seen that the influence of the assistant motor inertia on steering system varies

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Fig. 8 Step response of assistant motor (Jm1 = 3.0 9 10-4 kg m2, GKa = 15.4). a Step response of torque signal. b Step response of motor angular velocity

Fig. 9 Step response of assistant motor (Jm2 = 3.0 9 10-3 kg m2, GKa = 15.4). a Step response of torque signal. b Step response of motor angular velocity

from the moment of assistant motor inertia. There’s no significant change in motor starting speed with small inertia, while the motor velocity is much higher and the compensation effect is more obvious with larger moment of inertia.

4.2 Compensation at Different Assistant Ratios The effect of assistant motor inertia is not only closely related to the moment of assistant motor inertia, but also depends on the assistant ratio. To see the compensation effect at different assistant ratios more clearly, here set Jm = 3.0 9 10-4 kg m2 and GKa = 0, the step response of torque signal and assistant motor angular velocity signal between with and without inertia compensation are shown separately in Figs.10a and b. It can be seen from Figs. 8 and 10 that as the assistant ratio decreases, the effect of inertia compensation on system dynamic performance is more significant, even if the inertia is small. With inertia compensation the torque signal is smoother and

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Fig. 10 Step response of assistant motor (Jm = 3.0 9 10-4 kg m2, GKa = 0). a Step response of torque signal. b Step response of motor angular velocity

its dynamic performance is much improved, which means the hand feeling is better. The rising speed of assistant motor angular velocity is much faster after inertia compensation, which means the torque shortage at assistant motor starting stage is much improved.

5 Conclusion This chapter compared the angular acceleration signal derived from angle sensor and torque sensor respectively. Simulation shows that the angular acceleration signal derived from the former is not available, while that form the latter can achieve the aim of compensating the effect of assistant motor inertia, which improves the system dynamic performance significantly. The following conclusions have been drawn from the research. Under the same assistant ratio, the EPS dynamic performance deteriorates with the increase of the moment of inertia of assistant motor; when the assistant ratio is small, the effect of assistant motor inertia is much more obvious, and the effect of inertia compensation on system dynamic performance is more significant, even if the moment of inertia of the assistant motor is small.

References 1. Xia F (2008) Optimization of control strategy on electric power steering system: automotive engineering. Tsinghua University, Beijing (in Chinese) 2. Yasuo S, Yoshihiro O (2006) Control for moment of motor inertia on EPS. SAE chapter 2006, 01-1179 3. Shen R, Lin Y, Tai X et al (2007) Research on modeling and compensation control strategy of electric power steering system. J Agric Mach 7(7):5–9 (in Chinese) 4. Lv y (2011) Fundamental research on PMSM EPS control. Department of Automotive Engineering, Tsinghua University, Beijing, (in Chinese)