Proceedings of China SAE Congress 2022: Selected Papers 9819913640, 9789819913640

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Lecture Notes in Electrical Engineering 1025

China Society of Automotive Engineers Editor

Proceedings of China SAE Congress 2022: Selected Papers

Lecture Notes in Electrical Engineering

1025

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Napoli, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, München, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Gebäude 07.21, University of Karlsruhe (TH) IAIM, Karlsruhe, Baden-Württemberg, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Dipartimento di Ingegneria dell’Informazione, Sede Scientifica Università degli Studi di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Intelligent Systems Laboratory, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, Department of Mechatronics Engineering, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Intrinsic Innovation, Mountain View, CA, USA Yong Li, College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Subhas Mukhopadhyay, School of Engineering, Macquarie University, NSW, Australia Cun-Zheng Ning, Department of Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Bijaya Ketan Panigrahi, Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Federica Pascucci, Department di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, University of Stuttgart, Stuttgart, Germany Germano Veiga, FEUP Campus, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Haidian District Beijing, China Walter Zamboni, Department of Computer Engineering, Electrical Engineering and Applied Mathematics, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA

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China Society of Automotive Engineers Editor

Proceedings of China SAE Congress 2022: Selected Papers

Editor China Society of Automotive Engineers China Society of Automotive Engineers Beijing, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-99-1364-0 ISBN 978-981-99-1365-7 (eBook) https://doi.org/10.1007/978-981-99-1365-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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. 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. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

A Partial Network Management Design Method for Hybrid Network . . . . . . . . . Xiangnan Li, Yongfei Zhao, Shuo Feng, Zhaolong Zhang, and Yi Zheng

1

The Study on Re Test Judgement Based on Bayes Rule . . . . . . . . . . . . . . . . . . . . Quan Cheng, Haiming Liu, and Weimin Wang

13

Shift Strategy Development for Electric Bus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yonghui Wang, Disha Yang, Heyan Li, and Erhu Qu

26

The Study of Implementation of Precise Location of Automotive Digital Key Based on UWB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baohua Xia, Guoping Qian, Xibin Wu, Zhenghua Lu, Juntao Tian, and Liu Lianfang Platform-Based Design of the Hybrid Electirc Drive Load Spectrum . . . . . . . . . Xiane Ruan, Manli Li, Jun Lei, Huajun Kang, and Huan Liu Analytical Impacts of Li2 CO3 Developments on New Energy Vehicle Sales Based on Grey Theory and Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . Liuzhu Qian, Zhenfei Zhan, Jie Wang, Gaohui Lu, Kun Wang, and Ju Wang

40

55

69

Research on the Management of Autonomous Delivery Vehicles at Home and Abroad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mengxu Zhao, Boyang Zhou, Tianyi Kang, and Yabing Deng

86

The Role and Implementation Path of the Automotive Industry in Carbon Neutrality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fanlong Bai, Fuquan Zhao, Xinglong Liu, and Zongwei Liu

100

Development Strategies of Intelligent Automotive Industry Under the Background of Increasing Demand for Computational Capacity . . . . . . . . . . Wang Zhang, Fuquan Zhao, and Zongwei Liu

113

Development Strategy of Shared Mobility Enterprise for Smart Cities . . . . . . . . Yisong Chen, Ying Cao, and Yongtao Liu Adaptive Accident Sampling Investigation Method Based on Regional Traffic Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiqing Chen, Yujia Feng, Fengchong Lan, and Junfeng Wang

129

144

vi

Contents

Research on Influencing Factors of Front Windshield Collision Simulation Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuewu Zhu, Yue Feng, Shibin Wang, Boshuai Ma, and Longbo Ji

162

Multi-objective Optimization Design of Occupant Restraint System Based on Dynamic Weighted Injury Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Long Ying, Rui Liu, Jianquan Xu, and Zengcai Lin

177

Flexible Man-Machine Verification Model Control Method Based on Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhonghao Zhao

189

Retrospect and Prospect of Twist Beam Axles . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanlun Tan, Zhuo Tang, Tao Fu, Yong You, and Xiaoyong Zheng Experimental Study on the Resistance Characteristics of a High-Speed Amphibious Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Hu, Lilan Zhou, Tao Zhang, Guoquan Yang, and Liangbo Li Motion Control Strategy of Wheel-Legged Compound Unmanned Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaolei Ren, Hui Liu, Jingshuo Xie, Yechen Qin, Lijin Han, and Baoshuai Liu Research on Instability Characteristics of Ducted Fans in Ground Effect . . . . . . Dawei Zhou, Yiwei Luo, Yuzhi Jin, Yuping Qian, and Yangjun Zhang Application of Synchronous Combustion Analysis Method to Analysis and Control of Low Frequency Chattering Vibration of Vehicle . . . . . . . . . . . . . Meng Xu, Jihong Shi, Jian Li, Xiulan Qu, Xixiang Yuan, and Shuai Zhao Evaluation of Objective Sound Quality Feature Extraction with Kernel Principal Component Method in Electric Drive System . . . . . . . . . . . . . . . . . . . . Xin Huang, Zizhen Qiu, Fang Wang, Kong Zhiguo, Jifang Li, and Xiang Ji

205

218

228

247

261

277

Launching Rattle Noise Test Analysis and Improvement for a SUV with 6AT Automatic Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Zhang and Yongzhong Bao

288

Virtual Analysis of Vehicle Interior Gear Whining Based on Time-Domain Hybrid Modeling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junli Guo, Zilong Tian, and Chao Ren

299

Contents

Research on the Influence of Drive Shafts Angles on Vehicle Lateral Swing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feng Deng, Yueyun Zuo, Xicheng Wang, Shangbao Fei, and Junqing Gu Fine Simulation and Bench Test Development of Single Composite Leaf Spring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Wang, Jiaxing Sun, Xuewu Zhu, Kaixuan Tong, Chao Han, Xingping Wang, Long Cheng, and Xiaoming Guo Correlation Analysis of Subjective and Objective Test on Automobile Seat Pressure Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuegang Li, Tao Li, Xiaolin Huang, Xiaosheng Ouyang, and Zhenyan Li Research on Spark Splash Control of Resistance Welding Based on Big Data Analysis Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xianshui Jia, Dongwei Li, Yong Chen, Shibo Tao, Datong Sun, Rui Wang, and Xiaomin Huo Research on the Technology of Accurate Measurement for the Thread Cone Angle of Synchronizer Used in Gearbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhi Hu, Tianhua Dai, Nan Ding, and Yue Li The Wear Analysis and Sharpening Method of Involute Spline Broach . . . . . . . Sheng Chang, Kun Zhu, Yucheng Xu, and Riming Men Study on the Quality of Body Assembly Based on Tolerance Analysis by Linearized Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiang Wu, Jiulei Cao, and Zijie Dou Research and Application of High Vacuum Die-Casting Shock Tower Using Heat-Free Aluminum Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinsheng Zhang, Yunbo Zeng, Bofang Liu, Dejiang Li, Xia Pu, Sha Lan, Zhibai Wang, and Gang Feng Comparison of Mechanical and Corrosion Performances of AlSi-Coated PHS Plates with Different Coating Thickness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chao Yuan, Dayong Zhou, Hong Zhang, Fatong Jiang, Huaxin Li, and Zhen Sun Optimization Design of Airless Tire Based on Re-entrant Hexagonal Cellular Structure with Negative Poisson’s Ratio Characteristics . . . . . . . . . . . . . Ying Zhao, Binlin Wang, Jusan Yin, Keming Zhou, Boyuan Hu, Xuanming Liu, and Fangwu Ma

vii

311

318

332

341

351

363

372

384

399

410

viii

Contents

Research on Performance of LP-EGR System Fitted for Hybrid-Engine . . . . . . Shaoyuan Duan, Hanyun Tuo, and Dong Yan Research on Knock Test Analysis Method and Sensor Signal Recognizability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Liang, Liangcai Fu, Minglang Zhang, Kangquan Zou, and Lizhuo Bai Numerical Simulation Study on Flow Characteristics of the By-Pass Valve of a Supercharged Engine Based on the Finite Volume Method . . . . . . . . Wenping Jiang, Xin Lv, Kaifu Xie, Xiaodong Chen, Jianming Zan, Xiaotao Zhang, and Pengfei Dai Adaptive Route Planning on Simulation-to-Real Environments in the Autonomous Driving Platform of the DeepRacer . . . . . . . . . . . . . . . . . . . . Yanbo Liu, Chunrun Du, Huaming Yan, Shaobo Wang, Yang Xu, Kang Liu, Yanze Yu, and Weiqi Sun An Efficient Real-Time Object Detection and Tracking Framework Based on Lidar and Ins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuan Zou, Yuanyuan Li, Xudong Zhang, Guoshun Dong, and Zheng Zang Vehicle Model Constraint Based Visual-Inertial Localization Algorithm for Autonomous Driving in Closed Park . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Li, Kai Li, Weiqing Shi, Jidong Chen, and Xiaohui Qin Effective Objective Detection and Hybrid Predictive Control for Intelligent Vehicle Automatic Emergency Braking Under Curving Road Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bowen Wang, Cheng Lin, Xinle Gong, and Sheng Liang Automatic Parking System for Multi-vehicle in the Autonomous Driving Platform of the DeepRacer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huaming Yan, Chunrun Du, Yanbo Liu, Xiuyu Yang, Haikuo Du, Ping Han, Yanze Yu, Yang Xu, and Weiqi Sun ITGAN: An Interactive Trajectories Generative Adversarial Network Model for Automated Driving Scenario Generation . . . . . . . . . . . . . . . . . . . . . . . . Zeguang Liao, Han Cheng, Xuan Wang, Xin Tao, Yihuan Zhang, Yifan Dai, and Keqiang Li Online Perception Performance Estimation for Autonomous Driving Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ziyu Qin and Zhao Zhang

431

442

458

467

484

503

518

537

554

567

Contents

ix

A Trade-off Design Approach for Integrating Cybersecurity, Safety, and Other Aspects of Intelligent Connected Vehicles . . . . . . . . . . . . . . . . . . . . . . . Jinghua Yu, Feng Luo, Geguang Pu, and Mingsong Chen

577

Correlation Analysis of Driver Fatigue State and Dangerous Driving Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhengdong Lan and Mingyu Xu

593

Hazard Analysis and Risk Assessment Based on Electric Power Steering According to ISO26262 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lingyang Li, Jia Du, Xiaoming Ye, Xin Liu, Tao Wen, and Jiangtao Du

604

Design and Performance Prediction of a Supercharging System for Racing Cars with Electric Assist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhengxuan Shi, Yuanxin Huang, Jining Chen, Tian You, and Zhaohui Jin The Design and Optimization of FSAE Upright Under Real Load Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yifan Qiu, Da Wang, Yungujian Bai, Gengrui Jin, Zhaoyan Huang, Chen Mi, and Chenyu Shi Crash Analysis and Optimization of Front-End Structure of Formul SAE . . . . . Xun Zhang, Chun Ren, and Xiang Guo

628

648

669

Power Improvement and Vehicle Matching of Racing Engine Based on FSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xianan Ye, Chunyu Jia, Jiachen Jin, Jiarui Zhang, and Li Peng

681

Optimization and Control of the Variable Intake System Based on an Engine for FSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xihua Hu, Renhe Liu, Yuyang Guo, and Ningwei Jin

696

Design of Dry Lubrication System for FSCC Racing Engines . . . . . . . . . . . . . . . Jiarui Zhang, Jiachen Jin, Cheng Jiang, Huayu Jiang, Xianan Ye, and Xianqin Li Research on Low-Temperature Energy Consumption Test Procedures of Bev Based on Road Travel Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaopan An, Hanzhengnan Yu, Liu Yu, Yongkai Liang, Kunqi Ma, Xin Zhang, Xi Hu, and Jingyuan Li A Multi-objective Optimized Self-heating Strategy for All-Climate Batteries at Low Temperatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Tian and Cheng Lin

718

743

756

x

Contents

Analysis of Thermal Runaway Characteristics of NCM Lithium-Ion Battery and Research on Early Warning Control Strategy . . . . . . . . . . . . . . . . . . . Xudong Sun, Guofang Liu, Minghui Hu, Rongrong Gu, and Xiaoming Xu

769

Extended State Observer-Based Position Sensorless Control for Automotive Ultra-high-Speed PMSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yao Xu, Cheng Lin, Jilei Xing, and Xichen Li

787

Active Disturbance Rejection Control for Gear Shift and Speed Regulation Process of PMSM for Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . Huanran Liu and Cheng Lin

800

Comparative Study on Fast Calculation Methods of Broadband Electrochemical Impedance Spectroscopy of Power Batteries . . . . . . . . . . . . . . . Xueyuan Wang, Kou Yao, Haifeng Dai, and Xuezhe Wei

817

Research on Adaptive Control Strategy of Coasting Energy Recovery . . . . . . . . Hao Wu, Xuwei Luo, Daoliang You, and Guangjie Wei Emission Characteristics of Fine Particles from Different Fuel Injection Mode Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiyuan Xie, Fengbin Wang, Xintong Li, Ke Zhang, Jinlong Zheng, Shulin Lai, and Mingzhi Zhang Study on the Effect of Ozone on Diesel Engine Exhaust NOx Emission . . . . . . . Yan Lei, Chenxi Liu, Tao Qiu, Guangzhao Yue, Xuejian Ma, and Fanzhao Kong

833

847

859

Experimental Investigation for NH3 Emission Characteristics of Light-Duty Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hualong Li, Xianfeng Tu, Leigang Yu, and Hai Li

872

Research on Topological Optimization Technology of Swing Arm of Cab Suspension Stabilizer bar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yaoyu Cui, Xin Yan, Shenshen Li, and Jianhua Li

883

Commercial Vehicle Technology Roadmap for Green Transportation . . . . . . . . . Xuefeng Jiang, Weiqun Ren, and Heng Zhang

894

Skid Steering Control Strategy of Distributed Drive Unmanned Platform . . . . . Jingshuo Xie, Lijin Han, Xiaolei Ren, and Hui Liu

903

Contents

Design of Vehicle Motion Control Service System Based on Dynamic State Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minghong Tang, Hongyu Hu, Mingxi Bao, Jian Zhang, and Zhenghai Gao

xi

917

Design and Research of Flexible Closures Line Based on “6 + 1” Model . . . . . Xiaoming Rong, Xi Gong, Min Liao, Long Wang, Yong Wang, and Tian Yuan

937

Design of a High-End Rain-Detection Line for Passenger Cars . . . . . . . . . . . . . . Wang Yu, Meng Shuting, Teng Long, and Huang Ruihe

951

Research on New Adjustable Stamping Pallet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weimin Li, Dan Shao, and Mengtian Wang

958

Design and Research of the Control System of the Automobile Drum Test . . . . Mingyu Cui, Chunhui Yang, Fuqiang Liu, Yulei Wang, Hongdong Li, and Chunlai Liu

969

Simulation Analysis of Impact Damage of Automotive Coatings . . . . . . . . . . . . Li Chen, Chenqi Zou, and Mengyan Zang

980

The Establishment of a Vehicle Climate System Model Based on a Data-Driven Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiangxian Zhu, Yilun Lou, Zhao Jiang, Xin Lin, and Hong Zhou

991

Simulation and Optimization of Membrane Air Suspension Failure . . . . . . . . . . 1006 Xiaoyi Wu, Haixiao Chang, Gang Li, Shuai Shan, Yao Li, and Biyuan Zhu Computational Studies of Urea-Derived Deposits in a Close-Coupled SCRF System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1020 Qintong Mo, Kun Du, and Zhanxin Mao Research on NVH Performance Optimization of Permanent Magnet Synchronous Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1040 Pin Lyu, Xiaolong Deng, Chunxiang Yu, Tianbao Tang, Junfeng Hu, and Xiaoqiang Zhou Modeling and Application Study of Boundary Manikins Based on Chinese Anthropometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055 Lipeng Qin, Jing Yang, and Bowen Zhao

xii

Contents

Research on Equivalent Acceleration Method of Body-in-White Endurance Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1065 Xianxiao Hou, Xicheng Wang, and Xiuye Ji Research on an Acceleration Methon of Hydraulic Bushing Bench Test . . . . . . 1081 Xingming Zhao, Jinglong Yu, Chao Han, Liang Peng, Shuwei Ding, and Zheng Du A Research on Fatigue Analysis Method of Automobile Seam Weld Based on Improved Notch Stress Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094 Xin Yan, Tingyu Yin, Ying Wang, Xiaofeng Wan, Yaoyu Cui, Yunlong Zhai, Xiyu Zhou, Jiamei Sun, Yuting Cheng, and Yonghong Pei Experimental Study on Multi-dimensional Target Setting of Passenger Car Engine Cooling System Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107 Xiang Yu, Wentian Zhao, Zhengdong Chen, and Wenkui Wang Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121

A Partial Network Management Design Method for Hybrid Network Xiangnan Li1(B) , Yongfei Zhao1 , Shuo Feng2 , Zhaolong Zhang3 , and Yi Zheng3 1 BAIC Motor Co., Ltd., Beijing, China

[email protected]

2 Beijing Automotive Technology Center, Beijing, China 3 Beijing Electric Vehicle Co., Ltd., Beijing, China

Abstract. Aiming at the electronic and electrical architecture with both OSEK and AUTOSAR network management, we propose a design method for the partial network management. This paper introduces the design principles of network management in the hybrid network, and the cooperative sleep-wake-up strategy of OSEK and AUTOSAR for each node. After verification, the design method proposed in this paper can be applied to the electrical and electronic architecture of the hybrid network, and plays the role of partial network management. Keywords: Hybrid network · Partial network management · Sleep/awake synergism

1 Introduction The rapid development of intelligent functions has led to a substantial increase in the number of ECUs on the vehicle, which has brought heavy network load pressure to the distributed electronic and electrical architecture of the entire vehicle. With the improvement of in-vehicle chip capabilities, the centralized electrical and electronic architecture can merge and integrate the functions of different controllers and concentrate them in several key domain controllers. This design greatly reduces the load on the in-vehicle network, but it also leads to more complex vehicle network management design. In the centralized electrical and electronic architecture, the domain controller also assumes the role of the gateway while realizing its own function. Each node on the whole vehicle network can adopt the network management strategy of Open Systems and the Corresponding Interfaces for Automotive Electronics (OSEK), or the AUTomotive Open System Architecture (AUTOSAR). In such a hybrid network, the domain controller as the gateway needs to coordinate these two management strategies to ensure that all nodes on the vehicle can wake up from sleep correctly. In order to realize this requirement, we propose a local network management design method of hybrid network architecture, which adopts software design form to cooperate with OSEK and AUTOSAR network management mechanisms. At the same time, the design idea of local network management is adopted to reduce the unnecessary wakeup of the controller, further reduce the energy consumption of the whole vehicle, and prolong the services life of the controller. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 1–12, 2023. https://doi.org/10.1007/978-981-99-1365-7_1

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2 Design of the Electrical and Electronic Architecture Centralized electronic and electrical architecture is an important trend in the development of the industry. Based on a centralized electronic and electrical architecture as a design application carrier, we introduce the design method and application of local network management in hybrid network architecture in detail. In this electrical and electronic architecture, the network segment between the four domain controllers constitutes the backbone network of the entire vehicle. As shown in Fig. 1, CANFD network segments AB, BC, CD and AD are the backbone network of the electrical and electronic architecture, which play the role of data transmission and network management. The local controllers, sensors and actuators of the whole vehicle are linked to the subnet segments of their respective domain controllers through CANFD network segments a1, a2, b1, b2, c1, c2, d1 and d2. The vehicle Ethernet is connected between the domain controllers, but the Ethernet does not have the network management function, and the connection and disconnection of the vehicle Ethernet is controlled through the CANFD network.

Fig. 1. Topology of the centralized architecture

3 Principle of Partial Network Management Design In order to reduce energy consumption on the basis of ensuring various functions of the vehicle and avoid unnecessary wake-up of the controller, the network management of the whole architecture adopts the local network management of divided network segments.

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This design is that when the vehicle wakes up, only the network segment where the controller necessary to achieve a certain function is woken up, and the controller of the other network segments can still be in a dormant state to further reduce the power consumption. To realize the vehicle’s network management function of normal network sleep wake-up: it is necessary to ensure that the electric control node can keep other nodes wake up to receive required application messages, and to prevent the nodes from waking up from each other and unable to sleep when the function is completed. The network management designed in this paper adopts a “bottom-up” sleep sequence: that is, only the local controllers, sensors and actuators on the subnet segment sleep, and the domain controller on the subnet segment can sleep. This bottom-up sleep sequence uses the domain controller as the core of network management to control the sleep and wake-up of adjacent network segments. In order to reduce the cost, there are bound to be parts used in other models when developing a complete vehicle. Minimizing the design changes of used parts can improve the functional stability and economy of the vehicle. However, the follow-used components may use OSEK-OS operating system, which is in line with OSEK network management, or may use the AUTOSAR architecture, using a layered software protocol stack, which is in line with AUTOSAR network management. As a result, this vehicle network includes both OSEK nodes and AUTOSAR nodes. In view of this, in order to reduce the design changes of the following parts, we divide the subnet segment into OSEK segment and AUTOSAR segment, and the nodes on each segment only need to adopt one form of network management. For the domain controller with gateway attribute, it needs to have both OSEK and AUTOSAR network management, and achieve the cooperation of the two network management state machines. On the backbone network, AUTOSAR network management is adopted.

4 Format of Network Management Messages The traditional form of OSEK and AUTOSAR network management messages can not realize local network management. Therefore, it is necessary to add the content of the network segment requirement in the user-defined field in the network management message. 4.1 Format of OSEK Network Management Messages To add information requesting specific network segments to OSEK network management messages, the format of OSEK network management message is specified as shown in Table 1. Byte0 is the destination address or source address of the network management message. This byte is the source address, when the declaration bit in the network management packet is 1. And this byte is the destination address, when the declaration bit is 0. Byte1 is the control byte of the message. Bit0: The Alive bit indicates that the controller has entered the wake state from sleep. This bit indicates that the node applies for joining the token ring of network management.

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BYTE

BIT 7

6

5

4

3

2

1

0

0

Destination Address/Source Address

1

Idle

Idle

Sleep Ack

Sleep Ind

Idle

LimpHome

Ring

Alive

2

cd request

c1 requets

b2 request

bc request

b1 request

a2 request

ab request

a1 request

3

Idle

Idle

Idle

Idle

d2 request

ad request

d1 request

c2 request

4–7

Idle

Bit1: The Ring bit indicates that a controller normally manages the network. This bit indicates that the node is working properly and participating in the transmission of the network management “token ring”. Bit2: Network limphome is a sign that a controller cannot manage the network properly. This bit indicates that the node did not properly participate in the passing of the token ring. Bit4: Sleep Ind bit indicates that the controller determines that it has met the sleep condition. This bit indicates that the node no longer requires application packets from the network. However, to ensure the normal operation of other nodes, this node still sends its own application messages. Bit5: Sleep Ack bit indicates that the controller determines that the entire network segment can sleep. This bit indicates that the node determines that there is no longer any need to transmit network messages on the entire network segment. Nodes that receive this flag no longer send their own application packets. Byte2 to Byte7 are user-defined bytes of network management messages. In this paper, 12 bits are used to correspond to the backbone network and subnet segment of the architecture. In the network management design of this paper, if a node determines that it needs to wake up a certain network segment to realize a certain function of the vehicle, it will set the request flag position of the corresponding network segment as 1 in the network management packet sent by the node. 4.2 Format of AUTOSAR Network Management Messages To add information requesting specific network segments to AUTOSAR network management messages, the format of AUTOSAR network management packets is specified as shown in Table 2. Byte0 is the source address of the network management message, indicating the node that sends the network management message. Byte1 is the control byte of the packet,

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Table 2. Format of AUTOSAR network management message BYTE

BIT 7

6

5

4

3

2

1

0

0

Source Address

1

Idle

PNI

Idle

AWB

Idle

Idle

Idle

RMR

2

cd request

c1 requets

b2 request

bc request

b1 request

a2 request

ab request

a1 request

3

Idle

Idle

Idle

Idle

d2 request

ad request

d1 request

c2 request

4~7

Idle

Bit0: Repeat Message Request bit is used to request other nodes on the network segment to enter the repeat state and determine whether nodes on the network segment are online. Bit4: Active Wake-up bit indicates that the node actively wakes up the network. Bit6: Partial Network Information bit indicates the bit of local network information, indicating that the node requests a local network segment. In the network management design of this article, this bit is always 1. Byte2 to Byte7 are user-defined bytes of network management messages. The definition of AUTOSAR network management message is the same as that of OSEK network management message. In both OSEK network management messages and AUTOSAR network management messages, idle bits are filled with 0b0.

5 Design of Subnet Network Management 5.1 Design of OSEK Subnet Network Management 5.1.1 Design of State Machine of OSEK Network Management In the traditional state machine of OSEK network management, there are three sub-states in the wake state of network nodes: NMReset, NMNormal, and NMLimpHome. The normal work of nodes is in the NMNormal state. However, in practice, when the node determines that it has met the sleep condition, the sleep indicator bit in the network management packet sent by the node is 1. When the node determines that it does not meet the sleep condition and the sleep indicator bit in the network management packet is 0, the node is in the NMNormal state. As a result, two types of network management messages can be sent from the same state, resulting in network management chaos. In the design of network management in this paper, in order to avoid the node to achieve a certain function but did not receive the required application message problem. A domain controller on a subnet segment can sleep only when local controllers, sensors, and actuators on the subnet segment are asleep. For the subnet segment managed by the OSEK network, the domain controller can send a network management message

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with sleep indicator bit 1 only when all non-domain controller nodes send a network management packet with sleep indicator bit 1. To achieve this design, the NMNormal sub-state machine is subdivided into NMNormal Active and NMNormal Passive state machines. In NMNormal active mode, the sleep indicator bit of the network management message is 0. In NMNormal passive mode, the sleep indicator bit of network management message is 1. The state machine jump diagram is shown in Fig. 2.

Fig. 2. State Machine of OSEK Network Management

NMInit is the initialization state for network configuration. NMReset indicates the alive state entered after the initialization, and sends network management message with the alive bit as 1. NMNormal active state indicates that the node determines the status of needing application messages from other nodes on the network and sending network management messages with the ring bit set to 1 and the sleep indicator bit set to 0. NMNormal passive state indicates that, when working correctly, the node determines that it no longer needs application packets on the network and sends a network management packet with the ring bit 1 and sleep indicator bit 1. NMBusSleep indicates the network sleep state of a node. For a non-domain controller node on the OSEK network segment, the jump condition of state ➀ is: The node has no network requirements. Status ➁: All nodes on the network segment are in NMNormal passive mode. 5.1.2 Wake up of OSEK Nodes For a non-domain controller node on the OSEK network segment, the node considers that it needs the information of a certain network segment after wake-up, whether it is a local wake-up or a network wake-up. And it needs to set the sleep indicator position bit in the network management packet to 0, and the required network segment position bit

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to 1. If the messages from other nodes is no longer needed, the node would set the sleep indicator bit in the network management message to 1 and the flag bit of all network segments to 0. 5.1.3 Sleep of OSEK Nodes For a non-domain controller node on the OSEK network segment, after all nodes in the network segment entering NMNormal passive state and receiving network management packets with the sleep indicator bit and sleep acknowledgement bit set to 1, the node enters the NMBusSleep state. 5.2 Design of AUTOSAR Subnet Network Management 5.2.1 Design of State Machine of AUTOSAR Network Management For a non-domain controller node on an AUTOSAR network segment, its state machine conforms to the standard AUTOSAR network management state machine. Its state machine is shown in Fig. 3 below.

Fig. 3. State Machine of AUTOSAR Network Management

Repeat Message State is the state that the node wakes up from sleep and start to send network management messages. Normal Operation State is the state that the node determines that it needs messages from other nodes on the network, and sends network management packets and application packets. Ready Sleep State is the state that the node determines that it no longer needs messages from other nodes on the network but receives network requests. In this case, the node does not send network management messages but sends application messages. Prepare Bus Sleep State is the state that the node determines that it no longer needs external network management messages and no longer receives external requests for itself. In this case, the node does not send network management messages or application messages. Bus Sleep State is the network sleep state of the node.

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5.2.2 Wake up of AUTOSAR Nodes For a non-domain controller node on the AUTOSAR network segment, after the node is awakened, it determines that it needs information on a certain network segment and sends a network management message with the bit represent for the required network segment set to 1. If the node determines that application messages from other network segments is not required, it does not send network management messages any longer. 5.2.3 Sleep of AUTOSAR Nodes The non-domain controller node on the AUTOSAR network segment determines that it no longer needs the network and does not receive network management messages. Therefore, the node enters the sleep state.

6 Synergistically Design of OSEK and AUTOSAR of Domain Controller In the electronic and electrical architecture designed in this paper, the domain controller plays a crucial role in coordinating the network status of each controller in the subnetwork segment and managing the sleep and wake up of the connected subnetwork segment and the backbone network. According to the different subordinate network segments of the domain controller, the domain controller can be divided into two types: sub network segments are all AUTOSAR network and sub networks contain AUOSAR and OSEK network. 6.1 Sub Network Segments All AUTOSAR Networks When the subnetwork segments of a domain controller are all AUTOSAR network segments, each network segment connected to the domain controller has an independent AUTOSAR network management state machine. For domain controllers, unrequested network segments remain in the BSM state. The requested network segment is in different states according to its position in the architecture and the source of the request, as shown in Table 3 below. Table 3. Performance of Wake up of AUTOSAR No.

Segments Requested

Appearance of Segments Requested of Domain

1

AUTOSAR subnet segment

NOS

2

AUTOSAR backbone segment (request from the same backbone segment)

RSS

3

AUTOSAR backbone segment (request from other backbone segment)

NOS

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When the network segment requested is the AUTOSAR backbone segment and the request is from the same segment, the state of the segment of this domain controller is RSS. For example, the domain controller A receives network request from the segment ad which is also the requested segment. If the state of the domain controller A on the ad segment was NOS which means sending network management messages, both domain controllers are in the state of NOS, resulting two domain controllers keep wake up mutually and could not sleep. So the state should be RSS in this case. 6.2 Sub Networks Contain AUOSAR and OSEK Networks If the subnetwork segment of a domain controller has OSEK network segment, the domain controller has an OSKE network management state machine on each OSEK network segment and an AUTOSAR network management state machine on each AUTOSAR network segment. The state machines on different network segments are independent from each other. There are four sources of network requests: the wake-up request received from the OSEK subnet segment; the wake-up request received from the AUTOSAR subnet segment; the wake-up request received from AUTOSAR backbone network; the wake-up request received from the controller itself. Regardless of whether the source of the request is one or more, we designs the state machine of the domain controller on each network segment according to the network segment being requested. This is shown in Table 4 below. Table 4. Performance of Wake up of OSEK No.

Segments Requested

Appearance of Segments Requested of Domain

1

OSEK subnet segment

NMNormal active

2

AUTOSAR subnet segment

NOS

3

AUTOSAR backbone segment (request from the same backbone segment)

RSS

4

AUTOSAR backbone segment (request from other backbone segment)

NOS

Similarly, when a domain controller receives a request from a backbone network, the AUTOSAR state machine of this domain controller on this backbone needs to be designed to be in the RSS state to avoid being unable to sleep.

7 Implementation of Network Management In order to realize the local network management design on the OSEK and AUTOSAR hybrid network mentioned in this paper, the software and hardware of each node need to be required.

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7.1 Requirement of Hardware The local network management method mentioned in previous studies realizes the wakeup of specific packets by using PN transceivers to set transceiver packet filtering [1]. This design allows different nodes in the same network segment to maintain independent sleep and wake up. However, a PN transceiver increases the cost of about 3 yuan compared with a common transceiver [2]. It is difficult to use PN transceiver at all nodes in a low cost and price sensitive vehicle. The local network management proposed in this paper uses the network management according to the network segment. Each node on the same subnet segment sleep and wake up at the same time. However, different network segments realize independent sleep and wake up controlled by domain controllers. Therefore, the hardware requirements of the transceiver in this paper are ordinary transceivers, and no special PN transceiver is needed. Through reasonable function allocation architecture design, similar application effects can be achieved to meet the requirements of vehicle design. 7.2 Requirement of Software The local network management strategy proposed in this paper requires that AUTOSAR network management nodes in the architecture can identify local network segment requirements [3]. It is recommended to use the AUTOSAR protocol stack that meets the local network configuration to configure the network management module, and then configure the relevant network segment [4].

8 Verification In order to verify the local network management design, this paper uses the physical platform to carry on the key link design verification. Local area network management includes two aspects: wake up subnet segment and wake up backbone. Therefore, a physical platform containing two domain controllers was built, and several nodes were attached to the OSEK network segment under one of the domain controllers. The design is verified by using a domain controller to wake up the backbone network and a domain controller to wake up the subnet segment. Verification Condition 1: Domain controller B actively wakes up and requests backbone network ab. The node B actively sends a network management message, and the network segment in the message is 0x02. After receiving the network request, the domain controller A is awakened and sends network management messages. Therefore, the network segment bit in the network management packet is 0x00 because A does not request for network management on the network segment. After that, A enters the RSS state and no longer sends network management messages. In this case, only the backbone network is wakened. Other network segments are not wakened as shown in Fig. 4 below. This condition shows that the local network management design method proposed in this paper can only wake up the required backbone network, and other network segments remain asleep.

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Fig. 4. Result of the domain controller wake up the backbone network

Verification condition 2: Domain controller A actively wakes up its subnetwork segment a1. The node A actively sends network management messages on the OSEK network segment. The network segment in the network management packet is 0x01. After receiving the network management message, other nodes in the subnet segment are awakened and send their own network management messages. In addition, these nodes do not have network management requirements for network segments. Therefore, the network segment bit in the network management messages sent by these nodes is 0x00. In this case, only the subnet segment of the entire architecture is woken up, but the backbone network is not woken up. This is shown in Fig. 5. This condition shows that the local network management design method proposed in this paper can wake up the required subnet segment, and other network segments remain asleep.

Fig. 5. Result of the domain controller wake up the sub network

Through the verification of these two working conditions, it shows that the local network management design method of hybrid network architecture proposed in this paper can be applied in practice, and plays the role of local sleep-wake-up.

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9 Conclusion In the electronic and electrical architecture of complex network with AUTOSAR and OSEK, we propose a design method for local network management, which can realize the sleep-wake-up of the network segment of the vehicle network under the premise of saving the cost as much as possible. In this paper, a physical platform is built to test and verify the key aspects of the design. The results show that the local network management strategy can be applied in a hybrid network to realize independent sleep-wake-up on different network segments.

References 1. Li, M.: A design method for local network management (PN) based on AUTOSAR. In: Proceedings of the 2020 Annual Meeting of China Society of Automotive Engineering, vol. 1533 (2020) 2. NXP. High-speed CAN transceiver for partial networking TJA1145 (2018) 3. AUTOSAR. AUTOSAR Specification of CAN Network Management, Version R20–11 (2020) 4. AUTOSAR. AUTOSAR Specification of Network Management Interface, Version R20–11 (2020)

The Study on Re Test Judgement Based on Bayes Rule Quan Cheng1,2(B) , Haiming Liu1 , and Weimin Wang1 1 CATARC Automotive Test Center (Wuhan) Co., Ltd., Wuhan, China

[email protected] 2 China Automotive Technology and Research Center Co. Ltd., Tianjin, China

Abstract. In the electromagnetic compatibility test, the result of RE (radiation emission) is uncertain. When the result is close to the threshold, it is difficult to make an accurate judgment. CNAS-GL07 considers the influence of uncertainty on the decision and prescribes a decision method based on a single test. In this paper, Bayes rule is used to analyze the results of single RE test, which proves that there is a high possibility of misjudgment, and suggests that the number of tests should be increased to improve the reliability of the final decision. The method is improved and perfected on the basis of CNAS-GL07, and the result is good through experiment. It can provide some guidance for the determination of RE test results. Keywords: bayes rule · uncertainty · RE · test judgement

1 Introduction The test of radiation emission, conducted emission and disturbance power can be collectively referred to as electromagnetic interference measurement, which mainly measure the strength of electromagnetic waves of a specific frequency emitted by the vehicle or electronic components to the outside world [1]. or the magnitude of the current, voltage or power of a specific frequency conducted along the wire to the outside world. GB 14023–2011, GB 34660–2017, GB/T 18387–2017, GB/T 18655–2018 and other standards have requirements for this, is one of the automotive compulsory inspection test items, is the type of test often encountered by testing organizations. Due to the inevitable differences in the test environment, test layout, parameter settings and human operation of each test, there will always be some uncertainty in the RE test results, especially when the test results are very close to the standard limits, which will bring some trouble to the determination of the test results. For this case, the testing organization makes a determination based on the document issued by the China National Accreditation Service for Conformity Assessment (CNAS), namely CNAS-GL07《Guidance on Evaluating the Uncertainty in Electromagnetic Interference Measurement》 , which considers mainly the impact of measurement uncertainty on the determination [2]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 13–25, 2023. https://doi.org/10.1007/978-981-99-1365-7_2

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2 Influence of Uncertainty on Decision Uncertainty of measurement, is that there will always be some irreducible difference between the measured value and the true value, such difference makes we can never know the size of the true value. For example, the radiation field intensity measured in the radiation emission test and the current or voltage measured in the conduction emission test, are only an accurate estimate of the true value, and the true value is considered to fall within the value range estimated by us. For example, we measured the radiation disturbance field intensity of the sample at 39.93 MHz frequency point as 19.06 dBμV/m through quasi-peak scanning. According to our uncertainty analysis of this test, 95% confidence level was taken according to CNAS-GL07, and its extended uncertainty was considered to be 2.6 dB. This indicates that the actual radiation disturbance field intensity is 95% likely to fall within the range of 19.06 ± 2.6 dBμV/m [3].

Fig. 1. Influence of uncertainty of measurement on test evaluation

But the test result is essentially just a range of estimates, derived from the measured value plus extended uncertainty. When the test results take the 95% confidence level and the valuation range is far below or above the limit value of 32 μV/m, we can confidently give a pass or fail judgment because the true value is almost impossible to appear near the limit value (low probability of 5%). However, once the valuation range is in the limit value (such as 32.01 ± 2.6 dBμV/m), the true value in this valuation range may be higher or lower than the limit value [4], which will cause trouble to our judgment, as shown in Fig. 1. For this reason, CNAS-GL07 has provided for the determination of the test results. The file gives reference values (Ucispr ) for the extended uncertainty of different types of tests, This value is the extended uncertainty determined based on the data in Appendix A of CNAS-GL07 and after considering each uncertainty component, which is artificially prescribed [5]. When the measurement extension uncertainty of each test system in the EMC laboratory is less than the reference value, it can be considered to have a satisfactory accuracy. The values of Ucispr are shown in Table 1.

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Table 1. The value of the Ucispr Measured item

Measuring frequency

U cispr

conducted emission

9 kHz ~150 kHz

4.0 dB

150 kHz ~30 MHz

3.6 dB

disturbance power

30 MHz ~300 MHz

4.5 dB

radiation emission

30 MHz ~1 GHz

5.2 dB

After obtaining the measured value, the laboratory can analyze the extended uncertainty of the test to obtain the value of ULAB at 95% confidence level, and then combine it with Ucispr for determination. The determination process is shown in Fig. 2. The determination method of CNAS-GL07 takes into account the influence of measurement uncertainty on determination, and the testing institutions are also based on this, to carry out the determination of the test results. But after a careful study we find that this method is still not perfect, because for testing system cannot reach 100% accuracy, When the value is close to the limit, a single test is not a credible way to determine eligibility, It does not eliminate the influence of measurement uncertainty and there may be misjudgment. According to the general idea, if the single measured value is not credible, it can be considered to carry out multiple tests, and the arithmetic average of each measured value is used as the final measured value to determine.

Fig. 2. Determination process stipulated by CNAS-GL07

But this inevitably introduces type A uncertainty from repeated measurements, In order to achieve a high calculation accuracy, at least 6 measurements are needed, which is time-consuming. The obtained class A uncertainty needs to be combined with the class B uncertainty to get ULAB , and the whole process is rather tedious. Therefore, this article considers the analysis of the credibility of each decision result in multiple tests to explore a simple and reliable decision method.

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3 Model Establishment Based on Bayes Rule 3.1 Understanding of the Nature of Test Results Usually, we think that the test is qualified on behalf of the sample is qualified, in fact, this understanding is not accurate, we will analyze from the perspective of probability.

Fig. 3. Sample space and probability distribution

As shown in Fig. 3, in A sample space  with an area of S , A region A with an area of SA is circled. Then we take A random point in , and the probability that this point belongs to A is the ratio of SA and S . If we circle A region B with area SB , B and A intersect in some regions, according to the theory of probability, formula (1) to (4) can be obtained [6]. Among them, P(A) and P(B) are called prior probabilities, which can be intuitively obtained according to their proportion in . P(A ∩ B) is called the joint probability, which means the probability that A and B happen simultaneously, or the proportion of ω in the area of intersection between A and B; P(A|B) is called the conditional probability, said that in the case of B, probability of A, by P(A ∩ B) and P(B) calculated, intuitive, represents the B in the area of the intersection of A and B share; P(B|A) =

P(B)P(A|B) P(A)

(5)

Similarly, the meaning of P(B|A)are similar. Here, we can easily obtain formula (5). Formula (5) is put forward by the 18th century British mathematician Thomas Bayes formula for the core of the Bayes rule, namely the bayesian formula, in which P(B|A)

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said in A case occurs the probability of B. Obviously, SA = SB under the condition of P(B|A) = P(A|B), from here we can make the following extension. Set up event A “test pass” and event B “sample pass”. we think is in the validation of P(A|B), not to exceed limit, when the test result and the test results the confidence level of 95%, then P(A|B) is equal to 0.95, and be ready to determine the qualified. But in practice, our common understanding of “test pass” as “sample pass” reverses cause and effect. Our concern should be in the case of “test pass”, reflect the credibility of sample pass”, namely the value of P(B|A). According to this conclusion, not simply think that P(B|A) is equal to P(A|B). P(A|B) = 0.95, P(B|A) but not necessarily is 0.95. In other words, although the sample passes the test and is judged to be qualified, there is still a certain probability that it is not qualified. In the same way, although the sample fails the test and is judged to be unqualified, there is still a certain probability that it is qualified. 3.2 Bayes Rule is Used to Analyze the Determination Credibility P(A) = P(A ∩ B) + P(A ∩ B)

(6)

P(A) = P(A|B)P(B) + P(A|B)P(B)

(7)

P(B|A) =

P(B)P(A|B) P(A|B)P(B) + P(A|B)P(B)

(8)

According to Bayes’ rule, we need to require P(A), which reflects the occurrence probability of the event “test pass” under the two conditions of “sample pass” and “sample fail”, which is the sum of occurrence probability of the two conditions of correct judgment and wrong judgment, as shown in Formula (6) . P(B|A) = P(B|A) = P(B|A) =

P(B)P(A|B) P(A|B)P(B) + P(A|B)P(B) P(B)P(A|B) P(A|B)P(B) + P(A|B)P(B) P(B)P(A|B) P(A|B)P(B) + P(A|B)P(B)

(9) (10) (11)

formula (7) can be obtained by combining formula (3) and (4). According to formula (5) and formula (7) , we can obtain formula (8) . If A and B in formula (8) are replaced by their opposite events and, respectively, the equation still holds. Thus, we obtain formula (9), (10) and (11). P(B|A) = 1 − P(B|A)

(12)

P(B|A) = 1 − P(B|A)

(13)

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Q. Cheng et al. Table 2. The probability distribution of each event expression

Event

probability

P(A|B)

test pass|sample pass

0.95

P(A|B)

test pass|sample pass

0.05

P(A|B)

test fail|sample fail

0.05

P(A|B)

test fail|sample pass

0.95

By observing formula (8) to (11), formula (10) and (11) can be simplified to obtain formula (12) and (13). According to the above Settings, we use the Bayes rule to calculate P(B|A). Considering the influence of the uncertainty of measurement laboratory, by the test results the confidence level of 95%, by P(A|B) = 0.95 and P(|) = 0.95, according to the formula (12) and (13), the probability of the event as shown in Table 2. Where P(B) represents the probability of “sample pass”. Since the samples submitted for inspection are mostly newly developed products without empirical data as a reference, it is difficult to estimate the value of P(B). Therefore, we divided P(B) into 10 values from 0 to 1 and put them into formula (8), (9), (12) and (13) to calculate and draw the curve, as shown in Fig. 4.

Fig. 4. P(A|B) = 0.95 sample pass rate and determine the relationship between credibility

The abscissa of the chart is P(B), and the ordinate represents the probability of each event. It can be seen that with the increase of P(B), the probability of passing the test gradually increases and the probability of failing the test gradually decreases, which is consistent with the actual situation. At the same time, it can be seen that with the increase of P(B), the probability of misjudging the unqualified sample gradually increases. With the decrease of P(B), the probability of misjudgment of sample qualification gradually increases. When P(B) is equal to 0.9 or 0.1, the probability of misjudgment reaches 0.321. We try to change P (A|B) value to 0.8 and 0.6, get the following curve, as shown in Fig. 5 and Fig. 6.

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19

Fig. 5. P(A|B) = 0.8 when the sample pass rate and determine the relationship between credibility

Fig. 6. P(A|B) = 0.6 when the sample pass rate and determine the relationship between credibility

As you can see, the lower the accuracy of test system, namely, P(A|B) the smaller the value, can cause under the same P(B), the probability of correct judgement is reduced, the probability of misjudgment. This shows that improving the accuracy of the test system is very beneficial to increasing the reliability of the determination, which is also consistent with our practical experience. But on the whole, because we can’t be in the test before they know how likely it is that samples are qualified, even if P(A|B) = 0.95, our single test results are not very high credibility. It is not rigorous to judge whether it is qualified or not after only one test. 3.3 Methods to Improve the Credibility of Measurement Results We can improve the reliability of the test results by increasing the number of tests, and the Bayes rule can still be used for analysis here. The Bayesian formula has a special feature that it revises our estimate of the probability of an event by constantly acquiring new information. This idea can be well used in our testing work.For example, in the first test, we assume that P(B) = 0.01. According to the calculation of the formula (8) and formula (8), available P(B|A) = 0.161, because we have been assuming that the probability of “samples fail” is low, the result will definitely question, so we decided to conduct A test. Through the calculation results, the first test can be thought of as “samples fail” has been updated for the probability P(B) = P(B|A) = 0.161, into the

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formula (9) to calculate and easy to get formula (14): P(B|A) = 0.785

(14)

At this time, the credibility of the test result has been greatly improved. If the credibility of the result is still not satisfied, P(B) = 0.785 can be used to test again. When the test result is still unqualified, the probability of “sample unqualified” is as follows,formula (15): P  (B|A) = 0.986

(15)

The result has high confidence. After three tests, we have determined with full confidence that the sample is fail. After the above analysis, we can assume that when the actual test of a frequency point measurement value is very close to the limit, we also according to the CNASGL07 provisions of the determination method to determine, but still worry about the credibility of the determination, after all, it is only the result of a test. At this time, we can conduct repeated tests according to the ideas contained in the Bayes rule, and constantly revise our conclusions through tests to obtain a judgment with high credibility. 3.4 Selection of Repeated Test Times But there is still a question, since we do not know the possibility of the sample qualified, how to choose the number of repeated tests to ensure that the judgment results have high confidence? As shown in the curve in Fig. 4, it is easier to misjudge when P(B) is too large or too small. Therefore, extreme cases may be taken into consideration here, which can be divided into two cases: the possibility of “samples pass” is very high and the possibility of “samples pass” is very low. Set P(B) = 0.99 and P(B) = 0.01 for a total of 3 tests, and each test result was divided into two conditions: “pass” and “fail”, with a total of 8 combinations. Each time the test qualified to calculate P (B|A), when the test unqualified to calculate P(B|A), and P(B) and (B) the updated into the next, until get the test results for the third time. The calculation method refers to formula (8) and formula (9), and the calculation results are shown in Table 3 and Table 4. When P(B|A) or P(B|A) more than the value of 0.95, we think there is enough to make the right decision. In Table 3, group 1, group 2, group 7 and group 8 meet the conditions, and in Table 4, group 1, group 2, group 4 and group 5 meet the conditions. Considering Table 3 and Table 4, only group 1 and group 2 can be used as a reliable basis for determination. Here we can see that regardless of the probability of “sample pass”, as long as there are three consecutive test results of pass or fail, we can give a clear decision.

4

3

2

1

group

P (B|A)

0.01

P(B|A)

0.99946865

0.99997202

0.99946865

FAIL

P(B|A)

P(B|A)

PASS

PASS

0.784782609

0.161016949

PASS

P (B|A)

P (B|A)

0.99997202

0.99946865

FAIL

P(B|A)

P(B|A)

FAIL

PASS

2

PASS

1

Test number/preliminarily judge

0.161016949

P (B|A)

FALI

0.00053135

P (B|A)

FAIL

0.985771773

P (B|A)

FAIL

0.999998527

P(B|A)

PASS

3

Epokhe

Epokhe

FAIL

PASS

Evaluation of the result

8

7

6

5

group

0.99946865

P(B|A)

PASS

0.161016949

P (B|A)

FAIL

0.161016949

P (B|A)

FAIL

0.161016949

P (B|A)

FAIL

1

0.01

P (B|A)

FAIL

0.99

P(B|A)

PASS

0.784782609

P (B|A)

FAIL

0.99

P(B|A)

PASS

2

Test number/preliminarily judge

Table 3. Results evaluation of three repeated tests when P(B) = 0.99

0.99946865

P(B|A)

PASS

0.99946865

P(B|A)

PASS

0.838983051

P(B|A)

PASS

0.161016949

P (B|A)

FAIL

3

PASS

PASS

Epokhe

Epokhe

Evaluation of the result

The Study on Re Test Judgement Based on Bayes Rule 21

4

3

2

1

group

FAIL

P(B|A)

0.99

0.161016949

0.784782609

0.161016949

PASS

P(B|A)

P(B|A)

PASS

0.99997202

0.99946865

PASS

P(B|A)

P(B|A)

FAIL

0.784782609

0.161016949

FAIL

P(B|A)

P(B|A)

PASS

PASS

P(B|A)

2

Test number/preliminarily judge

1

0.99946865

P(B|A)

FAIL

0.838983051

P(B|A)

FAIL

0.999998527

P(B|A)

FAIL

0.985771773

P(B|A)

PASS

3

FAIL

Epokhe

FAIL

PASS

Evaluation of the result

8

7

6

5

group

0.161016949

P(B|A)

PASS

0.99946865

P(B|A)

FAIL

0.99946865

P(B|A)

FAIL

0.99946865

P(B|A)

FAIL

1

0.99

P(B|A)

FAIL

0.01

P(B|A)

PASS

0.99997202

P(B|A)

FAIL

0.01

P(B|A)

PASS

2

0.161016949

P(B|A)

PASS

0.161016949

P(B|A)

PASS

0.00053135

P(B|A)

PASS

0.99946865

P(B|A)

FAIL

3

Test number/preliminarily judge

Table 4. Results evaluation of three repeated tests when P(B) = 0.01

Epokhe

Epokhe

Epokhe

FAIL

Evaluation of the result

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4 Improvement of Re Test Judgment Method Through the above analysis, in order to reduce the possibility of misjudgment of the samples, we summarized the following suggestions to improve the RE test judgment method.

Fig. 7. The improved decision process

(1) If the measured value is far below the limit requirement, it indicates that the sample is highly likely to be qualified, and the qualified judgment can be given with great certainty. (2) If the measured value is much higher than the limit requirement, it indicates that the sample is highly likely to be unqualified, and the unqualified judgment can be given with great certainty. (3) if the measured value is very close to the limit value, should repeat the test, each measured value according to the provisions of CNAS-GL07 for preliminary determination, and then divided into the following two cases: a. When there are three consecutive qualified or unqualified preliminary judgments, the test will be terminated, and the last measured value and preliminary judgment result will be taken as the final judgment result; b. If the same preliminary determination results do not appear for three consecutive times after multiple tests (no less than or equal to 6 times), the arithmetic mean value of the multiple measured values can be calculated and the uncertainty of class A can be combined with the uncertainty of class B to get the ULAB, and then the final determination can be made according to the provisions of CNAS-GL07.The decision process is shown in Fig. 7.

5 Test Verification RE test required by GB 14023–2011 was carried out on a sample vehicle, and the average detector was used for measurement. For the first measurement, as shown in Fig. 8, the

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Fig. 8. Curve of the first test

Fig. 9. Repeat test curve

Table 5. Results of repeated tests Test number

1

2

3

4

5

frequency point(MHz)

164.75

165.5

163.55

165.00

164.95

measured value (dBμV/m)

29.68

29.16

32.6

31.27

31.58

margin(dB)

0.32

0.84

−2.6

−1.27

−1.58

preliminarily judge

PASS

PASS

FAIL

FAIL

FAIL

measured value at 164.75 MHz was 29.68 dB μV/m, which was lower than the limit value of 0.32 dB μV/m. The ULAB was 4.5dB, which was qualified according to the CNAS-GL07 determination method. According to the above analysis and considering the possibility of misjudgment, we conducted four consecutive tests on the sample vehicle at this frequency point. The test curves are shown in Fig. 8 to Fig. 9, and the test results are shown in Table 5. In the second test, test qualified 2 consecutive happen, refer to Table 4, decide the result credibility may only 0.785 at this time, when the third test, the unqualified, and

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continued until the 5th, a total of three unqualified preliminary judgement, according to the above analysis, the possibility of whether samples are qualified, The credibility of the third unqualified judgment has reached at least 0.986, and we can give the unqualified judgment conclusion with confidence.

6 Conclution This article through to the CNAS-GL07 electromagnetic interference measurement uncertainty evaluation guide “analysis, points out its determination methods for a consideration, only the lack of repeat test verification, the measurement result is close to limit is prone to misjudge, through calculation and analysis based on the Bayes rule, proved this misjudgment has high possibility, And the reasonable selection of repeated test times is discussed, which makes the decision result have higher credibility. Through the study of this articale, the RE test determination process is supplemented and improved. To improve the business level of testing institutions, reduce the misjudgment rate, improve customer satisfaction, has played a certain role in promoting.

References 1. GB 14023-2011, Radio disturbance characteristics of vehicles, ships and internal combustion engines Limits and measurement methods used to protect off-vehicle receivers 2. CNAS-GL07-2015, Guidance on Evaluating the Uncertainty in Electromagnetic Interference Measurement 3. Qifei, Z., Baiyun, Y.: Analysis of objection cases of drunk driving detection based on Bayes principle. J. Yunnan Police College 04, 83–87 (2020) 4. Ke, W., Chaoan, H.: The subjectivism and objectivism of probability view. Modern Bus. Trade Ind. 41(24), 146–149 (2020) 5. Yuan, L.: Study on uncertainty evaluation of radiometric emission measurement of automobile parts. Safety EMC 03, 85–89 (2019) 6. Huangyu, L.: Discussion on the application of Bayes formula. Guide Sci. Technol. Econ. 28(08), 165 (2020)

Shift Strategy Development for Electric Bus Yonghui Wang, Disha Yang, Heyan Li, and Erhu Qu(B) College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China [email protected]

Abstract. This paper studies the shifting strategy based on an electric bus. According to the development performance requirements of the bus, a dynamic shifting strategy and an economical shifting strategy are formulated respectively. Among them, the dynamic shifting strategy is mainly based on the principle of maximum acceleration to formulate corresponding shifting strategy, and the economical shifting strategy is mainly formulated based on the test data of the motor bench and the characteristics of the vehicle speed and efficiency curve under different accelerator pedal depths. Finally, build an AVL CRUISE vehicle simulation model to simulate and analyze the shifting strategy of the electric bus and a test platform for testing. Simulation and test results show that the proposed shifting strategy is feasible. Keywords: Shifting strategy · AVL CRUISE · Optimization

1 Introduction The application of computer modeling and simulation technology can effectively reduce the vehicle development cycle and cost. AVL CRUISE is an advanced simulation analysis software for the study of vehicle power performance, fuel economy, emission performance and braking performance. It is a positive and modular simulation platform, which not only improves the efficiency of research and development, but also has practical significance for industry exchanges and common progress. For an electric bus, the drive motor is its only power source. When climbing a hill or under heavy load, the motor needs to provide a large torque. In order to reduce the torque requirements for the motor, a multi-speed transmission is required. At present, multi-speed has become the development trend of electric vehicles. Formulating an appropriate gear shifting strategy has an important impact on improving the power performance and economy of the vehicle. Therefore, it is of great significance to study the shifting strategy of electric vehicles [2]. Based on an electric bus, this paper studies the dynamic shifting and economical shifting strategies respectively [3]. Among them, the formulating principle of the power shift strategy is: formulate the corresponding shifting strategy according to the principle of maximum acceleration [4]; the formulating principle of the economical shifting strategy is: according to the test map data of the motor bench, combined with different accelerator pedal depths, formulate according to the principle of optimal shift point efficiency [5]. Finally, the AVL CRUISE vehicle © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 26–39, 2023. https://doi.org/10.1007/978-981-99-1365-7_3

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27

simulation model and test platform are built to verify the feasibility of the shifting strategy.

2 Powertrain Matching There is a 12m electric bus. The parameters of the bus are shown in Table 1. The design indicators are: (1) the maximum speed is greater than or equal to 100 km/h; (2) the maximum grade is greater than 15% (the speed of climbing is 10 km/h), Continue to climb 7% of the vehicle speed (climb speed 15 km/h); (3) The acceleration time of 0–50 km/h is less than 20 s.

Table 1. Vehicle parameters Vehicle mass (kg)

18000

Windward area (m2 )

6.7

Drag coefficient

0.55

Transmission efficiency

0.92

Coefficient of Rolling Resistance

0.012

Main transmission ratio coefficient

5.7

First, determine the power of the motor. It needs to meet the requirements of the maximum speed, maximum grade and acceleration time of the bus [6]. Calculate the maximum speed demand power P1 according to the maximum speed index, calculate the demand power P2 according to the demand index of the maximum grade, and calculate the demand power P3 according to the acceleration performance index, Pmax > max[P1,P2,P3]. Calculate the required power at the maximum speed according to formula (1):  2  CD · A · vmax vmax m·g·f + (1) P1 = 3600ηT 21.15 Substituting the vehicle parameters into the above equation, the maximum vehicle speed and power demand curve can be obtained, as shown in Fig. 1. According to the design requirements, the maximum speed is 80 km/h. Therefore, the required power corresponding to the maximum speed of the electric vehicle is P1 ≈91.88 kw. Calculate the required power for the maximum gradeability according to formula (2):   CD · A · vi2 vi P2 = m · g · f · cos αmax + m · g · sin αmax + (2) 3600ηT 21.15 Substituting each parameter into the above formula, the road gradient and power demand curve can be obtained, as shown in Fig. 2. The required power for 15% gradient and 10 km/h is P2 = 84.58 kw.

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Fig. 1. Vehicle speed and power demand curve

Fig. 2. Power demand curve with a slope of 15%

Calculate the required power for the maximum gradeability according to formula (3):  2  vm vm CD · A · vm m·g·f +δ·m· P3 = + 3600ηT 7.2 · tm 21.15

(3)

Substituting the parameters into the above formula, the curve of vehicle acceleration performance and power demand can be obtained, as shown in Fig. 3. According to the design index requirements: 0~50 km/h acceleration time should be less than 7s, P3 = 123.31 kw.

Fig. 3. Vehicle acceleration performance and power demand curve

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According to the design index of the maximum speed, the maximum speed of the motor is determined by the formula (4), and nmax > 2982r/min is calculated. n=

ig · i0 · v 0.377 · r

(4)

According to the maximum climbing index and the continuous climbing index, the peak torque and rated torque of the powertrain are determined by formula (5) and formula (6). Substituting into the calculation can get M > 2737.1N.m, Me > 1346N.m. M ≥

Ft · r (mgf cos α + mg sin α) · r = η T · i0 · ig η T · ig · i0

(5)

Me ≥

Ft · r (mgf cos ∂ + mg sin ∂) · r = η T · i0 · ig η T · ig · i0

(6)

According to the existing powertrain products, a motor with a peak power of 150 kW, a peak torque of 1500 N.m and a maximum speed of 3200r/min was finally selected. Then, matched with a two-speed transmission, the first gear ratio was 2.5, and the second gear ratio was 1, the maximum output torque of the powertrain is: 3750 N.m. The powertrain parameter table is shown in Table 2. Table 2. Powertrain Parameters Transmission ratio

2.5/1

Motor peak power

150 kW

maximum speed(r/min)

3200

maximum torque(N.m)

1500

3 Shift Strategy Formulation The electric bus is driven by an electric motor, which is different from the traditional engine and has its own characteristics. The formulation of the shifting strategy affects the power performance and economy of the vehicle [7]. The shift timing and gear selection of the automatic transmission are determined by comparing the current vehicle parameters with the parameters in the control system [8]. According to the different control parameters, it can be divided into three types: single parameter (vehicle speed), two parameters (accelerator pedal depths and vehicle speed) and three parameters (acceleration, accelerator pedal depths and vehicle speed) [9]. Since the dual-parameter shifting strategy is easier to control and implement, Since the dual-parameter shift strategy control is easier to implement, the control strategy of the shift strategy in this paper is based on the dual-parameter type of acceleration pedal and vehicle speed, and the dynamic shift strategy and the economic shift strategy are studied respectively. And optimized the original shift measurement.

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3.1 Development a Dynamic Shifting Strategy The intention of formulating a dynamic shifting strategy is to maximize the driving ability of the motor [10], meet the driving force demand of the vehicle when climbing a hill, have sufficient overtaking acceleration ability, give full play to the backup power of the motor, so that the motor is in a high load state [11]. This paper adopts the principle based on the maximum acceleration to formulate the power shifting strategy, that is, the maximum acceleration is the same before and after shifting. According to the external characteristics of the drive motor matched with the reference vehicle model, the load curve of the drive motor under different accelerator pedal depths is drawn as shown in Fig. 4. 1500 Load=100% Load=90% Load=80% Load=70% Load=60%

1000

Load=50% Load=40%

T(Nm)

Load=30% Load=20% Load=10%

500

0

0

500

1000

1500 n(r/min)

2000

2500

3000

Fig. 4. Load curve of the drive motor

The driving equation of the vehicle is: Ttq ig i0 ηT Cd A 2 du = mgf + u + mgi + δm r 21.15 a dt

(7)

In the formula: T tq is the maximum torque; ig is the transmission ratio of the main reducer; i0 is the transmission ratio of the gearbox; m is the weight of the vehicle; f is the rolling resistance coefficient; C d is the wind resistance coefficient; A is the windward area; ua is the vehicle speed; ηT is the transmission efficiency of the transmission system; δ is the rotation mass conversion factor; r is the rolling radius of the vehicle tires; i is the maximum grade. Considering driving on a straight road (ignoring the slope), the expression for the acceleration obtained from the driving equation of the vehicle is:   1 Ttq ig i0 ηT Cd A 2 du (8) = − Gf − ua dt δm r 21.15 From the acceleration formula (8), calculate the magnitude of the acceleration under different gears and different accelerator pedal depths, and the values are shown in Fig. 5. According to the principle of maximum acceleration, the intersection of the acceleration curves of two adjacent gears under the same accelerator pedal depths is taken as the

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31

2.5

The top-down accelerator pedal depth is 100% 1st gear 2nd gear

2

a(m/s2)

1.5

1

0.5

0

-0.5

0

10

20

30

40

50

60

70

80

90

v(km/h)

Fig. 5. Acceleration curves under different gears & different accelerator pedal depths Table 3. Upshift speed value for dynamic shift strategy (km/h) Accelerator pedal depth

10%

20%

30%

40%

50%

1st gear up 2nd gear

16.4847

17.6474

18.7042

19.4686

21.6764

Accelerator pedal depth

60%

70%

80%

90%

100%

1st gear up 2nd gear

22.3268

22.9663

23.8659

24.9466

25.6465

shift point, and the vehicle speed corresponding to this point is the upshift speed [13], as shown in Table 3. Under normal circumstances, the downshift speed difference is 2–8 (km/h). In order to improve the power performance of the vehicle, a smaller value is selected for the downshift speed difference under large throttle. To avoid shifting cycles, a larger value is selected for the downshift speed difference under small throttle [14]. According to this principle, the downshift speed is obtained, as shown in Table 4. Table 4. Downshift Speed for dynamic shift strategy (km/h) Accelerator pedal depth

10%

20%

30%

40%

50%

2nd gear down 1st gear

14.4847

15.6474

16.6474

17.4686

19.6764

Accelerator pedal depth

60%

70%

80%

90%

100%

2nd gear down 1st gear

20.3268

20.9663

21.8659

22.9466

25.6465

3.2 Economical Shift Strategy Formulation According to the map data of the motor bench test, combined with the torque requirements of the motor with different accelerator pedal depths, the vehicle speed and motor efficiency curves under different accelerator depths were drawn [15], as shown in Fig. 6 to Fig. 15.

Y. Wang et al.

1st gear 2nd gear

E(Motor efficiency)

32

V(km/h)

Fig. 6. When the accelerator depths is 100%, the vehicle speed-efficiency curve

E(Motor efficiency)

1st gear 2nd gear

V(km/h)

E(Motor efficiency)

Fig. 7. When the accelerator depths is 90%, the vehicle speed-efficiency curve

1st gear 2nd gear

V(km/h)

Fig. 8. When the accelerator depths is 80%, the vehicle speed-efficiency curve

To sum up the analysis of Fig. 6 to Fig. 15, the intersection of the first gear and the second gear curve is taken as the optimal economic upshift point [16]. Table 5 is the strategy of 1st gear to 2nd gear (Figs. 7, 8, 9, 10, 11, 12, 13 and 14). Using the equal-delay shift strategy, the downshift speed difference is generally 2–8 (km/h), and the speed difference is 3 km/h [17], and the downshift point is obtained, as shown in Table 6.

E(Motor efficiency)

Shift Strategy Development for Electric Bus

33

1st gear 2nd gear

V(km/h)

E(Motor efficiency)

Fig. 9. The accelerator depths is 70%, vehicle speed-efficiency curve

1st gear 2nd gear

V(km/h)

E(Motor efficiency)

Fig. 10. The accelerator depths is 60%, vehicle speed-efficiency curve

1st gear 2nd gear

Fig. 11. When the accelerator depths is 50%, the vehicle speed-efficiency curve

3.3 Optimal Shift Strategy Formulation Combined with the urban road conditions and the above two shift schedules, the original shifting strategy is optimized for the 12m electric bus. Due to the low efficiency of the motor in the medium and low speed region, an economical shifting strategy is adopted. In the high-speed section, the power performance of the vehicle is mainly considered, so the dynamic shifting strategy is selected. Combining the two can take into account both economical shifting and dynamic shifting. The specific shifting strategy is shown in Fig. 16.

E(Motor efficiency)

Y. Wang et al.

1st gear 2nd gear

V(km/h)

Fig. 12. The accelerator depths is 40%, vehicle speed-efficiency curve

E(Motor efficiency)

1st gear 2nd gear

V(km/h)

Fig. 13. The accelerator depths is 30%, vehicle speed-efficiency curve

1st gear 2nd gear E(Motor efficiency)

34

V(km/h)

Fig. 14. The accelerator depths is 20%, and the vehicle speed-efficiency curve

Shift Strategy Development for Electric Bus

E(Motor efficiency)

1st gear 2nd gear

V(km/h)

Fig. 15. The accelerator depths is 10%, vehicle speed-efficiency curve

Table 5. Upshift speed value for economical shift strategy (km/h) Accelerator pedal depth

10%

20%

30%

40%

50%

1st gear up 2nd gear

10.4638

16.5398

21.8393

23.2600

26.7619

Accelerator pedal depth

60%

70%

80%

90%

100%

1st gear up 2nd gear

28.2059

30.873

22.9744

25.2718

26.4206

Table 6. Downshift Speed value for economical shift strategy (km/h) 10%

20%

30%

40%

50%

2nd gear down 1st gear

7.4638

13.5398

18.8393

20.2600

23.7619

Accelerator pedal depth

60%

70%

80%

90%

100%

2nd gear down 1st gear

25.2059

27.873

19.9744

22.2718

23.4206

Accelerator pedal depth value (%)

Accelerator pedal depth

1st gear up 2nd gear

2nd gear down 1st gear

Vehicle speed (km/h)

Fig. 16. Optimized shift strategy

35

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4 Shift Strategy Simulation Analysis Build a vehicle simulation model based on CRUISE, add the AMT Control module and the Gear Box Program module, and complete the connection of the corresponding data lines in the Gear Box Program. Figure 17 shows the built vehicle simulation model, set the Gear Box Program module as the Component Variation variable parameter, and use the Component Variation in the Calculation Center to add a Gear Box Program comparison module [18]. Import the unoptimized shift strategy into the Gear Box Program module in the original model, and set the shift strategy with the dual parameters of throttle and vehicle speed [19]; Simulation and comparison of two shifting strategies.

Fig. 17. Vehicle simulation model

The Cycle Run task of China’s typical urban working condition (CCBC working condition) is established. The continuous driving time of China’s typical urban cyclic working condition is 1314s, the whole journey is 5.83 km, the average speed is 16.10 km/h, and the maximum speed is 60 km/h [20]. The speed following curves are shown in Fig. 18 and Fig. 19. It can be seen from the figures that the two strategies are in good speed following states. In typical urban conditions in China, the actual gear is consistent with the target gear, and the shifting rules are feasible and calculated separately. The vehicle energy consumption data of the two shifting strategies shows that the power consumption per 100 km of the unoptimized shifting strategy is 76.68 kWh/100 km, and the power consumption per 100 km of the optimized shifting strategy is 73.54 kWh/100 km, which improves the economy 4.5%. Establish a full-load acceleration task (0–50 km/h acceleration time calculation task), and compare the power performance of the two shifting strategies, as shown in Fig. 20. The acceleration time of 0–50 km with the unoptimized shifting strategy is 17.13s, and the acceleration time of 0–50 km with the optimized shifting strategy is 15.68s (Tables 7 and 8).

Shift Strategy Development for Electric Bus

37

Fig. 18. Analysis of the following situation of the unoptimized shift strategy under the CCBC condition

Fig. 19. Analysis of CCBC vehicle speed following and shifting situation after optimization

Table 7. Simulation of economic results project

Unoptimized shift strategy

Optimized shift strategy

Electricity consumption per 100 km (kWh/100 km)

76.64

73.58

Fig. 20. Comparison of full throttle acceleration simulation results for two shifting strategies

38

Y. Wang et al. Table 8. Simulation of 0–50km/h acceleration time results

project

Unoptimized shift strategy

Optimized shift strategy

0–50 km/h acceleration time (s)

17.13

15.68

5 Test Verfication Based on the above research on the shifting optimization strategy, the optimized shifting strategy was applied to a 12 m electric bus, and the acceleration performance and CCBC operating conditions were tested on the hub. The test results are shown in Table 9. Table 9. Test results of 12 m electric bus project

Test results of automotive hub test bench

0–50 km/h acceleration time (s)

15.81

Power consumption per 100 km under CCBC cycle 73.72 condition (kwh/100 km)

6 Conclusion In this paper, the shifting strategies of the electric bus is studied, and the dynamic and economic shifting strategies are formulated respectively. Combined with the urban road conditions, the original shifting strategy is optimized for the 12 m electric bus, and AVL CRUISE is used to simulate and analyze the shifting strategies before and after optimization, and finally carry out experimental verification. Through this research on the shifting schedule, it provides a good reference for the development of the shifting strategy of electric buses. In the future, we will conduct more in-depth optimization and research on the shifting control strategy of electric buses.

References 1. 陈淑江, 秦大同, 胡明辉, 胡建军. 兼顾动力性与经济性的纯电动汽车 AMT 综合换档策 略[J]. 中国机械工程, 2013,24(19) 2. 李顺波. 纯电动客车综合换挡规律研究.长春: 吉林大学硕士学位论文, 2014 3. 杨易, 江青华, 周兵, 王继生. 纯电动汽车最佳动力性换档规律研究[J].汽车技术, 2011(3):1–5 4. 李大伟, 方锡邦, 吴哲.微型电动汽车 AMT 动力性换档规律的制定与仿真分析[J].重庆 理工大学学报 (自然科学) , 2011,1(25) 5. 张煜. 纯电动客车自动变速系统及控制策略研究[D].吉林大学硕士学位论文, 2012 6. 王立国.纯电动客车动力总成控制策略研究[D].长春: 吉林大学硕士学位论文, 2009

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7. 刘晓东,马建,贺伊琳,张一西,张凯.AMT驱动构型对纯电动客车综合性能影响研究[J]. 中国公路学报.2019(07) 8. 冉滔,方玲玲,彭思慧.纯电动汽车AMT两档换挡规律分析[J]. 汽车实用技术. 2019(13) 9. 卢代继.电动汽车用两挡AMT换挡规律的建模与仿真[J].汽车维修技师. 2021(06) 10. 刘拂晓,赵韩,江昊.纯电动汽车AMT换挡规律及仿真研究[J].合肥工业大学学报(自然科 学版). 2013(11) 11. 秦大同,龙海威,胡明辉.AMT中度混合动力汽车经济性换挡规律研究[J].中国机械工程. 2013(20) 12. 何安清,孙可华,沈利芳,宋国鹏,胡远敏.基于CRUISE与ADVISOR前后向仿真软件的汽 车动力性对比分析[J].客车技术与研究. 2018(02) 13. 席军强,王雷,付文清,梁万武.纯电动客车自动机械变速器换挡过程控制[J].北京理工大 学学报. 2010(01) 14. LIU H, LEI Y, LI Z, et al. Gear-shift strategy for a clutchless auto-mated manual transmission in battery electric vehicles[J] . SAE In-ternational Journal of Commercial Vehicles, 2012, 5(1) : 57–62 15. 沈波.电控机械式自动变速器换档品质的研究. 吉林大学硕士学位论文, 2003 16. 刘保林. AMT 起步和换挡品质研究. 吉林大学硕士学位论文, 2003 17. 王丽芳.自动变速器换挡规律方法的研究.汽车技术[J] .1998(6) 18. 刘大权,张小东,曲金玉. 公共汽车AMT最佳动力性换挡规律的建模与仿真[J].农业装备 与车辆工程. 2009(02) 19. 张兆红,刘涛,刘凯泽,王琳.纯电动客车两档变速器传动比优化仿真分析[J].汽车实用技 术. 2021(05) 20. 刘勤.配置AMT的纯电动城市客车换挡策略研究.长安大学硕士学位论文.2017

The Study of Implementation of Precise Location of Automotive Digital Key Based on UWB Baohua Xia(B) , Guoping Qian, Xibin Wu, Zhenghua Lu, Juntao Tian, and Liu Lianfang Beiqi Foton Motor Co., Ltd., Beijing, China [email protected]

Abstract. By reviewing the automotive key development history, to implement precise location is an essential way of automotive digital key system. This study decides to implement precise location method based on Ultra Wide Band(UWB) technology, following the guide line of briefly stating the ranging principle of UWB, designing three points location model, constructing UWB location deployment model of automotive cockpit, describing location algorithm and work flow of implementation of UWB location method in detail. This paper analyzes UWB location error which is within the theoretical precision accuracy range proved by test data. The advanced design idea tutors optimizing the algorithm, and the implementation of distributed computing of this location algorithm makes the study achieve the best system resources balance and power management goals. The study also implements precise location of automotive digital key based on UWB. Meanwhile the paper shows that automotive digital key fusing UWB location technology matches the direction of automobile intelligence developing. Keywords: Automotive Digital Key · UWB · Three Points Location · Automobile Intelligence

1 Introduction With a view of automotive key development history, it has past mechanical key phase and Radio Frequency (RF) key phase and is going into digital key phase. By giving a perspective of function of automotive key: mechanical key only implements lock, unlock car these elementary function by physical actions between key and automobile; RF key may implement remote lock, unlock car when the key is nearby automobile and it also implements other intermediate functions such as Passive Entry Passive Start(PEPS) by RF wireless communication with Electronic Control Unit(ECU); about digital key it can implement automatic lock, unlock car so for driver who is nearby the automobile actively has no any feeling, and it can implement advanced Telematics functions such as remote control car, start engine and air conditioner etc. by through wireless network, Bluetooth(BT) or Wireless Fidelity(Wi-Fi). This shows the automotive key is developing and evolving from a simple feature function component to an intelligence system.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 40–54, 2023. https://doi.org/10.1007/978-981-99-1365-7_4

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A perfect automotive digital key system should own following key elements: 1). The precise locations between key and automobile; 2). High stable Intrusion Prevention System(IPS) and cypher system; 3). Over The Air(OTA); Study focuses on element 1), the paper tries to set automotive key precise location model up basing UWB indoor positioning technology [1]: thus the automobile may get exact position of the key(including both distance and direction), then the automotive digital key system may set the proper application scenario of in or out of automotive cockpit to the key, so the driver who holds the key can get the very best driving experience.

2 Current Situation and Study Goal 2.1 The Location Method of RF Key System The location method of automotive key may recall to RF key, RF key can implements nearby control automobile function by over the air, such as finding car, start engine and so on. To support this implementation the essential technology is the location method between RF key and PEPS ECU. The location method of RF key and PEPS ECU is shown as in Fig. 1.

Fig. 1. Location Model of RF Key System

As shown in Fig. 1, in this location model of RF key system: equips two low frequency antennas on back of driver seat and front passenger seat, and equips two high frequency antennas on the automobile nearby handles of left doors and right doors. The PEPS does data communication with RF key by using high frequency antennas and looks for RF key by using low frequency antennas. How does PEPS implement looking for RF key? It mainly gets RF field strength value what is responded from RF key measuring RF field strength to the low frequency antennas: first PEPS broadcasts looking for signal through low frequency antennas, when RF key receives the looking for signal the RF key will measure RF field strength to the antennas and then broadcasts value of RF field strength

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by carrying on high frequency at once, last the PEPS receives the responding value of RF field strength from RF key through high frequency antennas. According to calibrated data of RF field strength and using RF distance equation after PEPS calculates, finally PEPS locates the position of RF key. This is implementation of the location method of RF key system [2]. 2.2 The Location Method of BT Key System With modern automobile intelligence evolving, smart phone integrates key functions into mobile Application (APP) firstly, this means that automotive key goes into digital key system. The smart phone implements nearby control automobile by through data communication between mobile BT key APP and Telematics Box (T-Box) and by basing Received Signal Strength Indication (RSSI) location method between BT antenna of smart phone and BT location antennas of T-Box. This location model is shown as in Fig. 2.

Fig. 2. Location Model of BT Key System

As shown in Fig. 2, there are four roles in this model: 1). 2). 3). 4).

Smart phone, it integrates BT key APP; T-Box, it connects to automotive Controller Area Network(CAN) bus; BT communication antenna; BT location antenna;

When user controls automobile by using BT key APP the smart phone switches data with T-Box through BT communication antenna. Since T-Box connects to automotive CAN bus then T-Box may send CAN signals to Body Control Module (BCM) and PEPS to control automobile, this is a nearby control automobile function of BT key system. First when T-Box is looking for BT key APP it broadcasts looking for signal to ask location BT key APP, then the BT key APP picks RSSI values to the BT location antennas of T-Box up and sends the RSSI values to T-Box through BT communication

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antenna, last the T-Box receives the RSSI values and calculates the position of BT key APP what is in coordinate system of automotive cockpit by using RSSI equation. Thus in this model it implements the location method of BT key system [3, 4]. For improving BT location accuracy of BT key system, T-Box may equip BT Angle of Arrival (AoA) modules and AoA measuring direction matrix antennas, the location method and accuracy are better than BT RSSI location technology [5]. 2.3 Study Goal Upon statement shows that both the location method of RF key system and the location method of BT key system are matured, and automobile manufactories have equipped these key system in some luxurious automobile. Recalls automotive key development history, RF key system began modern automobile intelligence, but RF key system is a single function system, it can’t to be fused with T-Box and smart phone, and it can’t connect to Telematics Service Provider (TSP), no function iteration or no OTA. When BT key system was born, it means that automotive key taking evolution into digital key system, so it shows advantages of automotive digital key system, such as: fusing with T-Box and smart phone, connecting to TSP smoothly, and doing function iteration and doing OTA conveniently. Gives a perspective to the BT key system from system point, there are these features: 1). Basing on RSSI location method, system needs over four location antennas, and it takes more times when T-Box switches from location antennas to communication antenna alternately, location performance and equation are complex; 2). Basing on AoA location method, location performance and equation improves very much then RSSI location method, but T-Box has to equip BT AoA matrix antennas and module, the cost is higher; The paper focuses on improving BT key system, study tries to fuse UWB positioning technology with T-Box to do precise location, the goal is going to do a solution design to separate an individual location system from digital key system, distributed design will improve location performance and location precision further more, of course the study tries to push automotive key system having long term evolution potentially.

3 Solution Design(Schema) 3.1 Ranging Principle of UWB UWB uses Time Of Flight (TOF) to implement Single-side Two-way Ranging(SSTWR), ranging principle is shown in Fig. 3.

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Fig. 3. Ranging Principle of UWB

As shown in Fig. 3, the mobile what is installed digital key APP sends location signal(data package) at Tm0, UWB base will receive the location signal at Tb0; UWB base delays for Treply and replies location signal at Tb1, mobile will receive location signal at Tm1.The equations of Tround and Treply are shown as below: Tround = Tm1 − Tm0 Treply = Tb1 − Tb0 Defines Tprop to be as flying time of location signal in air, then calculates Tprop as below: Tround − Treply 2 (Tm1 − Tm0) − (Tb1 − Tb0) = 2

Tprop =

Since the speed of location signal is light speed, it is C, then there is the equation of distance S what the location signal flies between mobile and UWB base in the air, as below(Eq. (1)): S = C ∗ Tprop (Tm1 − Tm0) − (Tb1 − Tb0) =C∗ 2

(1)

This is ranging principle of UWB [6]. 3.2 Three Points Location Model When uses single UWB module to locate mobile, it only knows that the mobile is on the circle of radius S around the UWB base. Actually in real scenario the digital key system needs to know where the mobile is in or out of automotive cockpit, to achieve this goal the study has to design precise location model, in this model there three UWB modules. The paper calls the model is three points location, it’s shown in Fig. 4.

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Fig. 4. Three Points Location Model

Sets up xOy coordinate system what is showed in Fig. 4, abstracts automotive cockpit to be as a rectangle, then places this rectangle in xOy coordinate system. Declares cockpit width is W and height is H, describes this rectangle is rectV: rectV = [a(0, H ), b(0, 0), c(W , 0), d (0, 0)] Equips three UWB modules on a, b and c point of the rectangle, as shown in Fig. 4 they are UWB A, UWB B and UWB C. When mobile is on m(x,y) of xOy, measures distance from point m to point a, b and c, invokes Eq. (1) then gets the distance, declares them to be as below: 1). Ra, the distance from point m to point a; 2). Rb, the distance from point m to point b; 3). Rc, the distance from point m to point c; In xOy coordinate system, to set point a to be as dot draws circle A of radius Ra; to set point b to be as dot draws circle B of radius Rb; to set point c to be as dot draws circle C of radius Rc. As shown in Fig. 4, there is an unique intersection point of circle A, circle B and circle C, this is the point m(x,y) and it’s position of the mobile. 3.3 Location Equation In xOy coordinate system sets Eq. (2) of point m(x,y) to circle A, circle B and circle C up as below: ⎧ 2 2 2 ⎪ ⎨ Ra = x + (y − H ) Rb2 = x2 + y2 ⎪ ⎩ 2 Rc = (x − W )2 + y2

(2)

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Solves Eq. (2) then gets Eq. (3) of m(x,y): ⎧ W 2 + Rb2 − Rc2 ⎪ ⎪ ⎨x = 2W 2 + Rb2 − Ra 2 ⎪ H ⎪ ⎩y = 2H

(3)

Sets relationship function of point m(x,y) and rectangle rectV up as below (Eq. (4)):  1, m(x, y) ∈ rectV (4) f (m(x, y)) = 0, m(x, y) ∈ / rectV Calling Eq. (3) calculates m(x,y), then takes m(x,y) into Eq. (4) and calculating result is: 1). 1, this means that mobile is in of automotive cockpit; 2). 0, this means that mobile is out of automotive cockpit; Summarizes, using ranging principle of USB through three points location model, after calls Eq. (1), (3) and (4) then the digital key system really knows that mobile is in or out of automotive cockpit exactly from calculating result.

4 Solution Implementation 4.1 Deployment Model Makes instance of Sect. 3, constructs UWB location deployment model of automotive digital key system, as below (Fig. 5):

Fig. 5. UWB Location Deployment Model

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As shown in Fig. 5, there are five roles in this deployment model: 1). 2). 3). 4). 5).

Mobile; T-Box; UWB A, B and C; Antennas of UWB modules; BT communication antenna;

From Fig. 5, T-Box fusions UWB A, B and C through CAN bus to construct automotive digital key system. 4.2 Work Flow On T-Box side the work flow of implementation of UWB location method is shown as in Fig. 6.

Fig. 6. UWB Location Work Flow

If T-Box wants to look for mobile BT key APP then the automotive digital key system executes work flow of Fig. 6 automatically: first T-Box sends location signal to UWB A, B and C through CAN bus to measure distance, when UWBs measure distance to mobile by calling Eq. (1) to get Ra, Rb and Rc then UWBs return values to T-Box; next T-Box takes Ra, Rb and Rc into Eq. (3) and calls Eq. (4) to calculate result; after that T-Box sends the result to mobile BT key APP by through BT communication, thus the BT key APP knows that current mobile is in or out of automotive cockpit; finally the automotive digital system sets the mode with the right application scenario.

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4.3 Algorithm Improvement Coding Eq. (4) using C language, the source code is shown as below (Eq. (5)):

(5)

From the source code of Eq. (5) study gets a conclusion: the available range value of x is (0,W) and the available range value of y is (0,H). So if m(x,y) is in an available range value set that it means the mobile is in of cockpit, otherwise the mobile is out of cockpit. Takes available range value of x and y into Eq. (2), it is possible to calculate the available range value of Ra, Rb and Rc, as below:  (0, H 2 + W 2 ) Supposes that it only judges if mobile is in or out of cockpit then as a matter of fact the UWB location method needn’t call Eq. (3) to calculate exact m(x,y) at all, so the step 5 in Fig. 6 will be optimized to judge if Ra, Rb and Rc are in the available range value set, and if the judge result is yes this means the mobile is in of cockpit and if the judge result is no that means the mobile is out of cockpit. As upon statement merging Eq. (3) and Eq. (4) together, the optimized source code is shown as below (Eq. (6)):

(6)

Hence automotive digital key system is no longer calling Eq. (3) to calculate m(x,y) but only runs the optimized source code of Eq. (6), thus this algorithm improvement will descend Central Processing Unit(CPU) loading more.

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5 Error Analysis 5.1 Error Condition The error of UWB location method depends on UWB module clock accuracy and data processing power: 1). Generally light may fly 30 cm per 1 ns, so if UWB module clock accuracy is low then 1 ns synchronization error will take 30 cm error; 2). If CPU couldn’t process data on time, then calculating distance is outdated; The excellent location algorithm may eliminate error from noise signal [7] and enhances location calculation when automobile is in movement[8]. So when designs automobile digital key system it has to care of upon error factories. 5.2 Error Illustration Supposes the error parameter of UWB location is ± D, after calls Eq. (3) then the error parameter of UWB location is ± D’. Takes them into Eq. (3) it is: ⎧ W 2 ±(Rb±D)2 − (Rc±D)2 ⎪ ⎪ ⎨ x±D = 2W 2 2 2 ⎪ ⎪ ⎩ y±D = H ±(Rb±D) − (Ra±D) 2H Expands and derives: ⎧ W 2 + Rb2 − Rc2 |Rb − Rc| ⎪ ⎪ ± ∗D ⎨ x±D = 2W W 2 2 2 ⎪ ⎪ ⎩ y±D = H + Rb − Ra ± |Rb − Ra| ∗ D 2H H Reduces Eq. (3) then gets: ⎧ |Rb − Rc| ⎪ ∗D ⎨ ±D = ± W ⎪ ⎩ ±D = ± |Rb − Ra| ∗ D H Actually max(abs(Rb-Rc)) is W, and max(abs(Rb-Ra)) is H, so from theory deriving then gets Eq. (7) as below: ±D = ±D

(7)

So in Eq. (7), if ± D is ± 10 cm, then real m(x,y) should be located in the circle which radius is 10 cm and which dot is the theoretical point m’(x,y), it is shown in Fig. 7.

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Fig. 7. Three Points Location Error Illustration

5.3 Error Evaluation To verify the error illustration what is shown in Fig. 7, constructs test model as below(Fig. 8): As shown in Fig. 8, this test model sets a 100*100 cm test field, and divides test field into a 10*10 grid along x-aixs and y-aixs every 10 cm, then there are 11*11 total 121 test points in the grid of test field. Test model demands to do test that covers all 121 test points, after that then picks all test data up for calculating UWB location error. Since there is length limitation of the paper so here selects m’(50,50) to be as characteristic test point(it’s the theoretical point m’(x,y) too), detailed test procedure as below:

Fig. 8. Three Points Location Error Test Model

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1). Places UWB A, B and C on point a(0,100), b(0,0) and c(100,0); 2). Places mobile on point m’(50,50); In theory on m’(50,50) the Ra = Rb = Rc≈70.71 cm. The test is to get real Ra, Rb and Rc through UWB location method from mobile to UWB A, B and C, after test then calls Eq. (3) to calculate m(x,y). This test totally does 10 times location, picks all test data up as shown in Table 1. Table 1. Error Analysis Test Data No

Ra/m

Rb/m

Rc/m

(x,y)/cm

(50-x,50-y)/cm

1

0.80

0.79

0.80

49.21,49.21

0.80,0.80

2

0.78

0.78

0.79

49.22,50.00

0.79,0.00

3

0.78

0.79

0.78

50.79,50.79

−0.79,−0.7

4

0.76

0.79

0.78

50.79,52.33

−0.79,−2.3

5

0.76

0.80

0.77

52.36,53.12

−2.36,−3.1

6

0.76

0.80

0.77

52.36,53.12

−2.36,−3.1

7

0.77

0.80

0.77

52.36,52.36

−2.36,−2.3

8

0.78

0.80

0.77

52.36,51.58

−2.36,−1.5

9

0.78

0.80

0.77

52.36,51.58

−2.36,−1.5

10

0.80

0.80

0.78

51.58,50.00

−1.58,0.00

As shown in Table 1 m(x,y) is in the circle of dot m’(50,50) and radius is 10 cm: 1). The max(50-x, 50-y) is (-2.36, -3.1), this is the farthest point to m’(50,50); 2). The min(50-x, 50-y) is (0.79, 0.00), that is the closest point to m’(50,50); Makes location accuracy analysis function of m’(50, 50) against to ± D, as below(Eq. (8)): p = 1 − ((

n  |x −50| i

D i=1

n  |yi −50| )/n + ( D )/n)/2

(8)

i=1

where, D = 10 cm n = 10 times Takes the data of Table 1 to Eq. (8) then gets: p = 0.835. The result shows that the location accuracy against to ± 10 cm is ± 8.35 cm, this means in Eq. (3) it does one location but it actually does three distance measuring by UWB modules, in other words to do a location there are three mean square error

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compensation. The result also shows if the algorithm of UWB location method may fusion Kalman Filter or other error compensation algorithm then it will improve location accuracy more [9]. The error evaluation proves that the illustration of Fig. 7 and Eq. (7) are absolutely right.

6 Advantage The study shows that individual location system has more advantages: for Eq. (6) the system may distribute calculation to per location UWB module, so this strategy has high calculating efficiency, and it will improve location real time very much. The distributed calculation model of Eq. (6) is shown in Fig. 9.

Fig. 9. Distributed Calculation Model

As shown in Fig. 9, the model distributes calculation to per location module, the three location modules outputs result to an AND gate circuit and directly inputs signal to T-Box, T-Box never do any location calculation but pays attention to digital key strategy servo. From perspective of system design the model of Fig. 9 has very good extensibility: it easily do to wake T-Box up, extension model is shown as in Fig. 10.

Fig. 10. Extension Waking Up Model

As shown in Fig. 10, when any location module detects that mobile is in available range of automotive key system then the location module may output waking up signal through an OR gate circuit to wake T-Box up directly, this model shows that the study may implements the best way to wake sleeping T-Box up.

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Summarizes upon, the design solution of this paper has advantages: 1). On software architecture side it shows distributed calculation benefit; 2). On hardware architecture side it only adds AND gate and OR gate circuit, low cost and stability; 3). On system architecture side it may fusions T-Box very well, and does the best power management of automotive digital key system;

7 Conclusion The UWB technology began from 1960s, against to Narrow Band(NB) technology it has more advantages in such as path loss evaluation metrics, etc.. Using the special impulse carrier pulse communication technology of UWB to do indoor position this way it has high precise location and high real time efficiency. This paper uses matured UWB precise location(indoor positioning) technology and fusions this technology into automotive digital key system, at same time the paper also designs an individual location model what is a necessary module for T-Box looking for mobile key APP and deploys this model to be as an isolated component, the study successfully distributes the precise location algorithm and strategy what consumes more CPU loading to the UWB modules to execute, the result achieves system resources balance and power management very well. The study gives current automotive digital key system on development and evolution side a very powerful practice thinking and very detailed implementation method. The study indicates that UWB may collaborate with BT and Wi-Fi smoothly on sharing frequency resource, and since the direction of automobile intelligence developing will fuse all kinds of available resources in the future, so the study of the paper matches the direction of automobile intelligence developing.

References 1. Guandi, L., Zhao, L.: Improvement and implementation of indoor intelligent following system based on UWB technology. Comput. Appl. Softw. 37(12), 95–100 (2020) 2. Guoping, Q., Yujie, J., Xibin, W., Juntao, T., Tao, Z.: Automotive PEPS positioning principle and new low frequency calibration method. In: China-SAE. 2020 Proceedings of China-SAE Congress, Beijing, China, Machine Press, pp. 303–307 (2020) 3. Hongliang, J., Baohua, X., Jianpeng, H., Yue, Y., Min, W., Changfeng, S.: A vehicle control system and method of vehicle and its applications, China, 201811173601.7, p. 2018-10-09 4. Xiabaohua. Positioning Improvement Solution of Vehicle Bluetooth Key. Electron. Technol. Softw. Eng. 181(11), 37–39 (2020) 5. Baohua, X., Guoping, Q., Xibin, W., Juntao, T., Lianfang, L.: The study of Bluetooth key system and precise location solution on vehicle. In: China-SAE. 2021 Proceedings of China-SAE Congress, China Machine Press, Beijing, pp. 325–330 (2021) 6. Hongmei, Z., Jielei, Z.: An overview of ultra-wide band indoor positioning algorithms. Telecomm. Sci. 34(09), 130–142 (2018) 7. JunChao, J:. Research on random error processing Nethod of UWB indoor positioning system. Comput. Appl. Softw. 34(10), 157–162, 184 (2017)

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8. Xiaotong, Y., yu, Z., Jundian, S.: UWB indoor Localiztion algorithm based on attention mechanism. Comput. Appl. Softw. 38(06), 198–201 (2021) 9. Jijun, T., Lijian, J., Yingjie, Z., Faqinqin, G., Yanjie, B.: The UWB indoor positioning system based on adaptive kalman filtering. J. Test Measure. Technol. 32(02), 93–99 (2018)

Platform-Based Design of the Hybrid Electirc Drive Load Spectrum Xiane Ruan, Manli Li(B) , Jun Lei, Huajun Kang, and Huan Liu Dongfeng Motor Corporation Technology Center, WuHan, China {ruanxe,liml}@dfmc.com.cn

Abstract. Hybrid electric drive assembly is currently a relatively new configuration in China, there are a variety of working modes such as driving, power generation and energy recovery, and there are also different working strategy in each mode. Various modes involve more factors, and the calibration of the real vehicle has a great impact on the load distribution, resulting in complex prediction of the load spectrum of each mode. Based on the basic theory of hybrid electric drive, combined with the actual vehicle projects and the distribution characteristics of measured data, the load generation mechanism and distribution of each mode were analyzed, and the load spectrum of each mode was quantified and compared. Based on the distribution strategy of each mode, and platform stratagem was considered, and the special load spectrum envelope design method was constructed for each mode, and the hybrid load spectrum construction method covering multiple vehicle platforms was generated. Keywords: hybrid electric drive · load spectrum · platformization

1 Introduction With the development and application of hybrid electric drive in China, low cost and compact design are needed to improve the product competitiveness. Load spectrum is a necessary condition for low redundant design of reliability. Therefore, a method for load spectrum generation that meets the vehicle real conditions is needed. In addition, with the application of hybrid drive assembly platforms, the load spectrum should also cover the requirements of potential HEV/PHEV platforms and different vehicle mass and speed ratio series. Hybrid electric drive assembly is a relatively new configuration in China. There are many working modes such as driving, power generation and energy recovery. Different working modes exist different conditions. There are many factors involved in each mode, and the load distribution is greatly affected by the real vehicle calibration, which leads to the complicated prediction of the load spectrum.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 55–68, 2023. https://doi.org/10.1007/978-981-99-1365-7_5

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2 Influence of the Hybrid Strategy on Load Specturm 2.1 Introduction of Hybrid Working Mode Figure 1 shows the typical hybrid working mode types. This system has six working modes, and the efficiency of the system is improved by selecting the appropriate drive mode according to the driving conditions. The first mode is called motor drive mode, hereinafter referred to as EV drive. At this mode, the vehicle is driven by a motor using electricity stored in the battery. The second mode is called series drive mode. At this mode, the engine power is converted from a generator to electricity, and the vehicle is driven by a motor that uses this electricity. When the generator produces less electricity than the motor consumes, the discharge of the battery will make up for the shortfall. When the generator produces excess power, it will be charged to the battery. The third mode is called parallel drive mode. At this mode, the engine and wheels are coupled by a clutch at a fixed transmission ratio, and the wheels are driven directly by the engine and motor. The motor performs auxiliary and charging functions, and discharges from battery or charges the battery. The fourth mode is called engine drive mode. At this mode, the engine and wheels are coupled by a clutch at a fixed transmission ratio, and the wheels are driven directly by the engine without a motor. The fifth mode is called energy recovery mode. At this mode, the engine and the motor do not participate in the driving.The motor recovers and the kinetic energy of the vehicle when coasting or braking. The sixth mode is called parking generation. At this mode, the vehicle is static and the coupling device is disconnected. The engine drives the generator to generate electricity or charge the battery.

Fig. 1. Typical hybrid working mode types

2.2 Drive Mode 2.2.1 EV Drive and Series Drive EV drive is primarily chosen in the range from startup to urban and other low-speed driving to avoid fuel economy degradation due to the engine operating inefficiency. When driving at medium speeds, taking into account the balance between engine thermal efficiency and battery charge/discharge losses, fuel economy can be improved by intermittent operation that switches between EV drive and parallel drive or between EV

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drive and engine drive as appropriate. At high speeds, either parallel drive or engine drive mode will be choosen to achieve the highest energy transfer efficiency. Therefore, the switching MAP of the engine can be determined by the efficiency difference λ between the battery motor system and the engine system under N and T operating conditions of the vehicle. The motor torque of EV drive depends on the motor power configuration and the high-voltage battery output capacity. Under the condition of low battery capacity, the output capacity is small. While the condition of high battery capacity, the same road condition has higher output capacity. The typical hybrid mode distribution is shown in Fig. 2 [1]. However, in real vehicle applications, the engine switching point also needs to consider the low speed operating range of the engine (the lowest stable operating speed range), the delay of entry and exit modes to avoid cyclic mode switching. Therefore, the switching point in Fig. 2 also needs to be adjusted according to the actual vehicle parameters. 2.2.2 Parallel Drive In parallel drive mode, the engine and wheels are coupled through a fixed reduction gear, so the engine speed is uniquely determined relative to the vehicle speed. When cruising on a flat road, the relationship between engine speed and torque is shown by the dashed line in Fig. 3 [1]. The engine drive mode is used in the high speed cruising zone, but the engine operating point will deviate from the low torque side of the minimum BSFC line when driving on flat roads. In this case, the engine torque is increased so that the engine operates at a more efficient operating points, and the increased torque is absorbed by the regenerative operation of the generator. In contrast, when the engine operating point deviates from the high-torque side of the minimum BSFC line, the engine torque decreases and the difference is compensated by the motor. In this way, control through generator regeneration and motor drive converges the engine operating points to a higher efficiency area.

Fig. 2. Typical hybrid driving mode distribution

Fig. 3. Motor operating mode under parallel drive

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2.3 Generation Mode Power generation mode includes parking power generation, series power generation, driving power generation and other modes. 2.3.1 Series Power Generation There is no constraint on the speed of the vehicle and the speed between the engine and generator. This means that the engine speed can be set arbitrarily relative to the speed of the vehicle. Therefore, basically the control is performed so that the engine operating point tracks a uniquely determined minimum BSFC line with respect to the engine output. In addition, on the blue minimum BSFC line as shown in Fig. 3, the engine output is adjusted toward the operating point with higher efficiency. At this point, any excess or shortage of generated power relative to the motor output is compensated by the battery energy. 2.3.2 Driving Power Generation At engine drive mode, the engine and wheels are coupled through a fixed reduction gear, so engine speed is uniquely determined with respect to vehicle speed. When cruising on a flat road, the relationship between engine speed and torque is shown by the dashed line in Fig. 3. The engine drive mode is used in the high speed cruising zone, but the engine operating point will deviate from the low torque side of the minimum BSFC line when driving on a flat road.In this case, the engine torque is increased so that the engine operates at a more efficient operating point, and the increase in torque is absorbed by the regenerative operation of the motor. 2.4 Energy Recovery Mode In electric and hybrid vehicles, this wasted kinetic energy can be converted into electricity through energy recovery technology and stored in battery. 2.4.1 Coast Mode During coast mode, the kinetic energy of the vehicle is converted into electric energy, thereby achieving moderate deceleration. At the same time, the motor capacity and the battery capacity also limit the recovery intensity. The optimal recovery capacity under the vehicle parameters is generally expressed as the curve shown in Fig. 4 [2]. Fmax is the maximum energy recovery, which is calculated according to Eq. (1). Fmax = m ∗ gmax

(1)

where, m is the vehicle mass (kg), and gmax is the maximum recovered torque of the vehicle corresponding to deceleration (g).

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Fig. 4. Recovery deceleration curve of the vehicle coast

2.4.2 Braking Energy Recovery In the braking process, the braking energy recovery system calculates the demand braking strength by detecting the sensor signal. The regenerative braking control strategy calculates the optimal regenerative braking force and friction braking force according to the current motor state, battery state and vehicle state, and allocates the regenerative braking force to the motor controller MCU.

3 Mesured Distribution Characteristics of the Load Spectrum 3.1 Analysis Method The actual vehicle distribution is affected by various specific vehicle/engine limits, such as battery SOC distribution and temperature, etc. It is impossible to accurately quantify the load distribution difference of each mode through pure theoretical prediction. Therefore, it needs to be sorted out according to the actual vehicle application and calibration situation. In this paper, a multi-mode hybrid vehicle developed on a platform is selected as an example to evaluate the load distribution differences under actual vehicle applications. As it is applied to multiple vehicle platforms, the representative hybrid HEV and plug-in hybrid PHEV models are selected (Table 1). In order to quantitatively evaluate the difference of load intensity in each mode, the load intensity was converted into an intensive value according to the life relationship. In this paper, based on the fatigue damage theory of ISO 9226 and Miner’s rule, the load spectrum time domain data consisting of torque and speed are converted into torque and the number of cycles, as shown in Eq. (2). n Ni ∗ T i λ = N ∗ T λ (2) i=1

where, N is the number of intensive cycles (rev), T is the intensive torque (Nm), Ni is the number of cycles at each working condition point (rev), Ti is the torque at each working condition point (Nm), λ is the gear damage relationship coefficient and three is defined in this paper.

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The parameter name

HEV vehicle

Full load weight of vehicle

1900 kg

2000 kg

Vehicle drive type

Two-wheeldrive

all-wheel-drive

Hybrid corresponds to axle load

N/A

55%

Tire radius

0.315 m

0.35 m

Ratio of motor to wheels

~10

Ratio of engine to wheels (1st gear)

~3.4

Ratio of gemerator to engine

~2.7

Maximum motor torque and power

300 Nm/130 kW

Maximum engine torque

230 Nm

Highest wheelside torque

3000 Nm

PHEV vehicle

Due to the difference of SOC distribution and its high influence on the conventional drivability of vehicles, it is difficult to quantify the distribution law under each SOC conditions. Therefore, the load distribution characteristics are analyzed by means of big data through testing the real vehicle data under various working conditions and SOC in this paper. The working conditions cover low-speed and acceleration as shown in Fig. 5. The usage boundary of vehicle speed and wheel side torque is shown in Fig. 6. This method basically covers all operating conditions.

Fig. 5. Actual working conditions of the vehicle

Fig. 6. Vehicle speed and wheel side torque

3.2 Distribution of Drive Conditions 3.2.1 EV Drive and Series Drive The speed-torque distribution in motor drive mode of HEV vehicle is shown in Fig. 7a. The engine speed torque in yellow part is basically distributed in the high speed (>3000

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rpm) and low torque ( ρ is satisfaction that 0 ≤ ρ ≤ 1, and ρ is taken as 0.5 in no special case. The mean squared deviation is the number of the model data. The mean squared error is a test of the error distribution, the smaller the mean squared error ratio C the better, and the larger the probability of small errors P the better. If the results of the above data tests are within the range, the model is valid. In this paper, we use the supply of Li2 CO3 from 2017 to 2021 as the base data [9], model and forecast the supply of in China from 2022 to 2026. To build the Li2 CO3 GM(1, 1) model: The first step, the level test: The Li2 CO3 data time series were established as follows: x(0) = (x(0) (1), x(0) (2), . . . , x(0) (5)) = (23.9, 28.07, 34.74, 40.1, 50.7) Find the level ratio λ(k) =

x(0) (k−1) x(0) (k)

to obtain:

λ = (λ(2), λ(3), λ(4), λ(5)) = (0.8514, 0.8080, 0.8663, 0.7909)

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The second step, level judgment. 2 2 Determine if all level ratios λ(k) are within tolerance coverage ∅ = (e− n+1 , e n+2 ) within, to determine whether the data model is able to use GM(1, 1) to make predictions. Since, λ(k) ∈ [0.7165, 1.330712], k = 2, 3, 4, 5 so it is possible to use x(0) as satisfactory to obtain GM(1, 1) model. The third step is to build GM(1, 1) model. First, the original data x(0) is summed once, i.e. x(1) = (23.9, 51.97, 86.71, 126.81, 177.51) Then a sequence is generated based on the x(1) mean value of the generated sequence: z(1) = (z(1) (2), z(1) (3), z(1) (4), z(1) (5)) where z(1) (k) = 0.5x(1) (k) + 0.5x(1) (k − 1), k = 2, 3, 4, 5. z(1) = (37.935, 69.34, 106.74, 152.16) Establishing grey differential equations: x(0) (k) + az(1) (k) = b, k = 2, 3, 4, 5。 The corresponding white differential equation is given by: dx(1) + ax(1) (t) = b dt Remember u = [a, b]T Y = [x(0) (2), x(0) (3) . . . , x(0) (5)]T Next, construct the data matrix B, i.e., the data vector Y: ⎤ ⎡ (1) −z (2) 1 ⎢ .. .. ⎥ B=⎣ . .⎦ −z(1) (5) 1 ⎡ (0) ⎤ x (2) ⎢ .. ⎥ Y=⎣ . ⎦ x(0) (5)

Based on the least squares method, the estimate that enables the minimum value u to be reached is found to. be:  −0.1934 ∧ ∧ ∧ T T −1 T u = (a , b ) = (B .B) B Y = 20. 69766 Thus we get a = −0.1934, b = 20.6976 Modeled as follows: dx(1) − 0.1934x(1) (t) = 20.69766 dt

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The solution gives: b b x(1) (k + 1) = (x(0) (1) − )e−ak + = x(0) (1) = (23.9 + 107)e−0.193k − 107 a a Find the generated data x∧ (1) (k + 1) and the model reduction values x∧ (0) (k + 1): The x∧(1) (1) = (x∧(0) (1) = x(0) (1) = 23.9 Let k = 1, 2, 3, 4 From the above time response function, we can calculate x^(1) From x∧ (0) (k) = x∧ (1) (k) − x∧ (1) (k − 1), take k = 2, 3, 4, 5, we get: x∧(0) = (x∧(0) (1), x∧(0) (2), x∧(0) (3), x∧(0) (4), x∧(0) (5)) = (23.9, 27.95, 33.8985, 41.1264, 49.92)

Table 1. GM(1, 1) model test table Serial number

Year

Original value

Model values

Residuals

Relative Error

1

2017

23.9

23.9

0

0

2

2018

28.07

27.95

-0.07

0.12

3

2019

34.74

33.8985

0.02

0.8415

4

2020

40.1

41.1264

-0.03

−1.0264

5

2021

50.7

49.92

0.015

0.78

After the residual test, the residual values below 0.2 meet the requirements, 8 and the residual values below 0.1 indicate the high accuracy of the model. According to the residual test analysis in the above table, the residual values are all below 0.1, which indicates the high accuracy of the model. That is, the established model is used to make an approximate forecast of Li2 CO3 supply in the next few years. That is, the model developed by x(1) (k + 1) = 130.92e0.1934k − 107 x∧

(0)

(k) = x∧

(1)

(k) − x∧

(1)

(k − 1)

The forecast values for the next five years were obtained as: ( 60.55, 73.468, 89.144, 108.164, 131.243)

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That is, the trend of Li2 CO3 supply from 2022–2026 is shown in Fig. 6 below:

Fig. 6. Supply trend of Li2 CO3

However, in terms of supply, there is a certain deviation between this paper and the actual one. The main source of deviation is that the data source and analysis in this paper are mainly based on the new energy vehicle-related industry chain for modeling, and the consumption of Li2 CO3 in energy storage is not considered, which has a large limitation.

3 Supply and Demand Analysis of Li2 CO3 3.1 Li2 CO3 Supply and Demand Difference According to the above demand and supply analysis of Li2 CO3 , it can be seen that the total demand for Li2 CO3 shows a rapid rise, which is inevitable under the background of global” carbon peak, carbon neutral”. In the next few years, with the progress of technology, the mining rate of Li2 CO3 will increase, and the supply will also increase year by year, but because of the difficulty of mining lithium resources, there has been a certain difference between the rate of increase in supply and the rate of increase in demand, which has led to the high price of Li2 CO3 and an upward trend, which has also played a role in the development of the entire new energy market. This is also detrimental to the development of the new energy market and the implementation of the global “double carbon” strategy. In this regard, this paper conducts an in-depth research and analysis in order to promote the healthy cycle development of the new energy vehicle industry chain. It is expected that from 2022 to 2025, the difference between Li2 CO3 and Li2 CO3 will be as shown in the following Fig. 7.

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Fig. 7. Supply and demand differential of Li2 CO3

3.2 Market Feedback on Li2 CO3 Price Increase Li2 CO3 prices rose sharply on the market feedback is mainly reflected in the direct result of the battery factory gross margin decline and new energy vehicle price increases. Li2 CO3 as one of the raw materials for the battery cathode, the price rose sharply, and did not quickly lead to new energy vehicle price increases. First brought about by the cathode material lithium iron phosphate and ternary materials have risen sharply, resulting in a relatively large increase in the cost of new energy vehicle batteries [10]. But after Li2 CO3 continued to increase in price, began to transfer to the new energy vehicles themselves. This does not necessarily make the market need to reduce, need to consider is the affordability of consumers and car production enterprises. According to the study of related information, it is found that the decision made by new energy vehicle manufacturers such as Tesla, Azera, Ideal, Xiaopeng, and BYD is the price increase of new energy vehicles, i.e., it brings a wave of price increase of new energy vehicles [7]. For example, Tesla [11]’s Model3 standard version, a single car experienced two price increases, the total price increase reached 24,000 / car, the single battery cost increase of about 0.35 yuan, its price increase can basically cover the increase in raw material costs. However, after the price increase exceeds a certain margin, it is necessary to consider the affordability of consumers. In summary, it is the low-end new energy vehicles that are most affected by their Li2 CO3 price increases. The essence of low-end new energy vehicles is to rely on their low prices to attract consumers and whether to increase prices to maintain profits, which has triggered a game between consumers and automakers. Its typical low-end models, such as the Great Wall Oula and Wuling Hongguang miniEV, have been most affected. Among them, Great Wall Euler announced the discontinuation of production and Wuling Macro miniEV announced a price increase [8, 9]. These are the market repercussions under the price increase of Li2 CO3 . The details are shown in the following char (Fig. 8)t.

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Fig. 8. Li2 CO3 price rising by company sales trend

4 Game Theory Based Analysis of the Li2 CO3 Price 4.1 Gray Correlation Analysis of the Game Theory Model First of all, gray correlation analysis [1] is needed. The idea of gray correlation analysis is to determine whether the sequences are closely related based on the degree of correlation of their curve geometries. The closer the curves are, the greater the correlation between the corresponding series, and vice versa, the smaller the correlation. In this paper, the production of Li2 CO3 , the production of lithium battery, and the sales of new energy vehicles are analyzed by gray correlation. Let the output of Li2 CO3 be X0 , The output of lithium battery (unit GWh) is X1 , The sales volume of new energy vehicles (million unites) X2 , The statistics of for each months from July 2021 to April 2022 are as follows [8]: X0 = [9.33, 11.09, 13.51, 15.89, 17.80, 20.06, 18.79, 20.05, 23.58, 18.62]. X1 = [17.35, 19.50, 23.50, 25.10, 28.2, 31.6, 29.7, 31.8, 39.2, 28.96]. X2 = [22.2, 24.9, 33.4, 32.1, 37.8, 47.5, 35.02, 27.2, 48.4, 27.3]. A scatter plot of the statistics for each month from July 2021 to April 2022 is shown in Fig. 9: According to the gray correlation theory analysis, intuition shows that the most similar to Li2 CO3 production curve is battery production, while new energy vehicle sales are also similar to Li2 CO3 production curve, and only very few months have different trends. So the game between Li2 CO3 price, battery production, and new energy vehicles can be analyzed comprehensively. The game model of Li2 CO3 price, battery production, and new energy vehicles is Integrated Gaming as follows (Fig. 10).

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Fig. 9. Scatter plot of each variables for 2021.07–2022.04

Fig. 10. Integrated game model diagram

4.2 The Game of Li2 CO3 and New Energy Vehicles In this paper, under comprehensive analysis, in order to more intuitively discuss the relationship between new energy vehicle sales and Li2 CO3 price, the three - way game of battery production, Li2 CO3 price and new energy vehicle sales is simplified and a two-sided game model between new energy vehicles and Li2 CO3 is established. Differential game theory is commonly used in joint actions of multiple people, each making independent decisions, across time influenced by common state variables scenarios [18]. The game model established in this paper takes a typical two-person differential game [19] as a reference, and the two-person differential game model is not discussed specifically here due to the space now.

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The model assumes that the new energy vehicle and Li2 CO3 have only one state variable and decision variable, and the new energy vehicle and Li2 CO3 maximize the cumulative gain from moment t0 to t1 by choosing the decision variable.

t1 f1 (t, x1 (t), x2 (t), a1 (t), a2 (t))dt U1 =

U2 =

t0 t1

f2 (t, x1 (t), x2 (t), a1 (t), a2 (t))dt

t0

Among them, the U1 , U2 represent the cumulative benefits of the new energy vehicles and Li2 CO3 , respectively ai (t) represents the decision variables of participant i. xi (t) represents the state variables of participant i at time t, and fi represents the gain of participant i at moment t. The new energy vehicle and Li2 CO3 are subject to both the state transfer equation and the initial values of the state variables, as follows. 

x1 = g i (t, x1 (t), x2 (t), a1 (t), a2 (t)), i = 1, 2 xi (t0 ) = xi0 , i = 1, 2 Among them gi represents the xi the expression of the transfer equation, and xi0 denotes xi the value at t0 the value at the time, i.e., the initial value. The open-loop strategy of differential games, in which each participant commits to the entire path of his action before the game begins, does not correspond to reality [18]. The feedback strategy, on the other hand, is in the spirit of the: optimality principle of dynamic programming: regardless of past states and decisions, the subsequent game chooses the optimal decision considering the current moment and state variables [18]. The Hamilton-Jacobi-Bellman equation [20] is a partial differential equation that is commonly used to find the differential game in Feedback strategy. Consider a continuous dynamic optimization problem with uncertainty:

T max f (t, x, a)dt E a 0 s.t.dx = g(t, x, a)dt + σ (t, x, a)dB(t), x(0) = x0 where the state transfer equation is a stochastic differential equation [21] and σ (t, x, a) is the stochastic influence factor, and B(t) is the Brownian motion [21]. The specific derivation procedure [19] is omitted due to space 1imitation. V (t0 , x0 ) represents the optimal value function at t0 the optimal value function at time, i.e.

T max f (t, x, a)dt E V (t0 , x0 ) = a t0 The optimal decision under this problem can be obtained from the HJB equation of the following equation.  1 0 = max E f (t, x, u) + Vt (t0 , x0 ) + Vx (t0 , x0 )g(t, x, a) + Vx (t0 , x0 )σ 2 (t, x, a) . a 2

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4.3 Li2 CO3 Feedback Model on New Energy Vehicle Sales In the whole new energy vehicle industry chain, Li2 CO3 belongs to the upstream of the industry chain and is the most important in terms of supply chain, while the midstream battery industry is influenced by raw materials as well as new energy vehicle manufacturers, which are affected by more factors. The downstream new energy vehicle manufacturers are mainly influenced by consumers and battery midstream battery supply. In summary, this paper makes a closed-loop model with the whole Li2 CO3 to new energy vehicle sales, sets the corresponding influence parameters, and analyzes the feedback of each closed-loop model on the whole industry chain according to the changes of each parameter. Set the Li2 CO3 production LC , which may be influenced by factors such as mining technology k, lithium reserves c, mining time t, etc., so the model established is L C (k, c, t). Set the yield of the battery as DC . The possible influencing factors are the price of Li2 CO3 jl , Li2 CO3 supply gl , the whole vehicle demand xz , the supply of other materials gq etc., Therefore, the model is established as DC(jl , gl , x z , gq ). Because the sales volume of the whole car is the purchase volume of consumers, that is, to establish the sales volume model of the whole car. Set the sales volumes of the whole car as ZX . The factors that may affect it are the supply of batteries gd . The demand of consumer xx , the price of the batter dj , innovation of technology jc , and battery range dx . The model is Z X (gd , x x , d j , d x ) Among them, if Li2 CO3 mining technology further innovation, also uncovered more lithium reserves, and subsequently reduce the time of mining, making Li2 CO3 production LC up, which is further transmitted to the battery industry, resulting in a certain reduction in the price of Li2 CO3 supply and a rise in the supply of Li2 CO3 , increasing the battery production DC The increase in battery production. Because the production of the whole car is affected by the supply of batteries, so the supply of batteries will rise, the price will also have a certain degree of decline, and then the price of the whole car remains unchanged, the consumer acceptance is higher, and to a certain extent can promote the sales of the whole car. This is the most ideal state, and its model cycle is shown in Fig. 11 below.

Fig. 11. Ideal state model conduction

But the ideal state is almost impossible. In reality, the mining of Li2 CO3 is limited by the mining technology as well as the reserves, etc. The mining time cycle is long and cannot keep up with the demand of the industry, resulting in a huge supply-demand gap and soaring prices of Li2 CO3 , whose model cycle is shown in Fig. 12 below.

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Fig. 12. Model Loop

4.4 Suggestions to Promote the Sustainable Development of New Energy Vehicles In terms of the production of Li2 CO3 : First, according to the above-mentioned reasons affecting the supply of Li2 CO3 , the country should conduct research and investment in the mining of lithium resources technology and stive for early production in order to reduce the difference between supply and demand of Li2 CO3 . Secondly, further exploration is needed for lithium resources to provide a more adequate reserve for future development. Finally, more mining machinery and manpower should be invested to reduce the mining time to ensure the supply demand. In terms of cell yield: the factors are Li2 CO3 price jl , Li2 CO3 supply gl and the demand for complete vehicles xz and the supply of other materials gq etc., and in order to promote the further development of the new energy industry, try to maintain the stability of the price of Li2 CO3 and sufficient supply, in order to promote the installed capacity of the battery at the source to rise and improve the gross profit of enterprises to promote technological innovation capacity. Because of the poor supply and demand of lithium resources, it is also necessary to develop battery recycling projects as soon as possible, led by the battery factory, which can both recycle and improve the production of enterprises. In terms of new energy vehicle sales: the main influencing factors are the supply of batteries 9a consumer demand xx the price of batteries dj innovation of technology jc and battery range dx . etc. The study concluded that the research on the performance improvement of China’s power lithium batteries should be accelerated to make the allocation of lithium resources more reasonable and the batteries have a better range to meet the needs of consumers. In terms of policy: there is also a need for consumer awareness and subsidies, such as the already existing “double points” policy subsidies and the upcoming subsidies for new energy vehicles to the countryside, etc., which can promote the sales of new energy vehicles and contribute to the “double carbon” goal. In addition, there is also a need for vehicle technology development. In addition, there is a need for innovation in vehicle technology, such as the light weighting of new energy vehicles, which can improve the range of new energy vehicles. Of course, it is extremely important to keep the price of batteries and the supply of production.

5 Conclusions In this paper, the impacts of Li2 CO3 supply and price changes on new energy vehicles are analyzed. Firstly, the supply forecasting analysis of Li2 CO3 based on the gray model

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GM(1, 1), is conducted. Then the correlation of Li2 CO3 production, new energy vehicle sales and battery production was analyzed based on gray correlation analysis method, and a game theory model was established. Finally, a closed-loop model which describe the relationship between the Li2 CO3 supply and new energy vehicle sales is established, results lays out some useful suggestions for sustainable development of new energy vehicle industry.

References 1. Liu, S.F.: Grey system theory and its application, Seven edition of Science Press (2017) 2. State Council: Opinions of the State Council of the Central Committee of the Communist Party of China on the Complete, Accurate and Comprehensive Implementation of the New Development Concept to Do a Good Job in Carbon Summit Carbon Neutralization (2021) 3. The Blue Book of Low Carbon Economy: China ’ s Low Carbon Economy Development Report, Social Science Literature Press (2017) 4. Ministry of Industrial Information Parallel management method of average fuel consumption of passenger car enterprises and integral of new energy vehicles (2020) 5. Wang, Q.S., Yuan, C.H., Xu, H.: Global lithium resource distribution and potential analysis. China Mining Industry 2, 10–17 (2015) 6. Liu, N.N., Liu, C.Z., Da, B.W., Zhang, T., Guan, F.Y.: Dependence and risk spillovers between green bonds and clean energy markets. J. Clean. Prod. 279, 583–595 (2021) 7. Sina Finance and Economics, New Energy Vehicles Continuing Price Increases Still Hard to Demand Tesla Delivery Cycle extended to 4–6 months. [DB/OL]. 2022-05-20/2022-05-23. https://finance.sina.com.cn/chanjing/gsnews/2022-05-20/doc-imcwipik0834381.shtml 8. Interface News, Cheng D. Euler Stops New Orders Rising Raw Materials into a Common New Problem for Mini Electric Vehicles. [DB/OL]. https://finance.eastmoney.com/a/202202 252289548255.html. 2022-02-21/2022-05-23 9. China.com Finance, Wuling Hongguang announced its new energy models price increase of 4–8 thousand yuan: “sales champion” whole car sold only 40,000 more. [DB/OL]. https://fin ance.eastmoney.com/a/202203242322845698.html. 2022-03-24/2022-05-23 10. Chen, X.H., Wang, Y., Li, X.H.: Research on the evolutionary stability of green technology transformation strategies of interregional enterprises under environmental regulation. Syst. Eng. Theory Pract. 41(07), 1732–1749 (2021) 11. Xu, X.H., Luo, Q., Bai, G.Q.: Optimal strategy analysis of multi-level green supply chain under carbon emission constraint and carbon trading mechanism. Indus. Eng. Manage. 21(01), 37–44 (2016) 12. Cheng, Y., Wu, D.D., Olson, D.L., Dolgui, A.: Financing the newsvendor with preferential credit: bank vs manufacturer. Int. J. Prod. Res. 59(14), 4228–4247 (2021) 13. Xu, X.H., Wang, Q., Zeng, Q., Peng, H.G.: Research on the optimal ordering decision of perishable goods under the condition of delayed payment. Chinese Manage. Sci. 29(02), 108–116 (2021) 14. Qin, J.J., Li, J.: Research on the optimal production strategy of financially constrained manufacturer alternatives under carbon trading mechanism. Oper. Res. Manage. 30(05), 88–94 (2021) 15. Luo, R.L., Fan, T.J., Xia, H.Y.: Game analysis of supply chain carbon emission reduction technology investment under carbon emission trading policy. China Manage. Sci. 22(11), 44–53 (2014) 16. Cavone, G., Dotoli, M., Epicoco, N., Morelli, D., Seatzu, C.: Design of modern supply chain networks using fuzzy bargaining game and data envelopment analysis. IEEE Trans. Autom. Sci. Eng. 17(3), 1221–1236 (2020)

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Research on the Management of Autonomous Delivery Vehicles at Home and Abroad Mengxu Zhao1,2(B) , Boyang Zhou1 , Tianyi Kang1 , and Yabing Deng1 1 China Society of Automotive Engineers, Beijing, China

[email protected] 2 China Automotive Technology Research Institute Co. Ltd., Beijing, China

Abstract. As a new form of intelligent and connected vehicles, autonomous delivery vehicles are a massive increase in the era of autonomous driving. Autonomous delivery vehicles, as a new type of convenient and safe “contactless delivery” tool, have attracted widespread attention from all walks of life amid the fickleness of the new crown epidemic. The large-scale application of autonomous delivery vehicles requires corresponding supporting laws, regulations, and standards. With the release of the innovative autonomous distribution management policies in several pilot areas of intelligent and connected vehicles, such as the Beijing High-level Autonomous Driving Demonstration Area, and the continuous investment of many companies in product technology and applications, China’s autonomous distribution industry may welcome the rapid landing, and a new logistics ecology based on autonomous driving technology will become possible. This technology-driven industrial transformation will eventually bring users a unique distribution experience. Through analyzing the management system and policies of autonomous delivery vehicles at home and abroad, this article aims to provide a reference for promoting the landing of autonomous delivery vehicles in China. Keywords: Autonomous Delivery Vehicles · Management · Policy · Rights of Road · License · Safety

1 Introduction In the future of intelligent transportation and smart cities, tasks such as transportation, logistics, retail, etc., will be completed by autonomous delivery vehicles instead of humans. The connotation of autonomous delivery vehicles will undergo significant changes, evolving from traditional delivery vehicles to intelligent carriers for various delivery tasks. In the past two years, the new crown epidemic has raged worldwide, and largescale epidemics broke out in many countries, spawning the demand for “contactless distribution” in various scenarios. Autonomous distribution has been widely used in hospitals, communities, office parks, and other scenarios. The state and society have highly valued its industrial development. Autonomous distribution means that there is no or only a small amount of manual participation in the circulation of items/commodities, and machines are used instead of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 86–99, 2023. https://doi.org/10.1007/978-981-99-1365-7_7

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manual distribution methods to improve efficiency and reduce costs. There are many demand scenarios, including express delivery, takeaway, retail, supermarket, restaurant, and other distribution needs. According to the distance range of the current autonomous delivery scenarios, they are divided into three categories: (1) 10–100 m: This range is primarily indoor environments with a large flow of people, changing environments, and high requirements for the performance of robots, such as hotels, office buildings, shopping malls, and other scenarios. (2) 100–1000 m: This range is primarily an outdoor environment with significant changes in light intensity, high environmental complexity, and more complex road conditions, such as community, closed parks, and other scenarios. (3) More than 1000 m: the outdoor environment conforms to the autonomous driving scene. As an emerging industry that has attracted much attention, the autonomous delivery business involves different management departments. Compared with autonomous passenger vehicles, supporting policies, laws and regulations, and standard systems have not yet been formed.

2 Development Status of Autonomous Delivery Vehicles Industry The autonomous delivery vehicle method has the characteristics of low labor cost and heavy load and can realize the multi-point connection. As the best solution to solve the “last mile delivery,” it can become the basis of autonomous life service solutions. The leading players in the foreign autonomous delivery market are primarily startups. The Chinese market has formed a layout with Internet and logistics companies as start-ups’ core and active participation. The autonomous delivery vehicles developed in my country have reached the highest international level in terms of application scenarios, breadth and depth of coverage, and demonstration scope. 2.1 Development Status Abroad The United States, the European Union, and other countries and regions started the research on autonomous delivery early. Through the guidance of standards and regulations, they promoted the deep integration and development of autonomous delivery vehicles, smart transportation, and smart cities. They supported the demonstration application of autonomous delivery vehicles. However, relevant standards and regulations have not yet been implemented. In 2018, the fully autonomous delivery vehicle R-1 was launched by Nuro, a start-up company in Silicon Valley, USA. It is the world’s first L4 autonomous delivery vehicle, which can be used on ground roads in most cities. The Nuro autonomous delivery vehicle began commercial trial operation in the same year and completed about 1,000 delivery services (Fig. 1).

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Fig. 1. Nuro Autonomous Delivery Vehicle

In April 2020, Nuro officially launched a new generation of autonomous delivery vehicles, R2, which was approved by the U.S. Department of Transportation (DOT) and the U.S. National Highway Safety Administration (NHTSA), which is also the first time a similar regulatory license has been approved in the United States. Nuro mainly cooperates with industry giants such as Walmart, Kroger, and Domino’s Pizza to carry out autonomous delivery of food, beverages, medicines, and other products.

Fig. 2. Starship Autonomous Delivery Vehicles

In 2019, Starship Technologies, an autonomous delivery vehicle startup founded in the U.K, received a license to legally test and operate on open sidewalks at Purdue University and nearby. In addition, AutoX launched an autonomous delivery service to deliver fresh food in the United States (Fig. 2). 2.2 Domestic Development Status The domestic autonomous delivery research started a little later than foreign countries, but domestic enterprises have more obvious scene advantages compared with other countries, mainly in the following aspects: (1) The domestic logistics and distribution business market are huge, and the demand for distribution far exceeds that of other countries.

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(2) The domestic population density is large, and the average delivery distance of each order is relatively short, so the requirements for the sustainable working ability of autonomous delivery vehicles are relatively low. (3) Driven by mobile Internet technology, the penetration rate of autonomous delivery, takeaway, and other services in Chinese cities is relatively higher, and the implementation scenarios of autonomous delivery vehicles are more abundant than those in foreign countries. Since 2015, China’s autonomous delivery vehicle industry has flourished, gradually forming joint innovations between enterprises and local governments and actively exploring innovative development models for autonomous delivery scenarios. Table 1 shows the domestic development status in the past four years. Table 1. The Development Status of China’s Autonomous Delivery Vehicles from 2018 to 2022 2018

2019

1. JD.com and Meituan’s autonomous 1. Dongfeng Sharing Box autonomous delivery delivery vehicles achieve normal operation in vehicle served the 2019 Military Games and Xiongan; achieved demonstration operation in Wuhan Intelligent Network Demonstration Zone; 2. Meituan launches autonomous delivery vehicle open platform and new L4 level autonomous delivery concept vehicle;

2. Demonstration application of BIT’s autonomous transport vehicle at the 2019 Smart Expo;

3. Suning autonomous vehicles are put into use in many stores;

3. The Cainiao autonomous delivery vehicle was put into use at the Xiong’an Citizen Service Center in Hebei;

4. Baidu and Neolithic released the “Neolithic 4. Suning has opened the road test of the AX1” autonomous logistics vehicle, which terminal 5G autonomous delivery vehicle to has been put into operation in Changzhou, the media; Xiong’an and other places; 5. Cainiao New Retail Autonomous Delivery Vehicles debut at Hangzhou Yunqi Conference;

5. Yiqing low-speed autonomous delivery vehicle debuted at Huawei HC2019 conference;

6. Neolix autonomous delivery vehicles landed in Changzhou, with monthly sales exceeding 10,000

6. On August 17, a new generation of Suning’s 5G Wolong autonomous delivery vehicle was tested on the ground;

7. The autonomous delivery vehicle “Qingtu Xiaozhi” of IDRIVERPLUS successfully completed the task of delivering books between Tsinghua University libraries.

7. The white rhino autonomous delivery vehicle is in trial operation in the Beijing Demonstration Zone, delivering real-time autonomous delivery of fresh items; (continued)

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2020

2021

1. Baojun E200 “autonomous delivery vehicle” 1. The Dongfeng Sharing Box autonomous built by SAIC-GM-Wuling has started normal delivery vehicle was unveiled at the Shanghai operation; Auto Exhibition and the first China International Consumer Goods Fair; 2. Unity Drive’s innovative autonomous delivery vehicles deliver vegetables to an epidemic area in Shandong;

2. Ali Xiaomanlu is stationed in 15 colleges and universities in 11 cities, serving more than 300,000 students;

3. Ali’s self-developed Xiaomanlu G series autonomous delivery vehicles have launched normal distribution and operation services in Hangzhou, Beijing, Tianjin, Shanghai, Chengdu, Xi’an, Wuhan, Nantong, and other cities;

3. Meituan announced that a new generation of self-developed autonomous delivery vehicles has officially landed and operated in Shunyi, Beijing;

4. Neolix’s autonomous delivery vehicle provides contactless meal delivery service for isolation points of Haidian Hospital;

4. HAOMO. AI ‘s autonomous delivery vehicle “Little Magic Camel” debuted at the 2021 Digital City Smart IoT Exhibition;

5. The autonomous delivery vehicle “Wobida” of IDRIVERPLUS provides food delivery service for Wenzhou Yueqing People’s Hospital.

5. The autonomous delivery vehicle launched by Haomo.Ai Co., Ltd. Was officially put into operation in Shunyi, Beijing; 6. JD.com, Meituan, and Neolithic won the first batch of autonomous delivery vehicle license issued by the Beijing High-level Autonomous Driving Demonstration Zone.

3 Analysis of the Management System of Autonomous Delivery Vehicles With the continuous acceleration of the commercial process of 5G networks and the continuous iteration of autonomous driving technology, the rise of autonomous delivery vehicles will start by optimizing the business model and promoting the construction of new industrial ecology. When the autonomous delivery industry develops to the stage of large-scale application, it is necessary to standardize the management of the entire autonomous delivery industry. For the definition and classification of autonomous delivery vehicles, there are currently three types of views in the industry: one is as a motor vehicle, the second is as a non-motor vehicle, and the third is as a robot. [1] But one thing is obvious, no matter what definition or classification, autonomous delivery vehicles all need to be registered, obtain licenses, and have complete insurance products. To ensure safety, they should be incorporated into the existing traffic management system for overall management.

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3.1 Classification and Naming of Autonomous Delivery Vehicles 3.1.1 Classification of Autonomous Delivery Vehicles There is still much controversy over the industry’s classification and definition of autonomous delivery vehicles. There are three different views as follows: The first is as a motor vehicle. According to the definition of “motor vehicle” in the “Road Traffic Safety Law,” a motor vehicle is a wheeled vehicle driven or towed by a power device and driven on the road for passengers or for transporting goods and special engineering operations [2]. Autonomous delivery vehicles are “wheeled vehicles used to transport goods and carry out special engineering operations.” This attribute is closer to the category of “motor vehicles,” However, if an autonomous delivery vehicle is defined as a motor vehicle, it must undergo field tests and open road tests before it can be legally compliant on the road. At the same time, it must also accept strict product standards, market access management, and license acquisition. The second is as a non-motorized vehicle. At this stage, autonomous delivery vehicles operating in various demonstration areas across the country are all driven in non-motor vehicle lanes by the driving rules of non-motor vehicles. According to the definition of “non-motorized vehicle” in the “Road Traffic Safety Law,” a vehicle driven by human or animal power and driving on the road, and although a power device drives it, its designed maximum speed, empty vehicle mass, and external dimensions conform to the relevant national standards of transportation such as disabled motorized wheelchairs, electric bicycles, etc. [2] Although the design speed of the autonomous delivery vehicle meets the legal requirements for non-motorized vehicles, its empty vehicle quality exceeds the definition of non-motorized vehicles by relevant laws. Therefore, the autonomous delivery vehicle does not meet the description of a “non-motorized vehicle.” The third is as a robot. During the epidemic, most companies’ autonomous delivery vehicles usually submit applications for pilot operation to the local government as “robots,” After the local government approves and records, it coordinates public security, traffic management, and other departments to give them pilot operation support. If autonomous delivery vehicles are managed as robots, the current law does not prohibit robots from going on the road. 3.1.2 Naming of Autonomous Delivery Vehicles At present, autonomous delivery vehicles have not yet obtained a unified name in the industry, and there is a lack of accurate definitions. Autonomous delivery vehicles launched by various companies have significant differences in design size, quality, speed, etc., as shown in Table 2, the common autonomous delivery vehicle parameters. According to the application fields and characteristics of products, there are various names, such as autonomous food delivery vehicles, autonomous sales vehicles, autonomous delivery vehicles, intelligent express delivery vehicles, etc.

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JD.com

Idriver Plus

GoFurther. AI

Neolix

White Rhino

Modai 20

Delivery

WBD-C81

Chaoying 800C

X3

White Rhito

Application Scenes

Instant Delivery

Express Delivery

Delivery Express Delivery

Delivery Express Delivery

Autonomou s Retail

Product Size (m)

2.45*1.01* 1.9

2.15*0.965* 1.69

1.855*0.817* 1.223

1.715*0.75* 1.69

Maximum Load(kg)

/

200

80

Maximum

45

30

Maximum Speed

/

Driving Rules

motorized

Enterprises Product Type

Unity Drive Kuafu

Kuafu mini

Instant Delivery

Delivery Industrial Logistics

Express Delivery Instant Delivery

2.5*1*1.7

2.5*1* 1.8

3.65*1.56 *1.95

1.77*0.915*1.75

200

500

500

2000

300

10

18

25

25

25

20

25

10

18

25

25

25

20

motorized

motorized

motorized

motorized

motorized

Motor Vehicles

Motor Vehicles

Figures

First of all, the uniform naming of autonomous delivery vehicles will help them obtain legal status and will also affect the formulation of relevant policies for the autonomous delivery industry in the future. For example, defining autonomous delivery vehicles as “motor vehicles” will affect the formulation of policies by relevant authorities to promote access, subsidies, annual inspections, and scrapping of autonomous delivery vehicles. On the other hand, autonomous delivery vehicles require the coordination and cooperation of the Ministry of Industry and Information Technology, the Ministry of Public Security, and the Ministry of Communications on issues such as identity attribute confirmation, supervision, road rights, and license plates. First, the Ministry of Industry and Information Technology should establish the definition of autonomous delivery vehicles in the current vehicle management structure and identify and supervise the identity attributes of autonomous delivery vehicles. The second is the Ministry of Public Security, which formulates the right-of-way management policy for autonomous delivery vehicles; the third is the Ministry of Communications, which issues road licenses for operating autonomous delivery vehicles. In the face of various industrial development problems, due to the lack of product naming, the lack of competent authorities to coordinate and promote related issues has affected the rapid growth of the autonomous delivery industry to a certain extent. Second, the lack of product definition leads to the lack of quality assurance of autonomous delivery industry products. Autonomous Delivery Vehicles lack specifications for production qualifications, factory inspection, and quality management systems. The lack of these specifications will lead to product quality cannot being guaranteed. For example, in terms of production qualifications, the access management of production enterprises is not clear, and there is a lack of constraints on the comprehensive conditions and scale of enterprises, whether they can perform contracts, and whether they can independently develop, produce, and test. Regarding quality management, there is a lack of

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a quality management system, such as inspection of production, R&D equipment, and standardization of factory inspection. Currently, autonomous delivery vehicles have not been included in the Ministry of Industry and Information Technology’s “Road Motor Vehicle Manufacturers and Products Announcement,” and each company has made its factory and related quality certification, which has specific potential safety hazards. There is a lack of uniform regulations on whether the parts of autonomous delivery vehicles must meet the vehiclelevel requirements, especially whether all aspects of autonomous delivery vehicles must meet the vehicle-level needs. At present, there is still much controversy in the industry. Considering the current development status of the autonomous delivery industry chain, most X-By-Wire chassis have reached the vehicle-level requirements. However, at this stage, key components such as sensors and domain controllers are still complex to meet vehicle-level needs. 3.2 Traffic Management System of Autonomous Delivery Vehicles Currently, the commercial operation of autonomous delivery vehicles on open roads in China is mainly concentrated in various intelligent connected vehicle demonstration areas across the country. It is promoted by the operating enterprises joint of competent authorities of the demonstration zone and in the form of a pilot. In a closed park, the park management department generally conducts customized management based on the park’s characteristics after obtaining government permission and supervises the vehicles through the remote driving system. In the demonstration area, the autonomous delivery operation is carried out. First, the user acceptance in the area is relatively high, and there is a rather large user group; second, the roadside infrastructure is pretty complete, and the 5G network coverage rate is high, which can provide an excellent normal for autonomous delivery vehicles. The third is that the local government has a more positive attitude and can give a better pilot policy. More than 26 provinces and cities have issued detailed management rules for road testing of intelligent connected vehicles, but none involve autonomous delivery vehicles. In January 2021, the Ministry of Industry and Information Technology, the Ministry of Public Security, and the Ministry of Transport jointly issued the “Management Specification for Road Test and Demonstration Application of Intelligent Connected Vehicles (Trial)” (draft for comments), in terms of vehicle types, to meet the requirements of vehicles engaged in special operations such as garbage trucks and cleaning vehicles, special operation vehicles have been added to the applicable scope [3], but autonomous delivery vehicles are not included. The appearance, registration, testing, traffic management, and temporary parking of autonomous delivery vehicles still lack standardized and unified management. In addition, the division of legal responsibility for autonomous delivery vehicles in road traffic accidents is unclear, the subject is not clear, and there is no explicit requirement for a safe takeover. For autonomous driving test vehicles, China’s current laws and regulations require that there must be a safety officer in the main driver’s seat, and the safety officer is the primary person responsible for traffic accidents. The autonomous delivery vehicles do not have a driver’s seat; in the early stage of the test,

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safety officers accompany it; after the last test is mature, there is only remote monitoring personnel. When a traffic accident occurs in an autonomous delivery vehicle, how to determine the responsible subject, who should bear the corresponding criminal, civil liability, administrative penalties, etc., has yet to be confirmed. 3.3 Safety Management System of Autonomous Delivery Vehicles Safety is a prerequisite for the large-scale commercialization of autonomous delivery vehicles. Urban low-speed autonomous delivery scenarios have exceptional complexity, and the entire delivery process is full of long-tail scenarios. For example, non-motorized vehicles and pedestrians are mixed on auxiliary roads, and poor signal and positioning problems are caused by roadside shade. Autonomous delivery vehicles need to ensure safety in the operation of open roads, and a complete safety guarantee system must be established to ensure the safety of autonomous delivery products, testing safety, and operational safety. 3.3.1 Product Safety The product safety of autonomous delivery vehicles mainly includes software security and hardware safety.

Fig. 3. ODD Definition of Autonomous Delivery Vehicles

Autonomous delivery vehicle software security requires autonomous delivery vehicles to adhere to security benchmarks from the beginning of their design. First, strict compliance with traffic rules and relevant laws and regulations should be prioritized. Secondly, the Operational Design Domain (ODD) of autonomous delivery vehicles should be clearly defined, as shown in Fig. 3. And when the autonomous delivery vehicle fails or exceeds the scope of the ODD, the “safe exit” of the autonomous delivery vehicle should be realized by establishing a safety diagnosis mechanism and redundant backup system design. Finally, the cyber security of the autonomous delivery vehicle is ensured by ensuring the security of the Internet of Vehicles communication. The hardware safety of the autonomous delivery vehicle mainly refers to the reliability and safety of the parts of the autonomous delivery vehicle. Like traditional vehicles, ensuring the hardware safety of autonomous delivery vehicles requires safe driving in any environment during the vehicle’s entire life cycle. This requires that the reliability and safety of autonomous delivery vehicle components must meet the corresponding standards and specifications. At the same time, it must be noted that there are differences between autonomous delivery vehicles and autonomous passenger vehicles regarding vehicle key technologies, information interaction key technologies, and basic support key technologies [4]. As well as, parts quality certification must be distinguished.

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3.3.2 Testing Safety Similar to the development of intelligent connected vehicles, the development process of autonomous delivery vehicles also includes software simulation testing, closed road testing, and open road testing. Figure 4 shows the test flow chart of autonomous delivery vehicles.

Fig. 4. Testing Flow Chart of Autonomous Delivery Vehicles [5]

Autonomous delivery vehicles must undergo many performance tests before achieving large-scale commercial operations. They must meet safe driving requirements in natural driving scenarios, dangerous working conditions, and “safe exit” scenarios beyond the ODD design area. 3.3.3 Operational Safety After meeting the relevant requirements for product safety and testing safety, autonomous delivery vehicles must also meet operational safety requirements during the commercial operation/trial operation stage, including road safety and delivery commodity safety. Interactions between autonomous delivery vehicles and other road users, as well as remote monitors/security officers, play a vital role in ensuring the safety of vehicle operations. For other road users, the existing strategy is to install dot-matrix screens and voice devices at the front, rear and left and right sides of the vehicle to convey driving intentions to other road users. For remote supervisors, the vehicle operation status and order delivery status can be monitored through the remote monitoring center platform. Remote supervisors can be reminded to intervene through voice/video signals under dangerous conditions. For safety officers, since autonomous delivery vehicles do not have driving conditions, safety officers need to supervise outside the vehicle and take over the vehicle in time under dangerous conditions to ensure operational safety. As for the safety of autonomous delivery of goods, it is necessary to ensure the safety of goods in the manual operation/automation process of sorting and loading and ensure that the goods are delivered to the corresponding users during the delivery process.

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4 Management Policy Development Status of Autonomous Delivery Vehicles at Home and Abroad Due to their simple application scenarios, autonomous delivery vehicles are expected to become the first landing scenario for autonomous driving to achieve large-scale commercial applications. In recent years, the United States, the United Kingdom, Germany, Japan, China, and other countries have promoted road testing and commercial trial operation of autonomous delivery vehicles through innovation in policies and regulations. 4.1 Foreign Autonomous Delivery Management Policy According to the different operating roads of the application scenarios of autonomous delivery vehicles, countries such as Europe, the United States, and Japan manage by adopting new laws, continuing to use old regulations, and pilot applications. 4.1.1 The United States In January 2021, the U.S. Department of Transportation pointed out in the “Automated Vehicles Comprehensive Plan” that autonomous delivery vehicles are an emerging application of autonomous driving technology that can provide long-distance unmanned transportation, unmanned distribution, and last-mile travel services are important aspects of future smart cities [6]. Currently, the United States divides autonomous delivery vehicles into three categories for management: The first category is to define smaller autonomous delivery vehicles as Personal Delivery Devices (PDD) [7], which need to comply with the PDD regulations promulgated by each state. The second category is large autonomous delivery vehicles that drive on motor vehicle lanes. They are managed as motor vehicles and need to comply with current motor vehicle laws and regulations in the United States. The last category is for areas without regulatory support or states without legislation that can apply for a pilot operation to obtain the right of way and test on the road. 4.1.2 Europe The United Kingdom defines autonomous delivery vehicles as “miniature mobile vehicles” [7] for supervision, and they are managed by the “motor vehicle” regulations. The vehicle operator is the main responsible unit for the accident. A certain degree of policy relaxation has been carried out regarding management and insurance. Germany adopts a strict licensing system to regulate the management of autonomous delivery vehicles. Not only does the vehicle need to obtain a license to operate on the road, but the operating route of the vehicle must also obtain government certification before it can be operated on the road.

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4.1.3 Japan In 2020, Japan’s Ministry of Land, Infrastructure, Transport, and Tourism issued the “Benchmark Relaxation and Certification System for Automatic Delivery Robots” to provide safety measures for testing autonomous delivery vehicles. In June 2021, the Japanese Police Department issued the “Road Use Management Regulations Related to Road Testing of Certain Automatic Delivery Robots,” which simplifies the license review process for autonomous delivery vehicles tested and operated on public roads and facilitates companies to upgrade massive road tests [7]. Overall, autonomous delivery vehicles in Japan can be operated on public roads. Still, at the same time, the Japanese government has restricted the size, appearance, and speed of autonomous delivery vehicles and formulated corresponding requirements. 4.2 Domestic Management Policy of Autonomous Delivery Vehicles Under the strong demand for autonomous delivery caused by the epidemic, various provinces and cities in China have issued management rules and guidelines for autonomous delivery to promote the accelerated commercialization of the application of autonomous delivery vehicles. The category attribution has not yet been clarified, and unified laws and regulations have not yet been formed. In May 2021, the Beijing Intelligent Connected Vehicle Policy Pilot Zone issued the “Implementation Rules for the Management of Autonomous Delivery Vehicles” (Hereinafter referred to as the “Detailed Rules”) and gave the first batch of autonomous delivery vehicles to three companies in Beijing, namely JD.com, Meituan, and Neolithic. The vehicle code of the autonomous delivery vehicle gives the autonomous delivery vehicle the corresponding right of way and conducts commercial trial operations in Beijing High-level Autonomous Driving Demonstration Area. As the first nationwide management policy for autonomous delivery vehicles, the “Detailed Rules” refers to the management of autonomous delivery vehicles in the region concerning non-motorized vehicle management and conducts targeted testing and verification of autonomous delivery vehicles by issuing vehicle codes. The right of way is granted to autonomous delivery vehicles, breaking through the problem that autonomous delivery vehicles cannot go on the road due to current laws and regulations. In August 2021, Wuhu promulgated the “Wuhu’s JD Autonomous Delivery Vehicle Trial Operation Management Measures (Trial),” which stipulates the operating entities, vehicles, the application process, operation management, safety management, traffic violations, accident handling of JD autonomous delivery vehicles. In September 2021, the Shunyi District of Beijing issued the “Implementation Guidelines for the Management of Autonomous Delivery Vehicles.” (Hereinafter referred to as the “Guide”); in terms of autonomous delivery policy, the “Guide” allows autonomous delivery vehicles to go on the road for public testing and operation, and grants autonomous delivery vehicles corresponding road rights, achieving a policy breakthrough from the park to the open road.” In June 2022, the tenth meeting of the Standing Committee of the Seventh Shenzhen Municipal People’s Congress officially passed the “Shenzhen Special Economic Zone Intelligent Connected Vehicles Management Regulations” (hereinafter referred to as the

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Regulations). Carry out the whole chain legislation in terms of access registration, user management, traffic violation and accident handling, legal liability, etc., to provide legal support for the commercial development of relatively mature application scenarios such as autonomous delivery vehicles [8].

5 Conclusion According to the development status of autonomous delivery vehicles at home and abroad, the analysis of the management system, and the research progress of policies at home and abroad, the next steps for the development of autonomous delivery vehicles in China are as follows:

Fig. 5. Autonomous Delivery Vehicle Management System

1. Define the identity of autonomous delivery vehicles and establish a complete production management system. Figure 5 shows the autonomous delivery vehicle management system. Combined with relevant management experience at home and abroad, clarify the identity definition of autonomous delivery, clarify its legal attributes, and promote the integration of autonomous delivery vehicles into the modern transportation system. At the same time, strengthen the coordination of multiple ministries and commissions, establish a complete product management system, and clarify the management methods for product access management, road rights, vehicle registration, license acquisition, and insurance of autonomous delivery vehicles. 2. Carry out standard formulation and revision to clarify the requirements of product performance specifications and establish a complete product standard and certification system. It can give full play to the characteristics of the group standard “first try,” compile a standard system related to the product performance of autonomous delivery vehicles, make a clear definition of the product performance and specifications of autonomous delivery vehicles, ensure product safety performance, and establish a relatively complete product certification system. 3. Strengthen policy innovation and further expand the urban demonstration of autonomous delivery business. Encourage local governments to carry out policy innovation continuously, gradually expand the opening time and space scope of autonomous delivery business and form larger-scale urban demonstrations.

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4. Build a complete autonomous delivery industry ecology and vigorously promote local autonomous delivery infrastructure construction. Establish an interdisciplinary, cross-industry collaboration and industrial collaboration mechanism in autonomous delivery vehicles, promote the cooperation between upstream and downstream enterprises in the industry, and at the same time strengthen the construction of autonomous delivery infrastructure such as 5G networks, IoT, stops, and charging piles. 5. Strengthen cyber security supervision to ensure the driving safety of autonomous delivery vehicles and network data security. The local government establishes a remote safety supervision platform and a perfect network data security supervision system to conduct unified remote monitoring of autonomous delivery vehicles in the region to ensure the security of the network and data of autonomous delivery vehicles.

References 1. Liu, Y.: Panorama of the global autonomous delivery industry and China’s future development. Transp. Manager World 2, 64–69 (2022). (Chinese) 2. The Fifth Session of the Standing Committee of the Tenth National People’s Congress of the People’s Republic of China. Road Traffic Safety Law of the People’s Republic of China, 1 May 2004. (Chinese) 3. Ministry of Industry and Information Technology, Ministry of Public Security, Ministry of Transport. “Management Specification for Road Test and Demonstration Application of Intelligent Connected Vehicles (Trial)” (Draft for Comment). 12 January 2021. (Chinese) 4. China Society of Automotive Engineers. Technology Roadmap for Energy-Saving and New Energy Vehicles, pp. 196–198. 2.0. Machinery Industry Press, Beijing (2019). (Chinese) 5. Zhang,Y., Xia, H., et al.: The identity and road safety of autonomous delivery vehicles [R]. China Electric Vehicle 100 Association, Beijing (2021). (Chinese) 6. Ni, J.: Development Status and Prospect of China’s Functional Autonomous Vehicle Industry. China SAE Congress & Exhibition, Shanghai (2021). (Chinese) 7. Li, Q.: Research on policy management and technical standards of functional autonomous vehicles in China. China SAE Congress & Exhibition, Shanghai (2021). (Chinese) 8. Standing Committee of Shenzhen Municipal People’s Congress. Shenzhen Special Economic Zone Management Regulations on Intelligent Connected Vehicles, 30 June 2022. (Chinese)

The Role and Implementation Path of the Automotive Industry in Carbon Neutrality Fanlong Bai1,2 , Fuquan Zhao1,2 , Xinglong Liu1,2 , and Zongwei Liu1,2(B) 1 State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China

[email protected] 2 Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing, China

Abstract. The automotive industry is facing enormous pressure to reduce carbon emissions. Due to its complex industrial chain and close relationship with energy transition, the research on its emissions mitigation strategy is of great significance and urgency. This paper studies the key aspects of the auto and related industries to achieve coordinated emission mitigation. As a vital component of emission mitigation, a carbon accounting framework considering emissions from both product and company’s value chain is established. Carbon emissions of vehicles with different powertrains are compared and automotive company’s life-cycle carbon emissions are studied. Besides, the potential of vehicle to grid and vehicle to everything in emission mitigation is analysed. Practical development countermeasures for automotive industry’s carbon neutrality are put forward. Keywords: carbon neutrality · automotive industry · emission accounting · emission mitigation strategy

1 Introduction With the proposal of China’s dual carbon target, the automotive industry is facing increasingly severe emission mitigation challenges. Currently, China’s transport sector accounts for about 10% of carbon emissions, of which road transportation accounts for about 80% [1]. While emissions from the transportation sector are still growing considerably, research on the carbon reduction strategy of the automobile industry is getting more and more attention. Compared with China’s goal of achieving carbon neutrality by 2060, the China Automotive Technology and Research Center believes that China’s auto industry should achieve carbon neutrality by 2050 [2]. Because the carbon neutrality goal set by most global auto companies is 2040–2050, and if the Chinese auto industry does not achieve -neutral carbon products by 2050, companies may not be able to expand their business in carbon-neutral countries and regions and have substantial considerable disadvantages in competing with carbon-neutral products in the market. Under the dual carbon goal, the challenges faced by the automotive industry are as follows: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 100–112, 2023. https://doi.org/10.1007/978-981-99-1365-7_8

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Firstly, the automobile industry is large in scale and attracts close great high attention. The automotive industry’s value chain is particularly long, and the range of industries associated with automobiles is wide. Consequently, carbon reduction in automobiles is of strategic significance. While China’s car ownership per thousand people is still underdeveloped [3], there is still much room for future increases. The automobile industry is facing the dual pressure of development and emission reduction. Secondly, the international economic and trade mechanism and the market competition environment have changed, making the situation of the automobile industry uncertain. Thirdly, upstream and downstream cooperation is required to achieve the dual-carbon goal of automobiles. The carbon emissions of automobiles are significant, and thus they are highly concerned for society. In addition, most of China’s auto companies cannot manage carbon emissions throughout the vehicle’s life cycle [4], especially when the managed object is transitioning from carbon emissions in the use stage to that of the industrial value chain. Therefore, it is significant and needs to analyze carbon emission reduction’s role and implementation path in the automobile industry.

2 The Role of the Automotive Industry under Carbon Neutrality Because of the close connection between the automotive industry and energy, technology, manufacturing, and other sectors, the automotive industry plays a particularly important role in achieving carbon neutrality. As Fig. 1 shows, the automotive industry is deeply engaged in developing energy, transportation, manufacturing, and circular economy to achieve cross-industry, diversified and collaborative green transformation.

Fig. 1. The relationship between the automotive industry and other sectors

(1) Energy transformation is the foundation of achieving carbon-neutral. As a major oil consumer, automobiles’ electrification will significantly reduce China’s fossil energy consumption [5], especially oil. In addition, China will vigorously develop renewable energy, while renewable energy’s consumption and storage problems have become a significant bottleneck restricting its development [6]. Electric vehicles can serve as a

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solution to this problem. As a flexible energy consumption, storage, and distribution carrier, vehicles, especially battery electric vehicles (BEV), will cooperate sincerely with the energy transition to jointly promote the green development of energy in China. (2) Manufacturing transformation: With the increasing pressure on emission control and the proposal of carbon taxes in various countries [6], emission mitigation ability will become an important measuring criterion for manufacturing companies. It is not only an important driver for enterprises to improve their international competitiveness but also an indispensable way for China to transform from a big manufacturing player to a powerful one [7]. (3) The transportation industry currently accounts for about 10% of the carbon emissions in China, while the average annual growth rate of carbon emissions in the past nine years is 5% [8]. It has become a key national decarbonized focus. As a vital part of production and life, transportation supports China’s economic development and people’s livelihood improvement. The transportation industry will steadily achieve green transformation while maintaining its growth in the future. (4) Circular economy emerges as a significant trend in China. It has a very close relationship with the automotive industry. With the rising-up of electric vehicles, the second use of batteries and car recycling has become more important. At the same time, the number of car parts is very large, indicating a huge recycling market in the future. (5) Technological innovation is the key to supporting the automotive industry’s transformation and other sectors. With the breakthrough of key low-carbon technologies and corresponding supporting policies, the high costs caused by green transformation will be gradually addressed [9]. Meanwhile, all kinds of negative carbon technology, manufacturing energy efficiency improvement technology, and resource recovery technology will play a strong role in promoting the green transformation of automotive and related industries. In short, the low-carbon transformation of the automotive industry is complex systematic engineering. In the future, we need to pay close attention to the dynamics and prospects of energy, transportation, manufacturing, circular economy, and diversified technological innovation, and seek the optimal solution for cross-industry synergistic development.

3 The Key to the Coordinated Transformation of Automotive-Related Industries 3.1 The Energy Industry As a flexible carrier of energy consumption and storage, automotive plays a special role in the energy industry. It is a major energy demand and an important energy supplier, which will make a huge contribution to energy’s transition. In terms of policy, the development of the energy industry focuses on the decarbonization of electricity. The carbon emissions intensity of the electric vehicles’ operation depends heavily on the source of electricity. Therefore, the promotion of renewable power is not only viewed as an important measure of the energy sector’s transition but also will make the electric vehicles truly zero-emission.

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China has started carbon emissions trading market. The power companies are included in the pioneering industry. Meanwhile, green electricity certificate trading and renewable energy subsidy policy are becoming mature. With the promotion of relevant power policies, the emission reduction benefits brought by electric vehicles will be more and more obvious. As for automobiles themselves, automobiles are the main consumer of petroleum in China. There is an undoubted pathway for passenger cars’ decarbonization, which is to develop battery-electric vehicles. However, with the rapid development of renewable energy in the future, hydrogen energy will also play an important role. In some hard-toelectrify sectors, such as heavy-duty long-haul commercial vehicles, the current technical route is still not clear because of battery’s limitations. Hydrogen can perfectly fit variable renewable energy with brilliant flexibility. It is widely believed that it will contribute to the decarbonization of commercial vehicles through the fuel cell. In the future, the coordinated transformation of energy and automotive industries needs to be considered comprehensively from the perspectives of carbon reduction efficiency and economy. Given the obvious imbalance between different regions in China, local resource endowment should be fully evaluated to determine the best application products. So, the automotive company should pay attention to the national overall energy transformation roadmap and value the energy transformation roadmap of key regions. Selecting and developing appropriate products based on emission mitigation potential, region’s specific demand, market value, and energy efficiency can produce more significant profits and make the auto company more competitive. 3.2 The Manufacturing Industry As a pioneer in the decarbonization and upgrading of the manufacturing industry, the automotive industry will continue to improve energy efficiency. The automotive company with a low-carbon brand image will have a huge advantage in the future. In terms of policies, the carbon emissions trading implemented by China has already included automotive enterprises in some regions, and the energy consumption restriction of manufacturing enterprises has been gradually tightened. Because it is difficult for the enterprise to actively transform itself due to the additional costs caused by decarbonization, policies will still play a leading role in the short term. In addition, with the incorporation of carbon tax into legislation by more countries, international carbon barriers and carbon tax regulation will become an inevitable challenge China’s company has to face. Therefore, whether manufacturing enterprises can achieve carbon reduction as soon as possible will become a core competitiveness in the future. In the short term, the scope of national carbon emission regulation will be expanded from the power industry to eight key sectors before the national auto industry gets included. At the same time, each carbon emission pilot may include the auto industry in the local assessment in advance according to the local development situation. 3.3 The Circular Economy In July 2021, the National Development and Reform Commission released the “14th Five-year” circular economy development plan. The recycling of used power batteries

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is one of the key actions of the development plan. It is emphasized to improve the traceability management system for the recycling and utilizing new energy vehicle traction batteries. The responsibility of related product recycling will be continuously detailed in the future. As one of the core components of electric vehicles, automotive traction battery accounts for more than 20% of carbon emissions in the total life cycle [10]. With technological advancement, its manufacturing and use mode will continue to be optimized in the future. Battery second use will enable retired batteries to realize cross-industry utilization, especially as an energy storage device for renewable energy generation [11]. V2G energy storage and traction battery second use will change the mechanism of product design, build a new operation ecology and increase the total value of batteries.

4 Decarbonization Pathways for Automotive Firm 4.1 The Framework of Industry’s Emission accounting

Input Technical parameters of vehicle + Energy Emission Factors + Travel characteristics + Low carbon regulatory impact (CAFC&NEV Credit, carbon credit, carbon taxes…)

Carbon emissions accounting mechanism for product LCA and company

Product LCA

Fuel cycle (use phase carbon emissions) + vehicle cycle (manufacturing and recycling phase carbon emissions)

Company Value Chain

Scope 1 (direct carbon emissions) + Scope 2 (indirect emissions from consumed energy) + Scope 3 (supply chains and downstream service)

Output Product carbon emission + Company carbon emission + Impact of Policies ···

Fig. 2. Carbon emission accounting model for auto company

At present, the automotive industry still lacks a uniform standard and carbon emission accounting model for company’s whole life cycle. In the future, automotive enterprises should establish the emission accounting model of two chains: the product chain and the company value chain. As shown in Fig. 2, the carbon emissions of both product and company can be obtained according to the technical parameters, energy carbon emissions factors, travel characteristics, and the influence of regulations. Auto companies can optimize their overall strategy and product plan based on the output to achieve the overall lowest carbon emissions. 4.1.1 Life-Cycle Carbon Emissions of the Vehicle The vehicle’s life cycle can be divided into two stages, namely the fuel cycle and the vehicle cycle. The fuel cycle refers to the carbon emission during a vehicle’s use stage,

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Carbon emissions (%)

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Raw material

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refrigerant escape

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Fig. 3. Left: Carbon emission ratio of passenger vehicles with different power types in different cycles; Right: Vehicle cycle carbon emission composition [12]

As can be seen from the left of Fig. 3, the carbon emission ratio of the vehicle fuel cycle and vehicle cycle varies greatly with different power. The vehicle cycle’s carbon emission of battery electric vehicle (BEV) accounts for about 46%, while that of internal combustion engine vehicle (ICEV) is only about 25%. With the decarbonization of electric power, its vehicle cycle carbon emission ratio will continue to increase, becoming almost the only emission stage of BEV in a zero-carbon electricity scenario. As the right of Fig. 3 shows, diesel vehicles have the most carbon emission during the vehicle cycle, which equals 81g CO2 /km. The raw material acquisition is the largest contributor among different stages in the in-vehicle cycle. And the vehicle-cycle carbon emissions of battery electric vehicles are more than those of gasoline vehicles due to large emissions from traction battery manufacturing. 4.1.2 Carbon Emissions of the Company Value Chain Considering the principle from the inside to the outside, the carbon emission of the whole value chain of auto companies can be divided into three scopes [13]. (1) Scope 1 refers to the direct emissions of the enterprise itself, such as emissions of company’s all vehicles and emissions from fuel combustion activities; (2) Scope 2 includes carbon emissions generated by the outsourcing energy such as electricity and heat, as well as water and other resources; (3) Scope 3 refers to the carbon emissions of the upstream supply chain and the downstream service chain, such as the carbon emissions associated with purchasing parts from suppliers. Using the three-scope carbon emission accounting tool, the emission boundary of automobile enterprises can be determined, and the life cycle emissions of products can also be analyzed more accurately and give more convincing results based on this mechanism.

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Fig. 4. An overview of automotive companies’ life-cycle carbon emissions

4.2 Automotive Firm’s Life Cycle Emission Accounting Mechanism The development and manufacturing process of the automobile is complicated [14, 15]. As shown in Fig. 4, the internal carbon emissions of automotive enterprises should be accounted for in a life cycle way, including strategic planning, design, development, production, use, recycling, etc. At the strategic planning stage, three external factors, which are policy, market, and social responsibility, should be fully considered. In terms of policies, auto companies need to pay a lot of attention to the national policy trend, especially incentives for key technologies and regulations on energy consumption and carbon emissions. Market and social responsibility can complement each other. On the one hand, automotive enterprises have a social responsibility to take mitigation measures [16]. On the other hand, actively adopting emission mitigation measures can improve their brand image in the market and lay a foundation for their market expansion. Combined with the above three external factors, auto companies need to consider their technology and capacity from the overall and cross-industry perspective to choose the optimal strategic route. The core of manufacturing lies in supply chain assessment and management. Automotive enterprises need to evaluate the carbon emission level of suppliers and comprehensively optimize procurement strategy considering carbon emission, cost, product, and other factors. Meanwhile, it can improve production efficiency and save energy by optimizing logistics management. The use phase focuses on the full life cycle assessment of the energy chain. With the withdrawal of fossil energy and the rapid development of renewable energy, enduse electrification has become a general trend, while China’s current generation is still dominated by coal. Alternative energy sources such as gas, hydrogen, and ammonia have gradually attracted the attention of the industry but also brought new challenges in the production, transportation, and storage of different energy sources, so it is necessary to realize the low-carbon vehicle energy from the perspective of life cycle analysis. In addition, car-sharing can also improve the use efficiency, which is viewed as the last mile of road traffic decarbonization. In the future, the car-sharing model needs the joint efforts of all parties in society to develop. The recycling phase is becoming increasingly important with the rapid growth of new energy vehicles [17], the recycling phase is becoming increasingly important [18, 19]. The electrification and intelligence of cars have changed the overall hardware. The

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recycling value of vehicles also increases significantly, with traction battery playing a major role [20, 21]. In the future, the key to the development of recycling is to carry out the refined utilization value evaluation of the vehicle, that is, to fully calculate and plan the key materials and utilization scenarios. 4.3 A New Emission mitigation driver of the Automotive Industry: Internet of things

Carbon-neutral goal poses an external constraint on the IoT

Carbon neutrality: decarbonized industrial ecology

Intelligent green ecology

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The IoT provides a variety of solutions for the low-carbon development of different industry and creates a new market

Intelligent transportation

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Building Smart and Green City

Fig. 5. The relationship between the IoT (Internet of Things) and carbon neutrality

As an important emerging scientific and technological concept, the Internet of everything will play an important role in the emission mitigation of the automotive industry in the future [22]. Meanwhile, a considerable synergy can be achieved through IoT and carbon-neutral goals. They will jointly build a super-intelligent green ecology (Fig. 5). As a major national strategic direction, the carbon-neutral goal will provide lots of decarbonization opportunities and pose a huge amount of pressure on different industries. IoT can drive the digital transformation of the industrial chain [23] and play a significant role in carbon emission accounting and management. The proposed carbon-neutral goal gives IoT a new mission and brings limitations, pushing IoT to provide a variety of technological possibilities for the industry’s low-carbon development [24]. As carriers of both carbon neutrality and IoT technology, vehicles will play a particularly important role. Firstly, the role of the vehicle will be extended from an energyconsuming device to an important energy supplier and power connector. Secondly, through vehicle-to-everything (V2X) technology, vehicles will give full play to the technological potential of IoT to achieve connectivity with transportation, buildings, and other infrastructure [25]. It can improve overall traffic efficiency and vehicle utilization and achieve integrated green travel. In addition, through vehicle-to-grid (V2G) technology, electric vehicles can help address the consumption challenge of variable renewable energy [26]. In the future, with the increase of BEVs, it has huge energy storage potential and can make a great contribution to micro-grid power regulation. With smart electric vehicles as the core, through V2X and V2G, it can optimize the matching of regional energy, resources, and transportation, achieving carbon neutrality at the minimum cost, and help build a smart green city.

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5 Recommendations As China’s carbon-neutral strategy advances, more carbon emissions regulations will be put into practice. The company’s emission standards are believed to become stricter, and the scope of regulation will be wider with a more specific regulatory target. Hence, the car companies need to pay more attention to the policy dynamics. The company should establish a special carbon accounting team to make an emission mitigation analysis and strategy plan. The accounting should be made from the product’s life cycle perspective and across the whole industry value chain. All internal and external factors of the company should be well considered. Besides the emission accounting and planning, several important and practical emission mitigation measures are to be taken. For example, it is necessary to increase the usage of renewable power since the market for green electricity is already well developed. There are mainly three ways to adopt green power. (1) Directly build the enterprise’s renewable power plant. Some leading auto companies like XPeng have already constructed their own solar photovoltaic power plant [27– 30]. The electricity generated by the plant can be used for the company itself, and the surplus power can be fed into the grid to produce extra profits. (2) Indirectly purchase green electricity certificates (GEC) or adopt green electricity through Public-Private Partnership (PPP). The GEC can be traded in the special electricity market, officially approved by the National Development and Reform Commission. But its international recognition is limited. A PPP is a long-term contract between a private company and a government or non-profit organization, of which the price is more flexible. The renewable energy PPP projects are still in their infancy in China [31–33], which means the trading mechanism and regulation remain unclear. (3) Invest in the centralized renewable project. By investing in renewable energy power projects, the company can partially own the renewable plant. Since it is an investment rather than a direct cost, its yield varies from region to region. The market is mature, while finding ideal investment projects is challenging. In addition, improving efficiency is also an important way to reduce emissions. Although the proportion of emissions in manufacturing is relatively small, the carbon emission of enterprises can be greatly reduced by upgrading manufacturing technology, which will also enhance the company’s competitiveness. As shown in Table 1, many well-known giant car companies worldwide have put forward their own carbon reduction goals, mainly for carbon emissions in the life cycle of products. In contrast, Volkswagen and Volvo not only set carbon neutrality targets for themselves but also clearly put forward carbon emission requirements for their suppliers. Carbon mitigation in the automotive company is a complex and long process. The auto company should implement decarbonization transformation orderly and evaluate carbon emission from a life cycle perspective.

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Table 1. Top auto company’s carbon mitigation targets Company

Carbon-mitigation targets

Toyota

By 2050, the average driving process of new cars will reduce carbon emissions by 90% compared with 2010

General Motors

By 2040, global products and operations will be carbon neutral. By 2035, fossil fuel vehicles will be completely stopped, and the company will be powered by 100% renewable energy

Volkswagen

Realize its carbon neutrality by 2050, and make carbon emissions an important selection criterion for suppliers

Volvo

Achieve carbon neutrality by 2040 and a 40% reduction in life cycle emissions per vehicle from 2018 to 2025

Continental

Achieve carbon neutrality in the production process by 2040, and achieve carbon neutrality in business activities and the entire value chain by 2050

ZF Friedrichshafen Achieve carbon neutrality across the entire value chain by 2040

6 Conclusion Achieving carbon neutrality is a highly complex national-level project that involves all parties in society. China’s dual-carbon targets pose multiple constraints and complex impacts on the automotive industry. Due to its complex industrial chain and close relationship with other industries, the automotive industry is significant in achieving carbon neutrality. This paper studies the impact of the automobile industry’s carbon mitigation on related industries, the management framework of the automobile industry to reduce carbon emission, and company’s practical countermeasures. The following conclusions are obtained. (1) The emission reduction of the automobile industry is a complex systems engineering and includes lots of cross-industry factors. Therefore, the key is to seek a coordinated emission reduction path across industries and comprehensively evaluate the best path for emission reduction in the automotive industry from different perspectives. (2) The foundation and prerequisite of achieving carbon neutrality are to establish a carbon emission accounting model. The emission accounting should be conducted life-cycle and include emissions from the product’s life cycle, industry’s value chain, and cross-industry impacts. Many parameters such as technology advancement, emission intensity, and policy change are taken into account. Based on this model, the automotive company can take corresponding carbon reduction measures at different stages of their product development. (3) The combination of IoT and the carbon-neutral target makes the low-carbon transformation of the automotive industry particularly complicated but also gives it more potential. With the help of digitalization, auto companies can more easily conduct emission accounting and have more powerful instruments to reduce carbon emissions. With the rapid development of renewable energy, automobiles can also

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contribute to energy transformation by serving as energy storage carriers. Two significant technologies, V2X and V2G, will make cars play an increasingly important role in the future smart and low-carbon ecology. (4) To achieve carbon neutrality, government, industry, and enterprises should reexamine policy and decide on new technology routes with a focus on carbon reduction. Considering the close relationship between different parties involved in carbon reduction, they should together define the direction and roadmap for key technology and promote collaborative innovation. Governments should subsidize key carbon-reduction technologies that are most common and vital across different industries. (5) The auto company should pay more attention to the automotive dual-carbon system research. The goals and boundaries of the automotive industry should be clarified so that Companies can take more targeted measures to reduce emissions.

Acknowledgements. This study is sponsored by the National Natural Science Foundation of China (U1764265). The authors would like to thank the anonymous reviewers for their reviews and comments.

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Development Strategies of Intelligent Automotive Industry Under the Background of Increasing Demand for Computational Capacity Wang Zhang1,2 , Fuquan Zhao1,2 , and Zongwei Liu1,2(B) 1 State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China

[email protected] 2 Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing, China

Abstract. The demand for computational capacity in intelligent vehicles is increasing rapidly, which drives the automotive industry to accelerate the integration with the information and communication technology industry. This study evaluates the demand for computational capacity from three levels: vehicle, driving environment and industrial chain. Based on deductive reasoning method, it is revealed that to realize the increase of computational capacity, the technical architecture of vehicles needs to be reshaped, the driving environment needs intelligent upgrade, and the industrial chain needs ecological development. Then, according to the development needs of relevant technologies and industries, brand-new market competition strategies, business relationships and ecological evolution paths are identified. Finally, some suggestions are put forward to original equipment manufacturers, chip enterprises and information and communication technology enterprises, which are related to computational capacity of intelligent vehicles. Keywords: computational capacity · intelligent vehicle · automotive computing platform · industry development strategy

1 Introduction The new round of scientific and technological revolution expands the connotation and extension of vehicles, drives the intelligentization of vehicles and the development of information and communication technology (ICT), transportation, energy and machinery manufacturing [1]. Intelligent automotive industry has become one of the biggest origin ecosystems in the future digital era, which is pregnant with many small technical ecosystems and application ecosystems [2]. In the digital era, data is the means of production, computational capacity is productivity, and algorithm is the relation of production. Therefore, computational capacity is the basic ability of intelligent vehicles, which supports the perception, decision-making, control and interaction of vehicles [3]. The increase of computational capacity promotes the development of automotive intelligent technology and the integration of artificial © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 113–128, 2023. https://doi.org/10.1007/978-981-99-1365-7_9

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intelligence (AI) and vehicles. The application of AI in vehicles brings about a surge in unstructured data, which makes the structure of automotive AI algorithm increasingly large and complex. Therefore, the computational capacity of vehicles is soaring, which will soon surpass that of personal computers and mobile phones. Intelligent vehicle will become one of the terminals with the highest computational capacity in human daily life. In addition, the automotive computing platform that provides computational capacity is the connecting carrier between different technical ecosystems [4]. The automotive computing platform is equipped with chips, operating system and application software, which are connected with a wide range of development and application ecosystems. Therefore, to develop intelligent automotive industry, computational capacity is the first priority. However, computational capacity is a comprehensive and complex concept, involving many kinds of hardware, software and technical services [5]. The confusion of concepts causes different enterprises to have different judgments on the development direction of computational capacity, which makes it difficult to effectively combine industrial resources. Much of the existing research literatures has focused on on-board computational capacity and relevant technologies [6–9]. While little attention has been directed to the whole industrial ecosystem of intelligent automotive computational capacity. The purpose of this study is to systematically comb and analyze the demand for computational capacity of intelligent vehicles and the influence of its growth. Furthermore, this study is expected to provide some development strategies for the intelligent automotive industry under the background of increasing demand for computational capacity.

2 Research Framework The demand for computational capacity of intelligent vehicle is multi-level, which not only serves the driving of vehicles inside the vehicle, but also serves the whole urban transportation system and industrial chain outside the vehicle [10–12]. As shown in Fig. 1, this study analyzes the demand for computational capacity from three levels: vehicle, driving environment and industrial chain. In addition, to realize the increase of computational capacity, the technical and industrial changes required by different levels are identified.

Fig. 1. Three levels of increasing computational capacity

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At the vehicle level, the analysis mainly focuses on the on-board computational capacity. The main driving force for the increase of on-board computational capacity is identified. In addition, based on the key evaluation index of on-board computational capacity, the evolution of automotive technical architecture is discussed. At the driving environment level, the analysis mainly focuses on the computational capacity of the road environment outside the vehicle. The trend and characteristics of the increase of off-board computational capacity are extracted, and based on this, the upgrading requirements of transportation infrastructure in different stages are discussed. At the industrial chain level, the analysis mainly focuses on the overall computational capacity of the automotive industry. The application scenarios of computational capacity in the upstream and downstream of the industrial chain and relevant participants are combed. Taking all scenarios and participants into consideration, this study describes the data flow and utilization demand in the automotive industrial ecosystem. After three levels of analysis, this study sums up three future main trends of the intelligent automotive industry. Based on these trends, reasonable strategic suggestions are provided for the main participants related to computational capacity.

3 Impact of Increasing Demand for Computational Capacity 3.1 Impact on the Vehicle Level The more advanced intelligence of vehicles needs more on-board computational capacity, which mainly serves the two fields of intelligent driving and intelligent cockpit. Higher-level intelligent driving requires vehicles to be equipped with more sensors and AI models [13]. According to the classification standard of Society of Automotive Engineers, the current industry average level has developed to mass production L2 [14]. As shown in Fig. 2, referring to the practical scheme of representative enterprises, this study predicts the demand for on-board computational capacity of L2 ~ L5 intelligent driving based on the data volume of different sensors and the data processing efficiency of the algorithm models. The results show that from L2 to L5, the demand for onboard computational capacity will increase from the order of single digit to the order of thousand digit, which means a qualitative change. The increasing trend of computational capacity of intelligent cockpit is similar to that of intelligent driving. As shown in Fig. 3, three growth paths of computational capacity for intelligent cockpit are identified. At present, the interaction between the intelligent cockpit and passengers mainly depends on the screen [15]. With the increasing number of screens and display functions, rendering high-quality images will require more computational capacity. Moreover, the mode of human-machine interaction is also developing to multi-modal active interaction [16]. The interaction based on touch, hearing and vision will be deeply integrated, which makes the AI algorithm more complex and the computational capacity required constantly increasing. In the future, the intelligent cockpit will connect a wider range of ecology and create an intelligent experience of cross-function, cross-domain and cross-scene, which will further drive the increase of on-board computational capacity [17].

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Fig. 2. Increasing trend of computational capacity for intelligent driving

Fig. 3. Increasing trend of computational capacity for intelligent cockpit

The increase of vehicle computational capacity depends on the progress of relevant technologies. The core technical indicators of on-board computational capacity can be expressed by formula (1): CEA = CETP ∗ UR ∗ AE

(1)

CE A is the actual computational efficiency of the vehicle. CE TP is the theoretical peak computational efficiency of computing hardware, which mainly depends on the core chip performance of the automotive computing platform. UR is the effective utilization rate of computing hardware resources used by automotive software. AE is the efficiency of the algorithm in handling specific tasks.

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Most original equipment manufacturers (OEMs) in the market use CE TP to promote products, which can not effectively reflect the actual computational efficiency. To objectively explain how to improve the on-board computational capacity, it is necessary to analyze the technical architecture of vehicles based on CE TP , UR and AE. 3.1.1 Concentration of the Computation: Higher CETP Traditional vehicle is developed based on distributed electrical/electronic architecture (EEA), whose computing components are electronic control units (ECUs) distributed all over the vehicle [18]. The computational efficiency of a single ECU usually does not exceed 10 tera operations per second (TOPS). In addition, the software and hardware of the distributed ECU are highly bound, which makes it difficult to share the computational capacity among them cooperatively [19]. Therefore, it is difficult to improve the theoretical peak computational efficiency by increasing the number of ECUs. To eliminate the limitation of ECU, centralized EEA has become the architecture of the next generation intelligent vehicle [20]. Centralized EEA takes a few domain control units or computing platforms with high computational capacity as the computing components to centrally processes the computing tasks of the whole vehicle. The domain control units and computing platforms take the system on chip as the core, which has a high degree of functional integration [21]. The theoretical peak computational efficiency can be improved by upgrading the core chips of domain control units or computing platforms such as central processing unit, graphics processing unit and neural-network processing units. However, even if vehicles adopt centralized EEA, there are bottlenecks in improving theoretical peak computational efficiency. As shown in Fig. 4, improving the theoretical peak computational efficiency of the chip will bring a chain effect to the area, number of layers, wiring length and electromagnetic interference intensity of printed circuit board, which means that the chip will face many challenges such as safety, cost, power consumption, cooling and manufacturing process [22]. 3.1.2 Software and Hardware Collaboration of Computing Platform: Higher UR As described in Sect. 3.1.1, it is technically difficult and costly to improve the theoretical peak computational efficiency of the chip. Improving the utilization rate of computing hardware resources used by automotive software is a better strategy. Different algorithms have corresponding operating system kernel which is more suitable for running [23]. For example, Linux is more suitable for AI algorithm and QNX is more suitable for functional security algorithm. Different application algorithms also have corresponding computing unit that is more suitable for running [24]. For example, GPU is more suitable for processing image data. Moreover, some computing units, such as field programmable gate array, can embed algorithms by designing a special computing architecture, thus further improving the computational efficiency [25]. Therefore, to improve the utilization rate of computing hardware resources, the chip should be compatible with the common operating system kernel in the market, the algorithm should be developed jointly with the chip, and the operating system should be able to match the optimal computing resources for algorithm.

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Fig. 4. Challenges of improving theoretical peak computational efficiency of chip

Through collaborative design of the chip, operating system and algorithm of the automotive computing platform, the utilization rate of hardware resources of the computing platform can be increased from 30% to 80%, thus helping OEMs to find a reasonable balance between computational capacity and cost [26]. 3.1.3 Fusion of Multi-source Data: Higher AE Improving the algorithm efficiency is also helpful to improve the actual computational efficiency of the vehicle. The large-scale deployment of neural networks in vehicles makes the computational capacity overwhelmed. Using the fusion of multi-source data to reduce the computational load of neural network has become the main means to improve the algorithm efficiency [27]. As shown in Fig. 5, the current fusion algorithms can be divided into three types: raw data-based, feature-based and target-based. The fusion algorithm based on raw data directly fuses the data of different sensors, so that only one sensing algorithm is needed. This algorithm has high accuracy without data loss, but its overall computing tasks are still too many, which leads to higher communication bandwidth. The fusion algorithm based on features first extracts representative features from the raw data of each sensor, and then fuses these features into useful vectors such as direction, speed and shape. Although this algorithm can minimize the computing tasks and effectively fuse the information reflecting all aspects of the target, some key information may be lost, resulting in insufficient accuracy of the results. The fusion algorithm based on targets is to fuse the targets identified by each sensor’s perception algorithm. Its accuracy is higher than the feature-based fusion algorithm, and its efficiency is improved compared with the raw data-based fusion algorithm [28]. Therefore, the application prospect of target-based fusion algorithm is better than the other two algorithms to balance accuracy and efficiency.

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Fig. 5. Three algorithms of multi-source data fusion

3.2 Impact on the Driving Environment Level Due to the limitation of cost and power consumption, the on-board computational capacity can’t increase infinitely. To support the intelligent functions of the vehicle, it is necessary to improve the off-board computational capacity and make it cooperate with the on-board computational capacity. China has made it clear that the development of intelligent vehicles will adopt the technical route of intelligent vehicle infrastructure cooperative system. According to the planning of technical roadmap, the collaboration between vehicles and driving environment can be divided into three stages: joint perception, auxiliary calculation and collaborative decision, which correspond to different levels of infrastructure capabilities [29]. As shown in Fig. 6, this study predicts the trend and characteristics of the change of off-board computational capacity and on-board computational capacity. Table 1 shows the functions of driving environment in different stages and the infrastructure that needs to be upgraded or added.

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Fig. 6. Trend and characteristics of the change of on-board and off-board computational capacity

With the development of intelligent vehicle infrastructure cooperative system, the computing tasks with relatively low real-time performance will gradually shift from the vehicle to roadside and cloud [30]. In the stage of joint perception, the roadside will have primary perception and message passing capabilities. Intelligent vehicles acquire the data from roadside and cloud through the technology of vehicle to everything (V2X) to fuse them, so as to receive traffic signals, road conditions, service areas, traffic violations and other information. In the stage of auxiliary calculation, the deployment of roadside units and edge computing units will form an intermediate scale, which can help the vehicle to process some data. Intelligent vehicles can get information about other traffic participants, highprecision maps and driving risks from outside. At this stage, the increasing rate of on-board computational capacity will gradually slow down. In the stage of collaborative decision, all kinds of infrastructures in the driving environment will be intelligently upgraded. Vehicles and driving environment will realize distributed dynamic collaborative perception and decision-making to support collaborative scheduling and management of vehicle-road-cloud. Intelligent vehicles will be fully integrated with the internet ecosystem of intelligent transportation and smart cities [31]. In an optimistic scenario, the roadside and cloud will take on most of the computing tasks, and the on-board computational capacity may even be reduced.

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Table 1. Infrastructure upgrading schemes in different stages Function Communication support

Message passing

Explanation

Relevant infrastructure

Primary

Throughput > 100 kbps

LTE base stations

Intermediate

Throughput > 10 Mbps

5 G base stations

Senior

Throughput > 100 Mbps

5 G base stations

Primary

Nearby traffic signal

Traffic signal facilities

Real time traffic flow

Traffic message facilities

Service area information

Roadside units

Location of traffic participants

Roadside sensors

Dynamic high-precision map

Roadside units

Real-time message interaction

Telematics equipment

Road network dispatching

Road dispatching center

Primary

Traffic violation

Road monitoring facilities

Intermediate

Risk reminder

Roadside sensors

Senior

Cooperative collision avoidance

V2X connecting units

Intermediate

Decision support

Edge computing units and cloud computing center

Senior

Cloud control

Edge computing units and cloud computing center

Intermediate

Senior

Safety warning

Driving assistance

3.3 Impact on the Industrial Chain Level The premise of intelligent vehicles is digitalization, which depends on the digitalization of the whole industrial chain. Therefore, the increasing demand for computational capacity will spread to the whole industrial chain. All links of research and development (R&D), manufacturing, marketing, mobility, operation and service will be digitized on the cloud. As shown in Fig. 7, digital R&D, intelligent manufacturing, digital marketing, digital operation and digital application ecosystem will progress simultaneously with the intelligentization of automobile products [32]. In this process, there will be more participants in the intelligent automotive industry. In addition to the OEMs, component suppliers and dealers, Internet companies, cloud service providers, travel service providers and other emerging players will bring new innovations to the industry, and make the automotive industrial chain develop towards industrial ecosystem.

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Fig. 7. Digitization of intelligent automotive industrial chain

It is foreseeable that the automotive industry will build more cloud computing centers in the future to support the digitalization of the whole industrial chain. To make full use of these computational capacity to maximize the data potential, the automotive industry needs to create an open big data ecosystem [33]. As shown in Fig. 8, this study divides the automotive big data into four types and designs the connection and interaction modes of different types of data. The first type of data comes from the functional development ecosystem, which is generated based on the automotive functions in the process of product use, and can be used for the subsequent in-depth development of products. The second type of data comes from the networked application ecosystem, which is generated based on the Internet applications in vehicles, and contributes to the integration of the automotive industry and the ICT industry. The third type of data comes from market research, including traditional voice of consumers and Internet user data, which is helpful for enterprises to judge consumers’ personalized preferences and respond to users’ needs in time. The fourth type of data comes from the external service resource ecosystem, which is generated based on the public services provided by the government and the derivative services of intelligent vehicles. These four types of data cross each other, but they belong to different industry participants. To ensure the smooth flow of data in the industry, it is necessary not only to build a big data platform as a bridge between different ecosystems, but also to establish a brand-new cooperative relationship among different participants to realize data-based sharing, co-creation and all-win.

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Fig. 8. Big data ecosystem of intelligent vehicles

4 Development Trend of Intelligent Automotive Industry 4.1 On-Board Computational Capacity Competition between OEMs Computational capacity determines the upper limit of automotive intelligence [34]. At present, intelligent vehicles are still in the process of rapid iterative evolution. The first judgment about the trend of intelligent automotive industry in this study is that OEMs tend to reserve more on-board computational capacity for the future when developing products, especially for luxury vehicles. There are three main reasons for OEMs to reserve computational capacity in vehicles. First, the algorithms related to intelligent driving and intelligent cockpit are rapidly iterating, far exceeding the iterative speed of hardware. Only by reserving enough computational capacity in advance can vehicles support the subsequent more complicated and large-scale algorithm deployment. Second, OEMs has not fully utilized the potential of sensors and V2X at present. With the development of the Internet of Vehicles and the exploration of the potential of sensors, the data volume will explode in the future [35]. Third, OEMs lack enough engineers with software and hardware coordination and debugging capabilities. It is difficult to determine to what extent the software can make use of the chip, so they tend to choose chips with high computational capacity when purchasing. Table 2 lists the possible growth points of computational capacity of intelligent vehicles in the next five years. OEMs will make vehicles carry more computational capacity to meet one or more key user needs, which can be used as a new selling point, to gain differentiated market competitiveness. In fact, vehicles with more than 1000 TOPS computational capacity have appeared on the market.

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Computational capacity growth point

Explanation

Types of targets for image detection

• For example, the front camera adds the function of detecting animals

Types of targets for behavior detection • For example, the in-car camera adds the function of children window climbing reminder Number of voice commands

• The voice assistant in the intelligent cockpit can recognize more commands

Speech recognition modality

• The speech interaction subject is expanded from drivers only to passengers

Image rendering quality

• Games, videos and other functions require 3D rendering

Running frequency

• Demand for low delay will make software and hardware of vehicles run faster

Number of noise cancellation

• More penetrating signals are needed to eliminate noise thoroughly

4.2 New Business Relationship Around Computing Platform With the increase of computational capacity, the computing platform will become the core component of the vehicle. Around the computing platform, there will be a brand-new industrial division of labor and cooperation. Industry participants need to establish new business relationships with each other. Figure 9 shows the future industrial ecosystem of the automotive computing platform predicted by the authors. Chip enterprise will play an increasingly important role, not only being responsible for the hardware design of chips, but also having the opportunity to participate in the R&D of software and algorithms and provide a series of services based on chips, such as R&D tools, IP authorization and reference design. Basic software enterprises need deep cooperation with chip enterprises to realize collaborative optimization of software and hardware. OEMs will directly establish supply and cooperation relationship with chip enterprises to master the definition right of automotive computing platform. To adapt to the new division of labor, relevant enterprises need new capabilities, organizations and processes.

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Fig. 9. Future sub-ecosystem of the automotive computing platform

4.3 Computational Capacity Growth Path Based on Cloud-Edge-End Collaboration When the computational capacity increases to a considerable scale, it is necessary to find a balance among cost, computational efficiency and time delay [36]. By optimizing the combination of computing resources of cloud, edge and vehicles, the automotive industry will explore an optimal growth path of computational capacity. This study recommends a reference cloud-edge-end collaborative computational architecture as shown in Fig. 10. Intelligent vehicles undertake the main functions of data acquisition, execution and interaction, and have the single-vehicle intelligence to complete safety-related decisions with high real-time requirements. A large number of roadside units are distributed in every key road section and intersection in the city, which are mainly used to connect clouds and vehicles. In addition, these roadside units undertake some tasks of perception and short-distance collaborative decision-making. Hundreds of small cloud servers are distributed at the edge of a city’s road network to undertake long-distance collaborative decision-making and data processing tasks with medium real-time requirements. The central cloud data center mainly undertakes non-real-time computing tasks, which is used to provide cloud services for the digital industrial chain, such as algorithm iteration, user big data analysis and smart city services. Under the cloud-edge-end collaborative computational architecture, it is most difficult to realize the collaborative decision making of intelligent driving and travel, so the automotive industry needs to continue to study deeply in this field.

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Fig. 10. Cloud-edge-end collaborative computational architecture

5 Conclusion The increasing demand for computational capacity of intelligent vehicles will lead to the reshaping of automotive technical architecture, upgrading of driving environment and ecological development of industrial chain, and then give birth to brand-new market competition strategy, industrial division of labor and ecological evolution path. Combining with the future development trend of automotive industrial ecosystem, this study provides the following suggestions for main relevant participants: (1) OEM should balance the cost and computational capacity according to the positioning of enterprises, and continue to promote the centralization of EEA, the collaborative design of software and hardware, and the data fusion algorithm to support the continuous upgrading of intelligent vehicles. (2) Chip enterprises, as the main provider of on-board computational capacity, should establish necessary software algorithm team to interact more fully with OEMs, Tier1 suppliers and technology companies. Moreover, chip enterprises should also deeply participate in the vehicle development process and adjust the service to meet the differentiated needs of different OEMs. (3) ICT enterprises will play an increasingly important role in the “cloud-edge-end” collaborative ecosystem. They should rely on their own advantages in big data, AI and cloud computing to enable the connection and circulation of data, thus promoting the integration of intelligent vehicles, intelligent transportation and smart city.

Acknowledgements. This work was supported by National Natural Science Foundation of China (U1764265).

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Development Strategy of Shared Mobility Enterprise for Smart Cities Yisong Chen, Ying Cao, and Yongtao Liu(B) School of Automobile, Chang’an University, Xi’an, China [email protected]

Abstract. The development of shared mobility is short and unbalanced. To promote a healthy development of shared mobility enterprises, in this study, the development trend of shared mobility from the perspective of market pattern, market capacity, and market ecology based on relevant data is analyzed. The business models of three typical enterprises, Didi, CaoCao, and Uber, are then analyzed from the perspective of value proposition, profit model, promotion model, and operation model, and then from the perspective of integration of resources, mode guidance, and technology driven to build the China’s shared mobility enterprise development hierarchy. Finally, from the perspective of new models, advanced technology, and industrial integration, development strategies and suggestions for future shared mobility enterprises are presented. Keywords: shared mobility · business model · typical enterprises · hierarchical structure · smart mobility

1 Introduction Shared mobility is one of the solutions to solve the urban intelligent upgrade. Extensive studies have been carried out on shared travel. Aleksandra Ko´zlak [1] analyzed the role of shared mobility in smart transportation, and reported the influence of shared mobility and smart transportation on the sustainable development of smart cities. Li Meng et al. [2] attempted to connect the government and enterprises with policies from the perspective of the government to launch multimodal shared mobility. Liu Zongwei et al. [3] proposed an all-weather shared mobility, and explored its business model and market promotion path. Chen Xuanmei et al. [4] studied the internal mechanism of enterprises under the background of sharing economy based on previous achievements, and analyzed the importance of strategic flexibility to enterprises. Qiao Yingjun et al. [5] recommended to improve the development strategy of shared vehicles combined with the development trend of sharing. Yan Kangli et al. [6] established a game mathematical model for system simulation. The data were used to illustrate the relationship between fixed-point shared, floating shared, and private cars. At a larger number of shared cars, more users tend to choose shared cars due to the corresponding reduction in the cost of searching for cars. Fixed-point car sharing was less competitive than the use of private cars. Floating shared cars can replace private cars in a certain period of development. The result predicted © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 129–143, 2023. https://doi.org/10.1007/978-981-99-1365-7_10

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a shared mobility market and provided a reference for the government to control the market and enterprise to product management. Chen Xiqun [7] sorted out the existing data to deeply excavate the shared mobility’s development status and existing problems from four levels of online shared mobility behavior analysis, government supervision, system simulation, and platform management. Through an analysis, shared mobility mining development, integrated platform operation optimization, market equilibrium evolution, and complex network simulation optimization aspects need to be improved. It was also proposed that the shared mobility industry will be supported by big data in the future, artificial intelligence, and digital twin technology, which will provide the development direction for each body of the shared mobility industry. Patrícia S. Lavieri et al. [8] constructed a model based on big data to find the influencing factors of users’ travel characteristics and travel frequency. The population density and privacy leakage of users are the main concerns of users in choosing car hailing. Therefore, shared mobility enterprises can launch the corresponding solution for such problem in the future. Chen Xiqun et al. [9] developed an ensemble learning algorithm model, based on real shared mobility platform data to analyze user’s travel characteristics and rank the importance of user carpooling factors. Acheampong Ransford A et al. [10] established a multivariable structural equation model. The cost of taking a taxi, demographic factors, economic attributes, etc., affected the users’ choice of online ride hailing.Yu Jingru et al. [11] innovatively proposed an efficient map matching technology based on cell, which was used to solve the problems of monitoring road traffic and identifying congested network, to provide corresponding solutions in time. Based on the underlying logic of alleviating traffic congestion and expanding traffic capacity, the shared mobility has formed an enterprise pattern of adhering to the people-oriented business philosophy, relying on a profit-oriented business model, equipped with high-technology integrated cutting-edge disciplines. The shared mobility market is moving forward steadily in the direction of a safe, efficient, open, and green market [12–15]. In conclusion, the shared mobility industry has become one of the major forces promoting economic resilience and vitality. Scholars worldwide have studied and analyzed it from the perspectives of policy implementation, enterprise mechanism, development strategy, travel characteristics, and technological innovation. However, the aforementioned studies are mostly from an industrial perspective. This study starts from the perspective of enterprises, analyzes typical enterprises, and proposes a future development strategy of shared mobility enterprises under the requirements of “Carbon Peaking and Carbon Neutrality Goals” in the era of “New Four Modernizations”.

2 Development Trend of Shared Mobility in China According to the public security traffic administrative department statistics, by the end of 2020, China had a total of 281 million cars, accounting for 76% of the national motor vehicle ownership. As the car ownership in China increased year by year, the traffic congestion increasingly becomes the largest obstacle preventing people from an efficient travel. The shared mobility can effectively alleviate the situation by maximizing the resource utilization [16–18]. The rise of shared mobility responds to the current demand for smart mobility in smart cities. The future development trends include the following aspects, as shown in Fig. 1.

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Fig. 1. Shared mobility development trend

2.1 Gradual Stabilization of Market Structure The shared mobility market structure in vehicle enterprises, leasing enterprises, Internet enterprises, and other startup enterprises is gradually becoming stable. Internet enterprises led by Didi and Meituan Taxi, vehicle enterprises led by Geely and BAIC New Energy, leasing enterprises led by Shenzhou and Shouqi, as well as startup enterprises led by Cargo and Nicigo compete with each other for resources. According to the China business intelligence network statistics, from September 2017 to September 2020, the financing projects of shared mobility in China gradually decreased, and the industrial development returned to rationality, as shown in Fig. 2. In the next ten years, relying on the big data platform, intelligent driving technology support, and acquisition and merger

Fig. 2. Number and amount of investments and financing for shared mobility in China from September 2017 to September 2020 data resources: China business intelligence network

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among enterprises, the mobility market resources will be more concentrated and the structure will be stable. 2.2 Continuous Expansion of Market Capacity The market capacity of shared mobility is gradually and continuously expanding in terms of user demand, infrastructure, data platform, and intelligent technology. According to the China business intelligence network statistics [19], the car-hailing users scale in China has reached 340.11 million. As shown in Fig. 3, with the influence of objective factors such as the odd–even license plate policy and subjective factors such as consumer psychology [19], the scale of car-hailing users in China will continue to expand in the future. 2021 is the first year of the “14th Five-Year Plan”; the market supply– demand relationship is more complex. To support the sustainable development of the industry, the shared mobility market needs to fully rely on infrastructure to ensure the industrial development system, dispatch a stable data platform to analyze the industry supply–demand relationship, and apply an advanced intelligent technology to improve the industrial service structure. Therefore, the cross-border integration of industries and coordinated development of innovation promote the continuous expansion of the market capacity in related fields.

Fig. 3. Scale and utilization rate of car-hailing users nationwide in 2018–2020 data resources: China business intelligence network

2.3 Market Ecological Wisdom Expansion The market ecology of shared mobility, such as information integration, service experience, industrial upgrading, and platform integration, is gradually becoming smarter. Shared mobility is a typical product integrating intelligence, electrification, networking, and sharing. Among them, sharing is an industrial consumption model dynamically

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derived based on emerging technologies. The electrification, networking, and intelligence predict the industry direction based on industry transformation, upgrading, and technology iterative development. As the automotive industry’s border is becoming blurred, the shared mobility industry relies on smart platforms such as big data and smart transportation, equipped with an intelligent hardware, and efficiently integrates information resources to form a networked mobility ecosystem, which can conforms the consumer demand, improves the travel service experience, and supports the shared mobility ecosystem. Shared mobility combines smart transportation to derive smart mobility and empower smart cities.

3 Development Status of Shared Mobility in China As a complex emerging concept in the new era, the shared mobility is in the midst of the continuous development and change in its business model, operation system, and social effect. In the context of the Internet, many enterprises join in the shared mobility field, Original equipment manufacturers (OEMs) promote vehicle circulation through leasing and solve over-capacity, and Internet enterprises support technology soft platforms through leasing. The shared mobility market has a strong regional competition in China, which mainly forms the competition structure led by Didi, CaoCao, and Uber. The healthy competition will also continue to optimize the growth atmosphere of shared mobility, further expand the social influence of the shared mobility, and promote a more autonomous and normalized development of the shared mobility. 3.1 DIDI – Comprehensive Penetration of Big Data Cloud Technology The Didi’s business model consists of the value proposition that permeates the mobility nature, promotion model of multichannel publicity, profit model of segmented and multidimensional promotion, and operation model of simultaneous internal and external development, as shown in Fig. 4. According to China EV100 and China industry planning consulting leader statistics, Didi has 100 million users, more than 2 million daily orders, and more than 80% share of the national car-hailing market. The huge user group, consumer orders, and multiple rounds of successful financing have verified the correctness of the Didi’s business model. Didi conceived its operational value concept from the nature of mobility service, promoted and strengthened its brand influence through various channels, defined the multidimensional profit model from different stages, and explored the operation path from the company itself layer by layer. During its development and expansion, Didi gradually formed a unique and unreplicable business model. During the epidemic, Didi launched the “Smart Epidemic Prevention Code”. Each code represents a small data platform, through which, users can know the current disinfection situation of the car in real time. In addition, Didi released D1, which is the first customized car hailing worldwide jointly created by Didi and BYD. Both “Smart Epidemic Prevention Code” based on big data and cloud platform and new-generation shared mobility product D1 reflect the comprehensive penetration of the big data cloud technology in Didi. The big data cloud technology is an indispensable driving factor

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Fig. 4. Didi business model

to build a smart city. Massive data require high technologies to extract data values. Enterprises use the platform to analyze data information and explore potential business opportunities. Thus, Didi has set a good benchmark for other mobility enterprises in terms of handling of redundant data and effective utilization of its data platform. 3.2 CAOCAO——New Sensing Technology Accelerates Innovation The CaoCao’s business model consists of the value proposition that conforms to ecological development, promotion model of dispatching industry resources, profit model of grasping consumer psychology, and operation model of ensuring safety and low-carbon operation, as shown in Fig. 5. The most noteworthy aspect of the CaoCao’s travel business model is that it uses the B2C model, relies on Geely, and uses new energy vehicles as a service platform, thereby reducing operating costs and consolidating the safety foundation. With the background of Geely OEM, CaoCao has successfully become the second largest online car-hailing company in China by relying on the B2C model. CaoCao modified the Geely’s “Geometry A” self-driving vehicle to start a road test. The vehicle is equipped with DeepRoute-Sense II developed by Yuanrong Qixing, which can effectively reduce the cost and volume of the computing platform and assist “Geometry A” to achieve L2 advanced autonomous driving functions. “Geometry C”, which is being developed, will be equipped with 5G network and integrate the internet of vehicles and new infrastructure to realize autonomous driving. The sensor is only a small part of the car. By modifying the parts, they can have a better role, and thereby solve a series of problems caused by the parts. It is also an expected result of the continuous iterative application of high technology. Thus, CaoCao provides typical cases for other mobility enterprises to infiltrate innovative technologies into the industry and soft space for enterprise development.

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Fig. 5. CaoCao’s business model

3.3 UBER——Visual Tool Technology Improves Safety The Uber’s business model consists of the value proposition that responds to the macroeconomy, promotion model of covering people and domains, profit model of recovering high rents, and operation model of shaping high-end brands, as shown in Fig. 6. As the first leading enterprise to enter the shared mobility market, the Uber’s development process and operation system have attracted continuous attention from the industry. The largest difference from other travel companies is that Uber has added many hightechnology and mixed services, which enable to continuously optimize the ride plan. Uber provides a model for other travel enterprises in terms of a technology-driven, model-led, and industrial integration. Other companies should boldly learn from the Uber’s development philosophy, innovate in reform, and develop in innovation under the premise of sufficient capital costs [20]. We consider AVS, a Web-based three-dimensional visualization tool launched by Uber, as an example. Compared to other visualization tools, AVS can not only help cars better identify pedestrians and other vehicles on the road, but also redefine data exchange and minimize components with an open and decoupled architecture. It also meets the needs of all kinds of people in the autonomous driving ecosystem. Visual tools are important components to ensure the driving safety of intelligent cars. The innovative research and development of visual tools can significantly improve the safety performance of cars. Uber continues to deploy and develop in the field of intelligent driving, and constantly aims to be safer. According to the typical development model and technical characteristics of Chinese shared mobility enterprises, Didi, CaoCao, and Uber are well developed, which aim for safety first in the operation process, but their value propositions are different. In the promotion model, the three enterprises use the combination of online and offline. The difference is in the specific form. Didi and CaoCao implement promotion from a single dimension of profit, while Uber comprehensively covers people and domains from multiple dimensions. In the profit model, the three enterprises have expanded their

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Fig. 6. Uber’s business model

business scope. Among them, Didi has many types of profit expenses, CaoCao devotes a close attention to consumer psychology and uses a low-price strategy, while Uber uses a high-price strategy and recovers a high rent. In the operation model, the three enterprises are quite different. Didi has implemented a series of dynamic adjustments from the internal organizational structure to an external image display. CaoCao has always adhered to the B2C model, while Uber mainly provides high-end and high-quality services. A summary is presented in Table 1. Table 1. Business comparison and analysis of Didi, CaoCao, and Uber Enterprise

Value proposition Promotion model

Profit model

Operation model

Didi

Permeate the mobility nature

Multichannel publicity

Segmented and multidimensional promotion

Simultaneous internal and external development

CaoCao

Conform to ecological development

Dispatch industry resources

Grasp consumer psychology

Ensure a safety and low-carbon operation

Uber

Respond to the macroeconomy

Cover people and domains

Recover high rents

Shape high-end brands

4 Shared Mobility Enterprise Development Hierarchy The development essence of shared mobility is to integrate idle resources, improve the resource utilization, and reduce the space pressure, which is the primary development

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stage of shared mobility—resource integration. Through the guidance of the enterprise’s new development model, the efficient integration of information resources is the secondary development stage of shared mobility—model guidance. An innovative technology is injected into shared mobility. Under the guidance of the enterprise development model, resource sharing, data integration, and information exchange are realized, which is the advanced development stage of shared mobility—technology-driven. According to the typical development model and technical characteristics of Chinese shared mobility enterprises, under the continuous change of resources, models, and technologies, shared mobility constantly reshapes the definition of travel, optimizes travel plans, improves the travel experience, and constructs smart travel under the epitome of smart city in the new era, as shown in Fig. 7.

Fig. 7. Shared mobility enterprise development hierarchy

4.1 Resource Integration The primary development stage of Chinese shared mobility enterprises is to integrate resources. It can reduce the pressure on the limited space by reducing the rate of idle and empty vehicles on the road. In the other hand, it can accelerate the integration of energy, internet, artificial intelligence, and other fields to supplement the public transportation gap for fixed commuting. The reorganization and integration of resources have realized the transformation from “owning” to “using”, from “heavy assets” to “light assets”, from “public commuting” to “24-h waiting “, and from “a private car to travel all over the place” to “transferring shared cars whenever and wherever needed”. For some OEMs that are still in the initial stage or whose core value is traditional car manufacturing, selling, and using, their travel brands are mostly based on the development logic of reducing resource waste and solving overcapacity, such as Faw, Changan, and Dongfeng, which jointly invested the T3 mobility service enterprise.

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4.2 Model Lead The secondary development stage of Chinese shared mobility enterprises is model guidance. Through a precise positioning of the enterprise concept, it points out the direction for a stable development of the enterprise. On the other hand, through the correct business models, it creates positive corporate brands. According to the typical development model of Chinese shared mobility enterprises, Didi, CaoCao, and Uber are well developed, which make own business based on the self-development situation. Different mobile travel platforms have different development models. Different development models produce different brand power and social benefits. The operating platform should conform to the travel market, gain insight into user needs, devote a close attention to consumer psychology, and find suitable development models, value propositions, promotion models, and profit models, to meet the needs of multilevel and diversified car use. 4.3 Technology-Driven Stage The advanced development stage of Chinese shared mobility enterprises is technologydriven. It can improve the mobility experience by superimposing high technologies. On the other hand, it can promote the aggregation of innovative elements to enterprises, and thereby form an enterprise format that fits smart city and smart mobility. According to the typical development model and technical characteristics of Chinese shared mobility enterprises, the Didi application of big data and cloud technology is a typical case of adapting to the times, aiming for the current social environment and user needs. The typical cases that CaoCao Travel is equipped with new sensors and Uber research visualization tools are considered from the perspective of two leading actors in the travel ecosystem, the enterprise and user. It not only reduces the costs for enterprises, but also improves the ride safety for users. The new development in the new era requires shared mobility enterprises to break the shackles of the traditional automobile business model, use “safety” as the core, formulate technical routes, promote a technology-driven and industrial integration, and realize the symbiosis of the real economy and virtual economy. The technology will not become an independent individual to support the mobility platform. Only under the guidance of new models, it can provide a fresh vitality to the travel ecology.

5 Development Strategies and Suggestions for Future Shared Mobility Enterprise The future mobility concept should be supported by new generation information technologies such as 5G, V2X, big data, cloud computing, and intelligent transportation driving, which connects various transportation means to construct a large mobility platform for the best travel plan, which is public transportation as the main travel, mobile shared mobility as a supplement, and private transportation as an alternative. It can share real-time mobility information. In the next five to ten years, there will be more enterprises crowding into the mobility market, improving the industry chain of shared mobility, and forming an open, harmonious, and innovative mobility ecosystem.

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In the evolution process of shared mobility to smart mobility, the focus of the automobile industry will shift from products to people, to ensure users’ personal safety and vehicle efficiency and emphasize users’ inner experience. The future development strategy path of shared mobility enterprises is shown in Fig. 8.

Fig. 8. Future development strategy path of shared mobility enterprises

5.1 New Models Promoting Sustainable Development of Enterprises Positive and negative profit is the key factor to maintain the normal operation of enterprises. An important standard to measure whether the enterprise model is correct is whether a positive profit can be achieved. The shared mobility enterprises’ commercial profit mainly originates from the fund difference between the users’ rental car and platform’s car maintenance. Limited by the single mode, the enterprises’ cash pool fluctuates largely in the short term, so that the business model needs to be updated. The new model combination of shared mobility enterprises is to conform to the “new four modernizations” trend. With the “carbon peaking and carbon neutrality goals” requirements, new-generation information technology as a foundation, and enterprise’s positive profit as a terminal goal, it combines various business models to achieve multidimensional balance in terms of vehicles utilization, passenger demand, and smooth road network, and eventually forms a business model that is in accordance with itself and sustainable development. The enterprises’ positive profit should ensure that the cost of car maintenance on the platform is low and that the profit of users’ rental cars is high. Among them, the low cost of car maintenance on the platform can be achieved through time-sharing insurance, centralized procurement, crowdsourcing, and other approaches. The users’ high profitability of car rental can be achieved by improving the experience, dynamic scheduling, dynamic pricing, accurate layout, and other approaches. In this regard, enterprises can consider based on the next-generation mobile communication architecture, comprehensively consider consumer psychology and after-market services, fully utilize the data

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advantages of the communication platform, provide a good analysis of shared mobility data, and propose a business model that conforms to the positive profit logic. In addition, they can develop a new model from the nature of profit, ensure from the new model, and point out the path to build a shared mobility ecosystem from the enterprises’ sustainable and stable development. Through this profit logic structure, we can grasp the market supply and demand and realize the organic match between enterprise operation and efficiency. 5.2 Efficient Field Technology Innovation Derivative Enterprise Development Innovative technology is an independent node that links various elements in the shared mobility ecosystem. The application of high technologies in product structure, supporting facilities, and market services is a key factor to measure whether shared mobility enterprises can bring privacy, efficiency, comfort, and economy to users. The technical architecture of the travel scenario is basically mature, but some technical bottlenecks still need to be broken, which can solve the dynamic optimization problem under the intersection of demand, road network, and spatiotemporal heterogeneity, such as the time window adjustment [21, 22], optimal path planning, digital twin technology, and other technical bottlenecks. Therefore, enterprises need to continuously improve the technological innovation mechanism. Technological innovation requires simultaneous efforts from both soft and hard levels. At the hardware level, vehicle suppliers, technology suppliers, and supporting facility suppliers in the shared mobility upstream industry chain build hard functional modules such as remote assistance, information security, human–computer interaction, and other hard function modules for the shared mobility middle industry chain by relying on infrastructure, simulation systems, high-precision maps, development tools, sensors, radars, etc. At the software level, midstream shared mobility service providers rely on smart platforms such as big data platforms and vehicle-road collaboration to provide a highquality travel service flow integrating smart distribution order, smart scheduling, smart experience, and smart travel for downstream users using shared mobility tools. Driven by technologies such as big data and artificial intelligence, learning mechanisms based on multitask dynamic optimization and platform construction based on deep learning and data mining [23] have been built to promote shared mobility, move toward “travel enjoyable”, and truly realize from “travel convenient” to “travel enjoyable”. 5.3 Multidimensional Industrial Integration Shaping the Diversified Development of Enterprises Industrial integration is a necessary condition to build a shared mobility ecosystem. A deep integration of industrial systems of different dimensions is an important judgment to measure whether shared mobility enterprises can expand their business coverage and bring diversified social effects. It is also a key element to measure whether the shared mobility ecosystem can form a virtuous cycle. In the future, the smart city mobility ecology will form a “1 + 1 + 1” mode, which implies that the government and other decision-making bodies will serve as city builders, OEMs will serve as core supporters, and Information and Communications Technology

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(ICT) enterprises will serve as a city link to jointly build the smart city. Therefore, enterprises not only need to conduct positive interactions around the industrial chain, but also transfer value with the entire travel market and development of the automotive industry. Industrial integration involves many commercial details, including cost input and revenue division, product definition rights, and transparency of shared resources. Different enterprise development models cannot be customized in the same manner. An advanced technology can only enhance the enterprise development by adapting to local conditions. Therefore, enterprises need to build an integrated platform that comprehensively considers information, energy, transportation, and other disciplines, and can connect upstream, midstream, downstream, and after-market services. By off-setting over-capacity with travel demand and mobilizing insurance, maintenance, user, and other life cycle systems, they can promote industry integration and development, reduce the gap between different models and technologies, open the ecosystem’s closed-loop development, form multiscenario and multimodel travel methods, maximize the shared mobility value, create disruptive innovations in the travel market, form an integrated solution for the automotive industry, create a convenient, comfortable, and economical travel model, truly achieve “travel enjoyable”, and promote carbon peaking and carbon neutrality goals.

6 Conclusion (1) After the industrial pain period, shared mobility has returned from blind expansion to rational development. On the new path of industrial upgrading and technological empowerment, it has gradually become the epitome of smart mobility under the smart city, and rekindles the spark. (2) Looking back at the development process of shared mobility typical enterprises, old and new technologies have been blended and replaced, and old and new models have repeatedly competed. The application of new technologies such as artificial intelligence, digital twin, image recognition, deep learning, and machine learning, as well as cross-border integration of industries such as finance and information, which provides enterprises with digital solutions such as vehicle asset and operation lifecycle management and services, provides users with efficient travel solutions such as personalized customization. (3) Regarding the provision of a more valuable business model of shared mobility enterprises, from the perspective of the enterprise, business models, innovative technologies, and industrial integration are indispensable components. From the perspective of the external support environment, government supervision and data platforms are strong support guarantees in the operation of enterprises. In the future, enterprises need to focus on the two major needs (society and users), find the correct enterprise positioning, adjust the revenue structure, dig deep travel scenes, revitalize idle resources, and ensure a good cross-border cooperation. Finally, the technology empowers the industry to achieve construction and sharing.

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Adaptive Accident Sampling Investigation Method Based on Regional Traffic Characteristics Jiqing Chen, Yujia Feng, Fengchong Lan(B) , and Junfeng Wang School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China [email protected]

Abstract. In the in-depth investigation of road traffic accidents, it is significant to ensure the reliability of the data source and the representativeness of samples in the database. A sampling investigation model is proposed based on regional traffic conditions and traffic accident data, and a regional transportation and accident characteristics evaluation system is established. Several regional transportation indicators are defined to divide relatively balanced regional sampling units(RSU) based on adjacent administrative divisions. The regional accident indicators are divided into two categories: accident scene classified index and accident damage evaluated index. Analytic Hierarchy Process(AHP) is adopted to calculate the weights of each type of scene according to the accident damage. The group of weights is applied to stratify the accidents in each RSU. It is also necessary to take into account the cost of sampling in practical investigation work. A two-stage sampling investigation model is established, which integrates regional indicators and investigation costs. The total number of accident samples is determined according to the given budget. In the first stage, the regional units are randomly selected. In the second stage, the accident cases are stratified and sampled in the selected units. The entire framework is experimentally implemented with data from Guangdong province, China. Keywords: Accident in-depth investigation · Road traffic accidents · Sampling survey · Analytic Hierarchy Process

Abbreviations AC AHP ADEI ASCI CI CR FT FTO LH

Accident Case Analytic Hierarchy Process Accident Damage Evaluated Index Accident Scene Classified Index Consistency Index Consistency Ratio Freight Traffic Freight Turnover Length of Highway

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 144–161, 2023. https://doi.org/10.1007/978-981-99-1365-7_11

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MSE PCMV PT RSM RSU RAI RTI RI RSF SV

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Mean Absolute Error Possession of Civil Motor Vehicle Passenger Turnover Random Sampling Method Regional Sampling Unit Regional Accident Indicators Regional Transportation Indicators Mean Random Consistency Index Regional Sampling Framework Sentinel Values

1 Introduction With the development of the economy and society as well as the improvement of people’s living standards, road traffic safety is paid more and more attention. The statistics of various traffic indicators, such as the national freight volume and the number of civil cars, have increased in the past three years, with an average increase of 6.19% and 7.67% respectively, increasing the pressure on road traffic. Although road accidents have decreased in frequency over recent years, there were still more than 240,000 road traffic accidents, and the direct property losses caused by road traffic accidents exceeded 1.3 billion RMB in 2020 [1], which undoubtedly affected people’s life and property safety. Hence, it is essential to implement an in-depth investigation of collision accidents, to further study the traffic accidents and the injury-causing mechanisms of the people involved in the accidents [2]. Other relevant deployments are supported, such as the optimization of automotive technical safety standards as well as the design of active and passive vehicle safety systems. The current research based on road traffic accident data focuses on the analysis of accident databases can be divided into two categories according to time sequence. One type is pre-accident prediction, which is a probability forecast and risk assessment of an accident occurring in a specific scenario based on a local accident database. Numerous machine learning models are adopted to analyze and predict traffic accidents. Liu et al. [3] fused accident characteristics and spatiotemporal features of traffic stream to predict future traffic flow, in which a grey convolutional neural network is adopted. Multivariate Regression Models and Artificial Neural Networks(ANN) are compared for the prediction of Highway Traffic Accidents based on 4 years of data for accident counts on the Spain freeway, Alqatawna et al. [4] proved that ANN produced a closer estimation. Six supervised learning classification models, viz. Logistic Reasoning, Linear Discriminant Analysis, Naive Bayes, Classification and Regression Trees, k-Nearest Neighbor, and Support Vector Machine are applied to predict the impacting unknown vehicles in hit-and-run road accidents, and Support Vector Machine has been found to have the maximum potentiality [5]. The other type is post-accident causation analysis, based on accident data to correlate the factors influencing the occurrence of accidents. Caliendo et al. [6] explained the correlated influence of various road traffic parameters on accident frequency based on

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a correlated random parameters approach, according to 226 unidirectional motorway tunnel accidents over 4 years. A mixed logit model with heterogeneity-in-means was proposed to study the effects of multiple investigated factors on the degree of driver injury, based on motorway traffic accidents that occurred in Heilongjiang Province, China, from 2013–2017 [7]. Kuran et al. studied the adaptive non-conform behavior in road accident investigation based heavy goods transport sector, which the data is supported by the Norwegian Safety Investigation Authority(NSIA) [8]. Based on accident databases in countries all over the world, multiple machine learning models were adopted with different parameters in various research projects. However, the above research was all done under the premise that the database is trusted by default. Research on the design and application of sampling methods for accidents is of equal importance. Sun et al. [9] conducted the global sensitivity analysis to quantify the individual effects of parameters in the main steam line break accidents based on sampling methods and surrogate models. A standardized soil sampling protocol was proposed to facilitate the collection of large, tractable samples, which is used to deal with the Fukushima Dai-ichi Nuclear Power Plant accident [10]. Sampling methods are widely adopted in similar accident research [11, 12]. A header framework for application to the field of traffic accident investigation is presented in this paper. Analytic Hierarchy Process(AHP) is a quantitative technique to solve a multi-criterion problem, which was applied to evaluate safety based on accidents [13, 14]. Traffic accidents have multidimensional indicators, which were necessary to be organized according to their relative importance [15]. In this paper, the AHP method is used to analyze regional indicators, combined with sampling methods to establish a framework. In the in-depth investigation of road traffic accidents, it is essential to study and optimize the sampling method in the process of accident data collection. In the actual accident investigation work, due to the excessive amount of data nationwide, it is extremely costly to conduct an in-depth investigation of each traffic accident. Therefore, there is an urgent need to design a sampling method for accident data collection work that is oriented to the overall data volume, the variety of accident characteristics, and the complex accident data fields. A sampling framework considering regional characte- ristics was established, using AHP hierarchical analysis and second-order sampling method, integrated regional road traffic characteristics and accident characteristics to calculate the sampling weights, combined with the survey cost and sampling accuracy to develop a sampling plan. The organization of this paper is introduced as follows. As shown in Sect. 2 in this paper, the regional sampling framework is established, which is illustrated by a sampling flow chart. In Sect. 3, the Regional Transportation Indicators(RTI) and the Regional Accident Indicators(RAI) are explained with public statistics. The framework is validated in Sect. 4, which is conducted based on the traffic data within a Province. Section 5 presents the conclusion and recommended future work.

2 Regional Traffic Characteristics Analysis In this section, the regional traffic characteristics indicators are defined in relation to the traffic data, which are divided into two aspects as Regional Transportation Indicators (RTI) and Regional Accident Indicators (RAI). All of the indicators contribute to an

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optional range, which ensure the adaptation of the entire framework, so that several indexes are selected for special investigation purpose. 2.1 Data Source Explanation The data about traffic development in the example region and the total number of regional accidents were sourced from the Guangdong Provincial Statistical Yearbook [19]. The relevant alternative regional road traffic data items included road mileage, passenger traffic, passenger turnover, freight traffic, freight turnover, and possession quantity of vehicles in each city. And the Relevant regional traffic accident indicators include information on accident road type, accident time and environment, and age or gender of accident participants. 2.2 The Regional Transportation Indicators RTIs is used to measure the development of the regional road transport industry involving motor vehicles, which includes statistical indicators as follows: Length of Highways(LH), passenger traffic, Freight Traffic(FT), Possession of Civil Motor Vehicles(PCMV), urban public transportation, passenger turnover(PTO), Freight Turn- over (FTO), total passenger traffic in cities in Cities and so on. Among them, LH refers to the length of highways built in conformity with the grades specified by the Technical Standards JTJ01–88 for Highway Engineering. The math relationship between A and B exists as shown in Eq. (1).  FTO = (1) (FT × Transport Distance) Based on the statistics of Guangdong Province, China in 2020, LH, PTO, FTO and PCMV is chosen as the RTIs, with the value curves showing RTIs in some cities are plotted after normalization, as Fig. 1. 0.8

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2.3 The Regional Accident Indicators Two aspects of indicators are used to measure the regional road traffic accident situation. One purpose of RAI is to classify the huge number of accidents, and this part of RAI are called ASCI. On the other hand, ADEI aims to measure the severity of traffic accidents in each scenario. The characteristics of the type of road where the accident occurred are set as the basis for accident classification. Therefore, its ASCI indicators include road type, road administrative level, road surface type, road section characteristics, road alignment, road access conditions, etc. Road feature terms influence the distribution of accident types and also affects the estimation results of the sample. For example, the most common type of road surface is dry, straight asphalt pavement, which have huge quantity in reality. So if the pavement type is used as the characteristic term, asphalt pavement will account for the vast majority of the share, and such statistics are meaningless. Furthermore, if a rare accident is analyzed, such as 0.67% of accidents on sharp curves and steep slopes, the road condition is used as a characteristic term to ensure sampling accuracy, where oversampling is conducted. In general, the two characteristic terms mentioned above are not selected as ASCI when estimating the entirety, which would produce a disparate share. The ADEI consists of four indicators, including the number of accidents, injuries, deaths, and Direct Property Loss(DPL), which are applied to evaluate the accident damage and the security of certain scenarios. The distribution of accidents under each type of characteristic can be explored to determine what is worth studying. The grade of the accident road, the accident nearby environment, and the information of the participants are selected as studied features, and the accident destructiveness curves are plotted under various scenarios, as shown as follows (Figs. 2, 5 and 6).

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With analysis of the statistical data, the accident distributions under different ADEIs appears obvious distinction. As shown in Fig. 3 and Fig. 4(a). The human injuries count is approximate synchronous, while the number of fatalities and direct property loss are different from the two. When comparing the two accident scene road types, highway

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1.075, and more benefits are obtained with higher speed. Keywords: Amphibious vehicle · Water resistance · Model test · Bow and stern flaps · Trim angle

1 Introduction Amphibious vehicle is capable of traversing across aquatic and terrestrial environment. It can be used for both military and civil field. Its resistance characteristic has always been concerned by designers, which is one of the important factors affecting the maneuverability of amphibious vehicle. Most of the amphibious vehicles were propelled by crawler paddling at the beginning, while a few used water jet propulsions, and the speed was generally low. The speed of infantry combat vehicles or amphibious tanks in the 1970s was usually lower than 10 km/h. At the end of the 20th century, higher speed of amphibious vehicle is focused. The high speed amphibious vehicles of Britain and Switzerland were first appeared in the world [1]. The research of amphibious vehicles in China begins relatively late. Until 1996, the technology of “increasing buoyancy in the vehicle” that proposed by Zhang [2], which opened a new wave of research on amphibious vehicles in domestic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 218–227, 2023. https://doi.org/10.1007/978-981-99-1365-7_16

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The resistance of amphibious vehicle is mainly composed of frictional resistance, viscous pressure resistance and wave-making resistance. Researches show that frictional resistance is about 5%–10% of the total resistance, viscous pressure resistance is about 70%–80%, and wave-making resistance is about 15%–20% [3], which provide some ideas for the hull optimization of amphibious vehicle. With the development of amphibious vehicle, high speed is an important requirement, but due to its high block coefficient and bluff body, the resistance and trim angle of amphibious vehicle are always large when sailing with high speed. With the spreading of energy saving concept, the optimization of vehicle hull and minimization of the resistance of amphibious vehicle are attracted more and more attentions in recent years. Gao (2009) calculated the flow field around the amphibious vehicle with wheels, with half lifting wheels and with full lifting wheels respectively. It is found that the flow field near the vehicle body changed by the protruding parts, which resulting in serious flow separation and poor hydrodynamic performance [4]. Cui (2011) designed an amphibious vehicle and optimized the inclined angle of bow, results showed that the inclined angle of bow has an obvious effect on the pressure distribution, and the viscous pressure resistance C vp and friction resistance C f can be improved by the change of inclined angle of bow [5]. Peng and Liu (2014) made an in-depth study on a wheeled amphibious vehicle that installed stern hydrofoil by CFD simulation, it was found that the resistance and trim angle can be reduced after installing the stern hydrofoil when the Fr ▽ < 2.087 [6]. Yuan (2014) adopted the numerical simulation method to study the influence of the wave suppression plates with different shapes and sizes on the resistance characteristics of the amphibious vehicle at high speed, and compared the simulated results with the test results. It was found that the resistance reached maximum when the Fr ▽ was close to 1.25, and the resistance reduced obviously by the installation of wave suppression plates when the Fr ▽ was 0.5 ~ 2.25 [7]. Yu (2015) simulated the motion of an amphibious vehicle model using dynamic mesh technology of FLUENT, and orthogonal analysis was adopted to improve computation efficiency, the optimal range of flap angles for improving the lift-to-drag ratio of amphibious vehicle was obtained [8]. Wang (2018) calculated the viscous flow field around an amphibious vehicle, the results showed that the hydrodynamic performance of amphibious vehicle with wave suppression plates was better than the one without wave suppression plates [9]. At the present, most of researches on the resistance characteristics of amphibious vehicle are simulated and calculated by using CFD software, and the focuses are mainly on the optimization of vehicle hull, the wheel retraction suspension device and the resistance characteristics under planning state. This study focuses on the resistance of an amphibious vehicle under displacement state and semi-planning state by experiments. The resistance characteristics of an amphibious vehicle model with different stern flap and bow flap are tested and discussed. A kind of bow flap and stern flap with smaller resistance will be found.

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2 Test Equipment The test is carried out in the towing tank of Wuhan University of Technology. The length and width of the tank is 132 m and 10.8 m respectively, the water depth is 2.0 m, the maximum speed of the towing carriage is 7.2 m/s. The resistance of the amphibious vehicle model is recorded by the resistance dynamometer. The trim angle is recorded by the clinometer. The navigation rod is used to restrict the lateral movement of the amphibious vehicle, and the amphibious vehicle is free to pitch and heave in the test. The amphibious vehicle model is made of wood and smooth painted; the scale ratio is selected as 1:4. Four identical wheels are installed in the front and rear of the amphibious vehicle model and a pair of tracks is installed in the middle. The wheel retraction cantilever, track connector and so on are simplified in the test; the wetted area and displacement distribution of the amphibious vehicle model are roughly similar to the prototype amphibious vehicle. During the test, the wheel is in the state of retraction, and the lower end of the wheel is flush with the bottom of the amphibious vehicle body. The bow angle of the amphibious vehicle is 35°, and there is a vertical wall in the front of the vehicle body. Transom stern is adopted to increase the "virtual length" and prevent too large “rooster tail” when sailing with high speed [10]. The angle between the inclined plane of the stern and the baseline is 39°. The schematic diagram of the amphibious vehicle is shown in Fig. 1.

Fig. 1. Schematic diagram of amphibious vehicle

3 Test Conditions The bow and stern flaps used in this paper are horizontal rectangular wooden plates, which are connected with the vehicle body through hinges and telescopic rods, and the changing of angle for bow and stern flaps is realized by the telescopic rods. After completing the angle adjustment, the telescopic rods are effectively fastened, and the edges of the plates are closely jointed with the surface of the vehicle body. The length of the bow flap is 18.3% of the vehicle length, and the length of the stern flap is 8.6% of the vehicle length. The width of the two flaps is equal to the breadth of amphibious vehicle model, and the thickness is 10 mm. The test is carried out for two schemes. Figure 1 is that the bow flap is installed at the intersection of the vertical wall at the front of the vehicle body and the inclined plane, and the stern flap is installed 20 mm above the intersection of transom stern and the inclined plane at stern, as shown in Fig. 2.

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The installation angle of bow flap is defined as α 1 , and the installation angle of stern flap is defined as β 1 . Anticlockwise is defined as positive in the study. Angles of bow and stern flaps tested in the simulation are listed in Table 1. Figure 2 is that the installation position of the bow flap is the same as that of scheme 1, and the stern flap is installed at the intersection of transom stern and the inclined plane at stern, as shown in Fig. 3. The installation angle of bow flap is defined as α 2 , and the installation angle of stern flap is defined as β 2 . Anticlockwise is defined as positive in the study. Angles of bow and stern flaps tested in the simulation are listed in Table 2. Among the tested cases, CASE 1 is the case that without flaps. The angle of the stern flap is kept constant under CASE 2 ~ CASE 5, and the angle of the bow flap is gradually reduced for these cases. The angle of the bow flap is gradually decreased, and the angle of the stern flap is increased under CASE 6 ~ CASE 10.

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4 Results and Analysis 4.1 Definition of Dimensionless Parameter The resistance coefficient [10] as shown in Eq. (1) is adopted to express the total resistance of amphibious vehicle model. Cd =

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4.2 Test Results and Analysis The resistances of the amphibious vehicle model with five different speeds (the speed step is 1 km/h) are tested for each scheme, the corresponding Fr∇ is 0.904–1.13, which is between the displacement navigation state and the semi-planning navigation state. Reference indicated that the total resistance per unit displacement of the amphibious vehicle increases with the speed for this kind of vessel [12].

Fig. 4. Comparison of resistance coefficient of amphibious vehicle in Scheme 1

Figure 4 shows the resistance coefficient C d of amphibious vehicle model with different speeds and with different bow and stern flap angles as illustrated in Fig. 1. It can be seen that the effects of flaps on resistance reduction are obvious. When the speed is constant, the resistance decreased with the installation of bow and stern flaps. For CASE2 ~ CASE5, the angle of stern flap β is constant, and the angle of bow flap α is decreased from CASE2 to CASE5. The resistance is reduced about 11% with Fr∇=0.904, and about 7% with Fr∇=1.13 for CASE5 compared with the one without flaps (CASE1).

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Figure 5 shows the trim angle θ of amphibious vehicle model with different speeds and with different bow and stern flap angles as illustrated in Fig. 1. Positive value of trim angle in the figure represents trim by stern. It can be seen that the trim angle of the amphibious vehicle model increases exponentially with the increase of the speed when without flaps. At lower speeds, the trim angle increased when with the flaps, but when Fr ▽ > 1.075, the trim angle decreased when with the flaps. As can been, the reduction of trim angle is becoming larger with the increase of vehicle velocity. The trim angle is reduced about 15% Fr∇=1.13 for CASE2.

Fig. 5. Curve of the relationship between trim and Fr ▽ in Scheme 1

Figure 6 shows the resistance coefficient C d of amphibious vehicle model with different speeds and with different bow and stern flap angles as illustrated in Fig. 2. Comparing with the results of Fig. 1, it can be observed that the resistance characteristics are greatly improved when the stern flap is installed at the intersection of transom stern and the inclined plane at stern. For example, at lower speeds, the resistance reduction is about 20% for CASE6, which is the lowest one of cases in Fig. 2. It may because the installation of the flaps at the intersection transom stern and the inclined plane at stern reduces the protruding sharp corners of the vehicle body and improves the distribution of the flow field at the stern, the energy consumed by the vortex is reduced, and the flow resistance around is reduced [6, 13]. According to the results of CASE8 (α = 12°, β = 25°) and CASE9 (α = 8°, β = 25°), it can be found that the resistance reduction is the largest when the angle of the bow flap α is about 10°for the amphibious vehicle model that we tested, and the resistance reduction increased with the increasing the angle

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of the stern flap β at higher speed. When the volume Froude number Fr∇=0.904, the resistance reduction of CASE10 is about 29% compared with CASE1 that without flaps. When the volume Froude number Fr∇=1.13, the resistance reduction of CASE10 is about 18%. Figure 7 shows the trim angle θ of amphibious vehicle model with different speeds and with different bow and stern flap angles as illustrated in Fig. 2. From the results of CASE8 (α = 12°, β = 25°) and CASE9 (α = 8°, β = 25°), it can be seen that the angle of bow flap α has little effect on the trim angle. From CASE7 (α = 12°, β = 20°), CASE8 (α = 12°, β = 25°) and CASE10 (α = 8°, β = 40°), it can be found that the angle of stern flap β has a great influence on the trim angle. The trim angle decreased with the increasing of the angle of stern flap β, and the trim angle reduction increased with the increase of speed when with the same β. At low speeds, the trim angle reduction is negative, which means the trim angle increased with flaps, but at high speed, the trim angle decreased with flaps, the trim angle reduction is about 27% at the highest speed.

Fig. 6. Comparison of resistance coefficient of amphibious vehicle in Scheme 2

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Fig. 7. Curve of the relationship between trim and Fr ▽ in Scheme 2

5 Conclusions The resistance and trim angle of a high speed amphibious vehicle model with different bow and stern flaps are tested and discussed. Conclusions can be drawn from the test results as follows: (1) The installation of bow and stern flaps can reduce the protruding sharp corners of the vehicle body and improve the distribution of the flow field, and optimize the model and reduce the resistance. (2) The resistance of the amphibious vehicles model is reduced by the use of bow flap and stern flap, and the angle of bow flap α and the angle of stern flap β have different influence on the resistance of amphibious vehicle. The resistance reduction is the largest when the angle of the bow flap α is about 10°for the amphibious vehicle model discussed in the paper. When the angle of the bow flap α is about 10°, the resistance reduction increased with the increasing of the angle of stern flap β especially at high speed. Compared with the model without flaps, the resistance is the smallest with 8° bow flap and 40° stern flap in our study, the resistance reduction is about 29% at the lowest speed and is about 18% at the highest speed. (3) The stern flap has a great influence on the trim angle of the amphibious vehicle model. When Fr ▽ < 1.075, the trim angle increased with flaps compared with the one without flaps. When Fr ▽ > 1.075, the trim angle increased with flaps. Compared with the model without flaps, the trim angle is the smallest with 8° bow

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flap and 40° stern flap in our study, the trim angle reduction is about 27% at highest speed. In order to explore more characteristics of amphibious vehicle with flaps, simulation works are necessary. For more accurate evaluation of the proposed flaps under the actual operating conditions, model tests and numerical simulations will be performed in waves in further research.

References 1. Song, G.: Main question research on reducing resistance and increasing speed for amphibious vehicle. Nanjing University of Aeronautics and Astronautics (2008) 2. Xu, Y.: Research the key technologies of amphibious vehicle based on motion characteristics. Jiangsu University of Science and Technology (2013) 3. Zheng, X., Fang, L., Wang, C., et al.: Research on rapidity design of amphibious vehicles. J. Sichuan Ordnance 11, 34–37 (2015) 4. Gao, F., Jiang, L., Pan, C.: Numerical Study of flow field around an amphibious vehicle and its improvement in shape. J. Natl. Univ. Defense Technol. 31(1), 114–119 (2009) 5. Cui, J.: Research on high speed amphibious vehicle’s virtual prototype technology based on wheel’s retracted and deployed. Nanjing University of Aeronautics and Astronautics (2011) 6. Kun, P., Ying, L.: Influence of empennage on resistance characteristics of a wheeled amphibious vehicle. Veh. Power Technol. 4, 15–19, 24 (2014) 7. Yuan, X., Zhang, M.: Influence of breakwaters on sailing characteristics of an amphibious vehicle. Veh. Power Technol. 2, 15–19,28 (2014) 8. Yu, Z., Liao, Y., Li, J., et al.: Simulation analysis of effect of different skateboard angles on lift-drag ratio of amphibious vehicle. Ship Eng. 37(03), 26–29 9. Wang, L., Zhang, J., Liu, T., et al.: Numerical analysis on effect of wave suppression plate on hydrodynamical characteristics of amphibious vehicle. Syst. Simul. Technol. 14(2), 113–117 (2018) 10. Ju, N.: Hydrodynamic Analysis and Simulation of Amphibious Vehicle. Weapons Industry Press, Beijing (2005) 11. Sheng, Z., Liu, Y.: Principles of Naval Architecture. Shanghai Jiaotong University Press, Shanghai (2005) 12. Zhao, L., Xie, Y.: Principles and Design of High-Performance Ships. National Defense Industry Press, Beijing (2009) 13. Zheng, X., Jia, X., Liu, X.: Numerical simulation of running resistance around a minitype high-speed amphibious vehicle. Ship Sci. Technol. 30(3), 139–142 (2008)

Motion Control Strategy of Wheel-Legged Compound Unmanned Vehicle Xiaolei Ren1(B) , Hui Liu1,2 , Jingshuo Xie1 , Yechen Qin1 , Lijin Han1,2 , and Baoshuai Liu1 1 National Key Lab of Vehicular Transmission, Beijing Institute of Technology, Beijing, China

[email protected] 2 Advanced Technology Research Institute (Jinan), Beijing Institute of Technology, Beijing,

China

Abstract. This paper proposes a motion control strategy for the wheel-legged compound unmanned vehicle, including attitude control and driving control. Firstly, based on tire mechanics, Lagrangian method and D’Alembert’s principle, the dynamic modeling of the driving system, wheel-legged system and vehicle body is carried out respectively. On this basis, a layered parallel control architecture is proposed. The upper-level controller consists of a driving system controller and a wheel-legged system controller. The former is used to control the longitudinal, lateral and yaw motions of the vehicle based on the model predictive control method, and the latter is used to adjust the longitudinal, pitch and roll motions of the vehicle based on PD control. The lower-level controller is used to control the output torque of the vehicle motor based on the PI control method. Finally, three typical working conditions are designed to verify the attitude control and driving control of the vehicle. The simulation results show the accuracy and stability of the designed controller to track the target command. Keywords: wheel-legged compound unmanned vehicle · motion control · attitude control · driving control · layered parallel control architecture · model predictive control

1 Introduction The wheel-legged compound unmanned vehicle (WLCUV) has the advantages of both wheeled and legged platforms, and has the characteristics of high movement efficiency and strong environmental adaptability [1]. It is widely used in military reconnaissance [2], disaster area rescue [3] and alien exploration [4] and other fields [5, 6]. The wheellegged compound unmanned vehicle body includes 6 degrees of freedom (DOF). Different from the traditional vehicle active suspension control, its posture is mainly controlled by the active coordination of the joints of the wheel-legged system, which is similar to the “large travel active suspension”. The control method significantly improves the mobility and pass ability of the vehicle, but it also poses a great challenge to the motion control of the vehicle. The motion control of WLCUV is mainly divided into two parts: centre © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 228–246, 2023. https://doi.org/10.1007/978-981-99-1365-7_17

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of mass (COM) attitude control and driving control. The former mainly draws on the control methods of quadruped robots, including PID control [7], Virtual Model Control (VMC) [8] and model predictive control (MPC) [9], etc. And the latter mainly includes direct driving and steering control. Generally, there are four control methods according to different vehicle structures. The first is the same Ackerman steering as traditional vehicles, which is limited by the layout of the steering mechanism, and it is difficult to adapt to wheel-legged vehicles; the second is to control the external swing joint of the wheel-legged system, such as the ANYmal [10] series of wheel-legged robots developed by the Swiss Federal Institute of Technology in Zurich, Switzerland. The single wheel-legged system contains 4-DOFs, and the driving direction of the robot is adjusted through the external swing joint. This multi-degree-of-freedom joint improves the flexibility of the robot as a whole, but it is very prone to deviations and understeer in straight driving. The third type is multi-wheel collaborative control, such as “Nezha” developed by Beijing Institute of Technology [11], which has the functions of four-wheel independent drive and four-wheel independent steering at the same time and the drive is flexible, but the control is difficult. The fourth type is the skid steering control, such as Handle [12] produced by Boston Dynamics and Ollie, a two-wheel-legged robot developed by Tencent [13], which can achieve in-situ steering and small-radius steering, also each wheel-legged has less DOF. Especially in high-speed driving conditions, it can be Effectively suppressing the phenomenon of straight driving and deviation. At present, most of the motion control for WLCUV focuses on the planning and control of the centre of mass (COM) of the vehicle, and its driving control is rarely mentioned. For this reason, an 18-DOFs dynamic model of the whole vehicle is established in this paper, including the driving system, the wheel-leg system and the vehicle body model. Then a layered parallel controller is designed. The upper controller includes the driving system controller and the wheel-legged system controller. The former is based on the MPC method to control the longitudinal, lateral and yaw motions of the vehicle and the PD control is used to adjust the motion in the vertical, pitch and roll motions of the body. The lower controller is based on the PI control method to control the output torque of the vehicle motor. Finally, the motion controller of the vehicle is verified through typical working conditions. The results show that the designed controller is accurate and stable in tracking the target command in complex terrain.

2 System Modeling 2.1 Structure Description The WLCUV adopts a modular design, and its overall structure and transmission mode are shown in Fig. 1.

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1-GPS, 2-Vehicle, 3-Radar, 4-Binocular camera, 5-Brake system, 6-Wheel, 7-Wheel motor, 8-Knee joint, 9-Calf, 10-Thigh, 11- hip joint

Fig.1. WLCUV structure

The whole vehicle mainly includes the body, wheel-legged system and braking system. The body adopts an integral box girder structure and is responsible for carrying multiple functional systems such as control system, perception system, positioning system, navigation, inertial measurement unit (IMU), and communication. The leg-system is mainly composed of the thigh, calf and wheel. The thigh is directly driven by the hip motor, and the calf is driven by the knee motor through the timing belt, and the wheel is driven by the wheel motor. The four sets of wheel-legged systems are symmetrically distributed on both sides of the body. The braking system adopts the method of disc brake, which has the functions of service brake and parking brake. 2.2 Coordinate System Establishment In order to facilitate the description of the motion and dynamic relationship of the multi-particle system, simplify the description of the model variables, and use different coordinate systems to represent the vehicle position, speed and acceleration, as shown in Fig. 2, the inertial coordinate system {OW } is firmly connected to the ground, and the coordinate system of the body {OB } is fixed to the COM of the body, where Bx is the longitudinal direction of the vehicle, and Bz is perpendicular to the body, and By satisfies the right-hand rule. The wheel-leg system coordinate system is established based on the Denavit-Hartenberg (D-H) method. {O0 } is the coordinate system where the hip joint is fixed to the body, and {O1 } represents the hip joint coordinate system that rotating with the thigh, and {O2 } represents the knee joint coordinate system rotating with the calf, and {O3 } is the wheel coordinate system.

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2.3 Dynamic Modeling The WLCUV has a complex structure which contains 18-DOFs. The dynamic model is mainly composed of three parts, namely the vehicle body model, the driving system model and the wheel-legged system model, as shown in Fig. 3. Among them, the body has 6-DOFs, including longitudinal, lateral, vertical, roll, pitch, and yaw motions. The driving system mainly controls the longitudinal, lateral and yaw motions of the body, and its components include motor model, wheel model and tire model. The wheel-legged system includes 3-DOFs, which mainly controls the movement of the vehicle body in the vertical, pitch and roll motions.

Fig. 3. Dynamic model block diagram

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2.3.1 Driving System Model Each wheel of the WLCUV is equipped with a wheel-side motor and controller, which can realize distributed driving. The wheel-side motor model is represented by a firstorder transfer function model, and the time constant τm of the motor is generally taken as 0.05 s according to the driving characteristics of the motor. Te,wheel =

1 Tc,wheel 1 + τm

(1)

where, Te,wheel is the target torque of the motor (N · m), and Tc,wheel is the motor output torque. The force analysis of the wheel is shown in Fig. 4. Fxij , Ffij , Fzij are the driving force, rolling resistance and vertical force provided by the ground to the wheels (N), respectively, and the subscript ij represents the front left (Fl), the front right (Fr), the rear left (Rl), and the rear right (Rr). Ω˙ ij is the rotational speed of the wheel (rad/s) and rij is the wheel rolling radius (m). Je is the equivalent moment of inertia of the wheel (kg · m2 ).

Fig. 4. Wheel model

Since there is no reducer between the motor and the wheel shaft, the equation of motion when the wheel rotates can be expressed as: Je ×

  d Ω˙ ij = Tij − rij × Fxij − Ffij dt

(2)

The tire model adopts the Unitire unified index tire model [14], which is suitable for skid steering under large slip and large cornering conditions, and its longitudinal slip rate and lateral slip rate are uniformly defined as the ratio of slip speed to rolling speed, and it can be given as: ⎧ ⎨ Sxij = Ω˙ ij rij −vxij , Sxij ∈ (−∞, +∞) |Ω˙ ij rij | (3) ⎩ Syij = −vyij , Sxij ∈ (−∞, +∞) |Ω˙ ij rij |

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where, Sxij , Syij represent the longitudinal and lateral slip rates of the tire, and vxij , vyij are the longitudinal and lateral components of the wheel speed (m/s), respectively. Under the combined conditions of tire longitudinal slip and side deflection, the relative total slip rate of the tire φij is expressed as:  ⎧ 2 + φ2 ⎪ φij = φxij ⎪ yij ⎨ Kx Sxij (4) φxij = μx Fzij ⎪ ⎪ ⎩ φ = Ky Syij yij

μy Fzij

where φxij , φyij represent the relative longitudinal slip rate and the relative lateral slip rate, and Kx , Ky are the longitudinal slip and cornering stiffness of the tire. μx , μy are the longitudinal and lateral friction coefficients, respectively. The tire dimensionless tangential force, longitudinal force and lateral force can be described as: ⎧ 

 2 − E + 1 φ3 ⎪ F = 1 − exp −φ − Eφ ij ⎪ ij 12 ij ⎨ φ (5) Fxij = μx φxijij Fzij F ⎪ ⎪ ⎩ F = μ φyij F F yij

y φij

zij

where E is the tangential force curve factor. 2.3.2 Wheel-Legged System Model The wheel-legged system is mainly used to adjust the vertical, pitch and roll motions of the vehicle, and transmit the force or moment generated by the ground load to the body. Therefore, the wheel-legged system dynamics model is jointly determined by the longitudinal, lateral and vertical forces of the tire. In this paper, the interaction between the legs is ignored, and the wheel-legged dynamics model is established, as shown in Fig. 5 for the right rear wheel-legged system. Equivalent thigh and calf as rods, assuming a homogeneous distribution across the legs. L1 , L2 are thigh and calf lengths, and Ch , Ck , Cw are the COM of the thigh, calf and wheel, respectively. In the generalized coordinate system, the leg joint variables are  T q = qh qk , qh , qk represent the hip and knee joint angles. According to the Lagrange, the dynamic model of the wheel-legged system can be obtained as:   M(q)¨q + C q, q˙ q˙ + G(q)= τ + J T Fext (6)   where M(q) ∈ R2×2 is a symmetric positive definite mass matrix. C q, q˙ ∈ R2×2 are centrifugal force and Coriolis force components. G(q) ∈ R2×1 is the gravity component. τ ∈ R2×1 indicates the joints driving torque. J T is the Jacobian matrix and Fext is the generalized force acting on the end of the legs.

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Fig. 5. Schematic diagram of the Rl wheel-legged system

2.3.3 Body Model

Fig. 6. 6-DOFs model of the body

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The vehicle body as a whole contains 6-DOFs, which are mainly connected to the wheels through the wheel-legged system, and bear the force or moment of the ground. The force analysis is shown in Fig. 6. Considering the influence of the body posture on the skid steering, a 6-DOF dynamic model of the body is established, which can be obtained from the d’Alembert principle: ⎧ ⎪ mb ax = Fxij − Ffij ⎪ ⎪ ⎪ ⎪ m b ay = Fyij ⎪ ⎪ ⎨ mb az = Fzij − mb g (7) ⎪ Ix θ¨ = B2 · Fzij − H · F yij ⎪   ⎪ ⎪ ⎪ Iy ϕ¨ = L2 · F ⎪ ⎪  xij − H · Fxij − Ffij ⎩ Iz ω¨ = B2 · Fxij − Ffij + L2 · Fyij where Ix , Iy , Iz are vehicle roll, pitch and yaw moments of inertia (kg · m2 ). mb is the body mass (kg). H is the initial height of the vehicle. L is distance from front axle to rear axle and B is the track width. The specific parameters of the vehicle are shown in Table 1. Table 1. Vehicle parameters No

Symbol

Quantity

Description

1

mb

50 kg

Body mass

2

mh

3.5 kg

Thigh mass

3

mk

1.5 kg

Calf mass

4

mw

2.4 kg

Wheel mass

5

L

0.735 m

Distance from front axle to rear axle

6

B

0.494 m

Track width

7

L1

0.300 m

Thigh length

8

L2

0.290 m

Calf length

9

H

0.717 m

Initial height

10

r

0.100 m

Wheel radius

11

max , τ max τe,h e,k

144 Nm

Joint peak torque

12

max Te,wheel

25 Nm

Wheel peak torque

3 Controller Designer In this paper, a layered parallel control structure is proposed to control the motion of the WLCUV. The COM controller is used for the analysis of different subsystem signals and the coordinated control of the COM, including the driving system controller and the

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wheel-legged system controller. The former is used to control the longitudinal, lateral and yaw motions of the body. The latter is used to adjust the vertical, pitch and roll motion of the body. The motor controller realizes the control of joints torque and wheels torque and the specific control frame is shown in Fig. 7. act des des act act T des e,w , τ e,h , τ e,k are the input torque of the wheel, hip and knee, and T e,w , τ e,h , τ e,k  T des des des des des des are the feedback torque. Gdes is the COM = xCOM yCOM zCOM θCOM ϕCOM ωCOM desired position and attitude of the COM.

Fig. 7. Vehicle motion control system framework

3.1 Driving System Controller 3.1.1 Speed Controller The main purpose of vehicle speed control is to enable the vehicle to track the desired speed when driving on unstructured roads. The vehicle speed controller adopts PID closed-loop control, and calculates the wheel speed requirement from the deviation

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between the target vehicle speed and the actual vehicle speed Ω˙ des . ⎧

⎪ ⎨ Ω˙ des = Kp e + Ki edt + Kd de dt ⎪ des ⎩ act e = x˙ CoM − x˙ CoM

237

(8)

des is the target speed and x˙ act where x˙ COM COM is the actual speed. e is the speed deviation. Kp , Ki , Kd are proportional, integral and derivative gain coefficients, respectively.

3.1.2 Yaw Rate Controller The lateral dynamics of the vehicle are mainly determined by the lateral velocity Vy and the yaw rate ω. ˙ Based on MPC theory and the vehicle 2-DOFs linear monorail model, this paper designs the yaw rate controller, and its control frame is shown in Fig. 8. For nonlinear continuous systems, the approximate discrete-time system dynamics equations can be obtained by the forward Euler method x˙ = f (x, u), as shown in Eq. (9).  xk+1 = Ac xk +Bc uk (9) yk = C c xk where, Ac ∈ R2×2 , Bc ∈ R2×1 are discrete state system matrices and C c ∈ R2×2 is the unit output matrix. The subscript k is the discrete step size, which is assumed to be in the prediction time domain.  The input vector is u = Vx Vx−1 , which represents the rate of change of speed difference. Compared with the Ackerman steering vehicle, under the same input value, the steering radius decreases with the increase of the input amount, and has the same magnitude [16]. Therefore, it is very suitable to select Vx Vx−1 as the skid steering input. T T   x = Vy ω˙ is the system state vector and y = Vy ω˙ is the system output vector, In summary, it can be seen that the system has full observability. In order to avoid excessive influence on the system and improve the stability of the system, it is also necessary to consider the rate of change of the control input. Therefore, the cost function in the finite prediction time domain under the discrete-time system is set as: ⎧ κ+N κ+N ⎪   p −1  T c −1  T ⎨ y ( κ|t)2 + u ( κ|t)2 J= P Q (10) κ=t κ=t+1 ⎪ ⎩ ∗ U = arg min(J) where s2ξ = sT ξ s represents the weighted 2-norm, and t is the current sampling time. Np is the prediction time and Nc is the control time. yT ( κ|t) represents the prediction of the system control input increment at time t to time κ. uT ( κ|t) is represented as an incremental sequence of input variables for the prediction system and P, Q are positive semi-definite weight matrices, reflecting the importance of each control objective. Convert Eq. (10) to the standard form for quadratic programming (QP) solution, given as: 1 J (x0 , u) = uT Hu + ug(x0 ) + c (11) 2

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Fig. 8. Yaw rate controller framework

where H is a symmetric matrix determined by the prediction model and the weight matrix and the vector g(x0 ) is determined by the initial state of the system, the system matrix and the weight matrix, and c is a constant. The control sequence in the prediction  ∗ T . Since the system input time domain is obtained by QP solution u∗ = u1∗ u2∗ · · · uNc vector is Vx Vx−1 , the wheel speed needs to be solved for the subsequent motor control: 

Ω˙ l = Ω˙ r =

Vx +Bω˙ r Vx −Bω˙ r

(12)

where Ω˙ l , Ω˙ r indicate the left and right wheel speeds, respectively. In motion control, the WLCUV needs to consider the torque execution capability of the motor, that is, the output range of the motor torque. The peak torque of the joint motor and the wheel motor is shown in Table 1. 



LB ≤ U ≤ U B 



(13)

where LB, U B are the upper and lower limits of the motor torque, respectively. The maximum yaw rate of the vehicle constrains the vehicle’s speed and yaw rate state, which can be expressed as: ⎧ ˙ ≤ ημx g ⎨ −ημ  xg ≤ Vx (k) · ω(k) (14) ⎩ Ky2 Kx2 + Ky2 < η < 1

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where μx is the ground attachment coefficient. g is the acceleration of gravity and η is the maximum yaw rate coefficient. With the positive and negative of the yaw rate of the vehicle, the rotation speed of the left and right wheels is constrained respectively, that is, during the skid steering process, the excessive slip of the outer wheel is constrained, and the rotation speed of the inner and outer wheels satisfies the following relationship:  ˙ l (κ) > Ω ˙ r (κ), ω˙ < 0 Ω (15) ˙ ˙ r (κ), ω˙ < 0 Ω l (κ) < Ω

3.2 Wheel-Legged System Controller The inverse kinematics model of the wheel-legged system can solve the joint angles when the end position is known. In this paper, by solving the vehicle body attitude change, through the inverse kinematics model of the vehicle, the joint angles in each wheel-legged system are mapped, taking the Fl leg as an example, as shown in Fig. 9.

Fig. 9. Inverse kinematics of wheel-legged system

The coordinates of the wheel-ground contact point O4 in the hip joint coordinate system {O0 } can be obtained through the body sensor, and the hip joint rotation angle and the knee joint rotation angle are expressed as: ⎧     L2 sin qk −1 Z4 −r ⎪ + tan ⎨ qh = tan−1 L1 +L  2 ·cos qk  X4 (16) 2 +(Z −r)2 −L2 −L2 X 4 −1 4 1 2 ⎪ ⎩ qk = − cos 2L1 L2   where O4 X4 Z4 is the wheel-ground contact point coordinates. In order to compensate the deviation of the WLCUV during the motion attitude adjustment process, an incremental PD control law is designed based on the desired

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attitude and the current attitude, which can be expressed as:     ϑ in = ϑ des + Kp ϑ des − ϑ act + Kp ϑ˙ des − ϑ˙ act

(17)

where ϑ des , ϑ˙ des are the position and speed of the desired attitude and ϑ act , ϑ˙ act are the position and speed of the current posture of the vehicle body, and its control framework is shown in Fig. 10.

Fig. 10. Wheel-legged system control framework

3.3 Motor Controller The motor controller includes a position loop, a speed loop and a current loop, and PI control is adopted as a whole. The specific framework is shown in Fig. 11.

Fig. 11. Motor controller framework

In summary, the whole motion control system of the WLCUV is formed.

4 Simulation Analysis In order to verify the effectiveness of the motion controller, this paper uses the SimulinkSimscape multi-body dynamics software for the service environment of the WLCUV to establish an 18-DOFs model of the vehicle and build typical working conditions, including 20° vertical slope, 400mm height limit rod and S-curve, as shown in Fig. 12.

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Fig. 12. Typical working conditions

Figure 13 shows the change of the body pitch angle when the WLCUV passes through a 20° vertical slope, in which (a) and (b) respectively represent that when the vehicle is going up and down the vertical slope, the body reaches the desired level through the wheel-legged system. The pitch angle is ± 20°. Figures 13 (c) and (d) represent the pitch angle of the body when the vehicle is uncontrolled. It can be seen that by controlling the pitch angle of the vehicle, the vehicle body can be kept in a horizontal state when passing through the vertical slope. Figure 14 shows the height change of the vehicle body when the vehicle passes the height limit bar. In addition, when the vehicle is passing through a large-angle vertical slope, the vehicle height and pitch angle can be adjusted at the same time to increase the approach angle and departure angle, improve the vehicle passability, and reduce the risk of collision between joints and vertical slopes. The overall simulation duration is set to 80 s, and the step size is 0.001 s. Figure 15 shows the change of the body pitch angle when the vehicle passes the working condition at 0 ~ 30 s. The red line is the desired pitch angle, and the blue line indicates that the actual pitch angle of the vehicle body can accurately track the desired pitch angle when the vehicle is under PD control. The body pitch angle can be obtained through the vehicle IMU unit, with a maximum tracking deviation of 0.016°, and the black line indicates that in the case of no control, the body pitch angle has a large deviation from the expectation.

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Fig. 13. Body pitch angle change

Fig. 14. Body height change

Figure 16 and 17 show the changes of the vehicle speed and yaw rate under the all conditions. It can be seen that when the vehicle passes through the longitudinal slope, the vehicle speed and the yaw rate have small fluctuations, and the maximum tracking deviation is 0.09 m respectively. 0.09 m/s and 0.0017 rad/s, the vehicle can maintain a good tracking effect during the entire driving process.

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25 20 20.02

15

20.01 20

Pitch Angle/°

10

19.99 10.5

5

10.54

0 -5 -10 -15 Desired pitch angle Pitch angle with PD control Pitch angle without control

-20 -25 0

5

10

15 Time/s

20

25

30

Fig. 15. Pitch angle change under condition I 3.5 3

Vehicle Speed/(m/s)

2.5 2 1.5 1 0.5 0

Desired vehicle speed Vehicle speed with PID control

-0.5 0

10

20

30

40 Time/s

50

60

70

80

Fig. 16. Vehicle speed

Figure 18 shows the joint torque output obtained by the motor feedback under the all conditions of the vehicle. It can be seen that when the vehicle passes through the vertical slope, the torque output is large due to the impact on the joints. The maximum torque of the hip joint and the knee joint are 135.24 Nm and 130.53 Nm, which are less than the peak torque 144 Nm. When turning, the maximum torque of the hip joint and knee

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0.3

Yaw Rate/(rad/s)

0.2 0.1 0 -0.1 -0.2 -0.3 -0.4 0

10

20

30

40 Time/s

50

60

70

80

Fig. 17. Yaw rate

joint motor is 44.91 Nm and 57.2 Nm, respectively. The maximum torque of the wheel is 14.99 Nm when passing through the vertical slope and when the steering is steady, the torque is 10.17 Nm, which are less than peak torque 25 Nm, as shown in Fig. 19. 150 Fl leg hip joint torque Fl leg knee joint torque 100

Joint Torque/Nm

50

0

-50

-100

-150 0

10

20

30

40 Time/s

50

Fig. 18. Fl joint torques

60

70

80

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20 Fl wheel torque Fr wheel torque

15

Wheel Torque/Nm

10 5 0 -5 -10 -15 -20 0

10

20

30

40 Time/s

50

60

70

80

Fig. 19. Wheel torques

5 Conclusion In this paper, by establishing an 18-DOFs model of the whole vehicle, including the driving system, wheel-legged system and body dynamics model, a layered parallel control structure is proposed for the motion control of the WLCUV. Its COM controller is used for the analysis of different subsystem signals and the coordinated control of the centroid, and the motor controller realizes the control of joint torques and wheel torques. The simulation results show that the vehicle speed, yaw rate, position and attitude can be accurately tracked under complex working conditions, and the joint torques and wheel torques are both smaller than the motor peak torque.

References 1. He, J., Gao, F.: Mechanism, actuation, perception, and control of highly dynamic multilegged robots: a review. Chin. J. Mech. Eng. 33(1), 1–30 (2020) 2. Jeans, J.B., Hong, D.: IMPASS: intelligent mobility platform with active spoke system. In: 2009 IEEE International Conference on Robotics and Automation, ICRA 2009. IEEE (2009) 3. Orozcomagdaleno, E.C., Cafolla, D., Castillocastaneda, E., et al.: Static balancing of wheeledlegged hexapod robots. Robotics 9(2), 23 (2020) 4. Mccloskey, S.H.: Development of legged, wheeled, and hybrid rover mobility models to facilitate planetary surface exploration mission analysis. massachusetts institute of technology (2007) 5. Buchanan, R., Wellhausen, L., Bjelonic, M., et al.: Perceptive whole body planning for multilegged robots in confined spaces. J. Field Rob. 38(1), 68–84 (2020) 6. Leng, Y., Lin, X., Huang, G., et al.: Wheel-legged robotic limb to assist human with load carriage: an application for environmental disinfection during COVID-19. IEEE Rob. Autom. Lett. 6(2), 3695–3702 (2021)

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7. Wong, C.Y., Turker, K., Sharf, I., Beckman, B.: Posture reconfiguration and navigation maneuvers on a wheel-legged hydraulic robot. In: Mejias, L., Corke, P., Roberts, J. (eds.) Field and Service Robotics. STAR, vol. 105, pp. 215–228. Springer, Cham (2015). https://doi.org/10. 1007/978-3-319-07488-7_15 8. Gehring, C., Coros, S., Hutter, M., et al.: Control of dynamic gaits for a quadrupedal robot. In: 2013 IEEE International Conference on Robotics and Automation, pp. 3287-3292. IEEE (2013) 9. Di Carlo, J., Wensing, P.M., Katz, B., et al.: Dynamic locomotion in the MIT cheetah 3 through convex model-predictive control. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1–9. IEEE (2018) 10. Bjelonic, M., Bellicoso, C.D., De Viragh, Y., et al.: Keep rollin’ - whole-body motion control and planning for wheeled quadrupedal robots. IEEE Rob. Autom. Lett. 4(2), 2116–2123 (2019) 11. Peng, H., Wang, J., Wang, S., et al.: Coordinated motion control for a wheel-leg robot with speed consensus strategy. IEEE/ASME Trans. Mech. 25(3), 1366–1376 (2020) 12. Piedra, A., Solorzano, T., Kalpoe, K., et al.: SkateBot: bipedal skating robot design. Robot. Auton. Syst. 62(3), 306–318 (2014) 13. Wang, S., Cui, L., Zhang, J., et al.: Balance control of a novel wheel-legged robot: design and experiments. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 6782–6788. IEEE (2021) 14. Guo, K., Yang, J.: Steady-state UniTire lateral force model under sideslip combined with large camber condition. J. Mech. Eng. 50(8), 95 (2014)

Research on Instability Characteristics of Ducted Fans in Ground Effect Dawei Zhou, Yiwei Luo, Yuzhi Jin, Yuping Qian, and Yangjun Zhang(B) School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China [email protected]

Abstract. Ducted fan is a potential propulsion unit for flying cars in the future, and it has a wide range of application prospects. In ground effect, due to the aerodynamic interference with the ground, the ducted fan easily enters an unstable state caused by rotating stall. In this paper, a simulation platform is built based on a modified MG model, and the simulation of the ducted fan in ground effect is carried out for hovering and landing conditions. The static and dynamic effects of the ground effect are discussed in detail and the sensitivity analysis of parameters is carried out. The results showed that the instability of the ducted fan increases with the decrease in altitude or the increase in speed under hovering conditions, while it doesn’t occur under landing conditions. In addition, the study pointed out that the pressure rise characteristics and flow characteristics of the ducted fan have a significant impact on its instability. The combination of high peak pressure rise coefficient and low corresponding flow coefficient can effectively improve the stability. Keywords: Ducted fan · Ground effect · Rotating stall · Pressure rise characteristics · Flow characteristics

1 Introduction With the rise of the concept of urban air mobility (UAM), flying cars with amphibious functions in land and air have become an important part of three-dimensional traffic in the future [1]. As the aerial propulsion unit of flying vehicles, the ducted fan has attracted extensive attention due to its advantages of low noise, high static efficiency and high safety. However, compared with efficiency, stability is a more important indicator in airworthiness standards. Many environmental factors may lead to instability of flying vehicles, which brings severe challenges to their flight stability. Ground effect (GE) is the most common external environmental disturbance of flying vehicles during vertical takeoff and landing. When the vehicles operate in ground effect, the ground affects the flow field at the outlet of the ducted fan, making the exhaust mode change from free jet to restricted jet, and the vertical velocity component at the ground boundary rapidly decreases to zero. The ducted fan presents the characteristics of thrust deviating from the design value and being easy to enter the rotate stall. The data from the helicopter suggested that 46% of accidents occurred during the vertical takeoff and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 247–260, 2023. https://doi.org/10.1007/978-981-99-1365-7_18

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landing state from 2014 to 2016 [2], and the strong interaction between the propeller and the complex air flow near the ground caused by the ground effect was an important cause of failure. The existence of ground effect seriously affects the safety of flying cars in the air, which is the key factor restricting the commercialization of flying cars in cities. It is necessary and urgent to carry out research on the instability in ground effect. Most of the existing researches focus on the thrust characteristics in ground effect. Mi [3] compared the flow field structures of the ducted fan with or without ground effect through numerical simulation. Ground effect changes the direction of the exhaust, which bounces off the ground and forms a vortex ring around the duct. The larger average pressure difference on the surface of the blade pressure side and suction side makes the rotor thrust increases, and at the same time reduced air velocity around the duct leads to a decrease in duct thrust. When the height between the ducted fan and the ground is further reduced, the vortex ring of the duct gradually moves up, and the duct thrust is further reduced. The total thrust of the ducted fan shows a slight upward trend. Deng et al. [4] carried out the flow field PIV measurement and pressure measurement at the import and export of contra-rotating ducted fans, and the results showed that when the height above the ground is reduced, a low-speed vortex core is formed between the blade wheel and the ground. High back pressure contributes to the increase in the rotor thrust. Existing studies show that the ground effect will lead to the reduction of the duct thrust and the increase of the rotor thrust. Only few studies focus on unsteady characteristics of ducted fans in ground effect. Wang et al. [5] made a dynamic measurement of the pressure downstream of the exhaust, and found that the pressure under the ducted fan presents asymmetric distribution. With the enhancement of the ground effect, the high pressure area under the ducted fan concentrates into the projection circle and there is an obvious boundary between high and low pressure areas. Jin et al. [6] studied the unsteady flow characteristics of the ducted fan and its influence on aerodynamic stability under near-ground conditions through experiments and numerical methods. The results showed that the ground and the duct together restrict the effective exhaust area and direction of the ducted fan, which leads to radial flow distortion at the outlet of the ducted fan, and induces large separation near the blade root. As the height from the ground decreases, the throttling effect increases, and the flow rate further decreases. The ducted fan enters a rotating stall state, and the stall cells appears in the blade tip area. The circumferential rotating speed of the stall cells is 21.4% of the fan blade speed, and the thrust appears unsteady fluctuation. The above literature shows that once the take-off and landing of the ducted fan aircraft enters the influence range of the ground effect, the internal air flow of the propulsion system is prone to instability, resulting in unsteady thrust fluctuation and unsteady torque. Losing control of the flying vehicles attitude and even crashing may happen in this situation. In this paper, the instability of ducted fans in ground effect is studied. By introducing the ground effect Moore-Greitzer model of the ducted fan, the mechanism of the height and speed on the instability in hovering conditions and landing conditions is analyzed, combined with the results of parameter sensitivity analysis. The influence law of the fan characteristics on the rotating stall is then summarized.

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2 Ground Effect Moore-Greitzer Model The ducted fan is actually an axial flow compressor system with low compression ratio, which changes the pressure difference between the front and back of the blade, and gives the air a backward velocity increment to generate forward thrust. Different from compressors, traditional compressors mainly focus on pressure ratio and efficiency, while ducted fans focus on thrust. As a consequence, it is feasible to use axial flow compressor system model to analyze the dynamic changes of the ducted fan in theory. In related research papers [7], some researchers have proposed a ground-effect instability model for ducted fans based on the modified Moore-Greitzer (short for MG) model. MG model is a classic model for studying compressor system instability, which was first proposed by Moore and Greitzer [8] to describe the dynamic response of axial flow compression system after instability. The model consists of three third-order nonlinear differential equations, which correspond to the change of compression coefficient, flow coefficient and instability disturbance with time. The Galerkin method can greatly simplify the higher-order differential equations, and the obtained first-order differential equations can be used to judge the occurrence of instability and describe the evolution process of rotating stall [9, 10]. When the equivalent operating point of the compressor system moves to the left of the surge line, it is considered that the compressor is unstable. However, the original MG model only aimed at the case of constant speed, and did not consider the transient change of speed. Therefore, Gravdahl and Egeland [11] proposed a modified MG model considering compressor speed, in which a new first-order differential equation of parameters was added as a function of speed transfer, in the following form z˙ = f (z) where z = (, , J , B),  represents the mean flow coefficient,  represents the totalstatic pressure rise coefficient, J represents the square of the circumferential distortion amplitude, which could be regarded as the magnitude of the instability, B represents parameter B. The dimensionless time is defined as, ξ=

Ud t R

where Ud is the expected linear velocity at the position of the average radius of the compressor blade, that is, the control signal of the motor. R is the average radius of the blade. Considering the change of speed, the parameter B in the original model also becomes a variable, and the following complete formula is obtained, −ψc0 φ d H + 1 + 23 ( W d ξ = lc (B) [− H lE Ud 1 1 φ 3 − 2 ( W − 1) − bH ] dJ dξ

 φ = J 1 − (W − 1)2 −

3aH · (1−m B a)W

J 4

−

− 1)(1 − J2 )

2Ud 1 (m−1)W 3bH

d dξ

=

2 B ( − T ) − 21 B

 dB dξ

= 1 B2 = 1 (u − τc )B2

Please refer to the literature for the specific derivation process [11], where H , W , and ψc0 are parameters in the characteristic curve of the compressor, lc , lE , and m the

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three-dimensional geometry parameters,  is the dimensionless torque, 1 , 2 , a, and b are simple representation of the parameters, which would be given later. Figure 1 is a schematic diagram of the compressor system described by the modified MG model.

Fig. 1. Schematic diagram of the compressor in the modified MG model

Different from the traditional rotating stall, the phenomenon of valve throttling will appear at the outlet of the ducted fan in ground effect, and the throttling effect becomes obvious with the decrease of height. Based on this, Jin et al. [6] adopted three-dimensional CFD simulation of flow field analysis and curve fitting way to set up the correct relationship between the valve coefficient and dimensionless height in MG model (The dimensionless height ignores the influence of differences in the size of ducted fans), which is shown as √ √ T = f (h) ·  = 1.09 · (1 − e−2h ) ·  h=

HIGE Rtip

where HIGE is the actual distance between the ducted fan and the ground, Rtip is the radius of the fan blade tip. The schematic diagram is shown in Fig. 2.

Fig. 2. Schematic diagram of variables related to the ground effect of the ducted fan

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Accordingly, the parameters related to the downstream plenum in the modified MG model are rewritten as follows, Vp = π R2tip · HIGE = π R3tip · h 

3 U π Rtip · h 2as Ac Lc  Ac Lc b = 2as π Rtip · h  Ac Lc ρR3 Ac 1 = · 2as IUd π Rtip · h  Ac Lc R 2 = · 2as Lc Ud π Rtip · h

B=

In order to simplify the calculation, the speed control of the ducted fan is added into a type P controller  = c(Ud − U ) where  represents the input torque of the fan, c represents the control parameter of the controller. According to reference [8], the speed and torque of the ducted fan can be calculated according to the following formula,  Ac lc 60 ·B· n = 2as Vp 2π R T=

m ˙ · pt ρω

3 Results and Discussions 3.1 Model Building MATLAB/Simulink provides a modular graphical programming environment, which is widely used in modeling, simulation and analysis of dynamic performance of multiple physical fields. Based on MATLAB/Simulink simulation platform, this study builds the ground effect MG model. The flow coefficient differential equation module (Phi equation module), pressure rise coefficient differential equation module (Psi equation module), rotating stall amplitude differential equation module (J Equation module) and parameter B differential equation module (B equation module) are established respectively.

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3.2 Parameter Settings In the above ground effect MG model, there are many parameters to be given, which can be divided into three categories: (1) Geometric parameters: Geometric parameters such as the moment of inertia I , the average radius of the fan R, the dimensionless length of the inlet flow section lI and outlet flow section lE can be measured in three-dimensional simulation software. (2) Performance parameters: Performance parameters obtained in the characteristic curve such as H , W , ψc0 can be measured partly by CFD simulation. Since the characteristic curves vary at different rotating speed, parameter values turn out to be different in different cases. (3) The rest of the physical constants: for example, air density ρ. This study considers only the near-ground conditions, and related parameters are given according to the sea-level standard atmosphere. See Table 1 for the detailed parameters of fan geometry and physical constants. The ducted fan performance parameters are obtained based on the reference [10]. As shown in Fig. 3, ψc0 is the pressure rise coefficient corresponding to the cut-off flow coefficient, H represents half of the difference between the peak pressure rise coefficient and the bottom pressure rise coefficient, and W is half of the flow coefficient corresponding to the peak. The pressure rise coefficient and flow coefficient are defined as follows = =

p 2 ρUtip

Vaxial m ˙ = Utip ρUtip π R2tip

where Utip and Rtip represent the fan tip tangential velocity and tip radius, Vaxial represents the average axial velocity, and p = pstatic,exit − ptotal,entrance is the difference between the static pressure at the fan outlet and the total pressure at the fan inlet.

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Table 1. Geometric and physical constant parameters of ground effect MG model fan Parameters

Values

Fan moment of inertia I

0.04048 kg·m2

Total length lc

0.3 m

Dimensionless inlet pipe length lI = LI /Lc

0.526

Dimensionless outlet pipe length lE = LE /Lc

0.395

Outlet pipe length factor m

1.25

Fan tip radius Rtip

0.32 m

Fan hub radius Rhub

0.06 m

Compressor lag factor a

0.3

Cross-sectional area at the fan Ac

0.3217 m2

Air velocity as

340 m/s

Inlet air density ρ

1.293 kg/m3

Slip factor σ

0.9

Fig. 3. Compressor characteristic curve related parameters diagram

After ANSYS CFX simulation, the characteristic curve of the ducted fan obtained is shown in Fig. 4, where the operating condition points calculated at each speed are marked with different symbols in the figure. The cubic polynomial suggested in literature [10] is used to fit all the operating points with the steady-state operating data, and the characteristic curve of the ducted fan is obtained. However, at the flow cut-off point, the cut-off pressure rise corresponding to each speed is different. The parameters from the simulation results are sorted out to obtain the values of the parameters H , W and ψ c0 corresponding to the speed, as shown in Table 2.

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Fig. 4. Characteristic curve of the ducted fan

Table 2. Sum of parameters related to characteristic curve of the ground effect MG model H

W

ψc0

500

0.00755

0.122

0.0708

Rotating speed (r/min) 750

0.0096

0.122

0.0667

1000

0.0109

0.122

0.0642

1250

0.0119

0.122

0.0621

1500

0.0127

0.122

0.0606

According to the data in reference [7], the initial values of other parameters are given as follows 0 = 0.2 0 = 0.06 J0 = 0.05 B0 = 0.1

3.3 Simulation Results of Hovering Firstly, a comparative simulation study is carried out for the rated speed of 1000 r/min, the dimensionless height of 1.0 (the actual height is close to 320 mm, and the unit dimensionless time step corresponds to 0.0095 s) and 0.3, respectively, to determine the height boundary of instability. The dynamic changes of mass flow rate, outlet pressure and instability amplitude (i.e. parameters in the model) are monitored, and the results are shown in Fig. 5. As can be seen from the figure, instability does not occur under the condition of h = 1.0, but the amplitude of instability increases gradually at h = 0.3 and finally stabilizes at a fixed value, indicating that instability has occurred under this condition.

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Fig. 5. Simulation results of hover conditions at different heights with speed 1000 r /min

In order to further investigate the instability situation of the ducted fan in the nearground conditions, the simulation is carried out for the operating conditions with the speed of 500, 750, 1000, 1250, 1500 r/min and the dimensionless height from the ground 0.3 to 1.0. The development time and amplitude of the instability of the ducted fan due to the ground effect are counted. The results are shown in Fig. 6. Several conclusions can be drawn from the study: (1) The occurrence of instability has nothing to do with the rotational speed, while the distance from the ground is the main influencing factor. When the non-dimensional height above the ground is more than 0.8, there is no instability of the ducted fan at all speeds. However, when the dimensionless height is lower than 0.8, the ducted fan enters the instability state in all cases, although higher speed corresponds to shorter instability time and larger instability amplitude. Therefore, it can be inferred that the rotational speed has little influence on the height threshold of instability. (2) With the decrease of height or the increase of speed, the instability of the ducted fan occurs faster with greater amplitude. In order to further explore the key parameters affecting the ground effect of ducted fans, the sensitivity analysis of fan performance parameters H , W and ψ c0 is carried out. The reference values are set at the design speed of 1000 r/min, and five design points are uniformly selected within the interval of 80% to 120% of the reference value. Other conditions remain unchanged. The simulation results of different dimensionless height are summarized in Fig. 7, where the missing data points represent no instability. According to the simulation results, it can be found that the fan characteristics have an impact on the instability of the ducted fan, and the influence of W and ψ c0 is more significant. Based on the simulation results, the following conclusions can be drawn: (1) (1) (ψc0 + 2H ) is the peak pressure rise coefficient, and the monotonic change of H and ψ c0 corresponds to the monotonic change of the peak pressure rise coefficient. By decomposing the influence of the peak pressure rise coefficient on instability (Fig. 7(a)(c)), it can be found that the cut-off flow pressure rise coefficient ψ c0 has

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a greater influence on instability at different heights, while the parameter H has a significant influence only near the critical height. (2) Flow characteristics are the biggest factor affecting instability. The lower the flow coefficient at the peak, the higher is the height threshold which triggers instability. It is obvious because the instability corresponds to the low-flow condition. When the fan can work normally in the low-flow condition, the instability threshold will be increased. (3) The pressure rise characteristics and flow characteristics have opposite effects on the instability of ducted fans in ground effect. With the increase of the peak pressure rise coefficient, the higher the height threshold of instability, the less prone the ducted fan is to instability. With the increase of the flow coefficient corresponding to the peak pressure rise coefficient, the instability of the ducted fan is more likely to occur. It can be seen that the characteristic combination of the high peak pressure rise coefficient and the low corresponding flow coefficient is effective for solving the instability problem. It is mentioned in the preface that the ducted fan is a low-pressure compressor. The only difference lies in that the compressor pursues the maximum compression efficiency, while the ducted fan pursues the maximum propulsion efficiency. The thrust of the ducted fan depends largely on the flow characteristics, so the following conclusions can be drawn from this part of the study that propulsion efficiency and stability may not be compatible in ground effect conditions. In the design process of the ducted fan, the propulsion efficiency should be paid attention to while the performance of the near ground condition should be taken into account.

4.0

500 r/min 750 r/min 1000 r/min 1250 r/min 1500 r/min

70 60

500 r/min 750 r/min 1000 r/min 1250 r/min 1500 r/min

3.5

Amplitude of instability

Occurance time of instability/s

80

50 40 30

3.0 2.5 2.0 1.5

20

1.0

10

0.5

0

0.0 0.3

0.4

0.5

0.6

0.7

Nondimensional height

(a)

0.3

0.4

0.5

0.6

0.7

Nondimensional height

(b)

Fig. 6. Simulation results of ducted fans with different dimensionless heights and different speeds

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20

15

10

70 60

5

50 40 30 20

0.009

0 0.09

0.012

25

h=0.7 h=0.6 h=0.5 h=0.4 h=0.3

20

15

10

5

10

0

0 0.12

0.15

0.060

H

W

ψc0

(a)

(b)

(c)

4.5

4.5

4.0

4.0

4.0

3.5 3.0 2.5 2.0 1.5 1.0 h=0.7 h=0.6 h=0.5 h=0.4 h=0.3

0.5 0.0 0.009

0.012

Maximum amplitude of instability

4.5

Maximum amplitude of instability

Maximum amplitude of instability

30

h=0.9 h=0.8 h=0.7 h=0.6 h=0.5 h=0.4 h=0.3

Occurance time of instability/s

h=0.7 h=0.6 h=0.5 h=0.4 h=0.3

Occurance time of instability/s

Occurance time of instability/s

30

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.09

257

h=0.9 h=0.8 h=0.7 h=0.6 h=0.5 h=0.4 h=0.3

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

0.12

0.15

0.075

h=0.7 h=0.6 h=0.5 h=0.4 h=0.3

0.060

H

W

ψc0

(d)

(e)

(f)

0.075

Fig. 7. Simulation results of parameter sensitivity analysis

3.4 Simulation Results of Landing The hovering condition is a typical static condition, but it cannot reflect the influence of dynamic changes on the instability characteristics. Common dynamic conditions include take-off and landing. The above research shows that height is the key parameter

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determining whether the ducted fan is unstable. Since the take-off process starts from the ground, based on the ground-effect MG model, the ducted fan is already in the stall state at this time, and the subsequent take-off process cannot be successfully realized. Therefore, in comparison, the landing process is the best choice to study the influence of ground effect on the instability characteristics under dynamic changes. In this section, we conduct a simulation study on the stall characteristics of aircraft under landing conditions. On the basis of the simulation model in Sect. 2, † parameters of the design point of the ducted fan, namely 1000 r/min, and referring to the flight control design data of the aircraft, the safe landing speed of 0.6 m/s is selected as the landing speed of the aircraft in the simulation. Considering that there is a threshold for ground effect instability between the dimensionless height of 0.7 and 0.8, the starting height of this landing condition study is chosen as the dimensionless height of 5.0, while the landing gear height of the ducted fan aircraft is 0.3. According to the uniform speed of 0.6 m/s, the dimensionless time of descent is 156 units. Since the initial value of the simulation could not be determined, the hovering condition is maintained for 500 units of dimensionless time at the initial stage, and the landing simulation is carried out when its value is fully converged. Simulation parameters of landing conditions are summarized as shown in Table 3. The simulation results are shown in Fig. 8. The total simulation time is 1000 time steps, and it starts to decrease at the 500th step and continues to the end of 656 steps. As can be seen from the variation of rotating stall amplitude in the figure, when the aircraft is in the landing stage, the ducted fan does not appear instability caused by ground effect from beginning to end, which is inconsistent with the above conclusion that stall will occur when the dimensionless height is below 0.8. However, by further increasing the simulation time to a total of 3000 steps (as shown in Fig. 9), it can be found that the instability caused by ground effect begins to develop significantly after 1500 time steps and is fully developed by 1700 steps, and its development time is also consistent with the results in Fig. 6. This indicates that the instability caused by ground effect will not occur in the moving condition of the ducted fan, and there is a time lag in the recovery of ground effect on the instability when the ducted fan stops moving. This conclusion is interesting because it raises the question of whether there is a threshold velocity for landing below which instability occurs and vice versa. Table 3. Simulation parameters of landing condition Parameters

Values

Rotating speed

1000 r/min

Landing speed

0.6 m/s

Initial dimensionless height

5.0

Final dimensionless height

0.3

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Fig. 8. Dynamic change of ducted fans in landing

Fig. 9. Dynamic change of ducted fans in landing (Prolonged simulation time)

4 Conclusions Aiming at the instability characteristics of the ground effect of the ducted fan, the instability model of the ground effect of the ducted fan based on the modified MG model is established. The instability characteristics and influencing factors of the ground effect of the ducted fan under hovering and landing conditions are analyzed by numerical methods. The main conclusions are as follows:

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1. The height is a key parameter affecting fan instability. There is a height threshold below which the fan will be unstable. With the decrease of height or the increase of speed, the instability time of the ducted fan is shorter and the instability amplitude is larger. 2. Parameters related to fan characteristic curve have significant influence on ground effect instability. The increase of the peak pressure rise coefficient and the decrease of the corresponding flow coefficient make the fan less prone to instability. The decrease of the flow coefficient means the decrease of the thrust, so it can be inferred that the propulsion efficiency and stability cannot be achieved simultaneously under the ground effect condition. 3. The instability caused by ground effect will not occur in the moving condition of the ducted fan, and there is a time lag in the recovery of ground effect on the instability after the ducted fan stops moving. Future research may include the influence mechanism and interaction law of ground effect on rotational stall in landing condition.

References 1. Zhang, Y.J., Qian, Y.P., Zhuge, W.L., et al.: Progress and key technologies of flying cars. J. Automot. Saf. Energy 11(01), 1–16 (2020). (in Chinese) 2. Zhang, J., Zhan, Y.M., Wang, Y.M.: Helicopter accident statistics and analysis of the World in 2014–2016. Helicopter Technique 3 (2017). (in Chinese) 3. Mi, B.G.: Numerical investigation on aerodynamic performance of a ducted fan under interferences from the ground, static water and dynamic waves. Aerosp. Sci. Technol. 100, 105821 (2020) 4. Deng, S.H., Wang, S.W., Zhang, Z.: Aerodynamic performance assessment of a ducted fan UAV for VTOL applications. Aerosp. Sci. Technol. 103, 105895 (2020) 5. Wang, H.T., Wang, Y.G., Deng, S.H.: Experimental study of in-ground-effect on force and spectrum characteristics of a contra-rotating lift fan. J. Propul. Technol. 39(12), 2703–2709 (2018). (in Chinese) 6. Jin, Y.Z., Fu, Y., Qian, Y.P., et al.: A Moore-Greitzer model for ducted fans in ground effect. J. Appl. Fluid. Mech. 13(2), 693–701 (2020) 7. Jin, Y.Z.: Flow Mechanisms and Control of Ducted Fans in Ground Effects. Tsinghua University (2020) 8. Moore, F.K., Greitzer, E.M.: A theory of post-stall transients in multistage axial compression systems (1985) 9. Greitzer, E.M., Moore, F.K.: A theory of post-stall transients in axial compression systems: part II—application (1986) 10. Moore, F.K., Greitzer, E.M.: A theory of post-stall transients in axial compression systems: Part I—Development of equations (1986) 11. Gravdahl, J.T., Egeland, O.A.: Moore-Greitzer axial compressor model with spool dynamics. In: Proceedings of the 36th IEEE Conference on Decision and Control, vol. 5, pp. 4714–4719. IEEE (1997)

Application of Synchronous Combustion Analysis Method to Analysis and Control of Low Frequency Chattering Vibration of Vehicle Meng Xu(B) , Jihong Shi, Jian Li, Xiulan Qu, Xixiang Yuan, and Shuai Zhao Beijing Automotive Technology Center Co., Ltd., Beijing 101300, China [email protected]

Abstract. Aiming at the low frequency abnormal vehicle vibration caused by engine sources, the process, key points and difficulties of synchronous combustion analysis are introduced in detail, especially the idea source of cylinder pressure external envelope post-processing method is emphasized. Firstly, the combustion and vibration data of four cylinders was collected and analyzed by synchronous combustion analysis method, and the external envelope post-processing method of cylinder pressure data was proposed for the first time. Secondly, by analyzing the cylinder pressure after envelope post-processing method, it was found that the root of abnormal order problem was the periodic lag caused by the pressure of each cylinder of the engine. In the detailed analysis of the cylinder pressure, the hysteresis law was determined as “2–4-3–1”. Finally, by optimizing the calibration data, the abnormal order vibration peak of the driver’s seat was reduced from 0.25g to 0.01g, which effectively controlled the low-frequency vibration of the vehicle. Keyword: Low frequency vibration · Combustion analysis · Vehicle chattering · Order analysis

1 Introduction With the intensification of market competition and the continuous improvement of users’ requirements for vehicle quality, the NVH performance of vehicles has been paid more and more attention by customers and the market. Thus, the vehicle front and rear low frequency vibration have become one of the key and difficult points of vehicle NVH development. Compared with the front-engine front-drive vehicles, the front-engine rear-drive and four-drive vehicles have longer transmission system dimensions and more subsystem modes. Therefore, it is easier to cause the front and rear low frequency vibration [1– 3]. There are various reasons for this problem. For example, when the torsional mode of the transmission system is excited, then the front and rear low frequency vibrations occurs [4–6]. The simulation calculation method is mainly used in China and abroad to obtain the torsional mode of the transmission system. In recent years, new modal test methods are also used to study the transmission system dynamics characteristics [7]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 261–276, 2023. https://doi.org/10.1007/978-981-99-1365-7_19

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In addition, the transient impact caused by large transmission clearance is also one of the important reasons. The influence of gear clearance on impact load is mainly studied [8–12]. Besides, the interior roar and the vehicle chattering caused by the additional torque, which is generated by unequal speed universal joints, are also studied [13, 14]. The performance of the vehicle vibration caused by the above reasons is the same, but the formation mechanism and control measures are different. Therefore, how to analyze and judge the mechanism correctly is the key to controlling the low frequency vibration. However, the domestic and foreign research on the front and rear low frequency vibration focuses more on the path (Transmission System) and control strategy [15–17]. The low frequency vibration caused by the vibration source (Vehicle Driving System) must be identified from the generation mechanism of the vibration source in order to be effectively analyzed and controlled. The process, key points and difficulties of the synchronous combustion method are explained first. The idea source and analysis process of the cylinder pressure external envelope post-treatment method is introduced in detail. Aiming at the front and rear low frequency vibration of the vehicles, the synchronous combustion analysis method is used to test the combustion and vibration data synchronously. The external envelope post-treatment method for cylinder pressure data is first used to determine the source of abnormal order. Through the analysis of the frequency domain and time domain of synchronous combustion data, it is determined that the combustion periodic lag is the root cause of the abnormal order, thus the problem is effectively controlled.

2 Synchronous Combustion Analysis 2.1 Synchronous Combustion Acquisition Synchronous combustion analysis is an analysis method that is gradually summarized in engineering practice. Its main innovation is to establish the communication between combustion data and NVH data, realizing the conjoint analysis of combustion information and NVH information on the time axis. It can not only take advantage of the powerful frequency domain analysis of NVH, for example, it can directly carry out FFT and order analysis of combustion data, but also can realize the decoupling of vibration source and path relying on the independence of cylinder pressure signal to directly judge whether the vibration source is too large or the path is amplified. In addition, through the acoustic playback function of NVH, the abnormal problem time can be determined. Meantime, by analyzing the cylinder pressure details of the abnormal problem time, the root cause of the problem can be further analyzed to clarify the direction for optimization. The connection diagram of synchronous analysis of the NVH system and combustion system is shown in Fig. 1. Firstly, the engine cylinder pressure signal is collected by the engine cylinder pressure sensor. The cylinder pressure signal is converted into the voltage signal, which is received and processed by the combustion analyzer, and then converted into a new voltage signal and output to the NVH data acquisition front end. At the same time, the vibration noise signal can be directly collected by the NVH data acquisition equipment, that is, cylinder pressure and vibration noise data are simultaneously collected.

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During the process of synchronous combustion acquisition, there are three key points: 1) All of the cylinder pressure needs to be acquired. It is recommended that all cylinder pressure should be acquired at the same time. Do not miss a cylinder or several cylinders, because the cylinder pressure of each cylinder is different. The excitation characteristics are determined by common analysis. Therefore, it is necessary to test completely the real-time cylinder pressure of each cylinder. 2) All of the cylinder pressure signal sensitivity should be set correctly. Since the combustion analysis equipment and NVH acquisition equipment work in series, the cylinder pressure sensitivity should be the product of the two sensitivities, which need to be focused on to obtain the correct data. 3) Enough cylinder pressure sampling rate should be guaranteed. Local time domain analysis of cylinder pressure is a necessary step to optimize combustion, so the sampling rate must be enough to meet the requirements of time domain analysis of cylinder pressure. It is suggested that the cylinder pressure sampling rate should be the same as that of the noise, that is, greater than 20000 Hz. 25600 Hz is used in this paper. The post-treatment method of cylinder pressure is a difficult point in the engineering practice of combustion synchronization analysis. The engine cylinder pressure signals are different. Therefore, how to fit together to form a whole cylinder pressure signal to characterize the common influence of cylinder pressure on torque output is the key to the cylinder pressure post-treatment. This integral cylinder pressure can also be compared with vibration and noise signals through the NVH analysis method. A new attempt is proposed accordingly, that is, the cylinder pressure external envelope post-treatment method. 2.2 Cylinder Pressure External Envelope Analysis It is known that the output torque of the engine is mainly affected by three parts: 1) The torque generated by combustion 2) Reciprocating inertia torque of crank connecting rod mechanism 3) The friction of mechanical structure. The analytical formula of engine output torque considering three influencing factors is shown in Eq. (1)–(3) [18].    3   Fp + Fj · R sin(α + iπ + β − γ ) − ρA − ρB (1) M = cos(β − ϕ − γ )/ cos ϕ i=0

Fig. 1. Synchronized Analysis Diagram of NVH System and Combustion System

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where, Fj = mj Rω12 [cos(α + iπ ) + γ cos 2(α + iπ )] Fp =

1 π D2 Pg (α + iπ ) 4

(2) (3)

where, D is the piston diameter. Pg is the combustion pressure in the cylinder. mj is the equivalent mass of the piston, that is, the sum of the mass of the piston assembly and the mass of the connecting rod assembly simplified to the small end. R is the turning radius of the crank. α is the crank angle. ω1 is the crank angular velocity. β is the swing angle of the connecting rod. γ is the angle between the action line of the connecting rod force and the big and small ends of the instantaneous connecting rod, that is, a geometric parameter in the calculation process. Figure 2(a) shows the relationship between the cylinder pressure and the crankshaft angle of a four-cylinder four-stroke engine. Based on the calculation of the analytical Eq. (1) of the engine torque calculation, it can be seen that the relationship between the engine torque output and the crankshaft angle is shown in Fig. 2(b) The fluctuation period of output torque corresponds to the peak or trough period of each cylinder pressure. It should be noted that there are two peaks of cylinder pressure in Fig. 7. The first peak value is generated by compression stroke, and the second is generated by combustion stroke. However, the interval periods of the two peaks are the same, that is, the interval frequency of the peak and trough of the pressure are consistent with the main output frequency of the output torque. It is known that the main energy of engine torque is concentrated in the ignition frequency of the engine. The ignition frequency calculation formula of the engine [19] is shown in Eq. (4). fen =

rpm × Ncy × 2 60 × Nstr

(4)

where, fen is the ignition frequency of the engine. Rpm is the engine speed. Ncy is the number of engine cylinders. Nstr is the number of engine strokes. Therefore, for a four-cylinder four-stroke engine, the ignition frequency of the engine is the second order, as shown in Eq. (5). fen =

rpm × Ncy rpm rpm × 4 × 2 =2× = 60 × Nstr 60 × 4 60

(5)

where rpm 60 is called the first order frequency of the engine, that is, the ignition frequency of the four-cylinder four-stroke engine is the second order of the engine. The second-order ignition frequency of the engine can be considered that when the crankshaft rotates for one revolution, there are two in-cylinder ignitions. That is, the crankshaft angle is excited twice by the peak value of combustion cylinder pressure within 0 ~ 360°. Therefore, inspired by the relationship between engine cylinder pressure and output torque frequency as well as the calculation principle of ignition frequency, it can be further considered that the fluctuation frequency of engine cylinder pressure peak and

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Fig. 2. Diagram of the relationship between pressure, torque and crank angle

trough can directly reflect the main frequency of torque output. The frequency of engine output torque is the result of the joint action of all combustion cylinders, that is, the four-engine combustion cylinders work together on the engine crankshaft to produce the overall torque output. Inspired by the above ideas, the cylinder pressure external envelope method is proposed and applied to analyze the engine output torque for the first time. The four independent cylinder pressure are integrated into a new overall cylinder pressure signal by the external envelope method to analyze the pressure data. The external envelope post-treatment method is to place all the engine cylinder pressure synchronously acquired on the same time domain signal graph. Only the data of the maximum values in all cylinder pressure curves are retained at each moment. Then all maximum values are connected to form a new cylinder pressure curve. Since the processed cylinder pressure curve is the external envelope of all cylinder pressure signals, it is called the external envelope post-treatment method.

3 Problem Introduction and NVH Analysis 3.1 Problem Description and NVH Test A certain sample vehicle in the development stage has obvious front and rear low frequency vibration within the engine speed range of 1500 ~ 2000rpm. The vibration is more intense especially in the acceleration climbing process at the fourth and fifth gears, which needs to be controlled urgently.

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First, the NVH test shall be carried out at multiple positions of the whole vehicle. According to experience, the powertrain causes vehicle vibration by translational and rotational vibration transmission. The translational transmission path is the “enginesuspension-frame-driver seat”. The rotation transmission path is “engine flywheel torsional vibration-transmission torsional vibration-transmission shaft torsional vibrationhalf shaft torsional vibration-wheel torsional vibration-body translation”. Therefore, the acceleration or magneto-electric/Hall speed sensors should be arranged at multiple locations such as the engine body, the suspension passive end, the car seat, the flywheel, the transmission shaft and the wheel to investigate the vehicle chattering problem. Sensor arrangement positions and test signals are shown in Table 1. The vibration data are the vibration signals in the X, Y and Z directions. The noise data is the scalar data represented by S. The speed is the single degree of freedom, defining the rotation direction corresponding to the forward direction of the vehicle as the positive direction. Table 1. Sensor Placement and Test Signal Description Number

Trasfer Path Analysis (TPA) Classification

Transmission Mode

Sensor Position and Direction

1

Vibration Source

Translation

Engine Body (X, Y, Z) Vibration

2

3

Transmission Path

Acceptance Source

Signal Type

Suspension Passive End (X, Y, Z)

Vibration

Rotation

Flywheel Speed

Speed

Translation

Frame(X,Y,Z)

Vibration

Rotation

AT Transmission Input

Speed

AT Transmission Output

Speed

Left/Right Wheel Speed

Speed

Driver Seat (X, Y, Z)

Vibration

Steering Wheel in 12 Clock Position (X, Y, Z)

Vibration

Driver’s Right Ear Noise (S)

Noise

Car Interior

3.2 NVH Data Analysis Through the analysis of NVH vibration data, it is found that the vibration of engine suspension is not obvious. The torsional vibration of the power transmission system is large and occurs synchronously with seat vibration. Therefore, it is considered that the

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vehicle chattering is mainly transmitted to the car interior through the torsional vibration of the transmission system. Time domain data of engine flywheel torsional vibration, rear drive shaft torsional vibration and seat rail vibration are shown in Fig. 3.

Fig. 3. Time domain signal of the flywheel torsional vibration, the rear drive shaft torsional vibration, and seat rail vibration

The spectrum of engine flywheel torsional vibration, rear drive shaft torsional vibration and seat rail vibration are analyzed, as shown in Fig. 4. The three synchronously show the abnormal 0.65th order peak value, while the vehicle without chattering has no abnormal 0.65th order torsional vibration. Therefore, 0.65th order is the root of vehicle chattering. The key to the problem is to investigate the source of the abnormal 0.65th order.

Fig. 4. Spectrum comparison of engine flywheel torsional vibration, rear drive shaft torsional vibration and seat rail vibration

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4 Synchronous Combustion Analysis 4.1 Synchronous Combustion Test Cylinder pressure sensors and NVH sensors should be arranged simultaneously in the synchronous combustion test. Firstly, cylinder pressure sensors are arranged respectively in the four combustion chambers of the engine. The output of cylinder pressure sensors is connected to the acquisition port of the combustion analyzer, and then connected to the NVH data acquisition front end through the output interface of the combustion analyzer. The combustion analyzer is used as the lower computer of NVH acquisition equipment. Secondly, the acceleration sensors and microphones are directly connected to the NVH data acquisition front end, acquiring synchronously the data of the combustion and the vibration noise. The equipment connection is shown in Fig. 5. The Ki-Box data acquisition equipment of Kistler Company is selected as the combustion analyzer in the actual project. The NVH acquisition front end is that of Siemens SCADAS data acquisition. The output parameter of the combustion analyzer is set as DC ± 10 V to match the Siemens hardware. 4.2 External Envelope Processing Method of Combustion Signal Firstly, the cylinder pressure signal under the vehicle problem condition is amplified in a short period, as shown in Fig. 6. According to the channel setting of the acquisition equipment, the red line is the Cylinder 1 pressure. The blue line is the Cylinder 3 pressure. The pink line is the Cylinder 4 pressure. The green line is Cylinder 2 pressure. It can be seen that the four-cylinder pressure reaches the peak value in the order of “1–3-4–2”, which conforms to the combustion characteristics of the four-cylinder engines. However, it is difficult to find anomalies through time-domain data. It is more difficult to connect with the abnormal 0.65th order data. The peak value of the cylinder pressure signal appears in the combustion stroke, while the cylinder pressure of other non-combustion cylinders is relatively small. So that it can be ignored. Therefore, a new signal processing method for engine cylinder pressure is proposed for the first time, that is, the external envelope processing method, which means only the maximum value in four cylinder pressure signals is retained at the same time. Since the processed signal is represented by the external envelope signal after the superposition of four-cylinder pressure, it is called the external envelope processing method.

Application of Synchronous Combustion Analysis Method

(a) Connection sequence of cylinder pressure sensor, combustion analyzer and NVH acquisition equipment

(b) Cylinder pressure sensor installation position

(c) Combustion analyzers Fig. 5. Connection diagram of synchronous combustion analysis equipment

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Fig. 6. Four-cylinder pressure data of engine

The cylinder pressure curve obtained after the cylinder pressure signal external envelope processing is shown in Fig. 7. The abnormal signal can be found. There is a regular abnormal trough of cylinder pressure. The continuous two troughs show that the interval period between the two is 0.06 s and the frequency is 17.3 Hz. Considering the engine speed is 1600rpm at this time, it is known that the order formula is shown in Eq. (6). Of −rpm =

60 × f rpm

(6)

where, Of −rpm is the order of the calculated frequency f corresponding to the speed rpm. f is the calculated frequency. Rpm is the rotating speed corresponding to the calculated frequency. It can be obtained that the order of 17.3Hz frequency corresponding to 1600 rpm rotating speed is shown in Eq. (7). Of −rpm =

60 × 17.3 60 × f = = 0.65 rpm 1600

(7)

The interval frequency of the two troughs is exactly 0.65th order, which corresponds to the vibration order of the front and rear vibration of the whole vehicle.

5 Synchronous Combustion Analysis Through the NVH frequency domain analysis of the cylinder pressure signal after the external envelope processing in Fig. 7, the frequency information of the engine output torque can be obtained. The synchronous frequency domain analysis of the cylinder pressure envelope signal and the vibration signal is shown in Fig. 8. It can be seen that the 0.65th order torsional vibration occurs simultaneously in the seat rail, transmission and

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Fig. 7. External envelope processing of four-cylinder pressure data

cylinder pressure envelope. It is further determined that the excitation source of the whole vehicle chattering is the excitation order jointly generated by the four-cylinder pressure. However, the torsional vibration and the seat vibration of the vehicle transmission system are mainly affected by the vibration source, which is manifested as forced vibration.

Fig. 8. Cylinder pressure envelope and torsional vibration / Vibration synchronous spectrum analysis

The 0.65th order excitation transfer process caused by engine combustion is shown in Fig. 9. The excitation source is the order of the engine combustion cylinder pressure. The transmission path is “flywheel torsional vibration-transmission output torsional vibration-rear drive shaft torsional vibration”, which is finally transmitted to the response point “seat rail” through the body structure. The subjective feeling is the problem of front and rear vibration.

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Fig. 9. Diagram of the excitation transmission path

5.1 Time Domain Analysis of Cylinder Pressure

Fig. 10. Partial analysis of cylinder pressure and its envelope signal of each cylinder

Another advantage of synchronous combustion analysis is that the signal in a small period can be analyzed in detail to further analyze the mechanism of the vibration source. Figure 10 shows the local analysis of four-cylinder pressure signals and their external envelope signals. It can be seen that the problem trough is caused by the periodic lag of each combustion cylinder. The next combustion cylinder cannot produce high cylinder pressure in time, so the trough in the cylinder pressure envelope is generated. For example, when the combustion of three cylinders is completed and the cylinder

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pressure decreases rapidly, the cylinder pressure cannot be established quickly due to the combustion lag of four cylinders, thus resulting in the trough of the external envelope cylinder pressure during the conversion from three cylinders to four cylinders. The sequence of cylinder pressure lag is “2–4-3–1”. The detailed analysis directly reveals the deep reasons and points out the way for the optimization of combustion control. 5.2 Combustion Optimization Comparison After the root cause of the problem is analyzed clearly, the control scheme also comes out accordingly. By controlling the combustion parameters of the engine, the regularity lag of cylinder pressure is eliminated. The scheme is specifically optimized for the problem condition, as shown in Table 2. it is the optimization of ignition advance angle. Table 2. Optimization of engine combustion parameters under problem conditions Number of cylinders

Ignition advance angle (°) Before optimization

After optimization

Cylinder 1

12

15

Cylinder 2

12

16.5

Cylinder 3

12

16.5

Cylinder 4

12

15

The comparison of the external envelope of the cylinder pressure before and after optimization is shown in Fig. 11. After optimization, the cylinder pressure has no abnormal trough. The trough is roughly around 9 bars. The interval frequency is the normal second-order ignition frequency of the engine. The trough before optimization is an abnormal 0.65th order frequency. The trough cylinder pressure is near 1 bar.

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Fig. 11. Comparison of the external envelope of cylinder pressure before and after optimization

After optimizing the combustion control parameters, the 0.65th order abnormal vibration source of engine combustion has been effectively controlled. The front and rear vibration peak of the driver’s seat is reduced from 0.25g to 0.01g, that is, the abnormal 0.65th order vibration peak disappears. Then the front and rear low frequency vibration of the vehicle can no longer be perceived subjectively. The subjective and objective comparison of the front and rear vehicle vibration is shown in Table 3. The score of subjective evaluation is obtained according to Table 4. Table 3. Subjective and objective evaluation of vibration Condition

Vibration peak (g)

Subjective evaluation score

Before optimization

0.25

4

After optimization

0.01

7

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Table 4. Subjective evaluation scoring table Evaluation score

Condition description

Feature description

1

Intolerance

Unacceptable

2

Uncomfortable

3

Objectionable

4

Not acceptable

5

To be improved

6

Acceptable

7

Good

8

Very good

9

Excellent

10

Perfect

Acceptable

6 Conclusions Firstly, the synchronous combustion analysis is extremely effective for analyzing the NVH problem caused by excessive engine excitation under vehicle conditions. It is found that there are regular troughs in the cylinder pressure external envelope from the time domain analysis, especially after the cylinder pressure external envelope method is adopted to process four-cylinder pressure data. From the frequency domain data analysis of the external envelope of cylinder pressure, it can be directly determined that there is an abnormal peak of 0.65th order, which is consistent with the order characteristics of the peak value of seat front and rear vibration. Finally, the vibration source of the vehicle chattering is determined. Secondly, through the analysis of cylinder pressure details, it is found that each cylinder’s pressure has a periodic lag. The order of the cylinder pressure lag is “2–43–1” cylinder, which points out the optimization direction for combustion analysis and control. Through the optimization of the engine combustion calibration data, the 0.65th order vibration peak of the driver’s seat is reduced from 0.25 g to 0.01 g. The subjective evaluation score is increased from 4 points to 7 points. The low frequency chattering problem of the whole vehicle is effectively controlled. In the future, the synchronous combustion analysis method can be applied to the early stage of engine combustion calibration to study the relative relationship between the results of synchronous combustion and the engine combustion stability parameters. Common combustion stability parameters such as the relative deviation of the cycle mean value of each cylinder of the engine, the rising rate of cylinder pressure, the fluctuation rate of the cycle mean value of cylinder pressure, etc., jointly analyze the impact of combustion data on the engine vibration and noise performance. Thus, NVH problems caused by engine combustion can be analyzed and controlled in the early stage of project development.

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References 1. Li, Y.: Torsional Vibration Analysis and Optimization of the Front-rear Drive Vehicle Transmission System. Yanshan University (2020) 2. Cai, Y.: Research and Optimization of Torsional Vibration Characteristics of Front-Rear Drive Vehicle Transmission System. Southwest Jiaotong University (2018) 3. Wang, Y., Zilong, T., Zhijian, Y., Kang, D.: Analysis and improvement of torsional vibration of the passenger vehicle powertrain under the acceleration condition. Autom. Eng. 40(01), 91–97+113 (2018) 4. Wang, K., Su, B., Ji, L., Li, S., Pang, C., Lv, Z.: Control and research on the influence of Torsional Vibration of driveline on MPV interior roar. Noise Vibr. Control 38(02), 77–82 (2018) 5. Tian, Y., Xiang, W., Hu, J., Li, H., Shi, Y., Ding, W.: Multi-platform co-simulation of vehicle roar caused by Torsional vibration. Noise Vibr. Control 39(05), 102–105 (2019) 6. Liu, X., Wu, Z., Lu, J., Xu, J.: Investigation of the effect of rotation speed on the torsional vibration of transmission system. J. Adv. Mech. Design Syst. Manuf. 13(4), JAMDSM0079– JAMDSM0079 (2019) 7. Luo, Q., Han, J., Lei, S.E.: Electric torsional shaker and its application in vibration modal test of driveline. Noise Vibr. Control 40(02), 243–248 (2020) 8. Yan, X.: Research on the Improvement of Torsional Vibration and Gear Knock Noise of a Passenger Vehicle Powertrain. South China University of Technology (2018) 9. Wan, L., Wang, B., Liu, X., Hou, Q., Yao, S., Shangguan, W.: Analysis method based on solving transmission gear knock. Vibr. Meas. Diagn. 194(6), 1218–1224+1360 (2019) 10. Chen, D., Gu, C., Li, H.: Study on the influence of torsional stiffness of driveline on the transmission gear knock abnormal sound. Noise Vibr. Control 38(05), 119–122+138 (2018) 11. Kosarev, O.I.: Influence of the contact ratios on the vibrational excitation in gear engagement. Russ. Eng. Res. 35(9), 643–649 (2015). https://doi.org/10.3103/S1068798X15090099 12. Crowther, A.R., Zhang, N., Singh, R.: Development of a clunk simulation model for a rear wheel drive vehicle with automatic transmission. SAE Tech. Papers (2005) 13. Yang, L., Zhang, F.: Research on torsional vibration of driveline based on working included angle vector of cross shaft universal joint. Mech. Eng. Autom. 213(02), 23–25 (2019) 14. Xu, J., Pan, Q., Chen, D.; Simulation analysis and experimental verification of transmission shaft optimization based on vehicle NVH lifting. Autom. Eng., 293(12), 1467–1474 (2018) 15. Ravichandran, M., Doering, J., Johri, R., et al.: Design and evaluation of EV drivetrain clunk and shuffle management control system. In: 2020 American Control Conference (ACC), pp. 4905–4912. IEEE (2020) 16. Reddy, P., Darokar, K., Robinette, D., et al.: Control-oriented modeling of a vehicle drivetrain for shuffle and clunk mitigation. SAE Tech. Paper (2019) 17. Deng, C., Deng, Q., Yu, C., et al.: Research on the mechanical mechanism of the shuffle problem of electric vehicles and the sensitivity to clearances. Electronics 11(13), 1935 (2022) 18. Song, L. Q., Niu, H. E., Zeng, L. P., Tian, H.:A study on the modeling and torsional vibration attenuation for vehicle powertrain system based on unit analysis. J. Automob. Eng. 5(8), 866–874 (2015) 19. Pang, J.: Vehicle Body Noise and Vibration Control. China Machine Press: Automotive Technology Innovation and Research and Development Series, 201501.448.7

Evaluation of Objective Sound Quality Feature Extraction with Kernel Principal Component Method in Electric Drive System Xin Huang1,2 , Zizhen Qiu1 , Fang Wang1,2 , Kong Zhiguo1,2(B) , Jifang Li1 , and Xiang Ji1 1 CATARC New Energy Vehicle Test Center (Tianjin) Co., Ltd., Tianjin 300300, China

[email protected] 2 China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, China

Abstract. This paper takes the electric drive system used in the electric vehicle as the research object, in which the objective sound quality of noise samples is extracted and evaluated based on the kernel principal component (KPCA) analysis method. Seven different power-level prototypes and their related parameters are firstly presented, while the sample library under different operational conditions has been established. Secondly, the KPCA method is employed to extract the contributions of eight objective psychological features. The results show that the KPCA method can effectively achieve multi-dimensional feature extraction. The cumulative contribution of sharpness and tonality is meeting 98.18%, which can fully represent the objective sound quality. Moreover, the sharpness and tonality are more sensitive to the speeds under different load conditions. Especially, tonality obtains a different pattern with SPL-A above 10000 r/min. This work can provide a theoretical and practical basis for predicting and optimizing the objective and subjective sound quality in electric vehicle applications. Keywords: electric drive system · sound quality · objective psychological feature · kernel function principal component analysis · experimental evaluation

1 Introduction At present, the implementation of electric drive systems brings new challenges in the areas of NVH (Noise, Vibration and Harshness) performance for the overall electric vehicle. The main difference between electric drive systems and conventional internal combustion engines concerns the structure of the electrified powertrain and the drive approach. The unique characteristics of the drive motor, such as wide-speed domain, higher load and complex electromagnetic harmonic components, significantly change the acoustic behaviour of electric vehicles [1]. While the conventional internal combustion engine noise is mainly characterised by low-frequency wide-band acoustics, the electromagnetic noise introduced by the electric drive assembly contains a large amount of variable high-frequency narrow-band acoustics [2]. Although electromagnetic noise is lower in intensity than internal combustion engine power noise, the caused subjective © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 277–287, 2023. https://doi.org/10.1007/978-981-99-1365-7_20

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feeling is very uncomfortable and even annoying [3]. Therefore, the study of the sound quality of the electric drive system is of great importance to improve the NVH of the vehicle level. In recent years, many scholars have conducted more in-depth research on the sound quality of electric driven systems. In the [3, 4], a comprehensive analysis of the mechanism of vibro-acoustic generation in electric driven systems was carried out. The results showed that the harmonic effect generated by high-frequency harmonics has a significant impact on the acoustic characteristics within the full-band spectrum. In terms of objective evaluation, Mosquera in [5], proposed a novel acoustic evaluation method related to the modified the evaluation coefficients to reflect the high-frequency (socalled sharp acoustic characteristics) presented by the electric drive system. In addition, to characterize acoustic samples comprehensively, it has been generally accepted by experts and scholars that the A-weighted sound pressure level (SPL-A) only represents noise energy or power, which can not fully reflect the human ear’s perception of the acoustic samples. Commonly used objective physical acoustic and psychoacoustic parameters include loudness, roughness, fluctuation, sharpness, tonality, prominence, and intelligibility index [6–8]. In the research area of subjective sound quality evaluation, many scholars have used semantic segmentation and pairwise comparison methods [9] to conduct evaluation tests to obtain subjective evaluation results of acoustic samples. A large number of subjective sound quality evaluation processes and results have shown that subjective evaluation teams require professional training and significant listening costs, in which the consistency of the results obtained is difficult to guarantee. Hence, the derivation of subjective and objective sound quality prediction studies. In [10], a sound quality prediction model based on an adaptive deep neural network approach was proposed. In [11], a sound quality prediction model for electric drive system was developed by the PSO-SVR method, and a multi-objective optimization scheme was proposed for the motor shell design. From the existing literature, it can be seen that there are still shortcomings in the existed studies. There is a lack of objective sound quality of electric drive systems, also a few evaluations associated with the objective psychometric parameters under different power levels. The feature extraction and contribution analysis of multi-dimensional objective psychological parameters are not yet clear. Therefore, this paper provides three different power-level electric drive systems under different operating conditions to establish the corresponding noise sample library. The Kernel Principal Component Analysis (KPCA) [12–14] method is used to evaluate and study the objective psychological parameters and its feature extraction. The extraction process of objective evaluation features can achieve multi-dimensional objective reduction, extracting the two objective parameters with the highest cumulative contribution to describe the acoustics of the electric drive system, which also analyses the influence on the objective parameters through the experimental and calculation results of different operating conditions.

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2 Electric Drive System Test and Acoustic Samples 2.1 Experimental Test Platform of Electric Drive Systems The extraction of objective evaluation features for the sound quality of electric drive systems first requires the acquisition of acoustic sample data under typical operating conditions. The acoustic sample tests described in this paper were conducted in a semianechoic chamber with a background noise of 18 dB, while the test standard is in accordance with QC/T1132–2020 “Noise Measurement Methods for Electric Powertrains for Electric Vehicles”. The experimental test hardware is shown in Table 1 and mainly includes the LMS Scada III multi-channel acoustic test and data analysis system, PCB-356A25 acceleration vibration sensor, GRAS-46AE acoustic sensor, etc. Table 1. Test equipment and technical parameters Item

Parameters

Data acquisition front-end

LMS SCADAS III

24 chs

Acceleration vibration sensor

PCB 356A25

10 ~ 500mV/g

Acoustic sensor

GRAS 46AE

17 ~ 138 dB

The acoustic samples of the electric drive system in this paper is selected from three electric drive systems with different power levels, consisting of a permanent magnet synchronous motor, gearbox and motor controller units. The key parameters of the electric drive systems are shown in Table 2. Table 2. Key parameters of the selected electric drive systems Parameters/Units

EDS1

EDS2

EDSS3

Rated voltage/V

380

400

560

Rated/peak torque/N·m

145/310

83/200

144/450

Rated/peak power/kW

65/160

41.5/100

70/215

Maximum operating speed/(r/min)

16000

16000

18000

During the noise sample collection process, the electric drive system is installed in accordance with the three-point suspension mounting as vehicle arrangement, as shown in Fig. 1. The experimental test platform is mainly composed of the electric drive system, load motor, drive shaft and coupling, etc. By changing the operating conditions of the load motor, the speed and torque of the electric drive system can be simulated. In addition, in order to reduce the noise impact of the load motor and other components, the load motor is placed outside the semi-anechoic chamber and the part connected to the electric drive system under test is isolated by a sound insulating cover (Fig. 2).

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Hemi-anechoic chamber

1m Controller

No·1

No·4(5) Measuring motor

1m

1m

No·2

Load

Load

motor

motor Coupling

Gearbox

Transmission shaft

LMS Scada III

Computer

Fig. 1. Experimental acquisition arrangement for electric drive systems

Fig. 2. Field diagram of the electric drive system noise experiment

2.2 Acoustic Sample Results The original noise sample results for the electric drive system are shown in Fig. 3, specifically the A-weighted sound pressure level (SPL-A) with state speed at different torques. The results show that SPL-A shows an increasing trend with increasing speed, also with increasing load torque, consisted with other studies [15]. With the speed reached 15,000 r/min, the SPL-A at 30 Nm and 50 Nm reached a maximum magnitudes of 90.67 dBA and 88.53 dBA. The SPL-A at 0 N-m torque reached a maximum value of 85.63 dBA when the speed reached 16,000 r/min.

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Fig. 3. Experimental results of SPL-A with different loads

3 Objective Sound Quality Evaluation Feature Extraction The objective evaluation features of sound quality mainly include physical acoustic parameters and psycho-acoustical parameters. Since the objective evaluation features of the input noise signal of the electric drive system are characterised by non-linear correlation. This section therefore focuses on a method to accurately obtain the main contribution quantities affecting sound quality for extracting strong correlation features, which orders to achieve a dimensional reduction of objective sound quality evaluation. to accurately obtain the main contribution quantities affecting sound quality. 3.1 Principal Component Analysis Based on Kernel Functions In this paper, based on the kernel principal element analysis (KPCA), the objective evaluation characteristics are used to simplify the objective evaluation dimensions and to improve the prediction accuracy for sound quality of electric drive systems. Essentially, KPCA is a combination of different types of kernel functions and principal element analysis methods [16], and the main computational flow is shown in Fig. 4. The mapping from the input space Rl to the feature space F is achieved by a nonlinear mapping function. Suppose the original dataset is X, which is transformed into the mapped dataset ϕ(xi ) after mapping, and then subjected to principal component analysis after non-linear transformation, λi ui = cui , i = 1, 2, · · ·, l

(1)

where c is the covariance matrix of the original data set, expressed as 1 ϕ(xi )ϕ(xi )T l l

c=

(2)

i=1

where λi and ui are the eigenvalues and eigenvectors in the covariance matrix, respectively.

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Fig. 4. KPCA calculation process

Both sides in (1) are simultaneously inner-producted for all samples ϕ(xi ), as λi ui , ϕ(xi ) = cui , ϕ(xi ) where the eigenvector u can be represented linearly by ϕ(x), giving ⎧ ⎨ ui , ϕ(xi ) = λKα ⎩ cui , ϕ(xi ) = 1 KKα l

(3)

(4)

where the kernel function matrix K = ϕ(xi ),ϕ(xj ); α = [a1 ,a2 ,…,al ]T . Combining the above analysis gives, lλα = Kα

(5)

where λ is the eigenvalue of the covariance matrix. For the kernel function, its selection needs to satisfy Mercer’s theorem, and in the actual use of KPCA method. The kernel functions represented by linear kernel function, polynomial kernel function, multilayer perceptron kernel function and Gaussian radial basis kernel function have been more widely used. The results show that the use of multilayer perceptron kernel function can achieve better feature extraction effect [17– 19]. The kernel function based on the multilayer perceptron can be defined as   K(x, y) = tanh c(x, y) + d , c > 0, d > 0 (6) where, x and y are the characteristic variables. c and d are the kernel function parameters.

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3.2 Extraction of Objective Evaluation Features for Sound Quality The acoustic samples of the related electric drive systems are collected at 12000 r/min, where a total of 12 samples are obtained by acceleration conditions and three torque modes. The SPL-A, loudness, roughness, fluctuation, sharpness, tonality, prominence, and intelligibility index are identified by comparing the methods described in provide studies [11, 15, 20]. The eight dimensional objective evaluation feature parameters are used as the input sample for KPCA analysis. Table 3 gives the results of the objective evaluation features for the 12 noise samples, where the serial numbers X 1 to X 8 correspond to the above objective evaluation features. Table 3. Input Spatial Data Sample Sets Sample

X1

X2

X3

…

X6

X7

X8

1

74.42

37.35

2.46

…

10.0

2.82

0.43

2

76.72

41.59

2.61

…

10.0

2.64

0.36

3

74.16

32.54

2.23

…

14.35

1.54

0.31

4

74.60

35.77

2.38

…

17.63

1.97

0.29

5

77.91

40.87

2.28

…

14.78

2.58

0.25

6

76.59

40.82

2.30

…

23.49

2.56

0.28

7

80.77

47.39

2.28

…

19.63

4.06

0.26

8

78.80

43.76

2.46

…

23.60

3.46

0.27

9

80.29

47.93

2.18

…

20.06

3.51

0.23

10

80.01

47.80

2.52

…

27.21

3.26

0.25

11

80.80

46.96

2.98

…

13.61

2.57

0.35

12

80.69

45.99

3.03

…

13.94

2.38

0.34

Table 4 gives the extraction results of objective evaluation features, where all acoustic samples are calculated by using the KPCA method. It can be seen that the KPCA method can reduce the dimensionality of non-linear objective parameters. The number of principal components with a cumulative contribution rate greater than 85% meeting as 2, which effectively extracts the main objective indicators. From the extraction results, it can also see that the cumulative contribution of the first two principal components has reached 98.18%, which basically retains most of the information of the original feature parameters. According to the analysis of principal component, if the cumulative contribution of the first n principal components reaches 85%, then these n principal components can reflect sufficient information [14], i.e. the objective sound quality parameters have been reduced from 8 dimensions to 2 dimensions.

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The first principal component and the second principal component evaluation function can be calculated as F1 = 0.1460X1 + 0.0427X2 +0.2017X3 +0.1316X4 +0.0083X5 − 0.0156X6 − 0.1996X7 − 0.1129X8

(7)

F2 = 0.1424X1 + 0.2018X2 − 0.0394X3 − 0.1227X4 +0.0282X5 − 0.2995X6 − 0.0950X7 − 0.2880X8

(8)

Table 4. Results of noise sample feature extraction with KPCA KPCA

Eigenvalues

Nuclear principal component contribution rate /%

Cumulative contribution rate /%

First principal component

1.4700

82.55

82.55

Second principal component

0.2800

15.63

98.18

Third principal component

0.0210

1.19

99.37

Fourth principal component

0.0051

0.63

100

Fifth principal component

0.0029

0.00

100

Sixth principal component

0.0012

0.00

100

Seventh principal component

0.0010

0.00

100

Eighth Principal Component

0.0005

0.00

100

From (7) and (8), it can be seen that the first principal component has the largest contribution of X 3 (sharpness) and X 7 (tonality). The second principal component has the largest contribution of X 6 (prominence) and X8 (intelligibility index). By calculating the contribution of each component, it can be seen that the contribution of the first principal component is 82.55%. Therefore, the cumulative contribution of X 3 (sharpness) and X 7 (tonality) is the largest in the whole acoustic samples and among the sound quality evaluation process. Based on the KPCA method, the objective sound quality features of the electric drive system are extracted as sharpness and tonality.

4 Experimental Results on the Principal Components The previous section extracts objective evaluation features of the sound quality of electric drive systems based on the KPCA method as sharpness and tonality. On this basis,

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this section evaluates the amount of objective sound quality features extracted for the influence of different operating conditions and further investigates the characterization laws of objective sound quality. Figure 5 shows the variation of the sharpness with state speed and different loads. It can be seen that the sharpness is sensitive to changes in the operating conditions of the electric drive system, where a significant upward trend with increasing speed. When the speed reaches 10,000 r/min, the sharpness of the electric drive system at 0 Nm torque reaches a maximum value of 4.58 acum. When the speed reaches 15,000 r/min, the sharpness at 30 Nm and 50 Nm torque reaches a maximum value of 6.57 acum and 6.17 acum, respectively.

Fig. 5. Variation of sharpness at different loads

Figure 6 gives the variation pattern of the tonality of the noise samples with state speed and different loads. It can be seen that as the speed of the electric drive system increases, the tonality tends to increase and then decrease. The maximum values at 10,000 r/min are 2.47 tuHMS (0 Nm), 3.44 tuHMS (30 Nm) and 4.00 tuHMS (50 Nm). Comprehensive analysis of the above experimental results shows that the variation of sharpness and tonality with different operating conditions basically coincides with that of SPL-A, which verifies the accuracy of feature extraction. For sharpness, as the speed conditions change, the frequency components of the electric drive system also change accordingly, with the high-frequency noise signal components gradually increasing and the narrow-band spectrum distribution widening, which can be considered as a main reason for making the sharpness amplitude increase. In addition, when the speeds above 10,000 r/min, the tonality show a different trend from the SPL, which can be used as one of the evaluation indicators for electric drive assemblies.

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Fig. 6. Variation of tonality at different loads

5 Conclusion This paper carries out feature extraction and contribution analysis of objective sound quality parameters based on the KPCA method, where the experimental analysis of the main features are fully analysis for different operational conditions. The conclusions reached are as follows. 1) The KPCA analysis method has been employed, where the multi-dimensional objective evaluation features could be extracted. The cumulative contribution of sharpness and tonality is calculated to be 98.18%, which can achieve a simplified characterization for sound quality. 2) The main components of the objective sound quality evaluation, sharpness and tonality, are more sensitive to changes in speed, even under different loads, which basically coincides with the changes in SPL-A, further verifying the accuracy of the feature extraction method. 3) Using the objective sound quality feature extraction, it is possible to further combine psychological evaluation factors on the basis of the SPL-A test evaluation, while provide reference for the optimal design of the NVH performance of electric drive systems.

References 1. Deng, W., Zuo, S.: Electromagnetic vibration and noise of the permanent-magnet synchronous motors for electric vehicles: an overview. IEEE Trans. Transp. Electrification 5(1), 59–70 (2019) 2. Muender, M., Carbon, C.C.: Howl, whirr, and whistle: the perception of electric powertrain noise and its importance for perceived quality in electrified vehicles. Appl. Acoust. 185, 108412 (2022)

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3. Fang, Y., Zhang, T.: Sound quality investigation and improvement of an electric powertrain for electric vehicles. IEEE Trans. Industr. Electron. 65(2), 1149–1157 (2017) 4. Qiu, Z., Chen, Y., Liu, X., et al.: Analysis of the sideband current harmonics and vibroacoustics in the PMSM with SVPWM. IET Power Electr. 13(5), 1033–1040 (2020) 5. Mosquera-Sanchez, J.A., Sarrazin, M., Janssens, K., et al.: Multiple target sound quality balance for hybrid electric powertrain noise. Mech. Syst. Signal Process. 99, 478–503 (2018) 6. Mosquera-Sánchez, J.A., de Oliveira, L.P.R.: A multi-harmonic amplitude and relative-phase controller for active sound quality control. Mech. Syst Signal Process 45, 542–562 (2014) 7. Jeon, J.Y., You, J., Chang, H.Y.: Sound radiation and sound quality characteristics of refrigerator noise in real living environments. Appl. Acoust. 68(10), 1118–1134 (2007) 8. Huang, H., Wu, J., Lim, T.C., et al.: Pure electric vehicle nonstationary interior sound quality prediction based on deep CNNs with an adaptable learning rate tree. Mech. Syst. Signal Process. 148, 107170 (2021) 9. Gonzalez, A., Ferrer, M., De Diego, M., et al.: Sound quality of low-frequency and car engine noises after active noise control. J. Sound Vib. 265(3), 663–679 (2003) 10. Chen, P., Xu, L., Tang, Q., et al.: Research on prediction model of tractor sound quality based on genetic algorithm. Appl. Acoust. 185, 108411 (2022) 11. Fang, Y., Zhang, T.: Sound quality of the acoustic noise radiated by PWM-fed electric powertrain. IEEE Trans. Industr. Electron. 65(6), 4534–4541 (2017) 12. Jiang, Q., Yan, X.: Parallel PCA–KPCA for nonlinear process monitoring. Control. Eng. Pract. 80, 17–25 (2018) 13. Sun, H., Lv, G., Mo, J., et al.: Application of KPCA combined with SVM in Raman spectral discrimination. Optik 184, 214–219 (2019) 14. Huang, H., Da, F.: A dictionary learning and KPCA-based feature extraction method for off-line handwritten Tibetan character recognition. Optik 126(23), 3795–3800 (2015) 15. Qiu, Z., Chen, Y., Kang, Y., et al.: Sound quality prediction for permanent magnet synchronous motors used in electric vehicles. Noise Vibr. Control 40(2), 146–151 (2020) 16. Jiang, X., Han, W., Mao, D.: Research on fault warning of power station auxiliary equipment based on kernel principal component analysis and GRU neural network. J. Eng. Thermal Energy Power 36(07), 93–98 (2021) 17. Mita, J.H., Babu, C.G., Shankar, M.G.: Performance analysis of dimensionality reduction using PCA, KPCA and LLE for ECG signals. In: IOP Conference Series: Materials Science and Engineering, vol. 1084, no. 1, p. 012005. IOP Publishing (2021) 18. Gao, C.: Feature Extraction Method Based on KPCA and Its Application. Nanjing University of Aeronautics and Astronautics (2009) 19. Weihua, F., Zhang, N., Youjie, J.I.N.: Investigation on sensitive warning index of earth dam by use of dimensionality reduction methods. In: IOP Conference Series: Earth and Environmental Science, vol. 69, no. 1, p. 012148. IOP Publishing (2017) 20. Zhang, J., Duan, C., Lin, J., et al.: Subjective and objective evaluation of acceleration sound quality of diesel engines in commercial vehicles. J. Tianjin Univ. (Nat. Sci. Eng. Technol. Ed.) 52(2), 150–156 (2019)

Launching Rattle Noise Test Analysis and Improvement for a SUV with 6AT Automatic Transmission Jun Zhang(B) and Yongzhong Bao Geely Automotive Research Institute (Ningbo) Co. Ltd., Ningbo 315336, Zhejiang, China [email protected]

Abstract. A SUV vehicle equipped with 1.4T engine and 6AT automatic transmission has an impact abnormal noise in the engine cabin during the low speed launching condition, which seriously affects the driving comfort performance. Based on the results of vehicle vibration and noise test and analysis, combined with the transmission power flow analysis of the D gear and R gear, as well as the theoretical assumptions of transmission clearance and rattle phenomenon, it is identified that the abnormal noise key element is the spline clearance of the intermediate output shaft driven gear. In addition, adopting the optimization of spline matching design parameters, the effectiveness of this counter-measures is verified by the vehicle driving subjective evaluation and objective test. This has important engineering guidance value for solving the driveline NVH problems under transient conditions. Keywords: Automatic hydraulic transmission · Transmission clearance · Power flow analysis · Spline · Impact noise

1 Introduction With the increasing attention of market customers to the vehicle NVH performance, the vibration and noise levels of transient conditions such as start-up, stop-off and rapid acceleration are gradually concerned in the process of the automobile research and development. The start-up abnormal noise problem of a longitudinal rear wheel drive commercial vehicle by adding a friction-reducing gasket between the output end of the manual transmission and the coupling flange [1]. Yue Chuanyuan found that the stick-slip phenomenon of the contact end face between the hub bearing and the driving half shaft is the main reason for the abnormal noise of a front wheel drive vehicle [2]. Cheng Lin solved the clutch abnormal noise problem in the starting process of a manual transmission vehicle by optimizing the clutch wave plate structure [3]. The DCT transmission impact noise in the process of reverse gear starting is solved by reducing the cone angle difference of synchronous gear sleeves [4]. The abnormal starting noise of a rear-drive pickup truck is solved by adding anti-friction gasket between the outer bearing flanges [5]. Zhu Lianjie studied the relationship between the rear row booming problem caused by the exhaust system and the engine starting auxiliary calibration parameters [6]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 288–298, 2023. https://doi.org/10.1007/978-981-99-1365-7_21

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Through the optimization of motor torque control strategy, Zhang solved the problem of knocking noise in the fast braking condition when the pure electric car runs at low speed [7]. Maruthi proposed a switching mode control method to solve the electric vehicle rapid acceleration problem caused by electric drive transmission gap [8]. Guangqiang Wu studied the influence of the parameters such as variable stiffness and backlash of gear meshing on the vehicle shake in the starting process [9]. At present, the NVH problem of vehicle steady-state driving conditions is widely studied in the industry, while the NVH problem of transient driving conditions is relatively less studied, and a systematic development control system has not yet been formed. Based on the investigation and analysis process of the abnormal noise problem of a vehicle starting process, this paper puts forward specific engineering improvement measures. Through the interference parameter design optimization of the intermediate output shaft passive gear spline, the transient impact abnormal noise of the transmission is effectively solved, which has reference and reference significance for improving the NVH performance design and development of the powertrain drive system.

2 Problem Description A horizontal front drive compact SUV is equipped with a 1.4T turbocharged engine and a 6-speed automatic transmission. When launching forward quickly in D gear mode or reversing in R gear, the occupants in the vehicle cabin can obviously perceive a clear impact sound in the engine compartment. There is no such abnormal sound during the driving process, and the probability of this abnormal sound is greatly reduced if the launching operation is slowly. Due to the low background noise in the launching condition, especially in the reverberation acoustic environment of the underground parking place, this abnormal noise will be more significant, which will seriously reduce the driver ‘s sense of security and easily lead to complaints and after-sales maintenance requirements. Generally, there are many factors affecting the transient abnormal noise problem of the powertrain transmission system. It is a challenging for quick diagnosis and troubleshooting.

3 Abnormal Noise Test Scheme Based on Vehicle In order to more accurately measure and analyze the fault characteristics of the abnormal problem, the noise and vibration signal are acquired to diagnose the potential cause mechanism under the low-speed braking condition of the vehicle. Therefore, vibration acceleration sensors are placed on the end cover housing on the side of the AT transmission, the differential output interface, and the hub bearing attachments, respectively. A microphone is arranged at the bottom of the engine compartment, the relative signals such as vehicle speed, transmission gear, and engine output torque are synchronously collected through the CAN bus, as shown in Fig. 1 and Fig. 2.

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The measurement procedure is that on the flat smooth road surface, the vehicle engine ignites and is engaged into the D-gear mode, quickly steps down the accelerator pedal to accelerate to about 15km/h, and then releases the accelerator pedal for braking and parking. It is to repeatedly operate multiple times and simultaneously collect the vibration and noise signals in the front engine compartment.

Fig. 1. Vehicle measuring sensor arrangement

Fig. 2. Vehicle measuring sensor arrangement on site

4 Abnormal Noise Analysis of the Vehicle Test Results According to the time-domain profile analysis of the measured signal and the repeated audio playback, combined with the comparative identification of the subjective and objective evaluation, some test analysis results and conclusion can be obtained, as shown in Fig. 3. When the vehicle starts to launch forward, almost at the initial moment of the vehicle speed and the engine speed, the microphone and the vibration signal have the same instantaneous impact characteristics, which is the subjective perception of the click abnormal noise. At this time, the engine torque output value is only 45 Nm, the vehicle traveling speed is 0.84 km/h, and the engine rotation speed is 960.5 Rpm. Because the background noise of the vehicle launching condition is relatively quiet, and the engine acceleration noise and tire friction noise are more dominant, the abnormal noise characteristics measured by the microphone in the front engine cabin are not significant, and the signal-to-noise ratio of the sound channel is low.

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Compared with the microphone sound signal, the transmission shell surface vibration signal has clear impact characteristics, and the signal-to-noise ratio of the vibration channel is high.

Fig. 3. Rattle noise measurement analysis in time-domain

In addition, as shown in Fig. 4, the vibration time domain measurement results of each channel are identified and compared. The instantaneous acceleration impact amplitudes of the differential output port and the 6AT transmission side shell position reaches to 16.5 g and 11.4 g, respectively, which vibration shock peak value is far greater 100 times than the left / right wheel assemble position. Although the vibration impact characteristic peak of the left wheel position is slightly larger than that of the right side, it is less than 0.2 g. So, it can be speculated that there is no stick-slip abnormal noise between the end face of the hub bearing and the outer cage of the drive shaft [10]. Therefore, it can be preliminarily inferred that the abnormal noise problem of the vehicle during the start-up process occurs inside the powertrain system and has a great correlation with the transmission.

Fig. 4. Analysis and comparison of vibration time-domain measurement

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In order to further eliminate the hypothesis of the cause of the abnormal noise of the hub bearing, the initial locking nut torque is reduced between the outer ball cages of the drive axle by more than 60%, or a resin anti-friction gasket is added. According to the vehicle driving evaluation verification, there is still abnormal noise in the vehicle launch process, which further indicates that the internal components and parts of the 6AT transmission may be the key element for the abnormal noise problem.

5 Transmission Power Flow Analysis in Launch Process As shown in Fig. 5, the front transverse 6AT transmission used in the vehicle is mainly composed of hydraulic torque converter, oil pump, planetary gear mechanism, differential assembly, shift actuator, valve body and TCU control unit. The front gear set is a single-stage planetary row, and the rear set is a ravigneaux two-stage planetary row. The intermediate output shaft and the differential assembly are arranged in parallel. The automotive transmission torque capacity is 280 Nm. The analysis of vehicle power flow is not only the basis of automatic transmission design and development, but also the fundamental underlying of the abnormal noise problem investigation.

Fig. 5. Internal structure diagram of a 6AT transmission

As shown in Fig. 6(a), when the vehicle makes a launch forward in D shift mode, the C1 clutch is locked, and the engine torque is transferred from the turbine shaft of the hydraulic torque converter to the outer ring gear of the front planetary row. The sun gear is fixed, and the front planetary carrier outputs power flow to the rear sun gear of the rear planetary row. The F1 one-way clutch is relocked, the rear planet carrier is fixed. The power flow is output from the outer ring gear of the rear planet carrier to the intermediate shaft passive gear, and then transmitted to the intermediate shaft active gear, and finally output to the driving half shaft through the differential assembly. As shown in Fig. 6(b), the C3 clutch is locked in the reverse gear launch condition. The power output is transferred from the front planet carrier to the front sun gear of the rear planet carrier. The B2 brake is locked, and the planet carrier of the rear planet row is fixed. The torque is directly output from the front sun gear to the outer ring gear, and then transmitted to the

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intermediate shaft gear. Because the vehicle occurs the instantaneous impact abnormal noise both in the forward and in the backward launch condition. Based on the power flow analysis diagram in Fig. 6, the cause of abnormal noise can be preliminarily excluded from the hydraulic torque converter with high damping characteristics, the front planet carrier solar wheel and the rear planet carrier. In the launch process of forward or reverse gear mode, the power flow will pass through the intermediate shaft and the differential assembly. Especially, compared with the planetary gear train and the differential system, the relative inertia moment of the helical gear pair transmission part in the intermediate shaft is smaller, and the relative speed ratio is larger. Therefore, the torque transmission load is also greater. Although the structure of the intermediate shaft gear set is simple, the response sensitivity to the change of torque load is higher.

Mode D1

(a) D1 Mode

R

Clutch

Brake

C1

C2

C3

B1

B2

O

— —

— O

—

—

—

— O

One-way Clutch F1 O —

(b) Reserved Mode

Fig. 6. Power flow diagram of a 6AT transmission D1/R gear mode

6 Mechanism Analysis Between Transmission Clearance and Rattle Noise Transmission components rattle noise is a nonlinear dynamic problem with many influencing factors and complex mechanism. For 6AT transmission system, the common rattle noise problems can be divided into two categories. The first is the wide frequency impact noise of internal transmission components or parts under quasi-steady state, when the vehicle is in idle static state, or creeping and slowly accelerating conditions. On-loaded empty sleeve gear pairs, sliding sleeves and gear rings are prone to radial impact problem caused by the torque or rotational speed fluctuation. The second is the rattle noise under instantaneous conditions such as launching, shifting or rapid acceleration. The main reasons are the radial or axial impact phenomena between gear pairs, spline components and the bearing rollers with transmission clearance. Due to design, manufacturing errors, wear and other reasons, there may be a certain gap or backlash between the transmission components in the transmission system. In the transformation process of the vehicle power torque paths, the transmission components clearance may cause the mechanical impact phenomena. The components shock excitation energy will be transferred to the transmission housing through the shaft system and bearing, and then the rattle noise results in radiation propagation to the surrounding environment. Therefore, the transmission components gap may destroy the of power flow

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Fig. 7. Transmission characteristics diagram on considering clearance

continuity from a stable meshing engage state to a non-contact state. Due to transmission discontinuity, the mechanical impact noise is susceptible to occurrence. As shown in Fig. 7, according to the critical value theory hypothesis [11–15] of the transmission system rattle noise, the impact phenomenon in the parts meshing process mainly depends on the driving torque of the driven component. The relationship between the driving torque, the dragging torque and the inertial torque is Eq. (1). Tdriving = Tinteria + Tdrag = I0 θ¨0 + Tdrag

(1)

In the above equation, Tinteria is the inertia moment of the driven part. Io is the inertia moment of the driven part, and θ¨o is the angular acceleration. Tdriving and Tdrag is the equivalent driving moment and the dragging moment of the driven part, respectively. The main factors of the dragging moment are to overcome the churning resistance and the bearing preload for the transmission components relative motion. As shown in Fig. 8, when the inertia moment amplitude Tinteria is less than   the drag torque Tdrag , the driving torque Tdriving and the dynamic meshing force F xij are positive. Therefore, the transmission pair components are in the normal engagement condition without impact phenomenon. When the inertial moment Tinteria is greater than the  moment Tdrag , the driving moment Tdriving and the dynamic meshing force  drag F xij of the driven component become negative, and the transmission pairs will be separated, which will regularly result in an rattle problem. Among the Fig. 8, F xij represents the nonlinear piecewise function of the dynamic resultant force between the transmission pairs with clearance, xij represents the real time clearance between the transmission pairs, and b is the backlash length.

Fig. 8. Transmission schematic diagram between backlash and impact noise

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7 Potential Causes Investigation In summary, the time domain peak characteristic of the abnormal vibration and noise is the instantaneous impact wave shape. The continuous interface friction abnormal noise of the bearing and gasket is excluded to cause the abnormal noise. Combined with the analysis of the correlation mechanism between the transmission clearance and the transient rattle phenomenon, it can be preliminarily inferred that the vehicle launch abnormal noise may be the single side lateral impact problem caused by the transmission components clearance. At the same time, as shown in Fig. 9, it is found that the fit clearance between the inner spline of the intermediate shaft passive gear and the outer spline of the output shaft is -0.007mm by the relevant parts design clearance check on the power transmission path. From experience, the small interference value scheme may cause that the spine fit backlash and spacing dimension parameters exceed the design specification after manufacturing and pressing processes. Therefore, after the actual transmission assembly, there is probably a certain gap between the intermediate output shaft gear splines, which may induce the rattle abnormal noise during the vehicle launching condition. Based on the above design judgment and manufacture process analysis, the driven gear is welded and fixed to the intermediate output shaft by manual. When the special modified transmission is reinstalled to the vehicle, the abnormal noise completely disappears. Such the similar experiments have repeatedly done on a few vehicles, the verification results are the same. Thus, it is further determined that the spline clearance fit state of the output shaft driven gear is the key factor of the abnormal noise. Rear Planetary Carrier Gear Ring Parking gear ring Driven Spine Thrust Bearing

Thrust Bearing Output Shaft Driving Gear Intermediate Output Shaft Assembly

Output Shaft Driven Gear

Fig. 9. Diagram of intermediate output shaft assembly

8 Improvement and Verification The driven gear design of the intermediate output shaft is used in the form of straight tooth cylindrical involute spline connection, which has the advantages of strong bearing capacity, high centering accuracy and high processing production efficiency. This type of spine connection method is widely applied in the gearbox assembly design of the shaft gear parts. A variety of spline assembly positioning forms will lead to a certain different spline fit deviation. In the actual engineering case of the involute spline assembly quality control, the spline tooth side fit backlash is often found too excessively large, which will

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result in the gear transmission abnormal noise or function fault. However, the spline tooth side fit clearance is too small, it will lead to excessive pressing force or cracking parts. Therefore, the process parameters of spline connection design and manufacturing quality of intermediate shaft gear need to be comprehensively verified and adjusted not only on the transmission bench test, but also by the vehicle driving evaluation. According to the definition of tooth width and tooth thickness, when the maximum tooth thickness of the shaft outer spline is greater than the minimum tooth width of the gear inner spline, the interference fit between the tooth sides can limit the circumferential relative rotation of the spline and avoid potential impact noise. Because the vehicle development cost and verification time cycle are limited, the factory existing gear spline assembly process level cannot be significantly improved, immediately. As shown in Table 1, the final adopted modification measures are to adjust the spine tooth side matching parameters design for increasing the shaft tooth assembly interference amount, such as to change the output shaft external spline tooth thickness and the driven gear internal spline tooth width, and to improve the quality grade requirement of the internal/external spline. Table 1. Design Optimization of Intermediate Output Shaft Passive Gear Spline Interference. Unit: mm Dimension item

Base

Modified

Output shaft external spline quality level

7 level

6 level

Output shaft external spline tooth thickness

1.922–1.972

1.964–2.014

Dirven gear inner spine quality level

7 level

6 level

Dirven gear inner spine slot width

1.868–1.918

1.884–1.934

Spline fit interference amount

−0.007–−0.107

−0.029–−0.129

After the spline interference modification of the intermediate driven output assembly, the driver and passenger in the vehicle have not subjectively perceived the abnormal rattle noise in the launch condition. In addition, through the comparison of the objective test data of vibration and noise, shown in Fig. 10, the instantaneous impact characteristics have disappeared. This means that the power torque transmission is more smooth, the driving quality of the vehicle is significantly improved.

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Fig. 10. Vehicle vibration and sound test Comparison after optimization

9 Summary With the increasing demand for vehicle comfort in the automotive market, it is very important to fully verify the NVH performance in all operating conditions as possible. Especially in the transient condition of the vehicle launch process, the discontinuity of the engine torque output is more likely to cause the impact noise problem of the power transmission system components. In this paper, the abnormal noise problem of a launching SUV equipped with a 6AT transmission is taken as the research case, and the process of measurement analysis and investigation is systematically expounded. Combined with the power flow analysis of the automatic transmission, as well as the mechanism hypothesis of the transmission clearance and the impact phenomenon, output shaft driven gear spline clearance is logically identified as a key factor affecting the abnormal noise. In addition, this paper also proposes an engineering improvement method for spline matching design parameters. The effectiveness of the measure scheme is verified by actual vehicle driving evaluation and objective test. This has important engineering guidance value for solving the power transmission system similar abnormal noise problem under the transient conditions. Because the starting torque output amplitude of the electric drivetrain is larger, the dynamic response is faster, and the switching of the torque direction is more frequent, more attention should be paid to the design control of the transmission clearance and torque calibration of the electric drive system. At the same time, it is necessary to strengthen the NVH performance verification test of the bench and the new energy vehicle under transient conditions.

References 1. Zhao, Y., Jun, Z., Mi, S., et al.: Analysis of stick-slip induced abnormal click noise at transmission output end and the corresponding improvement measures. Noise Vibr. Control 39(3), 128–132 (2019)

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2. Chuanyuan, Y., Cheng, Y., Jun, Z., et al.: Analysis and control of the starting stick-slip noise at vehicle front wheel end. Tech. Acoust. 38(4), 446–451 (2019) 3. Lin, C., Wenjie, Z., Tao, L., et al.: Modal analysis and optimization for the clutch driven plate of a mt vehicle. J. Vibr. Shock 37(24), 265–270 (2018) 4. Meng, Q., Feibing, Z., Bo, Y.: Verification and Modification of the Transmission Reverse Gear Abnormal Noise. In: SAECCE2020-NVH023, pp. 1265–1267 5. Qiang, F., Maogen, W., Yin, S., et al.: Analysis and Solution the Abnormal Noise at Starting and Reversing of Drive Axle. Autom. Parts 01, 83–85 (2020) 6. Zhu Lianjie, H., Dongjie, Q.Q., et al.: Study on interior booming noise of the smalldisplacement engine of a vehicle under launch process. Noise Vibr. Control 41(6), 250–254 (2021) 7. Jun, Z., Long, S., Ming, J., et al.: Analysis and optimization of the rattle noise problem of a EV vehicle one-stage transmission gearbox. In: SAECCE2021-EV031, pp. 311–315 (2021) 8. Ravichandran, M., Doering, J., Kevin, R.R., et al.: Design and evaluation of EV drivetrain clunk and shuffle management control system. In: American Control Conference, pp. 4905– 4912 (2020) 9. Guangqiang, W., Wenbo, L.: The impact of gear meshing nonlinearities on the vehicle launch shudder. In: SAE Paper (2015) 10. Bonnerjee, D., Bouzit, D., Iqbal, J.: Ting noise generation in automotive applications. In: SAE Paper (2017) 11. Yingming, Li., Weidong, C., Qi, C., et al.: Research of the influence of gear backlash on gear pair nonlinear vibration characteristic. J. Mech. Transm. 37(5), 1–4 (2013) 12. Hamed, M., Hassan, S.: Analysis of nonlinear oscillations in spur gear pairs with approximated modelling of backlash nonlinearity. Mech. Mach. Theory 51, 14–31 (2012) 13. Wu, G., Wu, H., Li, D.: Review of automotive transmission gear rattle. J. TONGJI Univ. 44(2), 276–285 (2016) 14. Padmanabhan, C., Rook, T.E, Singh, R.: Modeling of automotive gear rattle phenomenon. In: SAE Paper, p. 951316 (1995) 15. Forcelli, A., Grasso, C., Pappalardo, T.: The transmission gear rattle noise parametric sensitivity study. In: SAE Paper (2004)

Virtual Analysis of Vehicle Interior Gear Whining Based on Time-Domain Hybrid Modeling Method Junli Guo(B) , Zilong Tian, and Chao Ren Automotive Engineering Research Institute of Guangzhou, Guangzhou 511436, China [email protected]

Abstract. In order to analyze the influence factors of vehicle interior gear whining order noise from the mechanism, a time-domain hybrid analysis model of gear whining is built in this paper. The acceleration of the mount’s active side and nearfield noise result is acquired mainly based on the time-domain dynamic multibody model. Then the result data is used as input and loaded into the vehicle time-domain transmission path mathematical model, so as to calculate the vehicle interior gear whining order noise through virtual analysis. Compared with the real vehicle test results, the frequency error of the vehicle interior gear whining order noise calculated by this method does not exceed 6%, and the amplitude error does not exceeded 12%. On this basis, the method is applied to analyze the sensitivity of the factors affecting the gear whining on a certain vehicle type. It is found that the tooth shape modification and high-frequency vibration isolation of the mount etc. are the main causes of the vehicle interior gear whining. After the verification of the real vehicle test, the good accuracy of this modeling method is furtherly proved, which provides a powerful technical reference for the industry to solve this problem. Keywords: Whining · Time domain multi-body model · Time domain multi-body model · Time domain multi-body model · Virtual analysis · Order noise

1 Introduction With the development of technology in the Automobile industry, virtual analysis has gradually become a popular technical means in this field. The application of virtual simulation technology about autopilot is currently very popular in this industry. Virtual computation can save a lot of manpower and resource input costs caused by repeated attempts. In the process of vehicle vibration and noise development, tests are often used to find the cause of the problem and verify the effect of the solution. Similarly, there are many blind attempts. To reduce the input cost of trying tests in this field, and explain the root cause of the problem mechanistically at the same time, this paper proposes to use the vehicle-level virtual simulation technology to calculate the order noise of the vehicle interior gear whining and to find the problem mechanism, Then the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 299–310, 2023. https://doi.org/10.1007/978-981-99-1365-7_22

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corresponding optimization can be proposed from the main reasons. Simultaneously the optimization can be virtually modified in the model and repeated iteratively to achieve the best technical solutions. In the industry, software such as masta/romax is often used to simulate and analyze the transmission error, contact spot, misalignment, etc. of the gearbox body. People is trying to find out the influencing factors related to the excitation source that affect the gear order noise. Reference [4] uses dynamic method to analysis the vibration of the gearbox and the near-field radiated noise, and the method is applied to the real vehicle to verify the effect of the optimization. Miho Nakasuka used the OTPA method to analyze and test the contribution of the vehicle transmission path. The time-domain hybrid modeling method proposed in this paper builds a relatively complete model from the excitation source through the transmission path of the vehicle to the interior, which not only considers the influence of the gear excitation source, but also considers the influence of transmission path of the vehicle. At the same time, the two influences are related through a mathematical model, and their coupled responses and influencing factors are analyzed. So the evaluation and verification of vehicle interior gear whining is more comprehensive. Compared with the test results, the method has high accuracy. Also, the optimization proposed based on this method has been verified by the real vehicle test, and the improvement is remarkable. Not only the problem of the vehicle interior gear whining is solved, but also the accuracy of the method is proved.

2 Theoretical Model and Principle of Gear Order Dynamics in Time Domain 2.1 The Dynamic Theory of Gear Order Vibration The general dynamic model of gear transmission can be simplified to a simple springmass damping system. According to Lagrange’s theorem, abnormal signals such as gear eccentricity and tooth surface fault are not considered, then e(t) = 0 in Fig. 1,so the dynamic equation at the meshing point of the gear pair is as formula (1). m¨x(t) + c˙x(t) + k(t)[x(t) + E] = F0

(1)

where x(t) = x1 (t) − x2 (t) is the relative displacement of the gear teeth on the meshing displacement, E is the average static elastic deformation of the gear under load, c is the gear meshing damping, k(t) is Time-varying meshing stiffness of gears, m is the equivalent mass of the gear pair, F0 is the excitation exerted by the external load on the gear train when the gears are running normally, F0 = Tp /Rp = k0 (t)x(t), k(t) = k0 (t) + k1 (t), k0 (t) is static meshing stiffness, k1 (t) is dynamic meshing stiffness, so formula (1) can be transformed into formula (2). m¨x(t) + c˙x(t) + k0 (t)x(t) = −k1 (t)E − k1 (t)x(t)

(2)

The right side of the equation is the system excitation, − k1 E is linear excitation related to the static deformation of the system, − k1 x(t) is related to the output of the

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Fig. 1. Physical model of gear meshing

system, and is generated in the interaction between the negative feedback of the system’s vibration response x(t) and the meshing dynamic stiffness, it is a nonlinear excitation. In the powertrain model described in this paper, the external real load on the gear system is the excitation force on the right side of the dynamic Eq. (2) by derivation, which is transmitted to shell surface by the bearing, housing and mount,so the vibration of shell surface is formed. Among them, frequency components of the vibration response from the excitation force k1 E contain the base meshing frequency and its high-order according to the frequency retention of the linear system, and the meshing frequency corresponds to the meshing order of the gear. After conversion of k1 x(t), the frequency’s main components are also the base meshing frequency and its high-order. So the vibration of the housing surface includes the gear meshing order vibration. 2.2 The Theory of Gear Order Noise The computation of the order noise is based on the existing vibration model. The applied sound field computation method is the common direct boundary element method, and its theoretical formula is shown in the following (3). Ap = Bv

(3)

In the formula (3), A and B are the influence matrices of the sound pressure vector and the normal velocity vector separately, p is the sound pressure vector dB of the boundary element surface node, v is the normal velocity vector of the boundary element surface node, so as to obtain p and v of each node’s position on the surface. Finally the radiated sound pressure at any point x in the external field point grid can be obtained by interpolation as the following (4). p(x) = aT p + bT v

(4)

where a and b are the interpolation coefficient matrices of the nodal sound pressure vector p and the normal velocity v respectively. 2.3 Gear Order Rigid-Flexible Coupled Dynamic Model The powertrain gear order dynamic model in the vehicle condition mentioned as shown in Fig. 2 is built by the application software Virtual dynamic in this paper. The model

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includes mount, the detailed macro and micro parameters and the structural parameters of the transmission, the engine and other related components. Considering that the gear meshing excitation can be amplified because of the powertrain mode resonance, we build the Rigid-flexible coupled dynamic model by Replacing a rigid body for example the shafting, housing, crankshaft, cylinder block and other components with a flexible body model, and these flexible body is built by hypermesh. The parameters in this dynamic model involved mainly include the basic parameters of the engine, the macro and micro parameters of the transmission, etc. as shown in Fig. 3 and Fig. 4.

Fig. 2. Simulation model of gear meshing

Fig. 3. Basic parameters of the engine

Fig. 4. Macro and micro parameters of key gears

Under the condition of vehicle, for the load boundary of this model, this paper only considers the torque and the excitation force caused by the self-excited deformation of the gear, so engine cylinder pressure and The speed of the output shaft is applied to simulate torque excitation, and the gear self-exciting force corresponds to the detailed macro and micro parameters of the gear and the deformation of shaft system.The gear deformation contact force model is shown in formula (5), and the immersion contact

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impact model is used.   Fn = max kpe + cv, 0

(5)

where k is the meshing impact stiffness, e is the impact index, p is the contact immersion depth, v is the instantaneous velocity of contact, and c is the effective damping coefficient. This damping coefficient varies with the contact immersion depth p, as shown in Fig. 5.

Fig. 5. Relationship between damping and contact immersion depth

By building and debugging the model, the order vibration and noise of the powertrain active side under the acceleration condition of a vehicle is simulated. The time-domain vibration results of the mount active side are shown in Fig. 6, the powertrain near-field noise are shown in Fig. 7 below.

Fig. 6. Vibration of the mount active side

Fig. 7. Near-field noise of powertrain.

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3 Model Builing of Vehicle Interior Gear Noise 3.1 Analysis Principle of Vehicle Transfer Path in Time Domain The gear whining excitation is transmitted into the vehicle by structure and air transmission path, and forming the interior noise, as shown in Fig. 8. Mathematical models are applied such as formula (6) air transfer and formula (7) structure transfer.

Fig. 8. Schematic diagram of vehicle transfer path



Pi ∗ ATFi = Pa

(6)

Among them, Pi is the powertrain near-field sound pressure, ATFi is the acoustic transfer function, and Pa is the sound pressure transmitted to the interior of vehicle through air. The sound pressure through structure transfer is calculated as follows according to the time domain convolution.  api Fpi Psi ∗ ∗ = Ps aai ∗ aai api Fpi a

F

set up mti = apiai , ami = aPipi , NTFi = FPpisi Then the above formula can be written as the formula (7) below.  aai ∗ mti ∗ ami ∗ NTFi = Ps

(7)

Among them, aai is the vibration of the mount active side. mti is the vibration isolation of mount under operating conditions, that is, the isolation is Active side vibration divided by passive side. And ami is the static acceleration Impedance of the installation point on the passive side (body, subframe, etc.), that is passive side acceleration divided by force. NTFi is the body noise transfer function, that is passive side force divided by interior sound pressure. And Ps is the noise transmitted from the structure to the interior of vehicle. The above Pa and Ps are superimposed in the time domain, and the total noise of vehicle interior can be calculated. Table 1 shows the data status to be acquired. The test process is divided into three steps. The first step is to obtain the excitation source data, the mount active vibration and near-field noise data is collected on the dyno

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Table 1. Data sheet to acquire for test. excitation

Mount isolation

Transfer function

Vehicle interior

dynamic/static

dynamic

dynamic

static

dynamic

open/close cabin

open

close

open

close

structure transfer

aai

mt i

ami NTF i

Ps

acoustic transfer

Pi

——

ATF i

Pa

condition. It should be noted here that the cabin need to be opened when collecting nearfield noise data so as to obtain the powertrain direct noise. In the second step, the dynamic vibration isolation data of the mount is also obtained on the condition of dyno. The third step is to obtain the transfer function data in the full anechoic chamber, including the noise transfer function (ATF) and the structure transfer function (NTF and am). Finally, according to the above two calculation formulas (6) and (7), the noise transfer and the structure transfer are superimposed in time domain and the vehicle interior noise is calculated. 3.2 Hybrid Building Model Method of Vehicle Interior Gear Whining It can be seen from the above 1 that the vibration of the mount active side and near field noise can be acquired by the powertrain multi-body gear model under the vehicle operating conditions, So aai in Eq. (7) and Pi in Eq. (6) is known. As the load, it is input into the vehicle transfer path function model in time domain, and the calculated result Pa and Ps are superimposed in time domain to obtain the vehicle interior noise. Among them, the function data of vehicle transfer path in time domain needs to be obtained by using LMS/HEAD equipment to test, and the sensor placement is shown in Fig. 9.

Fig. 9. Sensor placement diagram of vehicle transmission path function test.

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4 Test Verification 4.1 Multi-body Simulation Analysis and Verification of Powertrain Gear Order Excitation Source According to the above theories and methods, a multi-body model of the powertrain under the vehicle operating conditions is built, the time-domain order vibration of the mount active side is calculated, and the result is converted into a colormap by frequency domain conversion, and the simulation results and test results are calibrated. Firstly, the order characteristics and the trend of the resonance band are evaluated. Secondly, the frequency error of the resonance band is evaluated by the method shown in Eq. (8) Ef , and the amplitude value is evaluated by the percentage error of Eq. (9) Em . The speed range of the problem proposed in this paper is as shown in Fig. 10, The simulation and test about the order characteristics and resonance band trends are consistent.

Fig. 10. Colormap of the order vibration for the powertrain mount active side

ftest − fcae ∗ 100% (8) ftest According to formula (8), it is obtained that the frequency error of the resonance band calculated in this paper does not exceed 9%. Ef =

Em =

Averg(test) − Averg(cae) ∗ 100% Averg(test)

(9)

Among Eq. (9), Averg(test) is the average value of the test amplitude, and set up the amplitude is a per 100rpm, then Averg(test) = (a1 + a2 + a3 + · · ·)/n, where n is the number of revolutions. Applying the above formula (9), it can be obtained that the amplitude error of the powertrain order vibration proposed in this paper does not exceed 15%. 4.2 Analysis and Verification of Transfer Path Fitting In order to verify the accuracy of the transfer path function, the vehicle interior noise is calculated through the mathematical fitting model of 2.1 with the excitation source data from real vehicle test, and then compared with the actual measured vehicle interior noise of the microphone, it is concluded that the mathematical model has high calculation accuracy. The trend is consistent, and the fitting analysis amplitude calculated according to formula (9) has an error of less than 8% compared with the test. The order curve of the vehicle interior whining is shown in Fig. 11. The red is the simulation result, and the black is the test result.

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Fig. 11. Comparison between simulation and test of whining noise in the vehicle interior (Color figure online)

4.3 Hybrid Modeling Analysis and Verification of Vehicle Interior Whining Noise According to the calculation results of the above powertrain multi-body model (under the vehicle condition) and the time-domain digital model of vehicle transfer path, the hybrid modeling method is used to obtain the fitting result of the vehicle interior noise. Then we compare the fitting result from hybrid modeling with the test result, as shown in Fig. 12, blue is the simulation fitting result, red is the test result, thus the fitting accuracy is very high, the frequency error does not exceed 6% according to formula (8), and the order amplitude error is not more than 12% according to formula (9).

Fig. 12. Comparison of vehicle interiol noise between simulation and test (Color figure online)

5 Virtual Optimization and Verification 5.1 Analysis of Virtual Optimization About Vehicle Interior Whining Based on the above models and methods, this paper simulates and analyzes a gear whining problem of a certain vehicle-grade and proposes a corresponding optimization for the speed range of 1500–2500 rpm about the problem. Gearbox optimization includes gear modification and shell reinforcement, and the improvement of vehicle interior noise is shown in Fig. 13, red is the result before optimization, blue is the result after optimization. Vehicle path optimization is as follows, first the hardness of the rear mount rubber is reduced (the vibration isolation of the mount is improved), second the body longitudinal beam is strengthened (the mount hole’s Stiffness is improved) as followed Fig. 14, third the front subframe increases the bushing (improves the noise transfer function) as shown in Fig. 15. After the virtual analysis of the gearbox and the vehicle path optimization, the order noise amplitude of the vehicle interior can be reduced maximally by 5–7 dB.

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As shown in Fig. 16, the red is the result before optimization, and the blue is the result after optimization, and the improvement is remarkable.

Fig. 13. Improvement effect of interior order noise after gearbox optimization

Fig. 14. Schematic diagram of reinforcement of body longitudinal beam

Fig. 15. Schematic diagram of adding bushing to the front subframe

5.2 Verification of Virtual Optimization About Vehicle Interior Gear Whining Noise The optimization proposed in 4.1 (gear modification, reduction of the hardness of the mount rubber, reinforcement of the body beam, and addition of the bushing to the front subframe) is made into real prototype. And the effect of the combined optimization is tested in the real vehicle. After the test verification, it is found that the vehicle interior noise problem within the speed range has improved significantly. As shown in Fig. 17, the pink color is the original result before optimization, and the black color is the optimized result. After optimization the gear order noise reduction amplitude of 1500–2000

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Fig. 16. The improvement effect of interior order noise after gearbox and vehicle path virtual optimization

rpm can reach 2–8 dB totally, the test trend of the results is basically consistent with virtual analysis, which furtherly proves the accuracy and reliability of the virtual analysis method.

Fig. 17. Real vehicle verification result

6 Conclusion 1. The time-domain dynamic model of gear order and related theories are introduced, and it is clarified from the mechanism about the rigid-flexible model of the powertrain gear order described in this paper.And under the action of the external load such as the engine torque and the meshing excitation force of the gear itself, a response is generated at the mounting side through the transmission path such as the bearing. At the same time, the basic method and related parameters of the powertrain dynamic model under the vehicle operating conditions described in the paper are expounded. 2. The method and theory of how the excitation source passes through the multiple transmission paths of the vehicle to superimpose the sound pressure of the vehicle interior are explained in detail, then the modeling method and related steps of the time-domain transmission path in this paper are introduced, so that readers can understand Path transfer mechanism for prediction of vehicle interior gear whining. 3. Applying the above methods and theories, this paper builds the powertrain multibody model and the digital transfer path model under the vehicle operating conditions, and loads the powertrain excitation into the transfer path model to predict the

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vehicle interior noise. Simultaneously, the accuracy calibration between simulation and test are carried out. Firstly,the simulation results of the order vibration characteristics and the trend of the resonance band of the powertrain mount active side under the vehicle working conditions are consistent with the test, the frequency error of the resonance band does not exceed 9%, and the order vibration amplitude error does not exceed 15%,Secondly,the amplitude error of the transfer path analysis is not more than 8%, Thirdly,the frequency error of the interior whining noise predicted by simulation and fitting is not more than 6%,the amplitude error is not more than 12% at the most speed range. 4. By applying the simulation prediction model of vehicle-level to analyze the scheme sensitivity of each subsystem, we propose the gear modification and shell reinforcement, the reduction of the rear mount rubber hardness, the reinforcement of the body longitudinal beam, and the addition of bushings to the front subframe, etc. It is found that the gear whining noise in the vehicle interior can be reduced by about 5–7 dB with the virtual optimization analysis, and the improvement effect is remarkable. 5. The above-mentioned optimization by virtual analysis is made into actual prototypes. After verifying the combined optimization scheme in a real vehicle, it is found that not only the effect is consistent with the simulation fitting prediction trend, but also the improvement effect is obvious, which effectively solves the problem of loud gear whining in the vehicle interior.The accuracy of this method is also proved. After applying this method, virtual simulation prediction of vehicle-level gear whining noise can be carried out for the concept or data design stage of the same platform or the same series of vehicles in the process of vehicle development, and the detect problems can be found in advance, thereby we can make relevant plans. So human and material resources for post-trial test can be greatly reduced. At the same time, it can effectively avoid the phenomenon that some optimization can not be applied due to project cycle problems. Simultaneously it provides an effective idea and method for solving problems in related fields in the same industry.

References 1. Li, Y., Ding, K., He, G., Lin, H.: Vibration mechanisms of spur gear pair in healthy and fault states. Mech. Syst. Signal Process. 81(5), 183–201 (2016) 2. He, G., Ding, K.: Study on Modulation Mechanism and Sparse Separation Method of Vibration Response of Compound Gear Transmission System 3. Akerblom, M.: Gearbox Noise, Correlation with Transmission Error and Influence of Gearing Preload. Ph.D.thesis, Department of Machine Design, Royal Institute of Technology, RITAMMK 2008:19. ISSN 1400–1179 (2008) 4. Kato, M., Inoue, K., Shibata, K. et al.: Evaluation of sound power radiated by a gearbox. In: Proceedings of the International Gearing, pp. 69–74 (1994) 5. Zhong, Z., Zhang, B., Zhang, A., Wang, K.: Simulation Study on the Noise Reduction of Transmission Based on Multi-body Dynamics (2017) 6. Velex, P., Ajmi, M.: On the modelling of excitations in geared systems by transmission errors. J. Sound Vib. 290(2), 882–909 (2006)

Research on the Influence of Drive Shafts Angles on Vehicle Lateral Swing Feng Deng1,2 , Yueyun Zuo1(B) , Xicheng Wang1 , Shangbao Fei1 , and Junqing Gu1 1 Dongfeng Motor Corporation Technical Center, Wuhan, China

[email protected] 2 Wuhan University of Technology, Wuhan, China

Abstract. The angles between the drive shafts and the power output shaft (hereinafter called the angles of the drive shafts) will generate the axial force. Excessive axial force will cause the vehicle to swing laterally. A vehicle, which appears lateral swing when accelerating at wide open throttle in third gear, is found that the frequency of the third-order vibration of the drive shafts is in good agreement with the frequency of lateral swing. The test result of the coordinate measuring machine (CMM) shows that the value of the angles of the drive shafts is larger than the design value. After reducing the angles of the drive shafts by adding weight, the lateral swing of the vehicle (hereinafter called lateral swing) is significantly weakened. In order to exclude the influence of the weight on the test results and considering the space constraints of engine room, the length of suspension spring is changed to reduce the angles of the drive shafts, and both subjective evaluation and test results show that the lateral swing is significantly weakened. Therefore, that the lateral swing is strongly related to the angles of the drive shafts is verified. This article could provide reference for the analysis of the lateral swing. Keywords: The angles of the drive shafts · Axial force · Lateral swing · Frequency

1 Introduction The drive system of a front-wheel drive vehicle usually consists of three parts: a drive shaft, a fixed constant velocity joint and an axially slidable constant velocity joint. Threepin shaft constant velocity joint (hereinafter called universal joint) is widely used in the modern automobile industry. While a vehicle is driving, due to the difference of road conditions and the number of passengers, there are usually angles between the drive shafts and the PTO, which causes the universal joint to generate a periodic third-order axial force [1, 2]. The value of axial force varies with the angles of the drive shafts. When the axial force exceeds a certain range, the vehicle may swing laterally and the abnormal noises may also occur [3, 4]. There are many articles on the study of axial force of the universal joint. Kei [5] established a differential equation that takes into account the influence of friction of each component of the drive shaft and could accurately estimate the internal force and torque of the drive shaft. Lim [6] proposed a multi-body dynamic © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 311–317, 2023. https://doi.org/10.1007/978-981-99-1365-7_23

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equation which could directly express the correlation between the axial force of the drive shaft and the lateral swing. Serveto [7] established a measurement model of the axial force of the drive shaft, by which the experimental results are in good agreement with the calculation results. Lee [8] used an apparatus to measure constant velocity joint force of different articulation angles and lubrication conditions for researching the friction, wear, and contact characteristics. Sa [1] found that the plunging force and the axial force of a constant-velocity joint are not the same treads as the joint angle, but depend on the type of a joint. The above articles are mainly theoretical research and bench tests, while the situation of the drive shafts on a vehicle is more complicated. Thus, researching the drive shaft on a vehicle is more helpful for technology accumulation and vehicle development of the original equipment manufacturer (OEM). This paper introduces the lateral swing when a vehicle accelerates at wide open throttle in third gear. The frequency spectrum analysis shows that the third-order vibration of the drive shafts is in good agreement with the frequency of lateral swing. The value of the angles of the drive shafts measured by the CMM exceeds the design value. It is suspected that the large angles of the drive shafts causes the vehicle to swing laterally. This speculation is verified by a temporary countermeasure (CM) which is increasing the vehicle’s weight to reduce the angles of the drive shafts. Both subjective evaluation and objective test show that the lateral swing is significantly weakened. To exclude the effect of the increased weight on damping lateral swing, the length of suspension spring is appropriately changed, and both subjective evaluation and objective test show that the lateral swing becomes better than orginal condition, but worser than the condition of increasing the vehicle weight. Thus, It is verified that the large angles of the drive shafts could cause lateral swing.

2 The Mechanism of the Axial Force of the Universal Joint Universal joint is composed of three-column groove shell, three-pin frame, needle roller, pin shaft, ball ring, drive shaft and other components. The three-column groove transmits the torque to the three-pin frame through the ball rings.The motion model is shown in Fig. 1.

Fig. 1. Coordinate system

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The three-column shell coordinate system is x 1 -y1 -z1 and is a fixed coordinate system. The coordinate system of the three-pin frame is x 2 -y2 -z2 . The input angle of the threecolumn shell is ψ. The output angle of the three-pin shaft is θ. When a ball ring is running in the three-column groove, it is subject to normal force, friction force and radial force of the chute and the three-pin frame. Moreover, there are both relative sliding and relative rolling, which could lead to rolling friction and sliding friction. Sliding friction force f s , along the axis of the pin, makes the ball ring slide axially. The rolling friction force f g , perpendicular to the axis direction of the pin, makes the ball roll around the axis of the pin. The combined force of f s and f g is along the axis of the chute [9]. The force of a single ball ring suffers during the movement in the chute is shown in Fig. 2.

Fig. 2. The force of a ball ring suffers

The friction force of a ball ring suffers on the chute can be expressed as following fh1 = ur Q1 cosax sign(sinψ)

(1)

  fg1 = ug Q1 cosγy sign cosγy sinψ

(2)

where f h1 is the sliding friction force on the axis of the chute, ur is the sliding friction coefficient, Q1 is the normal force, ax is the angle between x 2 and x 1 , cosax is the conversion formula of the sliding friction force which is projected to the axis direction of the chute, sign is the symbolic function, f g1 is the rolling friction force on the axis of the chute, ug is the rolling friction coefficient, γ y is the angle between y2 and y1 , cosγ y is the conversion formula of rolling friction force which is projected to the axis direction of the chute. The combined force of f h1 and f g1 can be expressed as following   f1 = fh1 + fg1 = ur Q1 cosax sign(sinψ) + ug Q1 cosγy sign cosγy sinψ (3) The angles between the three ball rings in the triple ball joint are 120°. Therefore, the combined force in the axial direction of the chute received by the other two ball rings can be expressed as following   f2 = ur Q2 cosax sign(sin(ψ + 2π/3)) + ug Q2 cosγy sign cosγy sin(ψ + 2π/3) (4)

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  f3 = ur Q3 cosax sign(sin(ψ − 2π/3)) + ug Q3 cosγy sign cosγy sin(ψ − 2π/3)

(5)

After summing the axial forces, it can be found that one rotation of the universal joint, the axial forces periodically change three times, and the change period is 2π/3, which is consistent with the distribution of the ball rings. During the rotation of the universal joint, the direction of the axial forces will also change, which is the root cause of axial movement of the drive shaft. In severe cases, lateral swing will occur.

3 Basic Information During the acceleration test of a vehicle at wide open throttle in third gear, the passengers feel obvious lateral swing of the floor and steering wheel when the engine rotates at 3000~5000 rpm. Test results show that the 0.4-order vibration of the steering wheel in the Y-direction is very obvious, as shown in Fig. 3. The gearbox of this vehicle is 7-speed dual clutch transmission, and the third gear ratio is 7.544. Through calculation, it is found that the 0.4-order vibration is consistent with the change period of the axial force of the drive shafts. The CMM is used to test the angles of the drive shafts, and it is found that the angle (under design state) between the left drive shaft and the power output shaft is 4.7° (hereinafter called angle of the left drive shaft) and the angle (under design state) between the right drive shaft and the power output shaft is 6.1° (hereinafter called angle of the right drive shaft), both of them exceed the design value, which is 4°. Article [8] points out that the larger angles of the drive shafts could lead to greater axial force. It is suspected that the larger axial force of the drive shaft causes the lateral swing.

Fig. 3. Steering wheel vibration in the Y-direction

4 Temporary CM Verification The angles of the drive shafts are determined by the vehicle attitude, which is composed of the tire load radius, the shape of wheel eyebrow and suspension attitude [10, 11]. After the concept of a vehicle is done, both the tire load radius and the shape of wheel eyebrow

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are fixed, the attitude of a vehicle can only be changed by appropriately changing the suspension attitude. The preload of the spring has a great influence on the suspension attitude, and it is relatively easy to change, the formula can be expressed as following δH =δFs · λs /Ks

(6)

where δH is the attitude variation, δF s is the spring preload variation, λs is the spring lever ratio, and K s is the suspension stiffness. Changing the spring lever ratio λs and the suspension stiffness K s involves the sample trail production and the chassis re-adjustment, both of which are heavy workload. Compare to them, it is much easier to change the preload of the spring. A simpler method is to change the vehicle load by adding or decreasing weight. Since the value of the angles of the drive shafts exceeds the design’s, the vehicle’s attitude can be lowered by appropriately increasing weight, which could also reducing the angles of the drive shafts. In order to reduce the influence of experiment error on the change of the axial force, the changed value of the angles of the drive shafts should be as large as possible. Calculation shows that the angle of the left drive shaft reduces to 3.6°, the angle of the right drive shaft reduces to 4.5° by adding 90 kg, which is the fully loaded state of this vehicle. Compared to original condition, the variations of both angles of the drive shafts are beyond 1°.In this state, a wide open throttle acceleration test in third gear is carried out. The result of the steering wheel vibration in the Y-direction is shown in Fig. 4. The 0.4-order vibration of the steering wheel in the Y-direction is shown in Fig. 5, the red line represents the result of original state of the vehicle, the green line represents the result of the state of adding 90 kg to the vehicle.

Fig. 4. Vibration in the Y-direction by adding weight

Fig. 5. The 0.4-order vibration in the Y-direction

It can be found that the vibration of the steering wheel in the Y-direction is significantly reduced at the 0.4-order, and the reduction is about 14 dB when engine rotates at 3000~5000 rpm. Subjective evaluation also finds that the lateral swing is significantly weakened. Considering that the increase weight of the vehicle not only reduces the angles of the drive shafts, but also may suppress the lateral swing. Moreover, the final CM cannot be implemented by increasing the weight of a vehicle to suppress the lateral swing, it is

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necessary to find out other CMs. The research shows that the free length of spring has a great influence on the attitude of a vehicle, and the relationship between them is almost linear, the ratio of them is close to 1:1 [11]. Naturally, reducing the free length of spring to reduce the angles of the drive shafts is taken into consideration. Considering that the layout of cabin and vehicle body is restricted and avoiding the influence of experiment error at the same time, the length of spring reduces by 20mm for experiment verification, and the installation point of the spring remains unchanged. Subjective evaluation shows that the attitude of the vehicle changed little before and after the spring change.

5 Valid CM Verification After the changed spring is loaded, the angles of the drive shafts are measured (under design state), and it is found that the angle of the left drive shaft is 3.4°, and the angle of the right drive shaft is 4.8°. Compared with the original state, the variations of the angles of the drive shafts are more than 1°. Wide open throttle acceleration experiment in third gear is carried out, the vibration of the steering wheel in the Y-direction is shown in Fig. 6. The comparison of the 0.4-order vibration in the Y-direction of the steering wheel of different vehicle states is shown in Fig. 7, the red and green lines are the same means as above, and the blue line represents the result of the state of reducing the length of spring by 20 mm..

Fig. 6. Vibration in the Y-direction after spring change

Fig. 7. The 0.4-order vibration in the Y-direction

Above figures show that the 0.4-order vibration of the steering wheel in the Ydirection is significantly weakened compared to the original state after the length of spring change, which is basically consistent with the subjective evaluation. When engine rotates at 3000~5000 rpm, the 0.4-order vibration of the steering wheel in Y is improved by about 9 dB compared to the original state, but deteriorates by about 5 dB compared to the state of adding 90 kg. As can be seen that the angles of the drive shafts after changing spring length are the same as the angles of the drive shafts after adding 90 kg, therefore, that heavier vehicle is beneficial for suppressing lateral swing is proved.

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6 Conclusion The angles of the drive shafts have a significant impact on the ride comfort of a vehicle. Large angles may cause lateral swing, and affect passengers comfort. This article takes the lateral swing of a vehicle at wide open throttle acceleration in third gear as an example, the reason why the drive shafts could cause lateral swing has been researched. Then, the effect of reducing the angles of the drive shafts on reducing lateral swing is verified by adding weight. Consider the infeasibility of introducing this CM to vehicles and base on relevant theories, a new CM of appropriately changing the length of the spring to reduce the angles of the drive shafts is proposed and the effectiveness of it is verified by experiments. This paper could provide reference for the analysis of vehicle lateral swing.

References 1. Sa, J.S., Kang, T.W., Kim, C.M.: Experimental study of the characteristics of idle vibrations that result from axial forces and the spider positions of constant velocity joints. Int. J. Automot. Technol. 11(3), 355–361 (2010) 2. Lee, C.H., Polycarpou, A.A.: Experimental investigation of tripod constant velocity (CV) joint friction. In: SAE 2006 World Congress & Exhibition (2006) 3. Jo, G.H, Kim, S.H., Kim, D.W., et al.: Estimation of generated axial force considering rollingsliding friction in tripod type constant velocity joint. Tribol. Trans. 61(5), 889–900 (2018) 4. Mariot, J.P., K’nevez, J.Y., Barbedette, B.: Tripod and ball joint automotive transmission kinetostatic model including friction. Multibody Syst. Dyn. 11(2), 127–145 (2004) 5. Kimata, K., Nagatani, H., Imoto, M.: Analysis of ball-type constant-velocity joints based on dynamics. JSME Int. J. Ser. C 47(2), 736–745 (2004) 6. Lim, Y.H., Song, M.E., Lee, W.H., et al.: Multibody dynamics analysis of the driveshaft coupling of the ball and tripod types of constant velocity joints. Multibody Syst. Dyn. 22(2), 145–162 (2009) 7. Serveto, S., Mariot, J.P., Diaby, M.: Modelling and measuring the axial force generated by tripod joint of automotive drive-shaft. Multibody Syst. Dyn. 19(3), 209–226 (2008) 8. Lee, C.H., Polycarpou, A.A.: Development of an apparatus to investigate friction characteristics of constant-velocity joints. Tribol. Trans. 48(4), 505–514 (2005) 9. Lee, C.H.: Development of a semi-empirical friction model in automotive driveshaft joints. Int. J. Automot. Technol. 9(3), 317–322 (2008) 10. Chakraborty, I., Tsiotras, P., Lu, J.: Vehicle posture control through aggressive maneuvering for mitigation of T-bone collisions. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference, pp. 3264–3269. IEEE (2011) 11. Zhao, R., Xie, W., Wong, P.K., et al.: Adaptive vehicle posture and height synchronization control of active air suspension systems with multiple uncertainties. Nonlinear Dyn. 99(3), 2109–2127 (2020)

Fine Simulation and Bench Test Development of Single Composite Leaf Spring Tao Wang(B) , Jiaxing Sun, Xuewu Zhu, Kaixuan Tong, Chao Han, Xingping Wang, Long Cheng, and Xiaoming Guo China FAW Group Co., Ltd., Product Development Center State Key Laboratory of Technology on Automobile NVH & Safety Control, Changchun, China [email protected]

Abstract. Fine simulation and bench tests were carried out based on the longitudinal and variable thickness composite leaf spring. The test results show that the deviation between the stiffness simulation and the bench test is less than 1.5%, and 500000 times of single composite leaf spring bench test, the vehicle four-channel bench test, and the vehicle load-bearing system endurance test have been passed. The simulation and bench test specifications are established to provide technical support for similar products to pass the tests at one time and shorten the product development cycle. Keywords: Composite Leaf Spring · Simulation · Stiffness · Bench test

1 Introduction With the increasingly stringent vehicle emission standards and the urgent demand to continuously extend the cruising range for new energy vehicles, the lightweight of key assemblies such as vehicle bodies and chassis has become the goal of engineers. Compared with traditional metal materials, in addition to the long fatigue life, high specific strength and specific modulus, great shock absorption and corrosion resistance, and good designability, fiber-reinforced resin matrix composites demonstrate excellent performance on lightweight. As such, the auto parts made of fiber-reinforced resin matrix composites can help save energy, reduce emission, and extend cruising range. Nowadays, the auto structure made of composite materials has gradually expanded from interior and exterior trim components to key chassis components that may affect the driving safety of automobiles, such as drive shafts, pedals, and leaf springs. Among them, the leaf spring is a widely utilized elastic element in the suspension system and may undertake complex stress conditions. The leaf spring made of composite material can contribute to two times greater fatigue durability life with half structure mass under the same stiffness condition compared with the metal spring. The leaf spring structure design frequently employs the equal stress design approach to create a variable thickness spring shape. Therefore, the layer design, simulation, and verification of the composite leaf spring are much more complicated than the traditional spring. Recently, more efforts have been devoted to composite leaf spring finite element © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 318–331, 2023. https://doi.org/10.1007/978-981-99-1365-7_24

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analysis (FEA) and bench tests. Great advancements have been achieved regarding the simulations with solid elements and durability bench tests in the vertical direction [4– 10]. Yet, there is less research on the simulations with plate-shell elements and durability tests considering all vertical, lateral, and longitudinal directions for the composite leaf spring. In this paper, the leaf spring we are focused on is longitudinally placed, with variable thickness, and made of glass fiber reinforced resin matrix composite. The detailed layer angle, length, and thickness of the composite leaf spring are constructed in Hypermesh. The accuracy of the simulated stiffness and strength of the leaf spring is validated by the stiffness bench tests. In addition, based on the measured wheel center load spectrum with the whole vehicle, the regulations of the vertical, longitudinal, and lateral durability bench tests for the composite leaf spring are developed according to the equivalent principle of pseudo-damage of the load spectrum. According to the established model and bench test specifications, the formed composite leaf spring passes the durability bench test directly and satisfies the design requirements of the decay stiffness and permanent deformation. Further, the strength and durability of the formed composite leaf are verified through the whole vehicle four-channel road simulation bench test and durability bench test. The established composite leaf spring FEA model and bench test specifications are effective in supporting the development of the glass fiber reinforced resin matrix composite leaf spring.

2 Maufacturing Technology and Design of the Composite Leaf Spring 2.1 Manufacturing Technology of the Composite Leaf Spring In the early stage of the composite leaf spring research, the simple hand lay-up process is usually adopted, which results in low stability in fabricating the leaf spring. Processes such as vacuum assist, filament winding, pultrusion, molding, Resin Transfer Molding (RTM), and the combined processes are then gradually applied to the fabrications. In general, the composite material leaf spring created by the molding process has become mature in terms of the curing process and engineering practice and has also been verified by a large number of experiments. The composite leaf spring structure investigated in the paper is constructed by the molding process. 2.2 Design of the Composite Leaf Spring The main parameters of the composite leaf spring are shown in Table 1. The assembly model shown in Fig. 1 is a single-leaf spring with constant width and variable thickness designed based on the iso-stress requirement. The material employed is E glass fiber with epoxy resin matrix composite. As shown in Fig. 1, the leaf spring is mounted to the bracket through the metal lifting ears at both ends. The metal lifting ears are fixed to the leaf spring through the bolts. The nylon and metal double-layer vibration damping pad is installed in the middle straight section of the leaf spring. In addition, U-bolts are utilized to connect the straight section of the leaf spring to the drive axle.

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No.

Parameter

Value

1

Action length (mm)

1498

2

Free arc height (mm)

147 ± 5

3

width (mm)

75

4

middle part thickness (mm)

42

5

Thickness at the ends (mm)

22

6

Vertical stiffness (N/mm)

140 ± 2

7

Fatigue cycles

> 5 × 105

8

Vertical stiffness decay

< 2%

9

Vertical permanent deformation (mm)

< 0.5

10

Vertical maximum load (KN)

26.6

11

Lateral maximum load (KN)

14.2

12

Longitudinal maximum load (KN)

14.8

Fig. 1. Schematic of the composite leaf spring assembly

3 Modeling and Simulationd of the Composite Leaf Spring 3.1 Refinement Modeling of the Composite Leaf Spring The vertical composite leaf spring with variable thickness is laminated with 0° unidirectional glass fiber reinforced composite materials. The glass fibers are laid along the length direction of the leaf springs to optimize the mechanical performance. The specific mechanical properties of the spring are shown in Table 2. The layer information is described in Table 3. The X direction in Table 2 is the length direction of the leaf

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spring defined as the 1 direction in the vehicle coordinate system. Correspondingly, the Y and Z directions are associated with the 2 and 3 directions of the vehicle coordinate system respectively. The positive directions for the 1, 2, and 3 directions are defined as the backward, right, and upward directions accordingly. Table 2. Mechanical properties of the composite leaf spring in the FEA model No.

Parameter

Value

1

Elastic modulus in X direction /MPa

39000

2

Elastic modulus in Y direction /MPa

9500

3

Elastic modulus in Z direction /MPa

9500

4

Shear modulus in XY direction /MPa

3860

5

Shear modulus in YZ direction /MPa

3500

6

Shear modulus in ZX direction /MPa

3860

7

Poisson’s ratio in XY direction

0.32

8

Poisson’s ratio in YZ direction

0.32

9

Poisson’s ratio in ZX direction

0.47

10

Tensile Strength /MPa

1100

11

Compressive strength /MPa

820

Table 3. Lamination information of the composite leaf spring Ply name

Ply angle

Single layer thickness(mm)

Ply length(mm)

Ply1

0°

1.23

1480

Ply2

0°

1.23

1480

Ply3

0°

1.23

1480

Ply4

0°

1.23

1480

Ply5

0°

1.23

1480

Ply6

0°

1.23

1480

Ply7

0°

1.23

1031

Ply8

0°

1.23

942

Ply9

0°

1.23

830

Ply10

0°

1.23

710

…

…

…

…

To accurately assign the properties of the single layer, the geometry of the composite spring is divided into parts according to the layer length before meshing. The upper surface of the spring is assigned as the base plane. The S4R elements are utilized for

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mesh partitioning in Hypermesh. To ensure the accuracy of the lay-ups, the normal direction of the plate and shell elements needs to be consistent with the lay-up direction as shown in Fig. 3. The schematic of the composite spring after lay-up is demonstrated in Fig. 4 (Fig. 2).

Fig. 2. Schematic of the composite leaf spring before and after geometric division

Modeling and Simulation of the Composite Leaf

Fig. 3. Schematic of the normal direction of the plate-shell element

Fig. 4. Schematic of the composite leaf spring after lay-up

3.2 Simulations of the Composite Leaf Spring 3.2.1 Simulations of the Composite Leaf Spring Stiffness The 1, 2, 3, 4, and 6 degrees of freedom of the front-end lifting lug are constrained. Correspondingly, the constraints on 2, 3, 4, and 6 degrees of freedom are applied to the rear-end lifting lug. The degrees of freedom mentioned above are in the whole vehicle coordinate system. The load is then applied to the flat section of the composite leaf spring as shown in Fig. 5 for the stiffness and strength simulations.

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Fig. 5. Boundary conditions for the stiffness and strength simulations of the composite leaf spring

The vertical load of 1000 N is applied at the loading point in Fig. 5, the maximum deformation of the leaf spring is 7.108 mm, and the vertical stiffness simulation result of the composite leaf spring is 140.7 N/mm (Fig. 6).

Fig. 6. Deformation contour of the vertical stiffness simulation

3.2.2 Simulation of the Composite Leaf Spring Strength In this section the cases of vertical impact, extreme turning to the lateral direction, and driving in the longitudinal direction are defined as the static limiting working conditions for the composite leaf spring. The fatigue working conditions are mainly examined in the vertical direction. For the defined static limiting conditions, the Cai-Wu safety factor should be less than 1. Correspondingly, the Cai-Wu safety factor for the fatigue conditions should be less than 0.8. The simulation results of the working conditions are shown in Figs. 7–10. Based on the simulation results, the designed composite leaf spring satisfies the static limiting loading and the vertical fatigue requirements.

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Fig. 7. Contour of Cai-Wu safety factor under the vertical impact loading condition

Fig. 8. Contour of Cai-Wu safety factor under the limiting turning loading condition

Fig. 9. Contour of Cai-Wu safety factor under the limiting driving loading condition

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Fig. 10. Contour of Cai-Wu safety factor under the vertical fatigue loading condition

4 Experimental Validations 4.1

Stiffness Bench Tests of the Composite Leaf Spring

The bench test setup for the composite leaf spring vertical stiffness measurement is shown in Fig. 11. The actuator is installed above the double-layer anti-friction pad of the composite leaf spring to apply force to the spring. As shown in Fig. 12, the 1 and 5 degrees of freedom in the whole vehicle coordinate system are released to construct the boundary conditions of the bench test.

Fig. 11. Experimental test for the vertical stiffness of the composite leaf spring

Based on the experimental setup shown in Figs. 11 and 12, three vertical stiffness tests are carried out. The measured vertical stiffness is respectively 141.6 N/mm, 139.2 N/mm, and 139.6 N/mm. The maximum discrepancy between the simulated results and experimental measurements is 1.1%.

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Fig. 12. Local boundary conditions for the vertical stiffness measurement of the composite leaf spring

4.2 Development of the Durability Test Regulations for the Composite Leaf Spring 4.2.1 Pseudo-Damage Equivalent Based on Measured Load Spectrum The multi-body dynamics model of the vehicle is established first. Based on the measured load spectrum from the whole vehicle test, the wheel road excitation is obtained through iteration. Further, the load spectrum of the three forces (Fx, Fy, and Fz) and three moments (Mx, My, and Mz) at the fixed connection between the composite leaf spring and the axle housing is obtained by simulations (Fig. 13).

Fig. 13. Multi-body model of the leaf spring

In the following section, the development of the lateral durability bench test is taken as an example to explain the procedures proposed by the study. First, based on the equivalent principle of pseudo-damage, when the number of the load cycle reaches 500,000, the equivalent force amplitude Fyd = A at the fixed point between the leaf spring and the axle housing is determined. Figure 14 demonstrates the constant amplitude load Fyd obtained by equivalent pseudo-damage and the iteratively calculated level crossings for the lateral load spectrum Fy. Secondly, the time-domain load spectrum data of Mx and Fy channels are fitted quadratically. The slope K of the fitting curve in Fig. 15 represents the equivalent force arm of Mx. By assuming the distance from the fixed point of the composite leaf spring and the axle housing to the bottom surface of the leaf spring is H,

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the distance between the bench test actuator and the bottom surface of the leaf spring is L = H + K as in Fig. 16. The development procedures of the vertical and longitudinal durability test of composite leaf spring are similar to that of the lateral test except for the requirement of fully preloading in the vertical direction.

Fig. 14. The constant amplitude load Fyd at the fixed point of the leaf spring and the level crossings of the iterative lateral load spectrum Fy

Fig. 15. Quadratic fitting of the load spectrum of Mx and Fy at the fixed point of the leaf spring

4.2.2 Durability Bench Test of the Composite Leaf Springs According to the constant amplitude load and the equivalent force arm obtained from the simulations for the three-direction durability bench test, the experiment platforms for the bench tests are constructed as shown in Figs. 17, 18, and 19. The experimental results of the lateral, longitudinal, and vertical durability tests of the composite leaf spring indicate that there is no fracture and delamination in the composite leaf after the durability tests. In addition, the appearance of the composite leaf spring remains well. The vertical stiffness decay and permanent deformation also meet the design requirements.

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Fig. 16. Schematic of the loading position for the lateral durability bench test

Fig. 17. Experiment platform for the lateral durability test of composite leaf spring

Fig. 18. Experiment platform for the longitudinal durability test of composite leaf spring

4.3 Durability Test of the Vehicle Load-Bearing System 4.3.1 Four-Channel Road Simulation Bench Test Compared with the endurance test on the proving ground, the vehicle four-channel indoor road simulation bench test can efficiently determine weaknesses of the product, guide

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Fig. 19. Experiment platform for the vertical durability test of composite leaf spring

the direction of product optimization and improvement, and is less affected by seasonal factors. Based on the measured spectrum data of the wheel center acceleration and suspension displacement, the load spectrum is compressed and accelerated according to the equivalent principle of pseudo-damage. The driving signal for the wheel displacement in the test field is generated through simulation iteration. The vehicle’s four-channel bench tests have been carried out for 440 units and continued for 50 days, which is equivalent to 29,400 km at the test site and 450,000 km of user mileage. The schematic of the whole vehicle four-channel bench test is displayed in Fig. 20, and the local view for the composite leaf spring utilized in the experiments is shown in Fig. 21.

Fig. 20. Experimental platform of the four-channel bench test

Fig. 21. Local view of the composite leaf spring in experiments

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The results of the four-channel bench tests indicate that the composite leaf spring has not broken or delaminated, and the appearance remains good. The indoor road simulation test results require further evaluation by the durability test of the whole vehicle on the proving ground. 4.3.2 Durability Test of the Load-Bearing System on the Proving Ground According to the test specifications on the proving ground, the two prototype vehicles have been utilized to carry out the endurance test of the whole vehicle bearing system simultaneously for 4 months. The proving ground mileage and user equivalent mileage are consistent with the four-channel bench test. Figure 22 displays the vehicle loadbearing system on the proofing ground in experiments for the durability tests.

Fig. 22. Schematic of the vehicle load-bearing system for the durability tests

The two prototypes have passed the endurance test of the vehicle bearing system, which further validates the strength and durability of the composite leaf spring meet the design requirements.

5 Conclusion According to the information on the layer length, the geometry of the composite leaf spring is divided into pieces. Based on the defined base plane of the layers and the angle and thickness of each layer, the refined laminated leaf spring model is constructed along the normal direction of the base plane. Based on the refined composite leaf spring model, the vertical stiffness and the strength are determined through simulations for the static limiting working conditions. The deviation between the simulated and experimental measured vertical stiffness is less than 1.5%, which further validates the accuracy of the constructed simulation model. Based on the measured load spectrum of the vehicle, a multi-body dynamics model is established. According to the virtual iteration technology, the load spectrums of all threeforce directions and three-moment directions at the fixed point of the leaf spring and the axle housing are obtained. Then the equal-amplitude durable bench test in vertical, lateral, and longitudinal directions are developed based on the equivalent principle of pseudo-damage.

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The designed composite leaf spring products pass the vertical, lateral, and longitudinal constant-amplitude durable bench tests directly. The vertical stiffness decay and permanent deformation meet the design requirements. The properties of the designed composite leaf spring are further validated through the load-bearing system durability test. The simulation modeling procedures and bench test specifications for variablethickness composite leaf are proposed to provide technical support for the rapid development of similar products.

References 1. Yang, D., Zhu, H., Zhang, L.: Research progress of composite leaf spring. Acta Materiae Compositae Sinica | Acta Mater Compos Sin 10, 84–89 (2014) 2. Wu, H., Li, Z., Jiang, Q.: Development and characteristics of composites leaf spring. Chin. Plast. Ind. 40(8), 103–106 (2012) 3. Gao, R., Gao, Z., Li, H.: Study on the design of new composite plate. Heavy Truck (2), 8–10 (2017) 4. Shijia, W., Li Zaike, W., Hui.: Finite element analysis and performance test of composite leaf spring for automobile. Automobile Technol. 4, 55–57 (2012) 5. Deling, C., Wei, G., Yongjin, S.: FEA analysis and test of a composite leaf spring. ShangHai Auto 9, 51–54 (2016) 6. Ang, Y., Sun Ying, W., Xiaoming.: Development and verification of the composite leaf spring for a heavy tractor. Automot. Eng. 37(10), 1221–1225 (2015) 7. Guo, H., Zhou, X., Zhang, H.: Structural design and stress field analysis of composite plate spring. Acta Materiae Compositae Sinica | Acta Mater Compos Sin (4), 66–71 (1996) 8. Li, J., Zhou, Z., Liu, B.: Structure design and experimental study of single composite longitudinal leaf spring. Chin. Plast. Ind. 46(6), 148–151 9. Jancirani, J., Assarudeen, H.: A review on structural analysis and experimental investigation of fiber reinforced composite leaf spring. J. Reinf. Plast. Compos. 34(2), 95–100 (2015) 10. Subramanian, C., Senthilvelan, S.: Joint performance of the glass fiber reinforced polypropylene leaf spring. Compos. Struct. 93, 759–766 (2011)

Correlation Analysis of Subjective and Objective Test on Automobile Seat Pressure Distribution Xuegang Li(B) , Tao Li, Xiaolin Huang, Xiaosheng Ouyang, and Zhenyan Li Automotive Research and Design Center, Guangzhou Automobile Group Co. Ltd., Guangzhou 511434, China [email protected]

Abstract. Through the analytic hierarchy process, this paper finds out that the pressure distribution on the hip area, leg area and waist area of the seat has a great influence on the subjective evaluation. The effectiveness of subjective evaluation method is verified through the correlation analysis between pressure distribution and subjective evaluation with the adoption of binary linear regression and curvature regression methods, which offers the theoretical basis for seat comfort design. Keywords: seat · pressure distribution · subjective evaluation · correlation

1 Introduction With the popularization of automobiles in our country, people require not only safety, functionality, and reliability, but also more and more on the comfort of automobiles at the same time. Seat, as the main interface of human-computer interaction with direct contact with the driver and passengers, is the main factor affecting the riding comfort of the driver and passengers. Pressure distribution is one of the main methods used to objectively evaluate seat comfort, the study and application of which is significant to improve seat comfort, reduce the probability of driving fatigue or of occupational diseases like back pain.

2 Subjective and Objective Correlation Evaluation of Automobile Seat Pressure Distribution According to the closed-loop diagram of “human body-seat” in the figure below, driver perceives the seat comfort level through body sensory organs, the brain sends instructions to guide the human body to adjust the sitting posture to adapt to the curvature and softness of the seat, to achieve the highest degree of comfort. As the body adjusts the sitting posture, it will inevitably cause changes in the pressure distribution on the seat. Therefore, the correlation analysis between the subjective evaluation score and the objective pressure distribution measurement data can be used to predict the subjective and objective evaluation model, and then validate the effectiveness of the subjective evaluation method (Fig. 1). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 332–340, 2023. https://doi.org/10.1007/978-981-99-1365-7_25

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Fig. 1. “human body-seat” closed loop evaluation

2.1 Study of Seat Pressure Distribution Positions Seat comfort covers the hip area, leg area, waist area and other body parts. In order to choose the most sensitive body parts of users to improve the legitimacy of subjective evaluation scores on comfort, this paper introduces the American operation-research scientist Satie’s AHP, which combines quantitative analysis and qualitative analysis, relying on the experience to judge the relative importance of each measurement, and then to reasonably give weight of each decision criteria, which is used to sort out the order of the pros and cons of each scheme. 2.1.1 Establish a Hierarchical Structure Model The top level defines the purpose of decision and the problem to be solved. The middle level looks at the factors to be taken into consideration and the decision-making criteria, which is the body parts that are contingent to seat comfort. The bottom level shows the alternatives for decision. As shown below (Fig. 2).

Fig. 2. Structure model of seat comfort

2.1.2 Construct Judgement Matrix The method of constructing the judgment matrix in the AHP is the consistent matrix method, which is: all factors are not compared together, but compared with each other; relative scales are used to minimize the difficulty of comparing factors with different properties to improve the evaluation accuracy. The judgement matrix A is constructed by the evaluators as shown in Fig. 3.

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Fig. 3. Seat comfort judgement matrix

2.1.3 Single Hierarchical Sorting and Its Consistency Evaluation The eigenvector corresponding to the largest eigen root λmax of the judgment matrix is normalized (the sum of the elements in the vector is 1) and denoted as ω. The element of ω is the sorting weight of the relative importance of the same level element to a factor of the previous level. According to A-λmaxI = 0, the maximum eigen root λmax is calculated. The maximum eigen root of matrix A is λmax = 6.2. Define consistency index (CI): CI =

λmax − n 6.2 − 6 = = 0.04 n−1 6−1

(1)

In the equation, λmax is the maximum eigen root; n is the number of criterion layer factors. In order to measure CI, the random consistency ratio RI is introduced, and the consistency ratio CR is defined (Table 1): Table 1. Random consistency ratio RI

CR =

0.04 CI = = 0.032, RI 1.24

It is generally considered that when the consistency ratio CR < 0.1, the degree of inconsistency of A is considered to be within the allowable range, there is satisfactory consistency, and the consistency evaluation is passed. The normalized eigenvectors can be used as weight vectors, otherwise the judgment matrix A should be reconstructed. The maximum eigen root of matrix A is λmax = 6.2, the corresponding eigenvector is: ω = (1,0.463,0.149,0.241,0.864,0.137)T . After normalization: ω = (0.35,0.162,0.052,0.085,0.303,0.048)T .

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2.1.4 Conclusion of Seat Pressure Distribution Evaluation Positions Through the analysis above, it is concluded that the evaluators conduct a pairwise comparative evaluation of the seats at the concept level according to each factor at the decision-making level. Subjective comfort scores for each seat are calculated: Y = 35%C1 + 16.2%C2 + 5.2%C3 + 8.5% C4 + 30.3%C5 + 4.8%C. Users are more sensitive to the comfort of the hip area, leg area and waist area, therefore these areas should be focused on. 2.2 Measurement of Pressure Distribution of Seat Comfort 2.2.1 Define Pressure Distribution Evaluation Index According to the subjective evaluation conclusion, this paper selects the user’s sensitive areas as the main positions for pressure distribution evaluation, and the evaluation index is defined as the average pressure, peak pressure and asymmetry coefficient. 2.2.2 Data Collection of Pressure Distribution This paper uses the “Xsensor” pressure distribution sensor to measure 4–5 testers of different heights and weights on a variety of seats, as shown in Fig. 4. Before the test, mark the A1, A2 and A3 points on the seat back and seat cushion of the concept layer (such as the stitched seat junction point), and place the calibrated seat pressure distribution pad on the seat cushion and backrest, making sure that the pressure pad covers the seat completely, the surface is flat, and there are no wrinkles and it is symmetrical so as not to cause abnormal values, and the coordinates of the pressure sensors at points A1, A2 and A3 are recorded at the same time. Select testers from the personnel database to collect the pressure distribution of the seats in the concept layer in turn. The testers can adjust the slide rail and backrest angle of the seat according to their driving or riding habits to get to their most comfortable position. Record more than 20 frames of test data, and select the average value of 20 frames as the final test result, as shown in Table 2:

Fig. 4. Tester requirements

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Fig. 5. Measurement of pressure distribution

Table 2. Measurement result of seat pressure distribution test Test Info Hip data

Leg data

Waist data

Seat 1

…

Tester 1

Tester 2

Average pressure x1 (p/N/cm2 )

Tester3

Tester4

0.45

Asymmetry coefficient x2

0.15

0.5

0.47

0.45

0.03

0.14

Peak pressure x3 (p/N/cm2 )

0.04

0.94

1.09

0.98

0.97

Subjective evaluation y

8

7.5

8

8

Average pressure x1 (p/N/cm2 )

0.17

0.18

0.2

0.2

Asymmetry coefficient x2

0.14

0.05

0.15

0.06

Peak pressure x3 (p/N/cm2 )

0.35

0.33

0.38

0.4

Subjective evaluation y

8.5

8

8

8

Average pressure x1 (p/N/cm2 )

0.26

0.22

0.25

0.21

Asymmetry coefficient x2

0.03

0.04

0.09

0.14

Peak pressure x3 (p/N/cm2 )

0.4

0.39

0.43

0.34

Subjective evaluation y

8

7.5

8

7

2.3 Correlation Analysis of Subjective and Objective Evaluation of Seat Pressure Distribution Combined with the distribution of subjective scores, this paper mainly uses regression analysis to analyze the correlation between dependent variables and independent variables. The regression analysis method includes linear regression and curved regression. Linear regression is further divided into univariate linear regression and multivariate linear regression. Since the subjective evaluation of the seat is affected by a variety of factors, therefore it requires the usage of multiple regression analysis methods with more than two variables and curved regression.

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2.3.1 Correlation Analysis of Seat Hip Pressure Distribution and Subjective Evaluation To establish a regression model between the explained variable (the hip subjective score) and each explanatory variable (the hip pressure distribution), it is first necessary to determine whether there is a dependency between them. The method is to use the explained variable to perform a simple regression on each explanatory variable and determine the importance of the explanatory variable by the value of the coefficient of determination. The greater the coefficient of determination, the more importance of the explanatory variable. Through the “regression” function, the regression analysis results of the hip subjective score y and x1 , x3 , x2 are as follows (Table 3): Table 3. Regression statistics Multiple R

0.853009

R Square

0.727625

Adjusted R Square

0.713289

Standard Error

0.340159

Observed Value

21

Table 4. Regression model coefficients Coefficients Intercept X Variable 1

14.86076 −15.7815

Standard error

t Stat

1.085849

13.68584

2.215141

−7.12438

From the calculation results above, it can be seen that the determination coefficient of y and x1 is R2 = 0.73, the coefficients of the regression model are: intercept β0 = 14.86, slope β1 = −15.78, and the regression model is y’ = 14.86–15.78x1 . The determination coefficient of y and x2 and x3 can be calculated in the same way, and they are arranged as shown in Table 5. Table 5. Sorting of determination coefficient Sorting order

1

2

3

Explained variable and explanatory variable regression

y and x1

y and x3

y and x2

R2

0.73

0.71

0.18

Table 4 shows that the importance of explanatory variables is x1 , x3 , x2 . Based on the regression equation y’ = 14.86–15.78x1 with the largest determination coefficient,

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introduce explanatory variables x3 and x2 into the model by adopting the stepwise regression method. The results are shown in Table 6 below. Table 6. Stepwise Regression Model Regression Model

R2

t x1

x3

x2

y’ = 14.86–15.78x1

−7.12

/

/

0.7276

y’ = 13.43–9.17x1 -1.6x3

/

-2.55

/

0.7999

y’ = 13.71–9.74x1 -1.53x3 -0.94 x2

/

/

−0.55

0.8034

According to the data in Table 6: When introducing the explanatory variable x3 into the regression model y’ = 14.86–15.78x1 , the binary regression model is y’ = 13.43– 9.17x1 –1.6x3 , when the degrees of freedom n-k-1 = 18, and the significant level α = 0.05, the absolute value of x3 is greater than the critical value t0.05 = 2.10, passing the ttest, and the determination coefficient is increased from R2 = 0.73 to 0.80, indicating that the introduction of the explanatory variable x3 is effective. In the same way, introducing the explanatory variable x2 , the ternary regression model is y’ = 13.71–9.74x1 -1.53x3 0.94x2 , when the degrees of freedom n-k-1 = 17 and the significant level α = 0.05, the absolute value of x2 is less than the critical value t0.05 = 2.11, which fails the t-test, and the determination coefficient (R2 = 0.80) has not been significantly improved, so the explanatory variable x2 should be disregarded. From the investigation above, there is a negative correlation between the seat hip feeling and the pressure distribution. The binary regression model is y’ = 13.43–9.17x1 1.6x3 . The explanatory variables x1 and x3 passed the t-test, and the determination coefficient R2 = 0.80, which means that 80% of the subjective evaluation can be explained by the changes of the two factors, the average pressure and peak pressure of the hip, and 20% of the factors are random errors, indicating that the subjective and objective correlation is high. 2.3.2 Correlation Analysis Between Seat Leg Pressure Distribution and Subjective Evaluation In the same way, it can be concluded that there is a negative correlation between the seat leg feeling and the pressure distribution. The binary regression model is y’ = 11.08– 8.37x1 –4.07x3 . The explanatory variables x1 and x3 passed the t-test, and the determination coefficient R2 = 0.78, which means that 78% of the subjective evaluation can be explained by the variation of the two factors, the average pressure and peak pressure of the leg, and 22% of the factors are random errors.

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2.3.3 Correlation Analysis of Seat Lumbar Pressure Distribution and Subjective Evaluation Combined with the scatter plot distribution of subjective evaluation scores, it is obvious that the waist area is different from the hip and leg areas, and a curved regression needs to be used. As shown in the figure below, the mean pressure and peak pressure pass the t-test (Fig. 6).

Fig. 6. Quadratic regression for each explanatory variable

Through the analysis, there is a quadratic regression between the seat waist feeling and the peak pressure and average pressure distribution, indicating that with the increase of pressure, the subjective evaluation score will gradually increase as well, and when the pressure increases to a certain point, the subjective evaluation score will not increase anymore, but decrease instead (Table 7). Table 7. Curved regression model Explanatory Factor

Curved regression model

t

R2

x1

x3

x2

average pressure

y’ = −14.22 + 170.12x1 -330.67x1 ^2

−5.96

/

/

0.66

Peak pressure

y’ = −3.66 + 48.65x3 -52.22x3 ^2

/

−6.62

/

0.71

Asymmetry coefficient

y’ = 7.16–0.13 x216.45x2^2

/

/

−0.14

0.03

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3 Conclusion This paper studies the objective evaluation method of seat pressure distribution focusing on the characteristics of the Chinese population, and at the same time conducts a correlation analysis between the objective evaluation results and the subjective evaluation and draws the following conclusions. (1) The AHP is introduced to determine that the user is most sensitive to the pressure distribution of the hip area, leg area and waist areas of the seat, which guides the direction for improving the seat comfort. (2) Through the subjective and objective correlation analysis, the peak pressure and average pressure of the seat are well aligned with the subjective evaluation scores. At the same time, multiple linear regressions and quadratic curve regressions are used to analyse the correlation between the subjective and objective evaluations, and the fitting accuracy is above 66%, and the subjective and objective evaluation of seat pressure distribution is highly correlated.

References 1. 谭纯: 杨枫等.基于层次分析法(AHP) 及体征分布的人机性能主观评价方法. 2020中国汽 车工程学会年会论文集 2. 张志飞: 袁琼等.基于压力分布的汽车座椅舒适性研究. 汽车工程2014第11期第36卷 3. 张萍: 张怡等.基于压力分布实验的汽车座椅人机形态研究. 人类工效学2017年6月第23 卷3期 4. 谢家发. EXCEL中的数据分析工具在多元回归市场分析中的应用. 中国商界2010第210 期

Research on Spark Splash Control of Resistance Welding Based on Big Data Analysis Algorithm Xianshui Jia(B) , Dongwei Li, Yong Chen, Shibo Tao, Datong Sun, Rui Wang, and Xiaomin Huo Chongqing Changan Automobile Co., Ltd., Chongqing, China [email protected]

Abstract. Welding is a key process in automobile body manufacturing, and its quality has an important impact on the connection strength and paint quality of vehicle body. It is worth noting that long-distance spark splashing during welding will not only damage the body paint, but also pose a threat to the personal safety. It is found that welding parameters are the direct factors affecting spark posture and splash distance. Therefore, the selection and optimization of welding parameters is very important. Aiming at the control of spark splashing distance during welding, this paper selects the welding process parameters that have the greatest impact on spark posture and splash distance: welding current and welding time, and uses fully connected neural network, Bayesian regression and Gaussian mixture model to mine the implicit relationship between spark splashing distance and the selected welding process parameters, the optimal welding process parameters can be deduced through the hidden relationship, so as to improve the quality, efficiency and safety factor of body welding. Keywords: Resistance welding · Parameter optimization · Fully connected neural network · Gaussian mixture model

1 Introduction Resistance spot welding is one of the four major processes in automobile manufacturing, which is often used to realize the welding of body in white, armature commutator and wiper module [1]. The welding quality will directly affect the connectivity of weldments and product performance, and the welding process parameters are the direct factors affecting the welding quality [2]. The spark splash generated in the welding process will greatly affect the quality of the body paint in the subsequent process, and will threaten the personal safety of the staff. Therefore, the energy efficiency and production safety can be greatly improved by automatically estimating the appropriate welding process parameters to control the spark splashing distance. The current mainstream spot-welding process, whether manual or welding robots, will set the welding process parameters in advance and fix them to ensure that the welding posture and attributes are consistent during the welding process and improve the welding quality. However, through data analysis, it is found that there is no explicit relationship © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 341–350, 2023. https://doi.org/10.1007/978-981-99-1365-7_26

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between the spark splashing distance and the welding process parameters, and the optimal welding process parameters corresponding to the desired spark splashing distance cannot be obtained through simple estimation, thus reducing the production efficiency. With the advent of the era of big data and high computing power, the fields of statistical machine learning and artificial intelligence have developed rapidly, and many reasoning tasks that have been troubled for many years have been effectively solved [3]. Aiming at the difficulty of welding spark splashing in the process of automobile body welding, this paper uses statistical machine learning methods (fully connected neural network, Bayesian regression and Gaussian mixture model) to mine the implicit dependency between welding process parameters and spark splashing distance in combination with the welding process and the collected welding process parameters and spark splashing distance samples. Through the mining distribution relationship model, the spark splashing distance in the welding process can be accurately controlled, so as to ensure the production safety, reduce the production loss cost and ensure the welding quality of the vehicle body.

2 Scene Establishment and Data Acquisition In this paper, the spark splash data photographed at the selected stations (with customized cameras) in the welding workshop is transmitted to the industrial control computer at the field acquisition module through the welding production network. The industrial control computer at the splash data acquisition module is connected to the 5G network (the 5G network deployed in the early stage of the workshop) through the intelligent industrial gateway and transmitted to the splash data processing platform system (data server) for relevant data cleaning, governance and constraint conversion. After that, it is transmitted to the splash data visualization analysis platform system (application server) through the factory’s office network for data analysis and algorithm prediction model display. As shown in Fig. 1:

Fig. 1. Scene construction and data acquisition

For general machine vision, the background image is so complex that it is very difficult to extract splash features in complex welding industry. The radiation spectrum

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range of the splashed metal particles in the high-temperature hot melt state is very wide, usually in the range of 400 nm–20 um, covering the visible light band, near-infrared band and infrared band. By customizing the filter to realize the specific spectral gating and eliminate the complex background interference, the splash characteristic distribution can be quickly obtained. According to the set parameters of the optical imaging system, the size and motion trajectory curve of the splashing spark can be calculated. Then according to the spark division rules, the splashing characteristic data of the solder joint is uploaded and the data collection is completed.

3 Data Analysis and Preprocess 3.1 Data Analysis In this paper, random Gaussian perturbation is added to the collected samples of the welding process parameters (welding current, welding time) and spark splashing distance (fire distance) during welding robot operation to simulate the real welding data distribution to the greatest extent. The data distribution of spark splashing data and welding process parameters are shown in Fig. 2 and Fig. 3:

Fig. 2. Data distribution of spark splashing distance and welding time

The data distribution in the figures shows that there is no common (linear and Gaussian) relationship between the spark splashing distance and the welding process parameters, and there is no explicit correlation. In this paper, it is assumed that there is an unknown implicit expression relationship between the welding process parameters and the spark splashing distance. It is necessary to use the given data samples to initialize the distribution characteristics, and update the initialized data distribution according to the existing data. With the selected measurement standard, when the probability of the collected data appearing in the fitting data distribution is the largest, the distribution parameters and the best posterior distribution of the given data are depicted, and the generation mode of the real data is restored.

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Fig. 3. Data distribution of spark splashing distance and welding current

3.2 Data Pre-processing The sample data used in this paper contains the characteristics of the splashing distance, height, width and frequency of sparks. The rich features can fully represent the semantic information of things and help to improve the reasoning performance of the model. However, the dimensions of these features are inconsistent, which will mislead the machine learning model to pay too much attention to the features of a certain dimension, thus optimizing in the wrong direction, resulting in unbalanced utilization of feature resources and poor generalization of the model. Therefore, this paper performs MinMax normalization on the sample data, unifies the unit dimension and representation range of each feature, and makes the model learn in the correct optimization direction. Min-Max normalization is to perform linear transformation on the original data and map the values between [0, 1]. Suppose we normalize all samples X = {x1 , x2 , x3 , …, xn } of a certain class of features to obtain the transformed feature Y = {y1 , y2 , y3 , …, yn }: yi =

xi − min(X) max(X) − min(X)

4 Research on Prediction Algorithm of Process Parameters After data pre-processing, a fully connected neural network is going to be established, Bayesian regression algorithm and Gaussian mixture model will be applied to mine the relationship between spark splashing distance and welding process parameters. 4.1 Fully Connected Neural Network Fully connected neural network, also known as multi-layer perceptron (MLP), is a mathematical model simulating the structure and function of biological neural networks in

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statistical machine learning and cognitive science, which is used to estimate and approximate functions. MLP is a kind of feedforward neural network, mainly composed of input layer, hidden layer and output layer, and there can be multiple neurons in each hidden layer. MLP is inspired by the central nervous system, which is connected by a large number of artificial neurons for calculation. In most cases, the neural network can change the internal structure on the basis of external information, so it is an adaptive system. MLP completes the adaptive process by training and optimizing the given data. For the input data, MLP obtains the corresponding output according to the weight of the neuron, the full connection calculation method and the nonlinear activation. The output and the real value calculate the loss. The loss updates the weight in the neuron through the back propagation mechanism, and finally achieves the purpose of fitting the distribution of the input data [4]. In this paper, a four-layer MLP is designed to learn the relationship between the splashing distance and welding process parameters. The spark splashing distance, width, height, number are selected as input data, and the welding current and welding time are selected as output data. The number of neurons in each layer is [5, 32, 16, 2]. The network structure is shown in Fig. 4:

Fig. 4. A fully connected neural network structure for predicting welding process parameters

In this paper, the input layers of fully connected neural networks differ between training and inference stages. In training stage, the input features are spark splashing distance, width, height, number, but in inference stage, the remaining feature dimensions are fixed, and the input features only contain spark splashing distance. The exponential moving average method is used to update the values of the remaining feature dimensions to make them more suitable for the overall distribution of the original data and to reduce the impact on the performance of the inference stage. 4.2 Bayesian Regression Bayesian regression is a linear regression method proposed by the Bayesian School, its idea is to estimate the distribution of parameters directly, rather than the specific parameter values. There are two main steps in Bayesian regression: parameter estimation and prediction [5].

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The task of parameter estimation is to solve a posterior probability distribution p (W|X; Y), where W is the model parameter and X, Y is the data. It is generally assumed that the posterior probability distribution is a Gaussian distribution N ~ (μ, σ), Therefore, only the attribute parameters μ, σ of the distribution need to be estimated, the distribution of the parameter w can be obtained soon afterwards. According to Bayesian formula p(W|X; Y) =

p(Y, W|X) ∝ p(Y, W|X) p(Y|X)

p(Y, W|X) = p(Y|W; X) ∗ p(W|X) = p(Y|W; X) ∗ p(W) where, p (Y|W; X) is the likelihood of the model, and p (W) is the prior probability of the model parameters. According to the assumption that the training data is extremely independent and identically distributed, the specific form of the model likelihood can be derived. n n     p(yi |w; xi ) = N wT xi , σ2 p(Y|W; X) = i=1

i=1

Input training data, and obtain the parameter W to be estimated by calculating the analytical solution of p (Y|W; X). In the prediction stage, the test samples are input and the prediction results are obtained through model likelihood calculation. 4.3 Gaussian Mixture Model Gaussian model is a commonly used variable distribution model, which is widely used in the field of mathematical statistics. The joint probability density function of multidimensional Gaussian distribution is: 1 T −1 1 e− 2 (x−μ)  (x−μ) p(X, μ, ) = 2πd/2  1/2 where d is a variable dimension, μ is the mean vector,  is the covariance matrix. Through the joint probability density function of a single multi-dimensional Gaussian distribution, it can be observed that most of its data are concentrated in a small area, resulting in a single expression form of the model and cannot be used to fit more complex data distribution. Gaussian mixture model (GMM) is a combination of multiple Gaussian distributions with different weight coefficients. GMM =

n 

p(X, μi , i ) ∗ πi

i=1

where p (X, μi ,  i ) is the probability density function of the ith Gaussian distribution, πi is the weight of the ith Gaussian distribution. This combination can make the model more complex, produce more complex samples, and meet the fitting requirements of different data distributions. Theoretically, if there are enough GMM fused Gaussian models and the weights given by each model are set reasonably, then any distributed sample set can be fitted.

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5 Experiments and Result Analysis Based on the collected sample data of spark splash and welding parameters, the distribution of sample data is estimated and fitted using MLP, Bayesian regression and GMM introduced in Sect. 3, and the implicit relationship between spark splashing distance and welding parameters is discovered. This section analyses the prediction results of each model from two aspects: test environment and result analysis. 5.1 Test Environment According to the principle of qualitative analysis, all the above models are trained and tested on the same data and hardware conditions. There are 1105 data samples used in this paper, and the data division is shown in Table 1, Hardware configuration conditions are shown in Table 2, The third-party tool library used and version information are shown in Table 3: Table 1. Data set division Data set

Number

Training set

663

Verification set

331

Test set

111

Table 2. Hardware configuration conditions Hardware

Configuration

CPU

AMD Ryzen 5 5600G with Radeon Graphics 3.90 GHz

OS

Windows 11

RAM

8 GB

Table 3. Third party library and version configuration Module name

Version

Pytorch

1.11.0

Sklearn

1.0.2

Sympy

1.10.1

Numpy

1.21.5

MLP is built and trained based on the Pytorch deep learning framework. Since the features include the spark splashing distance, width, height and number, and the output

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is the welding current and welding cycle, the number of neurons in the input layer is set to 5, the number of neurons in the middle two hidden layers is set to 32 and 16, and the number of neurons in the output layer is set to 2. Batch normalization (BN) is placed between layers to accelerate network convergence and prevent overfitting. Since all the sample data are positive, the activation function adopts ReLU, which increases the nonlinear expression ability of the network while maintaining the actual physical significance. The number of iterations of network training is set to 1000, the learning rate is 1e-4, and the loss function is evaluated by mean square error. In addition, this paper uses MLP to fit the sample data after adding random Gaussian noise, and the other settings remain unchanged. The purpose of adding random Gaussian noise to the original data is to make the training data more suitable for the real production scene. Bayesian regression model is implemented based on Sklearn, and the characteristics of samples are set the same as those of MLP. Different from MLP, this paper trains a Bayesian regression prediction model for welding cycle and welding current, which is used to decouple the output, increase the speed of training and reasoning, and improve the prediction accuracy. The Gaussian mixture model is implemented based on Python. The number of basic Gaussian cells is set to 4 and the variable dimension is 2. EM algorithm is used for parameter estimation [6]. By introducing hidden variables, EM algorithm solves the nonconvex optimization problem that the sum of logarithms of the maximum likelihood function still has a summation term. The iteration stop condition of the EM algorithm is set as whether the amplitude of the change before and after the parameter update is lower than the threshold. If it is lower than the threshold, it is considered that the parameter has been estimated correctly and the iteration is stopped. Otherwise, the iteration is continued. 5.2 Result Analysis In this paper, the L1 norm of the predicted value and the real value of the model is used to evaluate the performance of the model. The calculation method is as follows: loss =

N 

|pred − gt|

i=1

where pred is the predicted value of the model, gt is the real value, and N is the number of test samples. The smaller the loss value, the smaller the difference between the predicted value and the real value of the model, and the stronger the prediction performance of the model; On the contrary, the worse the prediction performance of the model. The prediction results of different methods are shown in Table 4: Table 4 shows that GMM has the best prediction effect on welding cycle, while MLP trained with original data has the best prediction effect on welding current. Furthermore, the training loss curve of MLP on the original data and the parameter iteration loss curve of GMM are proved to be effective. As shown in Figs. 5 and 6:

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Table 4. Prediction results of different methods Methods

Loss of welding time prediction

Loss of welding current prediction

MLP (original data)

10764.58588

229.2446594

MLP (Data with Gaussian noise added)

9573.843369

278.0999603

Bayesian regression

7478.09402

413.022103

GMM

7049.12276

439.3549445

Fig. 5. The training loss curve of MLP on the original data

Fig. 6. The parameter iteration loss curve of GMM

6 Conclusions On the basis of the accurate acquisition of the spark splash characteristics, through the deep learning method of the welding process parameters, the fusion analysis of the welding quality big data in the welding workshop is realized, and the quality improvement strategy and process improvement strategy are given:

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The dependence between welding process parameters and spark splashing distance is analysed by visualization technology, which lays a foundation for the selection of models to fit the dependence. The prediction of resistance welding process parameters for controlling the spark splashing distance is realized. A variety of methods based on parameter self-optimization and self-estimation are tested, and the data characteristics and model prediction performance are improved. The most appropriate parameters are searched according to the selected evaluation criteria, so as to realize the accurate control of the spark splashing distance of resistance welding. According to the experimental results, MLP and GMM are respectively selected to accurately predict the resistance welding process parameters under the expected spark splashing distance, which can reduce the impact of body surface impurities caused by spark splash on the coating and improve the production safety of the workshop on the premise of ensuring the connection strength of the body.

References 1. Zhang, B., Hu, Y.: Research on self-optimization of automotive armature resistance welding parameters based on big data analysis. Mech. Electr. Eng. Technol. 48(08), 67–70 (2019) 2. Cheng, F., Li, H., Lian, J., Wang, M., Zhang, Y.: Influence of variation of welding parameters on spot welding quality. Autom. Technol. (04), 35–37 (2005) 3. Peng, J., Chen, B., Zhu, P.: Intelligent design of welding procedure parameters based on neural networks. Trans. China Weld. Inst. (01), 21–26 (1998) 4. Zhang, Y., Gou, J., Zhang, E., He, L.: Welding deformation prediction of SMAW based on improved genetic neural network. Hot Work. Technol. 44(01), 208–210 (2015). https://doi.org/ 10.14158/j.cnki.1001-3814.2015.01.064 5. Sun, Y., Jiang, J., Wang, C.: Bayesian estimation of current status data with generalized extreme value regression model. J. Guangxi Normal Univ. (Nat. Sci. Ed.) 40(01), 82–90 (2022). https:// doi.org/10.16088/j.issn.1001-6600.2021060907 6. Liang, S.: Gaussian mixture model parameter estimation based on EM algorithm. J. Qiannan Normal Coll. Natl. 40(04), 5–8 (2020). https://doi.org/10.3969/j.issn.1674-2389.2020.04.002

Research on the Technology of Accurate Measurement for the Thread Cone Angle of Synchronizer Used in Gearbox Zhi Hu(B) , Tianhua Dai, Nan Ding, and Yue Li China Society of Automotive Engineers, Beijing, China [email protected]

Abstract. This paper aims to study questions about the thread cone angle of the cone lock-pin synchronizer used in the gearbox of a vehicle model, such as unstable measurement accuracy and single quality control method. This thesis introduced the basic working method of the equipment for testing the thread cone angle developed according to the tangent trigonometric function. The experimental data reported that the limitation and unreasonableness in the design of its original measuring mechanism were the main reasons that the measurement accuracy was unstable. Therefore, we selected another angle measuring scheme, that is, to develop new test equipment using the method of calculating the radian and then rotating the angle, and optimize the structure of mechanical measuring head to improve the measurement accuracy and reliability of the synchronizer’s thread cone angle, finally realizing that the processing quality of synchronizer parts was under control. Keywords: Synchronizer · Synchronizer ring · Cone angle · Radian

1 Introduction In order to reduce the noise and oil consumption of the automobile gearbox, prolong the service life of the gears and drive system and implement the gearchange in a smooth and rapid way, most modern car owners equip their synchronizers with gearboxes. The synchronizers can achieve the quick synchronization between the sliding sleeve and the gear ring to be jointed, and prevent the two from engaging before synchronization to dissipate the impact of shifting, shorten the time of shifting and simplify the process of shifting, making the gearchange simple and easy. At present, the common synchronizers use constant pressure structure and inertia structure, etc., among which the inertia synchronizers cover cone lock-pin type and sliding type. A synchronizer of this type has the advantages of large synchronous diameter, small inertia and impact, short friction time, large length of engagement, not easy gear loss, long service life and low manufacturing costs, etc. [1], which is widely used in the gearboxes of intermediate cars. However, there lacks an uniform standard for the test method of an important parameter, the thread cone angle of cone lock-pin synchronizers, in this industry. Most scholars have conducted © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 351–362, 2023. https://doi.org/10.1007/978-981-99-1365-7_27

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precision evaluation through simulation, which may directly affect the quality control of the gearbox due to being short of high accuracy. Taking a certain type of cone lock-pin type synchronizer parts for example, this paper analyzed the limitations of the measuring equipment on the mathematic model selection of the synchronizer’s thread cone angle at an earlier stage, as well as the phenomenon that the design defects of the detection mechanism result in the instability of measurement accuracy. In addition, regarding the above issues, this thesis redesigned a reasonable mathematic model for the cone angle, and innovated and optimized the reliability of detection mechanism to improve measurement accuracy at a later stage. The adaptability and matching degree between the measurement model and the tested hardware directly concerned the accuracy and reliability of parts measurement at a later stage.

2 Brief Introduction of Cone Lock-Pin Type Synchronizers The automobile synchronizer in the gearbox is to smoothly realize the shock-free addition and subtraction of the gearbox’s gears when gear and speed change operations are carried out on the moving car. A company adopts cone lock-pin type synchronizers in BE type gearboxes. Synchronizers with this structure have the advantages of small number of parts, short axial dimensions, large average radius of friction cone, large friction torque and short synchronization time, etc. Cone lock-pin type synchronizers are composed of cone synchronizer rings, lock pins, sliding gear sleeves, snap rings and other parts, whose outline structure is shown in Fig. 1. (This paper makes introductions by taking 5-gear synchronizers for example).

Fig. 1. The Outline Structure of 5-gear Synchronizers

The basic working principle of the cone lock-pin type synchronizer is that when speed changes, the sliding gear sleeve moves with the locating pin of the synchronizer,

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so that the two cone disks are in contact with each other and create frictional force instantly while the cone synchronizer ring is pressed toward the internal conical surface of the synchronizer gear’s cone disk. After the two conical surfaces are in contact, the gear sleeve with gear shift function makes the lock pin chamfer of the synchronizer be in conflict with and not engage in the lockpin hole chamfer of the gear sleeve. Positive pressure between the two conical surfaces creates friction torque, and the tangential force creates ring toggling torque. Only when the friction torque is greater than the ring toggling torque in design, the locking part can be reliably given rein to. At the stage of synchronization, the friction torque increases with the decrease of the cone angle α. In order to enlarge the capacity of the synchronizer, the cone angle α should be as small as possible. However, its limit value is restricted by the self-locking condition of the cone angle. In order to avoid the self-locking of the cone angle, the value of α should meet α ≥ arc tan μ (μ represents the friction coefficient). Since the friction coefficient μ is recommended to use 0.10 in design calculation, the cone angle α can be valued within 6°–7.5° generally [2]. In order to shorten the synchronization time, the company determined the cone angle, the height of the fixed diameter, the roundness and the relevant dimensions of the synchronizer ring as the main test items [3] according to the characteristics of its own products, whose technical parameters are shown in Fig. 2.

Fig. 2. The Main Test Items of Synchronizer Rings

According to the process setting requirements, the angle value of the synchronizer ring’s thread cone angle is set as α = 6°45’ ± 10’, the height of the fixed diameter from the Ø86mm section on the synchronizer ring to its inner side as 1.9+0.3 +0.05 mm, and the roundness of the Ø86mm section on the synchronizer ring as 0. 05mm.

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3 Study of Measurement Schemes of the SYNChronizer’s Cone Angle at an Earlier Stage 3.1 Study of the Working Mode of Measuring Equipment According to the design principle and working mode, the cone lock-pin type synchronizer increases the friction torque to destroy the oil film on the internal conical surface of the synchronizer gear. The conical surface of the synchronizer ring needs threading, as well as an oil drain tray in the vertical direction of the thread. The size and number of the oil tray are determined according to the diameter of the conical surface on the synchronizer ring. However, there remain many difficulties in the measurement of the synchronizer’s thread cone angle. The synchronizer ring is a conical surface with a short radius, and there are several axial oil grooves in the circumference of the conical surface. At the same time, the conical surface brims with threads, that is, on the surface of the synchronizer ring presents an intermittent thread cone shape [4]. Therefore, the design of the special test tool of synchronizer rings have an extremely high requirement for the accuracy of continuous measurement and measuring position. At an earlier stage, the company adopted the designing scheme of the parent company in France, and used the measuring system designed according to the trigonometric function model, and measured the value of the thread cone angle as α = 6° 45 ± 10 . Moreover, it developed a dynamic online measuring equipment, and the working mode is shown in (Fig. 3).

Fig. 3. The Dynamic Measuring Equipment of Synchronizers at an Earlier Stage

This dynamic measuring equipment is highly flexible, which can meet the measurement requirements of 1–2 gear synchronizers, 3–4 gear synchronizers, and 5 gear synchronizers at the same time (It has the same thread cone angle). Its hardware mainly includes human-machine interface display terminal, stepping motor for measuring roundness, main splined shaft that drives parts to rotate, a variety of interchangeable

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and adjustable spacer parallels, positioning table, lift cylinder, mechanical pressure head for measuring height, and other components. The equipment uses five displacement sensors to measure and calculate three process dimensions such as the thread cone angle, valuing radial height and roundness of synchronizers. The specific measuring process includes: Manually place the tested parts on the positioning table → manually press the “start” button → the lift cylinder drives the main splined shaft downward to position and hold down the parts → manually place the mechanical height pressure head in place, and the height sensor statically samples the fixed diameter height data from the Ø86mm section on the synchronizer to the inner side → the cone angle measuring unit drives the displacement sensor to sample the thread cone angle → the rotation of the stepping motor is realized by the main splined shaft driving the parts to rotate, and the roundness sensor samples the roundness → the human-machine interface analyzes, calculates and evaluates the data sampled by the five displacement sensors, and displays the cone angle, height, roundness and other data → each component retreats to reset → manually take the parts away, and the measuring process ends. However, in order to improve the measurement accuracy, we generally measure a single part three times in three directions to prevent misjudgment results caused by sampling errors. After this dynamic measuring equipment is normally put into use for half a year, the thread cone angle of the same part often generates a poor repeatability (see Table 1), and its measuring error has exceeded 30% of the design tolerance, which fail to conform to the quality control standards, and extremely easily cause batch quality incidents. Table 1. 10 The Measuring Results of the Same Part No.

Measuring Results of the Same Part

Measuring Extreme Value

Measuring Range

1

6°40 45

Maximum Deviation 17 38”

2

6°42 47

Maximum Angle 6°42 48” Minimum Angle 6°25 40”

3

6°42 48

4

6°30 15

5

6°33 36

6

6°35 50

7

6°28 40

8

6°25 10

9

6°27 16

10

6°29 22

3.2 Reliability Analysis of Dynamic Measuring Equipment After careful research of calculation model and measuring equipment, as we found, since the selection of the calculation method limits the manufacturing and installation

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accuracy of the mechanical mechanism, the mechanical fatigue caused after the measuring equipment is put into operation for a period of time leads to the drastic decrease in the measurement accuracy, affecting the detection precision of the parts. The testing principle of its original measuring equipment is shown in Fig. 4. Because the measuring mechanism is designed according to the tangent principle of trigonometric function, its calculation method is shown in Formula (1): α = (M1 − M2) ÷ 2 × 4 × (1 + 0.0034)

(1)

where, α is the thread cone angle (unit: degree); M1 is the large end diameter of truncated circle on conical surface (unit: mm); M2 is the small end diameter of truncated circle on conical surface (unit: mm).

Fig. 4. The Measuring Principle of Cone Angle at an Earlier Stage

There are the following shortcomings in practical applications: 1. The front-end contacts of the original equipment’s four sets of cone hoop excircle measuring units (that is, the upper and lower contacts in Fig. 4, with two on the left and right sides) swing the mechanism through the rotating shaft, and keep the measuring plane close to the tested thread cone busbar. However, the design of the distance A between the measuring surface and the rotation center is unreasonable and too far (about 6 –7 mm), the measuring plane width of the measuring head is about 2.5 mm, and this structural theory has rotational dead points, resulting that the measuring surface can not be closely fit to the tested thread cone busbar. Moreover, that the reset reed configured for the original contact loses efficacy due to fatigue makes the measuring head float sagging. When measuring, it is more difficult to snap the measuring plane to the tested thread conical surface, directly affecting the stability of measurement; 2. Compared to the thickness of the synchronizer ring of about 6mm, the measurement mode of two sets of cone hoop measuring units (namely, upper contact and lower contact) set on each

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side of the original equipment is unreasonable. The equipment adopts two independent measuring heads with the measured width B of about 2.5 mm. On one hand, its structure is complex and unreliable; on the other, the axial sampling length of its measuring cone angle is roughly: the thickness of the tested conical surface 6mm-2 × 2.5 ÷ 2the chamfering at both ends is about 1 mm = 2.5 mm. The too short sampling length influences its evaluating precision; it eventually contributes to a relatively poor stability and accuracy of the thread cone angle of the synchronizer ring.

4 Measurement Improvement Scheme of the Thread Cone Angle of Synchronizer 4.1 Selection of Mathematical Calculation Model Through a detailed study of the angle algorithm variety in the mathematical model, we discovered that the calculating method of first measuring the radian and then rotating the angle and height had a higher reliability than that of directly measuring the angle and height, which could more easily realize the assembly accuracy in mechanical design. This is because the angle is formed by the ray rotating around its endpoint. When rotating, the points on the ray must form an arc, and different points will form different arc lengths. However, all of arcs correspond to the same central angle, and its mathematical principle is shown in Fig. 5.

Fig. 5. The Relationship between Arc and Angle

According to Fig. 5, the radius OA is r, and the radius OA is r AB/r = A’B /r = a fixed value. Suppose α = n°, the arc length AB = ι, So,

ι=n×

2π r 360◦

2π r ι =n× r 360◦

(2) (3)

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According to Formula 2, Formula 3 is deducted, from which it can be seen that the right side of the equation excludes the radius, explaining that the ratio of the arc length to the radius is not related to the radius, but only to the size of α. Conclusion: The radius of the circle can be used to measure the angle as unit. The ratio between the truncated circle radius of the large end face on the synchronizer ring and the corresponding arc length is equal to that between the truncated circle radius of the small end face on the synchronizer ring and the corresponding arc length, which means that their central angles are also the same. Therefore, the angle can be calculated according to the function relationship between the arc length and radius of each truncated circle in the axial direction of the synchronizer ring, namely, the calculation method of first calculating the arc and then converting it to the angle is easier than that of using tangent trigonometric function [5, 6]. Thus, the distance from the truncated circle diameter in the thickness direction of the synchronizer ring to the medial surface must be 1.90 mm (i.e. the valuing radial height H), whose calculation method is shown in Formula (4):       H =1.0 × 1.1 ÷ 2 × 3 × tan π × 13.5◦ ÷ 180 ÷ 2 × M3 + (1 − 1 ÷ 3)      ÷ 2 × tan π × 13.5◦ ÷ 180 ÷ 2 ) × M4))) (4) where, H is the valuing radial height (unit: mm); M3 is the fixed point height diameter of truncated circle at the large end of the synchronizer ring (unit: mm); M4 is the fixed point height diameter of truncated circle at the small end of the synchronizer ring (unit: mm); On the contrary, the valuing radial height H is also the diameter of the truncated circle at this position of the synchronizer ring, which is Ø86 mm. Taking this truncated circle as a reference point, we calculated the corresponding radian value of the cone angle 6°45´ of the synchronizer ring under set process standards according to the function relationship between the arc length and radius, whose calculation method is shown in Formula (5): α = arctan(1.0 × (0.16667 × (M3 − M4))) = 0.1178 rad

(5)

where, α is the thread cone angle of the synchronizer ring (unit: radian rad); M3 is the fixed point height diameter of truncated circle at the large end of the synchronizer ring (unit: mm); M4 is the fixed point height diameter of truncated circle at the small end of the synchronizer ring (unit: mm); that is, if the diameter of the large truncated circle corresponding to the fixed diameter height from the Ø86 mm section on the standard synchronizer ring to the inner side is 0.1178rad, the corresponding angle is 6°45´. If the corresponding angle of each truncated circle diameter 0.1178rad in the axial direction of the standard synchronizer ring is also 6°45´, the cone angle formed by the normal extended lines of the large and small end faces of the entire standard synchronizer ring is 6°45´. Moreover, the process design requires that the tolerance range of the cone angle be 6°45´ ± 10´ (i.e. the maximum value of the cone angle is 6°55´, and the minimum value is 6°35´), whose corresponding radian-to-angle relationship is shown in (Table 2). Therefore, we only need to compare and measure each truncated circle radian in the axial direction of the synchronizer parts and the standard radian, transform the corresponding deviation range into a concrete angle value, finally calculate the thread cone angle of the

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synchronizer parts through the synthetic operations of computer measuring software, and determine whether the thread cone angle is qualified [7]. Table 2. Conversion of Angle and Radian Process Range

Degree System

Radian System

6°55

6.917°

0.1207

6°54

6.9°

0.1204

6°53

6.883°

0.1201

…

…

…

6°46

6.767

0.1181

6°45

6.75°

0.1178

6°44

6.733°

0.1175

…

…

…

6°37

6.617°

0.1155

6°36

6.6°

0.1152

6°35

6.583°

0.1149

4.2 Introduction of Secondary Improvement of Measuring Equipment Detection Mechanism According to the newly selected algorithm, we made secondary improvement of the original measuring equipment’s detection mechanism, and the scheme is shown in Fig. 6.

Fig. 6. Improved Detection Mechanism

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The measuring process is as follows: First ensure that the locking handle is in the original position, and then manually place the parts on the positioning table, limit the parts through three rough locating points on the testing tool, and turn the locking handle to the prelocking position → press the inching cylinder to raise the fastening cylinder, and then turn the locking handle to the locking position → press the start button again, and drive the cylinder forward, so that the mechanical measuring head is in contact with the external thread conical surface of the tested parts, and the cone angle displacement sensor on the backend of the mechanical measuring head starts sampling → after completion of the cone angle sampling, the rotating electrical machine drives the test parts to rotate through belt, and the displacement sensor samples the roundness of the parts → the measuring system calculates and evaluates the sample data, and the relevant results are displayed on the human-machine interface in real time → after completion of measurement, the mechanical measuring head is automatically retracted, the prelocking cylinder is automatically retracted, the locking handle is pulled back to its original position, and the parts are removed. A measurement cycle ends [8]. 4.3 Analysis of the Characteristics of Newly Improved Measuring Scheme Moreover, compared with the measurement of the original detection mechanism, the improved detection mechanism based on the dynamic measuring equipment (see Fig. 7) has higher measurement reliability, including: 1) The original two independent mechanical measuring heads (upper contact and lower contact) are changed to one wide mechanical contact covering the entire flat of thread, which avoids the crowning of the thread conical surface, and is simple and reliable; 2) the center F of the measuring head’s rotating shaft gets close to the tested flat of thread as much as possible (the distance is shortest at this time) to ensure the measuring plane is reliably attached to the tested busbar; 3) the sampling length is increased. Theoretically, the evaluating precision and stability of the angle parameters grow by 5.3 ÷ (6–2 × 2.5 ÷ 2–1) = 212%, compared with the precision of the original equipment.

Fig. 7. Improved Measuring Head Structure

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After transformation, the measurement stability and reliability are also guaranteed. Upon calculation, the same part is measured 10 times, and the change in the cone angle is only about 10% of the tolerance, meeting the acceptance standard for measuring accuracy. The measured data of the same part is shown in Table 3. Table 3. Ten Measuring Results of the Same Part No.

Measuring Results of the Same Part

Measuring Extreme Value

Maximum Deviation

1

6°44 25

2

Maximum Value 6°44 45” Minimum Value 6°43 35”

0.01944°

6°43 55

3

6°44 15

4

6°44 45

5

6°44 25

6

6°43 55

7

6°43 35

8

6°44 25

9

6°43 45

10

6°43 35

5 Conclusion 1) First, this paper introduces the main functions, and the appearance and structure characteristics of cone lock-pin type synchronizers, and determines the process test standard and test method of three main measurement items such as the cone angle, the fixed diameter height and the roundness of the synchronizer ring by taking a certain type of cone lock-pin type synchronizers for example. 2) Second, this thesis analyzes that the less rigorous mathematic model design and unreasonable measuring equipment structure of three measurement items such as the cone angle, the fixed diameter height and the roundness of the synchronizer ring contribute to the main reasons for the poor repeatability of the synchronizer parts’ measurement accuracy at an earlier stage; 3) Then, the author brings forward solutions, reselected an appropriate mathematical calculation model, as well as the calculating method of first measuring the radian and then rotating the angle and height, which has a higher reliability than the calculating method of directly measuring the angle and height, and conducts secondary optimization design of the main measuring mechanisms inside the equipment to improve the measurement accuracy and reliability of the cone angle and other measurement items of the synchronizer ring.

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4) Finally, through the research on the measurement accuracy of the thread cone angle of the gearbox’s synchronizer, this paper analyzes the correlation between the measurement calculation method and the measurement mechanism. The proper measurement calculation method is the scientific basis for ensuring the effectiveness of the measurement mechanism, and the reliable measurement mechanism is a technical method for measuring the accuracy of the measurement calculation method. Therefore, scientific measurement calculation method and measurement mechanism are key performances of measuring the advanced and effective quality control of part products.

References 1. Wang, Z.: Design refinement of lock-pin type synchronizers. China South. Agric. Mach. (4), 31–32 (2012) 2. Cao, X.: Design calculation of double-cone synchronizers of gearboxes. Mech. Manag. Dev. (6), 11–12 (2004) 3. Gong, C., Xia, W.: Improvement of synchronizer production line lean project. Automobile (8), 52–55 (2016) 4. Liu, X.: Design calculation of lock-pin type synchronizers of gearboxes. Mech. Eng. Autom. (10), 160–161 (2006) 5. Xu, Z.: Teaching design of radian system concept based on mathematical history. Hunan Educ. (Ed. C) (12), 41–42 (2008) 6. Zhang, H.: Application of conversion between radian system and angle system in angle measurement. J. Lishui Teach. Coll. (10), 71–73 (1996) 7. Zhu, X., Zhou, Q.: External conical surface diameter measurement research and special gauge design. Hydromechatron.Eng. (22), 176–178 (2015) 8. Lu, J., Zeng, L., Zhao, J.: Research on the design of online comprehensive test tools for the cone gears of gearboxes. Autom. Appl. Technol. (22), 150–151 (2019)

The Wear Analysis and Sharpening Method of Involute Spline Broach Sheng Chang(B) , Kun Zhu, Yucheng Xu, and Riming Men China National Heavy Duty Truck Group Datong Gear Co., Ltd., Datong, China [email protected]

Abstract. Involute spline broach is one of the main tools for processing internal splines in our company, which has the characteristics of complex structure and high cost. This paper focuses on the analysis of the reasons for broach wear, introducing the control method of the wear loss, and giving a detailed discussion of how to sharpen the broach correctly. Finally, a “Five-Step Operation Method” for cleaning and finishing the blade is proposed, which greatly improves the defects of the broach after sharpening as well as the processing quality of the workpiece, so as to prolongs the service life of the broach. Keywords: broach · wear · sharpening · grinding wheel · angle · operation method

1 Introduction Involute spline broach is a kind of spline broach whose tooth is type of involute. This broach is usually for processing the involute spline keyway, or involute inner spline. It is widely used in the automotive and aerospace industries. As we all know, any kind of machining tool after use will produce a different degree of wear, broach is no exception. It is necessary to resharpen the tool after machining a certain number of parts, otherwise the tool may be broken due to great cutting force. In our company, the major products are heavy duty transmission. The broach is mainly used to process the inner spline of synchronizer parts. The cost of broach is generally higher due to its large diameter (up to 204 mm), complex structure, long production cycle. Good resharpening method can not only improve the quality of processed products, prolong service life of broach, but also reduce the production cost of the enterprise (Fig. 1).

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 363–371, 2023. https://doi.org/10.1007/978-981-99-1365-7_28

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Fig. 1. Broach on site and its structure

2 Broach Wearing Analysis and Control 2.1 Broach Wearing Analysis During broaching process, abrasion may occur on the cutting edge, rake face, flank surface and tool nose due to the cutting force, strain hardening, mechanical friction and the extrusion and friction from tool teeth and machined surface. It is very possible that the tool teeth may be broken from the vibration, impact and the possible hard point on the workpiece. When the wear exceeds a certain limit or the tool broken, it is necessary to resharpen the broach. Broach wearing can be roughly divided into three stages. The first stage is of rapid wearing stage. All the broach, the new or re-sharpened one, their teeth wear quickly at the very beginning of broaching process. The reason is that the extrusion and friction between the grinding wheel and the broach generate the “virtual edge” on the flank surface of the tool. The “virtual edge” is as shown in Fig. 2. At the initial stage, the “virtual edge” wears off quickly, proceeding to normal wearing stage after trial broaching of 30–50 parts. The second stage is of normal wearing stage. After the previous trial broaching, the “virtual edge” and non-wear-resistant surface layer have been worn off, the teeth wear tends to be uniform, so the wear rate is much slower than the first stage. In this stage. The wearing is generally 0.1–0.15 mm. The third stage is rapid wearing stage. At this stage, the teeth are getting blunt, the cutting force is increasing, and the temperature at teeth is getting higher. At the same time, the rake angle becomes smaller, the chip gets jammed, the residual chip between the teeth is to scratch the workpiece surface, or even break the tool. So we need to resharpen the tool before it approaches to wearing limit.

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Fig. 2. Broach edge illustration

2.2 Broach Wearing Control Overing wearing may generate negative flank angle on the broach teeth as shown in Fig. 3. During broaching process, we should avoid overwearing as possible as we can for grinding off the negative flank angle may make the cutter size become smaller sooner. And the greater grinding amount may generate higher temperature on the tool to make it get annealed, reducing its strength and service life. In order to prevent excessive wear from breaking the tool due to greater cutting force, it is necessary to control the wearing amount of the flank angle to be less than 0.2–0.3 mm [1] and also mandatory to resharpen the tool every 800 pieces.

Fig. 3. Worn broach teeth

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3 Broach Resharpening See Fig. 4.

Fig. 4. Broach resharpening machine

3.1 Selection of Grinding Wheel Size and Mounting Angle

Fig. 5. Method A

Fig. 6. Method B

The grinding wheel size and mounting angle may directly affect the teeth space shape, rake angle, and surface roughness of the broach. The method A as shown in the Fig. 5 will increase the rake angle after resharpening. It will affect the chip escape direction and its curly radius, and more importantly, the increased rake angle tends to break the tool teeth and affect its service life. The method B as shown in the Fig. 6 is to reduce the rake angle after resharpening. It will affect the cutting sharpness and increase the cutting force. While escaping, the uncurled chip tends to break the tool teeth and affect the tool life.

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The commonly used grinding method is straight cone grinding method [2]. From the reference, the rake angle of the tool is related to broach diameter and grinding wheel diameter as shown in the Fig. 7.

Fig. 7. Broach resharpening illustration

D=

D1 sin(β − γ ) sin γ

where: D—Max Diameter of the Grinding Wheel(mm); D1—Outer Diameter of the Broach 1st Teeth(mm); β—Mounting Angle of the Grinding Wheel; γ —Rake Angle of the Broach. To facilitate the on site use, we have made the corresponding table of broach size, grinding wheel diameter and mounting angle [3], as shown in Table 1. Table 1. Grinding Wheel Diameter and Its Corresponding Mounting Angle D1 (mm) 120

130

D (mm)

Rake Angle of the Broach γ ° Mounting Angle of the Grinding Wheel β° 8

10

12

15

18

20

22

100

14.5

18.5

22

27.5

33

37

40.5

110

15.5

19

23

29

34.5

38.5

42

120

16

20

24

30

36

40

44

130

16.5

21

25

31.5

37.5

42

140

17.5

21.5

26

33

39.5

43.5

100

14

18

21.5

26.5

32

35.5

110

15

18.5

22

28

33.5

37

39 40.5 (continued)

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D1 (mm)

140

150

160

D (mm)

Rake Angle of the Broach γ ° Mounting Angle of the Grinding Wheel β° 8

10

12

15

18

20

22

120

15.5

19.5

23

29

34.5

38.5

42

130

16

20

24

30

36

40

44

140

16.5

21

25

31.5

37.5

41.5

100

14

17

20.5

25.5

31

34

37.5

110

14.5

18

21.5

26.5

32

35.5

39

120

15

18.5

22

28

33.5

37

40.5

130

15.5

19.5

23

29

34.5

38.5

42

140

16

20

24

30

36

40

44

100

13.5

16.5

20

25

30

33.5

36.5

110

14

17.5

21

26

31

34.5

38

120

14.5

18

22

27

32.5

36

39.5

130

15

18.5

22.5

28

33.5

37.5

41

140

15.5

19.5

23

29

34.5

38.5

42

100

13

15.5

19.5

24.5

29.5

32.5

35.5

110

13.5

17

20.5

25.5

30.5

33.5

37

120

14

17.5

21

26.5

31.5

34

38.5

130

14.5

18

22

27

32.5

36

40

140

15

19

22.5

28

33.5

37.5

41

According to broach specifications, we can quickly get the diameter and mounting angle of grinding wheel by looking up the table. Before resharpening, the grinding wheel needs to be refined, and try to ensure that the mounting angle of the grinding wheel is consistent with that of the table. During installation, the angle ruler can be used for measurement, as shown in Fig. 8.

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Fig. 8. Use angle ruler to confirm the mounting angle

3.2 Broach Resharpening Broach resharpening sequence is generally from the tail of the calibration teeth to the head of the cutter teeth one by one. In this way, we can get a consistent rake angle, and make the flank angle of the calibration teeth a little bit smaller than that of the cutting teeth. It is good to improve the wear resistance of the calibration teeth, ensure the tool processing accuracy while extending the broach service life. While resharpening, we should control the feed and retract route of the grinding wheel and the cycle time for each tooth based on the quality of broach rake face. At the same time, we should pay attention to keep the tooth lift evenly increased. In order to prolong the service life of broach, the first grind for calibration teeth should be for the first row, the second should be for the first and second row, so on in turn. Thus, we can reduce the grinding times of calibration teeth, delay the speed of calibration teeth diameter getting smaller, and prolong the service life of broach. 3.3 Cleaning and Finishing After grinding, if the broach is not cleaned well, the residual abrasive particles between the teeth are to stick together with the chip or just remain on the rake face while broaching, which will scratch the major, minor and tooth flank of the spline. If that very tooth with particles on can’t be located, the broach needs to be resharpened again, resulting in multiple ineffective grinding, leading to rapid reduction of cutter teeth diameter, reducing service life of the tool. There is often a “virtual edge” on the teeth after grinding. If not deal with well, the accuracy of the parts will be affected and more likely the broach teeth may be slightly broken, resulting in the tool cannot reach the normal usage effect and life. Based on our experience and on site situation, we have summarized the “five-step operation method” of cleaning and finishing the broach after grinding, namely: first brush, second blow, third dip oil, forth brush and fifth blow (as shown in Fig. 9). First step - brush: use a brush to clean the grinding wheel particles on the tool, mainly to clean out the particles stuck between the tool teeth;

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Second step - blow: blow the dust particles of the grinding wheel with the air gun, and pay attention to cleaning them row by row in order to prevent missing any row; Third step - dip oil: prepare a copper wire brush, dip the brush into the oil bucket and get a small amount of lubricating oil, this step is to provide lubrication for the next step, to prevent copper wire brush from scratching broach teeth; Fourth step - brush: brush the broach along the flank face with the copper wire brush. 5–8 rows of teeth can be brushed at each cycle to improve efficiency. This step is to polish away the “virtual edge”; Fifth step - blow: use the air gun to blow off the chips from step four. This procedure needs to be done row by row.

Fig. 9. Five step to clean and finish the broach teeth

The five-step operation ensures the cleanness of the broach after resharpening and avoid damage to the tool caused by the grinding wheel particles stuck on the rake face. By finishing the “virtual edge”, there is no longer need to resharpen the broach repeatedly, that is resharpening the tool after trial broaching of several parts.

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4 Conclusion In order to prolong the service life of the tool, we designed a ring nylon brush (as shown in Fig. 10). Installed under the machine flange, the brush is to clean the chip twice during feeding and retracting. After careful checking, the operator uses copper wire brush to clean the residual chips again to prevent the broach teeth from breaking.

Fig. 10. Ring nylon brush to clean the chip

From the one-year follow up and statistical analysis, we found that the cost for broaching process decreased by 32% compared with the same period of last year and the repetitive resharpening is totally avoided. It is expected that the savings for tools cost is to be more than 60,000 RMB per year. At the same time, we reduce the scrap rate by 39% and improve the production efficiency. The labor intensity of operators has also been alleviated. As a shop-floor operator, we only conduct analysis for the wearing and resharpening of the broach used on site. By improving the resharpening process, we achieved the anticipated goal in broaching effect. It is our objective to share our experience with other peers and extend its application to hob and shaver to get good fruits also.

References 1. Ye, W.: Method to prolong round broach service life. Precis. Manuf. Autom. 03, 65–66 (2006) 2. Shi, F.: Measures to improve round broach resharpening precision. Metal Mach. (Before HT) 23, 41–42 (2010) 3. Wang, X.: Broach Design Manual (2013)

Study on the Quality of Body Assembly Based on Tolerance Analysis by Linearized Method Qiang Wu(B) , Jiulei Cao, and Zijie Dou Chongqing Chang’an Automobile Company Limited, Chongqing, China [email protected]

Abstract. The tolerance analysis by linearized method could be used to confirm the tolerances of the different parts of body assembly, and to ensure the accuracy of the gap and flush of body-in-white assembly. Key parts and dimensions could be found by this way. The process capacity target, PPK, and I-MR control charts are considered as the way to control the quality of parts. The forward direction quality control model for body assembly is constructed by those two methods above, and that means the targets of the dimensions of body assembly could be reflected on dimensions of parts, and should be quantifyed. Based on the forward direction model, precision of parts is supervised, and this means it provides a fact to rectify parts. And this is reverse direction quality control model. Considering Time series of the parts and assembly, Bayesian theory was used to modify the datas, and also Correlation Coefficient could be calculated, in order to know the exact Contributions parts was made to the quality of body assembly. Keyword: Body assembly · Tolerance analysis · Process capacity · Linearized method · Bayesian

1 Introductions Under the background that enterprises in the whole automobile industry has entered into fierce competition with each other, to refine the visual quality of the vehicles has become an important competitive means, in order to enhance product competitiveness. Higher and higher requirements are put forward for the assembly quality of covering parts of the vehicles. However, the precision of the gap and flushness of BIW (Body in white) assembly directly affects the visual quality of the vehicle. Therefor, as an important index of BIW assembly quality, DTS (Dimensional Tolerance Specifications) is very important [1]. For body assembly quality problems, there are ways to analyze them one by one from the perspective of engineering technology and then take measures to solve them, but it is inefficient and requires a large amount of energy from technical personnel by that method above. And it also unable to meet the needs of quality controlling of current multi-model mass production. Therefore, it is very necessary to find a set of efficient and reliable methods to analyze the quality problems of body assembly, and to make it under control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 372–383, 2023. https://doi.org/10.1007/978-981-99-1365-7_29

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In order to control the dimensions of the body assembly, the targets of dimension tolerance should be determined firstly. Then each influencing factors of assembly dimension chains should be broken down. The quality controlling of the body assembly could be realized by controlling each of those bottom influencing factors. The dimension fluctuations of body assembly are often more derived from the dimension fluctuations of components in the system. While the dimensions of the components generally obbey a normal distribution. It is relatively simple and suitable to process datas that obey the normal distribution by linearized method [2]. The mathematical expressions could be established by linearized method, which connected the targets of dimension tolerance of the body assembly with dimension tolerance of the key influencing factors of the system. And in turn, the quality of the body assembly could be predicted and controlled by controlling the dimensional fluctuations of the influencing factors.

2 Establishment of Quality Control Model of Body Assembly 2.1 Tolerance Analysis by Linearized Method Tolerance is the allowable geometric and positional error defined for a part or assembly. Deviation is the difference between the actual dimension of the component or assembly relative to the theoretical dimension. At present, the analysis methods of tolerance include linearized method, statistical method and test method. However, in practical applications, the dimensions of the components are often subject to a normal distribution. So it is simpler and more efficient for data processing by linearized method. For the transfer of the three-dimensional dimension chain, each dimension can be regarded as a space vector from the starting point (x, y, z) of the dimension to the end point of the dimension, as shown in the example of Fig. 1.

Fig. 1. Schematic diagram of 3D dimension chain

For a dimension chain consisting of n spatial dimensions, the cumulative tolerance of the entire three-dimensional dimension chain could be calculated by → − → − → → − =− Z T1 + T2 + T3 + . . . + Tn

(1)

Linearization analysis methods include extreme value method and root mean square method. The expressions of the tolerance design function of the dimension chain by the

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extreme value method and the root mean square method are respectively as follows in Eq. (2) and Eq. (3) [2]: the extreme value method: n Z= (|α|Ti ) (2) i=1

the root mean square method: Z=

n i=1

  1 2 α2i T2i

(3)

where Z is the body assembly target tolerance, n represents a total of n influencing factors, Ti is the tolerance for factor i. α is the sensitivity of factor i to assembly tolerance, and it is the partial derivative of the assembly function with respect to the i-th factor in the (x, y, z) coordinate system along the Ti direction, and it reflects a change speed of the assembly tolerance Z with the change of the tolerance Ti of the factor i. The α could be expressed as Eq. (4) [3]. Z(ti + t) − Z(ti − t) ∂Z (4) = ∂ti 2t For the assembly dimension chain composed of a rigid body such as BIW, when allocating tolerance to factors in system, the change of Z could be approximately considered as the  acting on Z.  component of the dimension vector T The relationship between the tolerance designed Ti of each factor and the target tolerance Z of the assembly could be obtained through the formulas (1) to (4) above. When the assembly tolerance target Z has been clarified, the tolerance of the i-th part in the system should be designed as Ti. α=

2.2 Component Size Distribution and Control For components that obey a normal distribution, their size distribution satisfies the following Eq. (5). 1 2 2 e−(x−u) /2σ (5) F(x) = √ 2 2π σ where σ is the standard deviation of size distribution of the part. And u is the mean value of the size distribution of parts. The mathematical expression is as shown in Eq. (6). 1 n Xi (6) u= i=1 n For mass production, the dimensional accuracy of a single part couldnot effectively reflect the quality status of a batch of parts, and SPC must be used to control fluctuations [4, 5]. Here, the process capability index PPK is used as an indicator to measure the daily accuracy level of batch parts, as shown in Eq. (7). ppki = min[(USLi − ui )/3σi , (ui − LSLi )/3σi ]

(7)

In the Eq. (7), min[] means to take the minimum of the two, USL is the upper limit of tolerance, LSL is the lower limit of tolerance, and it satisfies Eq. (8). USLi − LSLi = T i

(8)

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2.3 Quality Control Model for Body Assembly Quality control of BIW assembly should include the accuracy control of components by positive design, as well as the analysis and rectification of the impact on accuracy of the BIW assembly when the components has actual deviation fluctuations. Corresponding to the arbitrary measuring point G of the body assembly, it includes the dimensions of the body coordinates x, y, z in three directions, and the actual accuracy of the measuring point is expressed as G(x, y, z), as shown in Eq. (9). G(x, y, z) = G0 (x, y, z) + U(x, y, z)

(9)

where G0 is the theoretical coordinate value of the measuring point G, U(x, y, z) is the actual deviation of the measuring point G. By the root mean square method, according to Eq. (3), we have Eq. (10). G(x, y, z) = G0 (x, y, z) +

n i=1

  1 2 α2i(x,y,z) t2i(x,y,z)

(10)

where ti(x,yz) is the actual size deviation of the i-th part in the (x, y, z) direction, αi(x,y,z) is the sensitivity of the i-th part to the deviation of the assembly in the (x, y, z) direction. It can be seen from this that to control the dimensional accuracy of G(x, y, z), we should pay attention to the deviation ti(x,yz) of part i. The size sensitivity of the part should be determined by αi(x,y,z) . Often according to the body assembly requirements, There is no need to control the three directions (x, y, z) of the assembly size at the same time, but only need to control one or two directions. At this time, the corresponding parts can be precisely controlled in the direction that contributes to the dimension chain. For example, when only x-dimension calculation is required for a certain measuring point, its possible relational expressions could be shown as Eq. (11).  1 2 G(x) = G0 (x) + [α21(x) t21(x) + α22(x) t22(x) + . . .

(11)

Through the ideas above, the quality control model for body assembly is obtained as shown in Fig. 2. It includes two analysis process models, The forward direction quality control model for body assembly and the reverse direction quality control model. The forward analysis of the body assembly quality is to confirm the sensitivity of each factor through the tolerance analysis by linearized method and the dimension chain analysis method after the completion of the body target tolerance setting, and then to find the key influencing factors of the parts. Finally, establish component control specifications to ensure results of assembly quality are well [6]. For the reverse precision analysis, considering that the daily sampling inspection data of parts and body assembly assembly inspection sampling data are not necessarily oneto-one correspondence during mass production, it is appropriate to use Bayesian theory to correct the measurement data [7–9]. By verifying the correlation of the accuracy results between the assembly and the components, it can be confirmed that whether the rectification direction is correct, and finally verify the rectification effect of the accuracy of the entire assembly system.

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Fig. 2. Forward and reverse quality control model of body assembly

The mean corrected by Bayesian Theory is as follows in Eq. (12). u =

mxσ −2 + nuS −2 mσ −2 + nS −2

(12)

In the formula above, u is the post-validation mean, u and σ are the pre-validation mean and standard deviation, respectively. The x and S are the new sample mean and standard deviation, respectively. The m and n are the previous sample size and the new sample size, respectively. In actual engineering practice, when production conditions such as equipment, personnel, materials, processing technology, and processing environment have not changed, the standard deviation of parts in a short-term time series generally does not change. In order to simplify the calculation, Eq. (12) can be simplified as Eq. (13). u =

mx + nu m+n

(13)

The formula for calculating the correlation coefficient r is as follows is Eq. (14) [10]. n i=1 (Xi − X)(Yi − Y) r =  (14) 1 n 2 n 2 2 − X) · − Y) (X (Y i i i=1 i=1 where X and Y are the mean values of the two groups, respectively. The criteria for judging Correlation Coefficient are shown in Table 1.

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Table 1. Correlation Coefficient Judgment Criteria Value Range

Conclusion

0.75 ≤ r ≤ 1 OR -1 ≤ r ≤ 0.75

Strong Correlation

0.3 ≤ r < 0.75 OR 0.75 < r ≤ 0.3

General Correlation

Others

Weak Correlation

3 The Example Analysis and Verification Based on the quality control model of body assembly above, the analysis and verification of the control of the DTS of the back door and the roof of a certain vehicle is carried out. It is known that the DTS of the back door and the roof of the vehicle is as follows. The flush that the back door relative to the roof is defined as: F = −11−1 mm And the flush direction is approximately the z direction, as shown in Fig. 3. According to the definition above, There are: Tolerance: T = ± 1 mm; Upper tolerance limit: USL = 0; Lower tolerance limit: LSL = −2 mm.

Fig. 3. Schematic diagram of the matching cross-section of the back door and the roof. 1. Back Door 2. The Flush of Back Door And Roof 3. Roof 4. Back Door Hinge

Through the analysis of the dimension chain, the three component assemblies ralated to the flush of the back door and the roof are the back door assembly, the body-in-white assembly, and the back door hinge. The dimension chain formed is shown in Fig. 4. The flush of the back door and roof is the component of the dimension t5 in the approximate coordinate z direction.

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Fig. 4. The matching size chain of the back door and the roof. t1 Roof to body hinge mounting surface dimensions. t2 Hinge surfaces between both ends dimensions. t3 Back door mounting hole position dimensions. t4 Back door hemming dimensions. t5 Back door to roof dimensions

According to Eq. (4), the sensitivity of each dimension along the approximate zdirection of the above-mentioned dimension chain is obtained, and the tolerance allocation is made according to the actual situation of the project. The results are shown in Table 2. Table 2. Tolerance Analysis Key Factor

dimension (i)

sensitivity (α)

Tolerance (Ti)/mm

Body

t1

0.9

±1.0

Hinge

t2

0.85

±0.3

Back Door

t3

0.85

±0.3

Back Door

t4

0.9

±0.5

By Eq. (3), the assembly tolerance T = 2.13 mm can be calculated. In fact, considering that the assembly tolerance T is optimized to within 2 mm, the tolerance requirements of the corresponding parts need to be improved, and the cost is too high. From the perspective of cost, the calculation result is considered acceptable, and the assembly tolerance T basically meet‘s the target requirement of ± 1.0 mm. Next, control specifications should be established for the 4 factors above. Through the statistics of the above four factors for a period of time, the corresponding I-MR control chart is established. And Fig. 5 is the control chart of the flushness between the back door and the roof. Figure 6 is the control chart of the back door hemming size t4. It should be pointed out that the upper and lower control limits are not the upper and lower limits of the tolerance. The upper and lower control limits of 0.3 mm are obtained according to the actual guarantee level of the size in the previous stage and the tolerance requirements. After completing the establishment of the control chart, the forward control model for the analysis of the accuracy of the flushness between the rear door and the roof has been established.

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Fig. 5. Control chart of flushness between back door and roof

Fig. 6. Control chart of back door

Next, the production statistical data during this period is used for reverse analysis and verification, and the initial sample size is 30 each, which is the data shown in Fig. 6. Through the monitoring of the control chart, it can be found that the control chart of the flushness between the back door and the roof shows that this group of data is out of

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control and exceeds the control limit of ±0.8 mm. The back door assembly is also out of control during this time period. Then, the process capability analysis is carried out on the back door assembly, the flushness between the back door and the roof respectively. According to Eq. (7), it can be calculated that the PPK of the back door assembly 0.55, and the PPK of the flushness between the back door and the roof is 0.49, which do not meet the requirement of PPK ≥ 1.33. [11]. The process capability distribution map can be get by the application named minitab. Before the process capability analysis, the normality test was carried out respectively. When the confidence level was 0.95, when P > 0.05, the two groups of data were considered to be in line with the normal distribution. Figure 7 is the normality probability map of the back door assembly accuracy data and the flushness data of the back door and the roof. Figure 8 is a schematic diagram of the process capability of the back door assembly.

Fig. 7. t4 and F Data normality test

Through the analysis above, it can be known that the process capability of the back door assembly and the flushness between the two parts in this detection time period does not meet the requirements. According to Eq. (14), calculating the correlation coefficient between the flushness data and the back door assembly data, there are r = −0.59.

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Fig. 8. t4 data Process Capability Analysis

At this time, taking the above 30 sample data as prior data, the mean value of the flushness between the back door and the roof is u = −1.484. Considering the hysteresis between the back door assembly and the body assembly inspection, continue to count the 10 sample datas of the body assembly, then use the Eq. (13) to obtain the mean u’ = −1.071 corrected by Bayesian Theory. Next, regenerate the two groups Corresponding to the sequence, and the corrected correlation coefficient r = −0.768 is obtained, as shown in Table 3. It shows that the fluctuation of the back door assembly is strongly related to the fluctuation of the flushness at this place. Table 3. Assembly flushness data correction, process capability and correlation analysis Assembly Flushness Sample

Mean

PPK

Mean Correction

Correlation Coefficient

30 sets of data (initial)

−1.484

−0.59

−1.003

0.49 0.55

−1.071

40 sets of data (10 additional sets)

−0.768

From this, it can be confirmed that the accuracy of the back door assembly needs to be rectified. This case is only an example to verify the correctness and feasibility of the control model, so the influencing factors of components are analyzed to the above three assemblies only. However, it should be pointed out that the underlying reasons for the deviation of the above three assemblies can also be further analyzed according to this model scheme. Inner plate welding process datas, edge wrapping process datas, etc. should be further analyzed by using the model above. This case will not be further shown here. Through further analysis in this case, the fluctuation here is due to the large

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process deformation during welding of the back door reinforcement. By solving the welding process deformation, the accuracy of the back door assembly and the process capability of the flushness between the back door and the roof can be improved. In fact, subsequent improvement results also show that this reverse verification model is correct and reliable.

4 Conclusions Based on the mathematical relationship between the target tolerance of the body assembly and the influence factors that constructed by the linearized tolerance analysis method, the sensitivity of each factor is obtained by analyzing the transfer relationship of the dimension chain, and key impact factors could be found out. The control chart of the key factors could be established based on the tolerance requirements and the actual accuracy status of the parts. Based on the method above, the forward quality control model of body assembly has been established. In addition, through the inverse model, the key factors that caused assembly deviation can be found out. In practical application, the combination of forward and reverse control models can better control and predict the quality of body assembly. The linearized tolerance analysis method can be used to analyze and control the size of parts, but for some dimensional deviations that do not obey the normal distribution, such as deviations caused by fixtures, it is not recommended to use the above model for quality control. In addition, when analyzing the assembly size by this method, it is best to first establish the monitoring data connection between the body assembly and the sub-assemblies, and then establish the connection between the sub-assemblies and the pieces in order to avoid too many errors caused by a very long dimension chain that directly from the piece to the body assembly, and which will affect the correlation judgment.

References 1. Yang, X., Zhang, X., et al.: Design of DTS of body closure parts. Autom. Technol. Mater. (3), 63–64 (2019) 2. Gu, L.: Tolerance analysis with general distribution dimension and its application in body fit, pp. 14–15. Shanghai Jiao Tong University, Shanghai (2007) 3. Wei, X., Guo, L., Zhang, Y.: Research and application tolerance analysis and design of guidance head components. Aeron. Manuf. Technol. (4), 75 (2006) 4. Guo, Y., Zhang, S.: Optimal process control for in-process assembly dimension quality assurance considering uncertainty. Mach. Des. Manuf. (9), 176 (2020) 5. Chiu, C.-C., Lee, K.-M.: Identification of process disturbance using spc/epc and neural networks. J. Intell. Manuf. 14(3–4), 379 (2003) 6. Peng, Z.: Research on quality management model of vehicle body dimension on six sigma method, pp. 32–33. Harbin Institute of Technology, Harbin (2018) 7. Jin, S., Zhao, T., et al.: The relativity analysis of BIW checking data based on Bayesian. Mach. Des. Res. 19(3), 57 (2003) 8. Liu, H., Jin, S., et al.: Fault diagnosis of body deviation based on Bayesian network. Machinery 36(3), 68 (2009)

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9. Liu, Y., Sun, R., et al.: Latent structure modelling and predictive quality control based on Multi-source data streams in the auto body assembly processes. China Mech. Eng. 30(2), 237–238 (2019) 10. Zhang, Y., et al.: Diversity Parameters Statistical Analysis Introduction. Science Press, Beijing (1997) 11. Pang, L.: Quality analysis and control of the body-in-white door assembly, p. 48. Tianjin University, Tianjin (2015)

Research and Application of High Vacuum Die-Casting Shock Tower Using Heat-Free Aluminum Alloy Jinsheng Zhang1,2(B) , Yunbo Zeng1 , Bofang Liu1 , Dejiang Li3 , Xia Pu1 , Sha Lan1,2 , Zhibai Wang1 , and Gang Feng1 1 Chongqing Changan Automobile Company Limited, Chongqing, China

[email protected]

2 Chongqing University, Chongqing, China 3 Shanghai Jiao Tong University, Shanghai, China

Abstract. By studying the Aluminum alloy high vacuum die-casting shock tower, the problems of casting deformation and high energy consumption of the traditional shock tower are solved through the research of heat treatment-free materials. In the casting rod statement, the developed Al-Mg series Aluminum alloy has a property of YS ≥ 160 MPa and A% ≥ 13%. The integrated structure design of the shock tower is carried out to achieve a weight reduction of 29%; the developed materials are used for trial production, the YS ≥ 170 MPa, and A% ≥ 8.5%, the gas content is 1.76–2.03 ml/100g Al, which meets the performance requirements of the shock tower. Keywords: Aluminum · High Vacuum Die-casting · Heat Treatment-free Materials · Shock Tower

1 Introduction In recent years, with the rapid development of new energy vehicles, the weight gain of the whole vehicle caused by the battery pack is relatively obvious, and the general weight gain is about 400 kg. In the market, Steel-Aluminum hybrid body or all-Aluminum body has begun to be used. A common feature of these two types of body is that the front shock tower area is generally made of Aluminum alloy and has an integrated die-casting structure. As the key structural component of bearing, the shock tower has extremely high requirements on mechanical properties and dimensional accuracy. Aluminum alloy shock towers mostly use high-vacuum die-casting technology, with a thickness of 2.5– 4 mm, which can reduce the weight by 25% compared with the steel solution [1]. The main Aluminum alloy high vacuum die-casting shock tower, there are two main types of materials, one is Al-Si series Aluminum alloy, and the other is Al-Mg series alloy. The commonly used Al-Si series material is Silafont-36 alloy (AlSi10MnMg) [2] which developed by German Rheinfeld Company. After die casting, T7 heat treatment process is adopted, which has problems such as heat treatment energy consumption and component deformation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 China Society of Automotive Engineers (Ed.): SAE-China 2022, LNEE 1025, pp. 384–398, 2023. https://doi.org/10.1007/978-981-99-1365-7_30

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Later, the company developed Castasil-37 alloy (AlSi9MnMoZr). This alloy can directly reach an elongation of more than 8% in the die-cast state, but due to the addition of Mo and Zr alloy elements, the cost is relatively high. Alcoa has developed a C611 Aluminum alloy [3], which also requires T7 heat treatment. Tesla company released its one-piece die-cast rear floor assembly in early 2021, announcing the use of a selfdeveloped heat-free Aluminum alloy, which mainly controls the Si content to about 6.5%, and adds Cu to improve the as-cast strength [4]. The typical grade of Al-Mg series alloy is Magsimal-59 alloy (AlMg5Si2Mn). This alloy has better mechanical properties, but slightly poorer fluidity, which requires higher casting process and is mainly used in Europe. Now many OEMs and parts suppliers in the industry are developing heat-free Aluminum alloys [5–7] (Table 1). Table 1. Typical high vacuum die casting aluminum alloy materials [2–4]

In this paper, based on the Al-Mg series alloy, the composition design is carried out. The specific composition is Al-6.5Mg-0.6Si-0.6Cr-0.5Cu, the yield strength ≥ 160 MPa, and the elongation is greater than or equal to 13%. The developed materials are trialmanufactured, with yield strength ≥ 170 MPa and elongation ≥ 8.5%, meeting the performance requirements.

2 Research on Heat-Free Aluminum Alloy Materials In this paper, the optimization idea of alloying is adopted to optimize the composition of Al-Mg system alloy. Adding Si element to the Al-Mg system can improve the casting performance on the one hand, and on the other hand, the two elements, Mg and Si, exist in the form of Mg2 Si compound, which can play a strengthening effect. The addition of Cr element can form a Chromium-containing intermediate compound to hinder the nucleation and growth process of recrystallization. When the Fe content is high, the microstructure of the iron-rich acicular phase can be improved, and the toughness of the Aluminum alloy can be improved. The Mg:Si mass ratio of Mg2 Si compound is 1.73:1, and the Mg:Si mass ratio in the alloy will significantly affect the microstructure and properties of the alloy. When

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the Mg:Si mass ratio is greater than 1.73, there will be excess Mg. Due to the high solid solubility of Mg in Aluminum, the excess Mg is mainly dissolved in the matrix. Formation of a small amount of Al-Al3 Mg2 eutectic phase. When the Mg:Si mass ratio is less than 1.73, there will be excess Si, and the solid solubility of Si in Aluminum is very small. The excess Si is mainly segregated on the grain boundary to form a long Al-Si eutectic structure, which seriously reduces the plasticity. And corrosion resistance.) Based on the above research and analysis, on the basis of Al-4Mg alloy, different contents of Si were added to control the strengthening effect of the alloy by precisely controlling the mass ratio of Mg:Si, and then 0.6% Cr was added to improve the needlelike shape of the Fe-rich phase. Appearance to improve toughness. Taking Al-4Mg-xSi-0.6Cr (x = 0.6,1,2.3,5 wt.%, represented by alloys 1, 2, 3, and 4, respectively) die-casting Aluminum alloy as the research object, Table 2 shows the actual composition after detection by inductively coupled plasma optical emission spectrometer(ICP-OES).The actual mass ratios of Mg:Si in alloys 1, 2, 3, and 4 are 6.6, 3.56, 1.72, and 0.77, respectively. Table 2. Chemical composition of Al-4Mg-xSi-0.6Cr alloy (wt.%) No.

Mg

Si

Cr

Al

Mg:Si (wt.%)

1

3.96

0.61

0.58

Bal.

6.6

2

3.92

1.10

0.57

Bal.

3.56

3

3.95

2.31

0.62

Bal.

1.72

4

3.91

5.06

0.64

Bal.

0.77

The scan images of alloys 1, 2, 3, and 4 as die-cast are shown in Fig. 1(a–d). The XRD phase analysis results of the four alloys are shown in Fig. 2. The microstructure observation shows that with the increase of Si content, the mass ratio of Mg:Si elements decreases, and the microstructure changes from excess Mg solid solution in Al matrix to excess eutectic Silicon.) The mechanical properties of Nos. 1–4 were tested, and the test results are shown in Table 3. The results show that the increase of Si content can increase the strength, but bring about a decrease in elongation. The Aluminum alloy shock tower is connected by riveting. According to the riveting test results, when the elongation ≥ 8%, the riveted joint will not crack, which is more reliable. In the actual casting process, due to factors such as trace impurities, gas, and inconsistency in solidification, the elongation of the part body sampling will be lower than the elongation of the material test bar, so the elongation is the key factor to be satisfied first. The focus is on selecting No.1 alloy (Al-4Mg-0.6Si-0.6Cr), and further optimization, under the premise of ensuring that the elongation is not significantly reduced, mainly to increase the yield strength and improve the hot cracking performance. Further adding Cu, Zn and other elements that have a large solid solubility in Aluminum and are not easy to form compounds with Mg. At the same time, Mg also has

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(a)No.1Alloy

E No.2Alloy

(c) No.3Alloy

(d) No.4Alloy Fig. 1. SEM morphology of die-cast alloy

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Fig. 2. XRD analysis results of four Al-4Mg-xSi-0.6Cr alloys as die-casting

Table 3. Mechanical properties of Al-4Mg-xSi-0.6Cr alloy No

YS (MPa)

TS (MPa)

Elongation (%)

Riveting elongation requirements

1

130

230–250

14–16

≥8%

2

160

300

8–10

3

230

300

2–3

4

300

310