Proceedings of China SAE Congress 2019: Selected Papers [1st ed.] 9789811579448, 9789811579455

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Lecture Notes in Electrical Engineering 646

China Society of Automotive Engineers Editor

Proceedings of China SAE Congress 2019: Selected Papers

Lecture Notes in Electrical Engineering Volume 646

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, 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, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany 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, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA 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 Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA

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China Society of Automotive Engineers Editors

Proceedings of China SAE Congress 2019: Selected Papers

123

Editors 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 978-981-15-7944-8 ISBN 978-981-15-7945-5 (eBook) https://doi.org/10.1007/978-981-15-7945-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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

Study on Air-Conditioning Refrigeration Performance of Electric Vehicle Based on Flow Field Optimization . . . . . . . . . . . . . . . . . . . . . . . Lefang Ding and Yong Zheng Three Dimentional Anemometer Using Thin Film Temperature Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takafumi Matsumoto, Xiaolin Guo, Hirohito Matsui, Yukikatsu Ozaki, Yasutaka Kamiya, Koji Kondo, and Hirotaka Miyano Design of Front Subframe Fixture Based on System Identification and Fatigue Life Calculation of Front Subframe . . . . . . . . . . . . . . . . . . Peng Tang, Lingshan Jiang, Jianhong Huo, Ying Zhang, Xiang Qi, and Weimin Yao Fatigue Analysis of Truck Frame Using Virtual Proving Ground . . . . . Chao Fang, Tian-bing Li, Hao-ren Wang, and Xiao-jie Sun A Digital Road Construction Method Applied in Virtual Proving Ground Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuai Zhou, Yunping Zhou, Jian Shao, Hua Wang, and Zhongling Jiang Research on Evaluation Methodology of Durability of the Plastic Tray System for Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junjie Duan, Hailong Mei, Guangyao Wang, Chengzhi Sun, and Shizhan Zhang

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Test and Analysis on Distance Measurement Accuracy of Commercial Vehicle Forward Collision Warning System . . . . . . . . . . . . . . . . . . . . . . Chengyong Niu, Zhanling Su, Kunlun Wu, Xiong Hu, and Jianxun Xu

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Car Body Durability Analysis Based on Modal Superposition Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zichun Zhang, Changpeng Wu, Zhaoming Wu, and Yanbing Lei

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Virtual Proving Ground Simulation in Practice for Vehicle Durability and Ride Comfort Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Jian Shao, Yunping Zhou, Hua Wang, Shuai Zhou, Zhongling Jiang, and Wenjuan Wang Research on Optimization Analysis Method of Exhaust System Under Multiple Loading Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Yan Qiao, XinTian Qu, GuiQi Yu, ShuangXi Zhan, and WenJun Kang Optimization Design of Sealing Strip Section Based on Finite Element Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 LiPing Ma, ShuGuang Xu, and JunPing Qiao Parametric Modeling and Structure Optimization Technology of Automobile Twist Beam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Nuo Xu, Chengzhi Sun, and Xiao Wang Investigation of Vehicle Water Drainage in Virtual Shower Room . . . . 177 Guohua Zhuang, Chang Qiu Liang, Kai Xu, Xiao Liu, Jun Ni, and Zhi Ding Research and Improvement on Welding Joint Failure of Subwater Tank Installation Bracket . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Jie Ren, Dexia Zhu, Shixing Chen, and Jingru Bao Research on the Influence of Lightweight Design of DAB Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Ding An Research on the Optimization Method of Body Torsional Stiffness Base on Flexible Joint Simplified Model . . . . . . . . . . . . . . . . . . . . . . . . . 219 Chengjie Huang, Shusheng Di, Xuemei Zhao, Dan Zhang, Zhixin Zhao, and Changchu Wang Design and Application of Flexible and Collective Painting for Truck Beds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Fujia Zhang, Wenping Xing, and Jin Li Construction of Virtual Simulation System for General Assembly Process Based on Digital Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Gong Jian, Wang Long, Dong Feixiang, and Li Qiang Application of Three-Dimensional Collision Detection in Painting Shop Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Yang Dong Development and Application of Flexible Welding Line in Overseas Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Yongsheng Fu

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Instant Centre Impact Loads Transfer on Double Wishbone Suspension While Car Traveling Straight . . . . . . . . . . . . . . . . . . . . . . . 287 Yongchen Zai, Weiguo Liu, Wenlin Chen, Bo Li, and Hongxi Lu Vehicle Side Slip Angle Estimation Using Stiffness-Update Method and Extend Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Guangxu Che, Mengjian Tian, and Bingzhao Gao Study of the Subjective and Objective Correlation on Vehicle Ride Comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Yi Lu, Lifa Ma, and Jianjun Guo Study on Vehicle Driving State Estimation for Four-Wheel Independent Drive and Steering Electric Vehicle . . . . . . . . . . . . . . . . . . 339 Li Gang, Fan Dongsheng, and Wang Ye Ride Comfort Optimization Method for Commercial Vehicle Based on Nonlinear Damping and PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Keren Chen, Shuilong He, Enyong Xu, Wei Wang, and Zhansi Jiang Study on Real Road Driving Emission Characteristics of Light-Duty Gasoline Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Qinggong Zhu, Dongdong Guo, Fulu Shi, Zhengjun Yang, and Jiaxin Luo Extraction and Analysis of High Emission Conditions of Heavy Diesel Vehicles Based on Real Road Driving Conditions . . . . . . . . . . . . . . . . . 385 He Lyu, Jingyuan Li, and Mengliang Li Analysis of PHEV Utility Factor and Fuel Consumption Based on Real Road . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Xiaopan An, Yu Liu, Jingyuan Li, and Yan Yan Crash Failure Analysis of a Carbon Fiber Full-Wrapped Hydrogen Cylinder with an Aluminum Liner and the Optimal Design of Its Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Chen Liang and Ping Chen Design and Implementation of Full Speed Range Control of Permanent Magnet Synchronous Motor for Electric Vehicle . . . . . . . 431 Kai Xu, De-qian Tang, and Zhi-fei Sun Model-Based Peak Power Estimation of Lithium-Ion Batteries for Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Xiang Shun, Zheng Ying, Hong Munan, Zhu Qian, Guo Yazhou, Liu Bo, and Yang Bo Study on Maneuverability Control of Four In-Wheel Motor Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 Zeyang Zhang, Jianpeng Shi, Chunlai Zhao, Qiulai Wang, and Hongtao Li

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Fuel Consumption Optimization for Dual-Motor Planetary Coupling PHEV Based on Adaptive Multi-target Compensation Factor . . . . . . . . 471 Cong Liang, Xing Xu, Feng Wang, and Zhiguang Zhou Power Performance and Efficiency Analysis of Two ElectricContinuously Variable Transmission for Hybrid Electric Bus . . . . . . . . 485 Xiaohua Zeng, Xingqi Wang, Dafeng Song, and Guanghan Li Comparative Study on Energy Consumption Characteristics of EV and FCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Tian Yang, Yanxin Nie, Xu Wang, and Peiliang Yu Driving Condition Recognition and Optimisation-Based Energy Management Strategy for Power-Split Hybrid Electric Vehicles . . . . . . 511 Weida Wang, Qian Chen, Changle Xiang, Zhongguo Zhang, Haonan Peng, and Zehui Zhou Research on Lateral Active Collision Avoidance Algorithms for Intelligent Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Jiaxiang Qin, Rui He, Yan Liu, Weiwen Deng, and Sumin Zhang Research on Classification of Vehicle Driving . . . . . . . . . . . . . . . . . . . . 543 Shuang Chen, Ziyang Zhu, Yuan Wang, Xiaozhen Qu, and Yang-shan Tang Research on Trajectory Tracking Lateral Control of Intelligent Vehicle Based on Nested Sliding Mode . . . . . . . . . . . . . . . . . . . . . . . . . . 561 Bingli Zhang, Pingping Zheng, Jie Zhang, Wei Tang, and Gan Shen Driver Drowsiness Analysis Based on Eyelid Feature . . . . . . . . . . . . . . . 575 Shu Wang, Zhao Zhang, Zheng Wu, Jie Liu, and Chunmei Mo A Unified Spatio-Temporal Description Model of Environment for Intelligent Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 Sijia Wang, Kun Jiang, Shichao Xie, Yuanxin Zhong, Pengwei Guo, Qun Wu, and Diange Yang Effect of Air-Flow Organization in Cylinder of Gasoline Engines on Particles Emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597 Wenzhong Zhao, Chao Zhang, Dongyu Qian, Liangchao Zhang, and Renyu Ruan Optimization Analysis of Buckling Strength of Connecting Rod for Internal Combustion Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Kunpeng Xu, Guangquan Wu, and Lin Xu Impact Analysis of the Aging Silicone Oil in the Damper on the Torsional Vibration of the Crankshaft of the Heavy-Duty Vehicle Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 Liming Zhai, Enzhen Wang, Weiguo Xu, Chenghai Huang, and Zhenguo Yang

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Experimental Study on Ethanol Gasoline Flash-Boiling Spray Characteristics Using Multi-hole GDI Injector . . . . . . . . . . . . . . . . . . . . 635 Jiakun Du, Hong Chen, Yuhuai Li, Lin Ye, and Genkun Li Lightweight Optimization for Engine Hood Based on Forward Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 Chuan-qing Wang, Li-ling Zhang, Lei Tian, and Cun-li Jia Lightweight Research of the Rear Apron Assembly Based on Cross-Section Torsion Coefficient and Shape Characteristic Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 Bensheng Xiong, Yongping Jiang, Yugong Wei, and Zhimin Liu Optimization Design of the Structure of the Automobile Bonnet Made of Fiber Composite Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 Hui Ye, Chang Liu, and Kangkang Yan The Application Practice of Inner Diameter Pneumatic Probe in Automobile Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 Ye Zongmao and Zhao Yanhui Solutions to Common Problems of Forged Steel Piston . . . . . . . . . . . . . 707 Jianjun Wang, Xinshuai Hu, Yi Yao, Denghe Chen, Guohui Cao, Jun Gu, and Chengzhong Yang Research on the Control Method of Empty Stroke of the Brake Pedal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 731 Qimin Yang, Lisheng Tian, and Xiuren Li Research and Application of Innovative Automotive Wax Injection Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 Huigang Pan, Haibo Zhou, Xuebing Xiang, Yalei Zhang, Zhongqiong Xu, Liye Sun, and Jun Sun The Innovation & Practice of New Model Early Verification Based on the Target of Smoothly SOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753 Hong Wang, Weihua Zhuo, Weimin Gong, and Ze Lu Application of Advanced Measurement Technology and Equipment in Transmission Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765 Xiaolong Geng, Hongbing Yang, Zhengqi Li, and Yingsu Li The Effect of Si, Mg and Mn/Fe on the Fluidity of AC2B Alloys . . . . . . 777 Weizheng Wang, Siyu Li, and Jiajie Ning Application of Air Quenching Technology to Engine Cylinder Head . . . 785 Weizheng Wang, Jiajie Ning, Zhengwu Yan, and Rui Hu Promote In-Process Measurement Technology Application in Intelligent Grinding Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793 Qi Yang

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Development of New Environmentally Friendly Waterborne Coating for Engine Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805 Yuning Li and Zhen Wang Simulation Study on the Idle Shake Control of a Vehicle . . . . . . . . . . . 815 Bocong Lu, Zheng Xu, Ming Chen, Xiaohu Zhang, Shuaiju Zhang, Lin Liu, and Wenjie Zhao Experimental Study on Mechanism of Cavity Filler Block to Reduce Interior Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829 Dejian Meng, Ziyi Wang, and Lijun Zhang A Study of Diagnosis Method for Turbocharger Abnormal Noise . . . . . 847 Yongjin Qiu, Fangyu Wang, and Dong Ren Research and Application of Reinforcement Beams Supporting Body Panel on Attenuation of Low Frequency Vibration and Sound Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 861 Jie Zhang, Jian Pang, Yuping Wan, Liang Yang, and Wenyu Jia Modal Parameters Identification Research of Commercial Vehicle Drive Shaft Based on Experimental and Simulation Hybrid Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873 Boqiang Zhang, Xinping Wu, Zhentao Li, Tianpei Feng, and Haiyang Yu Analysis of the Exterior Rearview Mirror Layout and the Blind Area of the Forward Visibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 889 Gang Yang Research on Development and Application of Virtual Reality System for Car Styling Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 903 Jingfeng Shao, Yunrong Zhang, and Zhiqiang Yu The Aural Discomfort Inside the Car Analysis and Control in the Instant the Door Closes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913 Mao Guangjun, Xi Shuxiong, Zhang Kaige, Jiang Guang, Li Xin, and Zhao Hualei Study on Thorax Certification Evaluation Strategy of Thor-50M dummy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 927 Enyi Shang, Dayong Zhou, Dandan Yan, and Xinkang Cui Floor Impact Bench-Test Method Based on Mass-Spring Model . . . . . . 937 Dong Liping, Wang Xiaowei, Zhang Xinqi, Bu Shaoxian, and Liu Guoqing Research on Measuring Method of Dynamic Collapse Velocity of Steering Column in Vehicle Collision Test . . . . . . . . . . . . . . . . . . . . . 947 Jiayao Li, Changqing Yin, Ai Xu, Xiaolei Li, Taisong Cui, and Hui Zhao

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Study on the Influence of Accelerometer with Damping and Without Damping on the Results of Head Impact Test at Front Windsheld . . . . 957 Yinhui Wan, Dongdong Tan, Chengjing Zhou, Linchun Zhong, and Fanjun Tai Shenzhen Mobility Research for Different Travel Scenes . . . . . . . . . . . . 967 Zan Li, Fuquan Zhao, and Zongwei Liu Research on “Dual-Credits” Policy of Automobile Enterprises . . . . . . . 977 Yunlei Yin and Zhenfei Zhan Software Development Management Research Based on Vehicle-Controlled ECU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 989 Yun-lin Ma, Hui Zhang, Dong-ping He, Yu Zhou, and Xiu-quan Tan Investigation on the Gearbox Radiated Noise Optimization Under Acceleration Conditions of Electric Bus Based on Gear Micro-modification Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997 Yong Chen, Ningning Qiu, Hai Liu, Miao Yu, and Changyin Wei Effects of Supporting Stiffness on Meshing Characteristics of Helical Gear Under Multiple Load Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 Yong Chen, Libin Zang, Kai Li, Huidong Zhou, Wangyang Bi, and Jinkai Li Steady-State Thermal Analysis of Electric Vehicle Two - Speed Automatic Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027 Yong Chen, Yuheng Wang, Guangxin Li, and Miao Yu Four-Parameter Real-Time Thermal Model for Dry DCTs . . . . . . . . . . 1045 Zhiyang Qiu, Zhi-Lin Charlie Zheng, Dongxiao Miao, and Li Chen Determination of Throttle Setpoint Control of Turbo-Charged GDI Engine Based on Newton Raphson Iteration . . . . . . . . . . . . . . . . . . . . . 1059 Long Qin, Fanwu Zhang, Lei Liu, Chunjiao Zhang, Jianbo Zheng, Xue Lei, Liuchun Yang, Fengmin Tian, and Fangxun Zhao Analysis of Cruise Failure Caused by Multi-function Steering Wheel Switch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1069 Qian Sui, Haihong Cao, Wei Yu, Hui Cao, Meilin Xiao, Yan Zhang, and Yong Hu Research on Fuzzy Energy Control Strategy for Four-Wheel Hubrid Elestric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1079 Cheng Li, Xu Wang, Zhongwen Zhu, and Qing Wei Online Identification Strategy of Sand Terrain for SUVs . . . . . . . . . . . . 1093 Jian Zhang, Yu Wang, Fei Xie, Sutie Zhang, Jian Zhao, and Weixiang Wu

Study on Air-Conditioning Refrigeration Performance of Electric Vehicle Based on Flow Field Optimization Lefang Ding and Yong Zheng

Abstract Considering the attractive appearance and reduction of aerodynamic drag, more and more electric vehicles adopt the closed front grille, which causes the insufficient air intake of the condenser and weakens the heat exchange performance of the condenser. Through looking for the new air intake passage between the front cabin and the condenser, the heat exchange performance of the condenser is improved. The refrigeration effect of air-conditioning system before and after the improvement of the air intake passage of condenser is compared and evaluated by CFD numerical simulation and environmental chamber test method. CFD numerical simulation results show that the air intake mass per second of the condenser of optimized electric vehicle increased by 15.17%, and air-conditioning refrigeration performance of environmental chamber tests show that the average cabin temperature decreased by more than 3 °C under various operating conditions. The measures to look for a new air intake passage for condenser can significantly improve the refrigeration effect of air-conditioning of electric vehicles and the comfort of cabin environment. The new scheme has been successfully applied to mass-produced vehicles. Keywords Electric vehicle · Closed grille · Air-conditioning refrigeration performance · Flow field optimization

1 Introduction The air conditioner ensures good refrigeration performance of the vehicle, providing passengers with a comfortable environment, reducing the fatigue strength of drivers and improving the driving safety. The air conditioner has become one of the symbols to measure whether the vehicle function is complete or not and refrigeration performance of air-conditioning system has become one of the important performances of the whole vehicle. In recent years, more and more automobile manufacturers have developed the electric vehicle business, so that the electric vehicle has shown L. Ding (B) · Y. Zheng BeiQi FOTON Motor Co., Ltd., Beijing, China e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_1

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the “blowout” development [1]. However, restricted by the battery capacity and endurance mileage, most electric vehicles adopt closed front grille to reduce aerodynamic drag and energy consumption. However, it also leads to seriously insufficient air intake of the condenser and results in substandard refrigeration performance of airconditioning system [2]. Aiming at the poor refrigeration effect of VAN-type electric vehicles with the closed front grille, this paper puts forward the scheme of assembling the lower deflector and seals around the radiator to improve the refrigeration effect of air-conditioning system and achieve the designed performance. Based on CFD numerical simulation and environmental chamber refrigeration test, the rationality and feasibility of flow field optimization are studied [3].

2 Air-Condition Refrigeration Performance Test and Evaluation 2.1 Test Methods The refrigeration test of the air conditioner is conducted on the VAN-type electric vehicle with 14 seats in the environmental simulation chamber in accordance with QC/T 658-2000 Air-conditioning Refrigeration Performance Test Method for Motor Vehicles (Fig. 1). The standard stipulates that under the conditions of ambient temperature 38 °C, relative humidity 50% and solar radiation intensity 1000 W/m2 , the refrigeration performance of air conditioner is evaluated under RECIR, MAX COLD, HIGH BLOW, FACE mode. The average cabin temperature value should be measured and calculated at the end of the every operating condition (operating condition I: running for 45 min at 40 km/h, operating condition II: running for 20 min at 60 km/h, operating condition III: running for 20 min at 100 km/h, operating condition IV: idle speed for 30 min).

Fig. 1 Sample vehicles, (a) Baseline vehicle (unclosed front grille), (b) Test electric vehicle (closed front grille)

Study on Air-Conditioning Refrigeration Performance …

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2.2 Evaluation Index In combination with the enterprise evaluation standards and the baseline vehicle level, the comparison table of air-conditioning refrigeration performance indexes of this VAN-type electric vehicle is formulated, and the refrigeration test data and the standard-reaching situation of this vehicle before the flow field optimization are summarized, as shown in Table 1. Compared with the baseline vehicle, the time it takes the test vehicle to achieve an average cabin temperature of 26 °C is 24 min slower, and 16 min slower than that required time of the performance index, and the rate of failure to meet the standard is 36%. At the end of idle speed, the average cabin temperature of the test vehicle is 3 °C higher than that of the baseline vehicle, and 1 °C higher than that required by the performance index, and the rate of failure to meet the standard is 4%. The air-conditioning refrigeration test data analysis results show that the average cabin temperature of the VAN-type electric vehicles with the closed grille decreases too slowly, and the average cabin temperature is too high at the end of idle speed, which seriously affects the comfort of the drivers and easily leads to the driving hidden dangers. Table 1 Comparison table of air-conditioning refrigeration performance index for electric vehicles Item

Baseline vehicle (unclosed front grille)

Performance index requirement

Test electric vehicle (closed front grille)

Standard-reaching situation of test electric vehicle

Time (min) to reach the average cabin temperature of 26 °C

37

≤45

61

Substandard

Average cabin temperature (°C) at the end of idle speed

24.36

≤26

27.09

Substandard

2.3 Reason Analysis Based on the comparison and analysis of the air-conditioning refrigeration data of the baseline vehicle (sample vehicle 510, unclosed front grille) and the test electric vehicle (sample vehicle 263, closed front grille) (as shown in Fig. 2 below), it is found that the front air intake temperature of the condenser of the test electric vehicle is about 10 °C higher than the ambient temperature (38 °C). This is mainly due to the decrease of the front supply air rate after the front grille is closed, and many returns of the fan heat airflow to the front-end condenser due to the suction of high-speed

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70 60

Temperature(ºC)

50 40 30 20 10 Time(min) 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121

0

263 3500rpm Inlet air of Condenser 263 3500rpm Outlet air of Condenser 263 3500rpm Average in-vehicle temperature 263 3500rpm Average temperature of air outlet of front air conditioning blowing surface 510 3500rpm Inlet air of Condenser 510 3500rpm Outlet air of Condenser 510 3500rpm Average in-vehicle temperature 510 3500rpm Average temperature of air outlet of front air conditioning blowing surface

Fig. 2 Comparison of refrigeration test data between baseline vehicle and test electric vehicle

fans. So new passages need to be appended for the front-end air flow source of the condenser. Thus, flow field simulation analysis should be carried out for the baseline vehicle and the test electric vehicle respectively, in order to determine flow field optimization scheme.

3 CFD Numerrical Simulation 3.1 Modeling The front-end air intake of the condenser can be evaluated and understood through the CFD flow field simulation calculation [4]. In this paper, STAR-CCM+ , a commonly used fluid software, is used to build Multiple computational domains, which includes main fluid domain, the porous medium domains such as condenser and radiator, the

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fan fluid domain with rotational behavior, etc. A total of 20 million volume meshes are generated. The physical model of three-dimensional, steady and incompressible flow is used to simulate the vehicle under hood flow field situation under the conditions of ambient temperature of 38 °C and vehicle speed of 40 km/h (operating condition I of air-conditioning refrigeration test). The wind tunnel simulation model is shown in Fig. 3 below.

Fig. 3 The wind tunnel simulation model

3.2 Simulation Results of the Original Design The simulation analysis is conducted for the baseline vehicle (unclosed front grille) and the electric vehicle (closed front grille), respectively, to analyze the front-end air intake mass of the condenser (as shown in Table 2 below) and the thermal reflux (as shown in Figs. 4 and 5 below). After the front grille of the electric vehicle is closed, the air intake mass per second (kg/s) of the condenser is decreased by 34.6%, and thermal reflux behind the fan is increased significantly. The freshness of the inlet air of the condenser is also decreased significantly, which has a great influence on the heat exchange of the condenser. Table 2 Comparison table of the air intake mass per second of the condenser with closed front grille and unclosed front grille Item

Baseline vehicle (unclosed front grille)

Electric vehicle (closed front grille)

Change rate for the air intake mass of condenser of electric vehicle compared with baseline vehicle

The air intake mass per second of condenser (kg/s)

1.002

0.7441

34.6% ↓

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b

a

More front-end air intake

Less front-end air intake

Fig. 4 Streamline in front of condenser, (a) Baseline vehicle (unclosed front grille), (b) Electric vehicle (closed front grille)

b a

No thermal reflux

Too much thermal reflux

Fig. 5 Streamline contrast chart of thermal reflux, (a) Baseline vehicle (unclosed front grille), (b) Electric vehicle (closed front grille)

3.3 Simulation Results of the Optimization According to Sect. 2.2, there exists a thermal reflux flow around the radiator of electric vehicles with closed front grille, which can be prevented by sealing the gap around the radiator. Further analysis [5] of the air flow in the under hood of electric vehicle shows that the airflow from the lower part of front bumper directly flows to the bottom of the vehicle, and there is air waste (as shown in Fig. 6 below). The airflow from the lower part of front bumper can be diverted to the condenser by assembling a deflector. The specific scheme models of seals around the radiator (Fig. 7) and lower deflector (Fig. 8) are as follows:

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Fig. 6 Velocity scalar graph of Y = 0 longitudinal section of electric vehicle (closed front grille)

a

b

Fig. 7 Seals around the radiator, (a) Front view, (b) Back view

Fig. 8 Lower deflector

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For the electric vehicle, comparing the proposals between only assembling the lower deflector (Proposal I) and assembling lower deflector and seals around the radiator (Proposal II), it can be concluded the difference of the air intake mass per second of the condenser (as shown in Table 3 below) and the reflux (as shown in Figs. 9 and 10 below) [6]. Compared two Proposals, it is concluded that: 1) The air intake mass of the condenser is increased by 14.58% after assembling the lower deflector; 2) The air intake mass of the condenser is increased by 15.17% after assembling the lower deflector and the seals around the radiator; 3) For the electric vehicle (closed grille) by only assembling the lower deflector, there is still thermal reflux to the front of the condenser, which still has an negative effect on the heat exchange performance of the condenser. After assembling the seals around the radiator, the heat exchange performance of the condenser is guaranteed to a greater extent. Table 3 Comparison table of air intake of condenser Item

Original design

Proposal I

Proposal II

Change rate for the air intake mass of condenser between proposals and original design Proposal I

Proposal II

The air intake mass per second of condenser (kg/s)

0.7441

0.8526

0.8570

14.58% ↑

15.17% ↑

a

b

Fig. 9 Streamline chart for front-end air intake of condenser, (a) Proposal I, (b) Proposal II

Study on Air-Conditioning Refrigeration Performance …

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b

a

Still existence of hot reflux, but reduction

Basic elimination of hot reflux

Fig. 10 Streamline of thermal reflux, (a) Proposal I, (b) Proposal II

4 Optimization Scheme Test Verification 4.1 Embodiment of Proposal II The Proposal II is embodied in the modification of the sample vehicle, as shown in Figs. 11 and 12 below. The restructured sample vehicle is tested and verified in the environmental chamber in accordance with QC/T 658-2000 Air-conditioning Refrigeration Performance Test Method for Motor Vehicles.

4.2 Test Results After Optimization In the test process of optimized vehicle, the driver feels cool and comfortable subjectively on the face and feet, and the air-conditioning refrigeration effect of the vehicle is well satisfied.

Fig. 11 Detail of added lower deflector

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Fig. 12 Seals around the radiator

The test data show that the air intake temperature of the condenser is about 42 °C, 42 °C, 38 °C and 48 °C under the operating condition I (vehicle speed of 40 km/h), operating condition II (vehicle speed of 60 km/h), operating condition III (vehicle speed of 100 km/h) and operating condition IV (idle speed), respectively. It shows that thermal reflux can be better suppressed for the optimized vehicle in the low and high speed conditions. The average facial temperature of the passengers in the optimized vehicle is 23.6 °C, 22.2 °C, 21.8 °C and 22.9 °C, respectively, at the end of the operating conditions of 40 km/h, 60 km/h, 100 km/h and idle speed (as shown in Fig. 13 below). The refrigeration effect of the original design and the optimized electric vehicle (closed front grille) under different operating conditions is summarized and compared, as shown in Table 4 below. Compared with the original state, the optimized electric vehicle(closed front grille) achieves an average cabin temperature of 26 °C, which is 31 min faster than the original design. The improvement rate is 50.8%. It is found that the time to reach the average cabin temperature of 26 °C of the optimized state is 15 min faster than that required by the performance index. At the end of idle speed, the average cabin temperature of the optimized vehicle is 3.7 °C lower than that under the original design. The improvement rate is 13.7%. It is also found that the average cabin temperature of the optimized state is 2.6 °C lower than that required by the performance index. The refrigeration test data analysis results show that the average cabin temperature drop time and the average cabin temperature at the end of idle speed of the VAN-type electric vehicle (closed front grille) of the optimized state can all meet the performance index requirements.

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50.0

60.0

45.0

50.0

40.0

40.0

35.0

30.0

30.0

20.0

25.0

10.0 Time(min)

0.0

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116

20.0

humidity(%)

Temperature(ºC)

Study on Air-Conditioning Refrigeration Performance …

ambient temperature( ºC) the air temperature in front of condenser( ºC) average in-car temperature( ºC) ambient relative humidity(%) the average facial temperatures of passengers( ºC)

Fig. 13 Refrigeration effect of electric vehicle (closed front grille) after optimization Table 4 Comparison table of refrigeration effect between optimized vehicle and original design Item

Original design vehicle Optimized vehicle (closed grille) (closed grille)

Improvement rate of optimized vehicle compared to original design

Performance index requirement

Time (min) to reach the average cabin temperature of 26 °C

61

30

50.8%

≤45

Average cabin temperature (°C) at the end of operating condition I

28.2

24.3

13.8%

No requirements

Average cabin temperature at the end of operating condition II

25.9

22.7

12.4%

No requirements

Average cabin temperature at the end of operating condition III

23.4

22.6

3.4%

No requirements

Average cabin temperature at the end of operating condition IV

27.1

23.4

13.7%

≤26

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5 Conclusions 1) Based on CFD simulation analysis, by means of flow optimization, the air intake mass per second of the condenser of the VAN-type electric vehicle with the closed front grille is increased by 15.17%. 2) The environmental chamber air-conditioning refrigeration test data under four operating conditions shows that the air-conditioning refrigeration performance of the VAN-type electric vehicle(closed front grille) of optimized state is well satisfied, which verifies the feasibility of the flow field optimization measures. 3) In combination with simulation and test, the problem of insufficient refrigeration effect of the VAN-type electric vehicle(closed front grille) is solved. The improvement rate of time to reach the average cabin temperature is up to 50.8%.

References 1. Chunlin G, Zijian Z, Li W et al (2012) Prospects and key factors analysis of electric vehicles development. Automot Eng 34(9):852–858 2. Xiayi Y, Zhengqi G, Yi Y et al (2009) Numerical simulation on vehicle under hood cooling. Automot Eng 31(9):843–847 3. Bao X, Qiming T (2016) Thermal management and optimization of automobile cabin based on CFD. J Automot Saf Energy 7:115–122 4. Alajbegovic A, Xu B, Kongstantinov A, Amodeo J (2007) Simulation of cooling, airflow under different driving conditions. SAE International 2007-01-0766 5. Patidar A, Gupta U, Marathe N (2013) Optimization of front end cooling module for commercial vehicle using CFD approach. SAE Paper 2013-26-0044 6. Manna S, Kushwah SY (2015) Optimization of a vehicle under hood airflow using 3D CFD analysis. SAE Paper 2015-01-0349

Three Dimentional Anemometer Using Thin Film Temperature Elements Takafumi Matsumoto, Xiaolin Guo, Hirohito Matsui, Yukikatsu Ozaki, Yasutaka Kamiya, Koji Kondo, and Hirotaka Miyano

Abstract Two different types of three dimensional anemometers were proposed in order to analyse airflow in engine compartment. It consists of multiple thin film temperature elements formed on a sphere surface. The measurement principles of each anemometer were established. Feasibilities of the principles were confirmed. Keywords Anemometer · Heat fluid · Air cooling system

1 Introduction In power-saving and fuel economy of automobiles, it is important to develop thermal management technologies to utilize unused thermal energy from combustion engines. The exhaust heat in engine compartment is transferred by complex airflow, therefore it is important to measure and understand the three dimensional airflow in engine compartment in order to develop effective thermal management technologies [2, 3]. Hot-wire anemometer and multi-hole pitot tube are well-known as compact anemometers which can be installed in engine compartment [4]. However, these anemometers have limited capability to detect large fluctuation of wind direction due to their limitation in measurement range of wind direction. For example, a hot-wire anemometer has a limited measurement area of 60° of the wind direction [5]. Optical measurements such as PIV have also difficulties to be applied in engine compartment due to their optical access. Therefore, we have developed two types of three dimensional anemometers deployable in engine compartment which can measure the wind velocity vectors T. Matsumoto (B) · X. Guo · Y. Kamiya Soken, Inc., Nisshin, Japan e-mail: [email protected] X. Guo e-mail: [email protected] H. Matsui · Y. Ozaki · K. Kondo · H. Miyano Denso Corporation, Kariya, Japan © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_2

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for different applications. One type can be applied to measure natural convection during dead soak of the engine. Another type can be applied to measure the engine exhaust for thermal management.

2 Configuration We have developed two types of three dimensional anemometers for following applications (Table 1).

2.1 Central Heating Type We have developed a three dimensional anemometer to measure the natural convection in engine dead soak time; under a low change rate of wind velocity. We called it “central heating type” anemometer. Figure 1 shows the configuration of this anemometer. Table 2 shows its specification. It consists of a plastic sphere with an electric heater in its centre and 16 thin film temperature elements formed on its surface. A temperature distribution generates on the sphere surface due to difference of the local heat transfer coefficient from the sphere to the air when there is a distribution of the air current on the sphere surface. This temperature distribution enables measurement of the wind velocity (Fig. 2). Table 1 Targeted applications Subject of measurement

Velocity change rate

Air temperature

Required response

Natural convection

Low

Constant

Low

Engine exhaust

High

Unstable

High

Fig. 1 Configuration of the sensor

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Table 2 Specifications Sphere Temperature element

Material

PEEK

Diameter

S φ 9.5 mm

Material

Nickel

Outside dimension

1.35 mm × 1 mm

Thickness

0.2 μm

Line width Heat resistant temperature

50 μm 150 °C

Fig. 2 Thin film temperature element

Fig. 3 Heat transfer of “central heating type”

2.2 Temperature Elements Heating Type In thermal management, wind direction greatly changes in a short time while engine exhaust heat moving to air-conditioner heat-exchanger by air current. High response less than 1 s is required to the sensor under such conditions. However the central heating type does not have enough capability of measurement under such conditions due to the heat capacity of the plastic sphere as shown in Fig. 3. Therefore, we have

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Fig. 4 Heat transfer of “temperature elements heating type”

Fig. 5 Configuration of temperature elements

also developed another high response anemometer called “temperature elements heating type” for such applications. In order to reduce the heat capacity and improve the response, the temperature element of metal film on the sphere surface was used as a heater instead of installing a heater in the plastic sphere (Fig. 4).

3 Measurement Principles 3.1 Central Heating Type 3.1.1

Measurement Principle of Wind Direction

In order to measure temperature distribution on sphere surface, we located the temperature elements as in Fig. 5. The origin of the polar coordinate system was set to the

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centre of the sphere. Z axis was set to the vertical direction, the angle between Z axis and the direction of the sensor location from the origin to be ϕ. X axis was set on the perpendicular plane of Z axis with its origin on the plane, the angle between X axis and the direction of the sensor location from the origin to be θ. The temperature elements were located in three aspects of ϕ = 45°, 90°, 135°. Eight temperature elements were located in the aspect of ϕ = 90°, four elements in each aspect of ϕ = 45° and 90°. The angle θ between the temperature elements located in the aspects of ϕ = 90 ° and ϕ = 45° (or 135°) was set to 22.5°. This layout of the sensors enabled measurement of three-dimensional wind directions. The wirings on the sphere surface were formed as thin films as well as the sensors and connected the sensors to the connectors located on the lower part of the sphere surface. In order to reduce the influence of electric resistance of the wirings in the temperature measurement, the four terminals method was deployed for the wirings. The heat from the heater in the sphere centre is radiated toward the sphere surface. A temperature distribution is formed on the sphere surface when a distribution of heat transfer rate exists on its surface due to the low thermal conductivity of the plastic sphere. The heat transfer rate on the sphere surface is larger at the windward than the leeward. Therefore, the wind direction can be identified as the vector that connects the origin with the lowest temperature location on sphere surface. In order to identify the lowest temperature location on the sphere surface, several lower temperature points were extracted using outputs from all temperature elements and distribution of surface temperature Ts was approximated with a quadric surface Eq. (1). The direction of the lowest temperature location (θw, ϕw) can be identified by identifying coefficients a, b, c, d, and e in Eq. (1). TS = aθ 2 + bθ + cφ 2 + dφ + e

3.1.2

(1)

Measurement Principle of Wind Velocity

The average heat transfer coefficient h from the sphere surface to the air was represented by the following formulas [6]. h=

Nu D

Nu = C · Pr 1/3 · Re1/2 Re =

V·D v

∴ V ∝ h2

(2) (3) (4)

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Nu: Nusselt number, Pr: Prandtl Number, Re: Reynolds number, V: Flow velocity, D: Sphere diameter, ν: Kinematic viscosity of air. These shows that the wind velocity V is proportional to the square of the average heat transfer coefficient h. Also, the average heat transfer coefficient h can be calculated by Eq. (5), where Q is the heat value of heater, A is surface area of temperature element, Ts_ave is the average of all temperature element outputs, TA is the air temperature at distantly positioned from the anemometer sensor. h=

Q   A Ts− ave − T A

(5)

Then, the relationship between wind velocity V and Ts_ave-TA could be shown as Eq. (6) when the surface area A and the heater heat value Q are constant. V ∝

1 Ts− ave − T A

2

(6)

Thus, with a calibration line of wind velocity V and Ts_ave-TA obtained beforehand, the wind velocity V can be calculated by temperature element outputs.

3.2 Temperature Elements Heating Type 3.2.1

Measurement Principle of Wind Direction

The measurement principle of wind direction with “temperature elements heating type” anemometer is the same as that of “central heating type” Sect. 3.1.1.

3.2.2

Measurement Principle of Wind Velocity

Equation (6) shows that the air temperature TA has to be obtained beforehand when measuring the wind velocity with “central heating type” anemometer. However the air temperature cannot be regarded as constant in heat management because the heat is transferred by air current. Therefore, wind velocity has to be obtained without the influence of the air temperature. The relations of heat value Q, surface area A, and the difference of the measured temperature by the temperature elements and the air temperature TA at distantly positioned from the anemometer is shown as Eq. (5). The heat value can be controlled by regulating electric current to the temperature element. Temperature measured by the temperature element changes under pulsed heat value as in Fig. 6.

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Fig. 6 Temperature change under pulsed heat value

The heat transfer rate h is expressed with simultaneous equations Eq. (7) and derived by solving them as Eq. (8). The heat transfer rate depends on the wind velocity and therefore the wind velocity can be obtained from Eq. (9) without the air temperature TA . 

h= h=

QH A(TH −T A ) QL A(TL −T A )

(7)

QH − QL T H − TL

(8)

(Q H − Q L )2 (TH − TL )2

(9)

h∝ V ∝

1 ··· 2 ···

QH : higher heat value, QL : lower heat value, h: heat transfer rate, TH : temperature measured with the element at QH , TL : temperature measured with the element at QL , TA : air temperature, V: wind velocity

4 Formation Method of Thin Film Temperature Elements Sputtering is generally known as one of the formation method of the film temperature element. However, it is difficult to make a mask pattern of the elements precisely on the sphere surface. Therefore, we have developed a new production method by molecular joining technology [7, 8] and laser processing technology. In this method, we formed a metal film on the whole plastic sphere surface by molecular joining technology and then removed unnecessary area of metal film from the sphere surface with a picosecond laser. Detailed procedure is expressed below.

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We applied a molecular joining technology in order to bond different kind materials by strong chemical bond to strengthen the combination between the plastic surface and the metal film. Binding molecules are firstly formed on the sphere surface before adding a metal film by immersing the sphere in the solution of binding molecules and then dried it. When we irradiated ultraviolet rays to this sphere, N2 can be isolated from the azido groups of the binding molecules and formed to be a nitrene shown as in Fig. 7. Nitrene peels H from C-H bond in PEEK and bonds with C in a benzene ring covalently. A part of the benzene ring of the plastic is combined with a binding molecule by this reaction. There are 3 hydroxy groups and 3 nitrogen atoms of triazine ring which have noncovalent electron pairs in this binding molecule. They connect with metal (nickel) and form the metal film on the plastic. The plastic surface was applied with the electroless nickel plating. Related reactions are shown in Eq. 10–12. Boron remained as nickel boride in the metal film (Fig. 8). Fig. 7 Molecular bonding

Fig. 8 Nickel-Boron plating

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3Ni2+ + (CH3 )2 NHBH3 + H2 O → 3Ni + H3 BO3 + (CH3 )2H2N+ + 5H+

(10)

4Ni2+ + 2(CH3 )2 NHBH3 + 3H2 O → 2Ni + NiB + H3 BO3 + 2HN+ + 6H+ (CH3 )2 NHBH3 +3H2 O → H3 BO3 + (CH3 )2 H2 N+ + 3H2

(11)

(12)

The conductivity was provided by the plating above mentioned, but the rate of resistance change due to temperature change (resistance temperature coefficient) is very small and resulting to low sensitivity as the temperature sensor. Therefore we formed the high purity nickel film by electric nickel plating based on the conductivity of nickel boron film (Fig. 9). As a result we improved the resistance temperature coefficient and raised the sensitivity of temperature elements (Fig. 10). We removed the unnecessary area of metal film from sphere surface to form the patterns of temperature elements and wirings with a picosecond laser (Fig. 11). The wirings were extended to the lower part of the sphere and connected to the wiring connections. The electric circuit of temperature elements was shown in Fig. 12. There are 16 temperature elements tandemly connected to a constant current power supply, and the resistances of temperature elements were calculated from their voltages. The relations between temperatures and resistances were obtained beforehand and therefore the temperatures can be calculated from the resistances of temperature elements.

Fig. 9 Nickel electroplating

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Fig. 10 Temperature coefficient of resistance

Fig. 11 Laser patterns of temperature elements

Fig. 12 Electric circuit of temperature elements

5 Feasibilities of the Measurement Principles 5.1 Central Heating Type We confirmed the principle of the wind velocity vector measurement in order to ensure the adequacy of the placement of temperature elements as shown in Fig. 5. The constitution of evaluation equipment is shown in Fig. 13. We installed an electric heater in the center of the plastic sphere and applied black paint (emittance 0.94) to the sphere surface. Then, we set this sphere in a wind tunnel, and measured the

Three Dimentional Anemometer Using Thin Film Temperature Elements

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Fig. 13 Wind tunnel

Fig. 14 Temperature distribution on sphere surface

sphere temperature distribution using a thermography. In addition, we measured the wind velocity with a commercial anemometer which were placed close to the sphere and changed the wind velocity in the range of 0.5–15 m/s. We estimated the heat value of the heater on 90 mW for the measurement on the breeze, and 400 mW for the measurement on the strong wind. Because a lower heat setting on the breeze could suppress the influence of the natural convection and setting a higher heat value could generate an obvious temperature distribution on the sphere surface even at the strong wind. The measurement of the temperature distribution of the sphere surface is shown in Fig. 14, where the white points were the temperature element positions. The result shows that the temperatures on the windward side are low. We have also confirmed if the real temperature distribution on the sphere surface was similar to a quadric based on Eq. (1). The temperature distributions on the sphere surface at ϕ = 90° measured by a thermography were shown in Fig. 15. The angle θ was varied from 0 to 90°, where 0° represent windward direction. The solid lines were the measurements, and the dashed lines were the second approximations of measurements. The measured results show a good agreement with secondary approximation lines under the real wind velocity of 0.5 m/s (heater heat value 90 mW) and 15 m/s (400 mW). Therefore, it was confirmed that the approximation method of Eq. (1) is appropriate to detect the wind direction.

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Fig. 15 Temperature distribution (measured and approximated)

Fig. 16 Correlation between wind velocity and Ts_ave TA

Then, we will discuss about wind velocity measurement. The wind velocity was calculated by the temperature element outputs those were installed on the sphere surface of the average temperature Ts_ave. Figure 16 shows the relationship between wind velocity V and Ts_ave - TA . It was confirmed that Ts_ave - TA changes according to the wind velocity. The absolute value of the leaning degree of Ts_ave - TA became lower under the heater heat value 90 mW when the wind velocity increased. The precision of the wind velocity measurement falls in this case and it is recommended to set a high heat value to ensure the precision of wind velocity measurement under strong wind conditions.

5.2 Temperature Elements Heating Type We formed a film temperature element on the plastic plate and installed it in a wind tunnel to confirm the measurement principle of “temperature elements heating type” as in Fig. 13. We changed the wind velocities as in Fig. 17 and checked the response of wind velocity measurements with the temperature elements. The result shows that TH - TL

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Fig. 17 Temperature element output in pulse heat value

Fig. 18 Responsiveness of the anemometer

followed a change of wind velocity. When the wind velocity was changed 63.2%, there was a 200 ms delay for the reference wind velocity (Fig. 18). Figure 19 shows relations between wind velocity and TH - TL . This shows that the higher heat value ensures measurement of the strong wind velocity up to 12 m/s. We also measured TH - TL under different air temperatures with constant wind velocity. Figure 20 shows that TH - TL is constant even under different air temperatures. This indicates that the wind velocity can be measured with the proposed anemometers without the influences from the air temperature.

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Fig. 19 Sensitivity of wind velocity

Fig. 20 Influence of air temperature

6 Conclusions (1) We have proposed two types of three dimensional anemometers deployable in engine compartment which can measure the wind velocity vectors for different applications. One type consists of a plastic sphere with an electric heater in its center and 16 thin film temperature elements formed on its surface. Another type employs 16 thin film temperature elements on the sphere surface as heaters. (2) We have confirmed the feasibilities of two principles of measuring wind velocity vectors in a wind tunnel. (3) We have suggested a new method of plating metal film temperature elements on a sphere surface and patterning them with a pico-second laser. We adopted a molecular joining technology to strengthen the cohesion between plastic and metal in the procedure.

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References 1. Matsumoto T (2018) Three dimentional anemometer using thin film temperature element. In: JSAE Congress (Autumn), No 106 2. Obara H (2016) Innovative technology of unused heat energy. In: JMSE The 21st national symposium on power and energy system 3. Hashimoto M (2004) Utilization of optimization method in automotive thermal management system. In: JMSE The 6th optimization symposium 4. Okajima A (1990) Measure the wind-measuring instrument and measuring system. JAWE 1990(42):58–108 5. Kasagi N (1997) Fluid Experiment Handbook. Asakura Shoten, Tokyo 6. Okazaki K (1986) Heat Transfer Engineering Materials, 4 edn. JSME, Maruzen, Tokyo, p P60 7. Mori K (2007) 21century adhesive technology-molecular adhesion technology. J Adhes Soc Jpn 43(6):242–248 8. Mori K (2017) Resin plating using molecular bonding technology (special issue: new plating technology for plastics). Jpn Mater Sci J 54(3):88–91

Design of Front Subframe Fixture Based on System Identification and Fatigue Life Calculation of Front Subframe Peng Tang, Lingshan Jiang, Jianhong Huo, Ying Zhang, Xiang Qi, and Weimin Yao

Abstract Aiming at the road spectrum simulation test rig of the front subframe of a car, the design of the test rig fixture based on multi-objective Optimization and the simulation prediction of the fatigue life are introduced in detail. Our engineers optimize the stiffness of the test rig by using the finite element software, so that the stiffness of the car body can be accurately simulated, and the force characteristics of the subframe on the test rig are the same as on the real car, thus the validity and accuracy of the bench test results are ensured. The fatigue test of the subframe is simulated by using the fatigue life software FEMFAT and the fatigue life of the subframe is predicted, greatly reducing the test time. Keywords Front subframe · Multi-objective optimization · System identification · Fatigue analysis

1 Introduction The vehicle is a complicated mechanical mechanism, which has the risk of fatigue fracture when bearing alternating load. The subframe suffers the impact load from road and improves the anti-torsion capability of the vehicle body, it has the ability to reduce vibration and noise. Therefore, in order to ensure the reliability of the subframe during driving, the fatigue tests are very necessary. In order to occupy the market, vehicle companies need to update constantly their products to improve the consumers’ appeal. Based on this, the traditional test methods can no longer support the current fast-paced R&D cycle. From the initial road tests

P. Tang · L. Jiang · J. Huo · Y. Zhang · X. Qi (B) FAW-VW Automotive Company, Changchun 130011, China e-mail: [email protected] W. Yao Jilin University, Changchun 130022, China © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_3

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and the test site tests to the current test bench tests, the methods of subframe performance tests are also constantly improving. Engineers use the computer-aided engineering (CAE) technique to simulate influence on components, which first predict the performance of components, and then optimize and improve them. Thus, it shortens the development time and improves work efficiency, which has strong practical significance.

2 Build FEA Model 2.1 Mesh Generation According to the CATIA model of the subframe and the body-in-white, the model is imported into the HyperMesh software. After the geometric cleaning (geometric repair and geometric simplification), meshing, quality inspection and simulation and simplification of the connection relationship (simulation of solder joints and bolts, simplification of the steering gear and the stabilizer bar), we have established a finite element model of the body with subframe system. The model has totally 1,375,853 units and 1,274,153 nodes. In order to calculate accurately the stress of the subframe, it is necessary to improve the mesh precision of the parts before the middle beam in the vehicle body, so the mesh size is small. In order to reduce workload, computer load and improve the work efficiency, it is suitable to enlarge the mesh size of the parts in the rear of the middle beam in vehicle. That will be effective to improve the calculation efficiency [1]. Because the number of displacement modes of the quadrilateral shell element is higher than that of the triangular shell element [2], and the calculation accuracy is also higher, so we control the proportion of triangular shell element below 2%. Finally, the model quality examination is in process according to software grid quality standard.

2.2 Material Properties The material of subframe is QSTE420TM, the material of body-in-white is uniformly set to high-strength steel, and the thickness of each part is given according to the thickness of the original parts. Among them, the thickness of the subframe body is 2 mm, and the thickness of the subframe tower is 2.5 mm.

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3 Model Verification In order to verify the accuracy of the established finite element model, it is necessary to carry out a static test. The load is applied to the subframe-body-in-white system to obtain the strain data of the gauge point, and the stress value is calculated by the relevant mechanical formula. At the same time, the subframe-body-in-white finite element model is constrained and loaded under the same conditions as the test. The static simulation analysis is completed and compared with the test results to improve the accuracy of the finite element model.

3.1 Test Results 12 strain rosettes are installed on the subframe where the damages are likely to occur, then the body on door sill beam is fixed by T-slot square box. Load is exerted from the left, right control arm and the engine swing support to measure gather strain signals of the strain gauge. According to the stress calculation formula [3], the strain results are transformed into stress results and compare with the static simulation results of the subframe-body-in-white system under the same conditions (Fig. 1). Strain rosettes stress formula of 45◦ In three axis    1+μ E 1−μ max 2 2 σmin = (ε0◦ − ε−135◦ ) + (ε−135◦ − ε90◦ ) (ε0◦ + ε90◦ ) ± √ 1 − μ2 2 2 Principal strain direction tan 2ϕ =

2ε−135◦ − ε0◦ − ε90◦ ε0◦ − ε90◦

According to the Fourth Strength Theory: Fig. 1 Subframe-body-inwhite static test

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Table 1 Subframe-body-inwhite stress results

Stress value Patch point 1

201.4 MPa

Patch point 2

47.33 MPa

Patch point 3

296.5 MPa

Patch point 4

153 MPa

Patch point 5

83.95 MPa

Patch point 6

206.6 MPa

Patch point 7

65.57 MPa

Patch point 8

323.4 MPa

Patch point 9

324.8 MPa

Patch point 10

178.1 MPa

Patch point 11

239 MPa

Patch point 12

95.58 MPa

 σ Mise =

(σmax − σmin )2 + σmax σmin

The material used in the model is QSTE420 steel. Elasticity modulus: 2.1E5 MPa, Poisson’s ratio: 0.3. The stress results are shown in Table 1. In the subsequent tables of this paper, one test condition is used: applying 16 kN on the left and right control arms, 5 kN on the engine swing support (maximum load measured one in test site) to represent the whole test process.

3.2 The Loading and Result of the FEA According to the test condition, the subframe-body-in-white is restrained as shown in Fig. 2. The six degree of freedom of the white body sill beam is constrained in order to avoid vertical movement of the control arm and restrain the Z-axis movement. The maximum load of each channel is applied to the loading point the same as the static test. Obtain the stress map of the subframe (Fig. 3 and 4). Comparing the test stress value with the simulated stress value at the position of the strain rosettes, the errors show in Fig. 5. The results show that the errors of most stress location are less than 20%, indicating that the finite element model of the subframe-body-in-white basically satisfy the analysis requirements.

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Fig. 2 Finite Element model of the subframe-body- in-white

Fig. 3 Stress nephogram of the subframe (Top view)

Fig. 4 Stress nephogram of the subframe (Inferior view)

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Fig. 5 Errors histogram between the simulation and test

Fig. 6 The FEA model of the subframe test bench

4 The Design and Optimization of Subframe Test Fixture 4.1 Build the Model for Optimization In the subframe test bench, the fixture directly connects to the subframe. In order to ensure that the characteristics of the subframe on the test bench are the same as those on the body-in-white, it is necessary to ensure that the stiffness of the test bench fixture is the same as the body-in-white [4]. In order to calculate accurately the stiffness of subframe test bench, it is usual to simplify that as cantilever beam model according to the structural characteristics, which means base fixture and loading from upper end. The subframe test bench finite element model is established in HyperMesh, you can see details in Fig. 6. In the figure, the fixture 1 and the fixture 2 connect to the tower, and the fixture 3 and the fixture 4 connect to the subframe body. Load 16 kN to the left and right control arms, meanwhile load 5 kN to the engine support, and the load direction is the same as the static test load with subframe-body-in-white. The six-degree freedom at the bottom of the finite element model of the fixture is constrained to simulate the constraint of the subframe on the test rig. Since all the fixtures have similarities in length and width, but they are not exactly the same. So it is necessary to set the fixture 1 and the fixture 2 in the same initial

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Fig. 7 The fixture design variables

Table 2 Variable statistics table

Ghtname

Initial value

Ghtrange

Ghtheight

bar1_1

20.00

20.00–55.00

100.00

bar1_2

35.00

20.00–55.00

bar2_1

20.00

20.00–55.00

bar2_2

35.00

20.00–55.00

bar3_1

35.00

20.00–55.00

bar3_2

50.00

20.00–55.00

bar4_1

35.00

20.00–55.00

bar4_2

50.00

20.00–55.00

205.50

size, and the fixture 3 and the fixture 4 in the same initial size. The design variable name and specific location of each fixture are shown in Fig. 7. Considering that the diameter of the bolt connecting the subframe with the fixture is 18 mm, the low limit is set to 20 mm and the upper limit is 55 mm. The initial value and variation range of the design variables are shown in Table 2.

4.2 Optimization The sequential quadratic programming method (SQP) can effectively solve the optimization problem of nonlinear constraints. The SQP method not only use the value information and the first derivative information of the objective function and the constraint function, but also use the second derivative information of the objective function and the constraint function. Therefore, the convergence speed is faster and the efficiency is higher. It is recognized as one of the best nonlinear constrained optimization algorithms. Usually there are three stages for the SQP method: the first

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is the update of the Hessian matrix of the Lagrangian function (using the BFGS method); the second is the solution of the quadratic programming (QP) problem; the third is the calculation of the one-dimensional search and the objective function [5]. Take the optimization problem with equality constraints as an example Min f (x) s.t.h(x) = 0 Corresponding Lagrange function L(x, λ) = f (x) + λT h(x) Taylor expansion at the current point       T   1    L x (k+1) , λ(k+1) = L x (k) , λ(k) + ∇ L x (k) , λ(k) x (k+1) − x (k) + x (k+1) − x (k) H (K ) x (k+1) − x (k) 2

H(K) is the Heisen matrix in the Formula In order to avoid the intervention of λ(k) , replace it with variable scale matrix of quasi-Newton method. 令 s (k) = x (k+1) − x (k) In order to ensure the feasibility of the detection point, Taylor expansion should be done for the equality constraint function in X ˆ((k). Get the linear approximate expression of the constraint function and make the detection point satisfy the constraint condition, that is:

 T h x (k+1) = h x (k) + ∇h x (k) s (k) = 0 As for the above formulas, omit the constant term and the term containing Lagrange multiplier, a quadratic programming sub problem is formed.  T 1  T min Q P s (k) = ∇ f x k s (k) + s (k) H (k) s (k) 2 (k)  k T (k) (k)

=h x + ∇h x s.t.h s s =0 The optimal direction s (k) can be obtained by solving the above quadratic programming subproblem. The next iteration point is obtained by one-dimensional optimization along s (k+1) . For non-linear programming problems with inequality constraints: min f (x)

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h(x) = 0 s.t. g(x) ≥ 0 It is recognized that the corresponding quadratic programming sub-problems can be obtained by the same deduction method:  T 1  T min Q P s (k) = ∇ f x k s (k) + s (k) H (k) s (k) 2 

 T h s (k) = h x (k) + ∇h x k s (k) = 0  T s.t. (k)

= g x (k) + ∇g x k s (k) ≥ 0 g s The solving process of the Sequential quadratic programming: (1) Select the initial point x0 , ak and corresponding positive definite symmetric matrix Hk ; (2) Solve the quadratic programming sub problem QP, determine the Lagrange multiplier vector λk+1 and the search direction vector dk . (3) Determine the step factor ak , let xk+1 = xk + dk ak ; (4) According to the convergence of the analysis structure, if it satisfies ||xk+1 − xk || ≤ ε, the iteration can be stopped. (5) Modify Hk , let k = k + 1, iterate again In the process of the subframe’s research and development, the fatigue test bench is built. Considering the forward design method, it is necessary to set the less error stress value of the patch points 1, 3, 4, 7, 8, 9, 11, 12 in the static simulation analysis as the target for multi-objective optimization design. Select the System-identificationoptimization method, after 236 iterations, the appropriate fixture size is obtained while approaching the target value. The objective function value and the stress value at the eight patch points and the errors of the subframe-body-in-white system simulation stress value are shown in Figs. 8 and 9. Fixture sizes are selected according to the optimization results of sequential quadratic programming and the accuracy in the actual manufacturing process as shown in Table 3. The optimized fixture is shown in Fig. 10. The stress and test values at the optimal solution and their errors are shown in Table 4.

4.3 Fixture Verification Based on Static Test Engineers build the test bench according to the optimized fixture size, and then do the static test. The instrument and test process used in this test are the same as the subframe-body-in-white static test. In the test, the same subframe is used as that in the subframe-body-in-white static test with strain gauges. The corresponding stress results are calculated according to the collected strain results. From the results, it is

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Fig. 8 Objective function Value in optimization process

easy to find the strain gauge of number 9 has been damaged and the other 11 strain gauge are valid. The stress errors between the subframe test bench and the subframe-body-in-white test are made into a histogram, as shown in Fig. 11. The blue in the figure represents a 9 kN load applied to the control arm, the yellow represents a 16 kN load applied to the control arm, and the gray represents a −7 kN load applied to the control arm. In order to verify the difference between the optimization result and the test result, we collect the simulation stress value of the subframe and the test value of the subframe test bench and the relative errors in Table 5 when it is applied 16 kN load to the control arm and 5 kN load to the engine support. It can be seen from the Table 5 that there are 6 patch points where the errors between the simulated stress value and the test value are within 20% among the 11 effective patch points. And only the patch point 2, 6, 10, 11 have large error, so basically it meets the finite element analysis accuracy requirements.

5 Fatigue Test for Subframe Using FEMFAT fatigue analysis software to analyze the fatigue life of the subframe, predicting the dangerous point of the subframe, finding the weakness in its structural performance, and comparing it with the fatigue test results to verify the accuracy of the simulation can greatly improve the efficiency of the test bench.

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Fig. 9 The errors value with the target in iterative process of sequential quadratic programming Table 3 Optimized fixture size Variable

bar1_1

bar1_2

bar2_1

bar2_2

bar3_1

bar3_2

bar4_1

bar4_2

Results

20.0

31.1

20.0

28.5

24.2

54.1

27.0

55.0

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Fig. 10 The optimized fixture

Table 4 Comparison with the optimization objectives value Optimization value

Target value

Error

Patch point 1

211.58

201.4

5.05%

Patch point 3

282.25

296.5

−4.81%

Patch point 4

126.01

153

−17.64%

Patch point 7

87.36

65.57

33.23%

Patch point 8

255.29

323.4

−21.06%

Patch point 9

237.07

324.8

−27.01%

Patch point 11

208.99

239

−12.56%

Patch point 12

98.10

95.58

2.64%

Fig. 11 The error between the stress measurement on bench and test of subframe-body-in-white

5.1 Real Bench Fatigue Test When the engineers do the fatigue test on the test bench, the road spectrum collected from the Strengthened pavement test site is loaded. In order to verify the repeatability of the test, the test number is 5. The subframe test bench layout is shown as Fig. 12. The test results are shown in Fig. 13.

Design of Front Subframe Fixture based on System Identification … Table 5 Comparison of simulation value and test result of subframe fixture

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Simulation

Test

Patch point 1

211.58

221.11

4.50%

Patch point 2

77.4

44.45

42.57%

Patch point 3

282.25

290.40

2.89%

Patch point 4

126.01

146.16

15.99%

Patch point 5

113.6

112.20

−1.23%

Patch point 6

350.1

205.40

41.33%

Patch point 7

87.36

106.83

22.29%

Patch point 8

255.29

304.60

19.32%

Patch point 9

237.07

//

//

Patch point 10

117.71

243.71

107.04%

Patch point 11

208.99

300.92

43.99%

Patch point 12

98.10

111.90

14.07%

Fig. 12 Layout of subframe test bench

5.2 FEMFAT Simulation Fatigue Calculation The finite element model of the subframe fatigue test rig is designed by the multiobjective optimization design of the subframe test bench fixture, and the stress results of the unit load applied to the left and right control arms of the subframe and the engine mount are calculated respectively. It is associated with the load spectrum of the three loading points of the left and right control arms and the engine mount. Stressbased fatigue analysis and design are used due to the high cycle fatigue performance of the front subframe [6]. The S-N curve fitted with QSTE420TM static parameters is shown in Fig. 14. Engineers use FEMFAT software to set the manufacturing process parameters, set the surface roughness to 60 (Smoothed), set the surface treatment process to the general surface treatment factor, and set the survival rate to 90%. It is necessary to keep other parameters as the software default settings, for example, solder joint

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Fig. 13 Crack locations Fig. 14 S-N curve of materials

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evaluation, stress correction, wall thickness processing, etc. Next step is calculating the damage value of the subframe based on the Average-Stress-Effect method in FEMFAT4.1. After calculation, the cloud map of the subframe fatigue life shows in Fig. 15. In order to compare the simulation results with the test results, the fatigue life test results of the subframe are combined with the dangerous area of the fatigue calculation results. It can be seen that the fatigue calculation results cover most of the test results, but there are still some differences. In the figure, P1 is crack position of the fatigue tests. In the simulation calculation, some of them are also dangerous areas, such as P1, P2, P3, P7, P9, etc. These positions are low cycles in the simulation, which may lead to fracture; There are many dangerous areas shown in the calculation, which basically include the location of cracks in the test, but some areas (such as the F1) show as dangerous areas, and no cracks appear in the test.

Fig. 15 Simulation fatigue life results

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There are also some differences between the test results and the simulation results. For example, the simulation result of the test crack position P6 and P8, is a safe area. The reasons are summarized as follows: 1. The test crack position P6 is located at the joint of the subframe and the strut, but in the simulation part, because the rod is not the main bearing part, so this should be simplified considering the finite element calculation efficiency. When calculating the fatigue, there is no fatigue damage at this point. In the future, it should pay attention to this when our engineers do this related simulation. 2. Similarly, as for the crack P8, there is no solder joint near it and it does not appear near the corner. There is also no stress concentration at this point. When engineers do the simulation, there is also no damage at this point. And P8 only appears once in five tests, so it may be caused by factors such as parts production process.

6 Conclusion As conclusion, this report includes taking one front subframe as the research object, building the finite element model of the subframe and the body-in-white, and doing the simulation analysis. Moreover, it is important to verify the accuracy of the model by comparing with the test results. Setting the stress value in the static simulation analysis as the target for the multiobjective optimization design and comparing with the physical test bench to verify the feasibility are great methods to design the fixture. According to the designed fixtures, calculating the fatigue life and comparing the fatigue analysis results with the test results reflect the actual crack location. It is verified that the fatigue life simulation method based on the test bench is feasible. In this paper, it is significant to build the technical process of the subframe fixture optimization design. This method has high feasibility and promotion, which can be applied to the design of most parts of automobiles. Finally, it also has high engineering use value.

References 1. 杨越凯 (2006) 某轿车车身有限元建模及静动态特性分析.东北大学 2. 张胜兰 (2007) 郑冬黎. 基于Hyperworks 的结构优化设计技术. 北京:机械工业出版社, pp 80–90 3. 聂毓琴 (2009) 孟广伟. 材料力学. 机械工业出版社, 北京 4. 陈栋华, 胡文伟, 周炜, 李文斌 (2011) 轿车副车架道路模拟试验台架优化设计及应用[J]. 汽 车与配件 (36):42–43 5. 叶秉良, 俞高红, 戚金明 (2008) 基于SQP法的拖拉机最终传动可靠性优化设计. 农机化研 究 (04):5–8+13 6. 李波, 徐泽民, 李方, 张新申 (2003) 试验设计与优化. 中国皮革 (01):26–28

Fatigue Analysis of Truck Frame Using Virtual Proving Ground Chao Fang, Tian-bing Li, Hao-ren Wang, and Xiao-jie Sun

Abstract Aiming at the problems of long test cycle, high cost and unable to evaluate the development scheme of vehicle in the early stage, the durability solution based on virtual proving ground has been widely used in vehicle development. In this paper, the fatigue analysis of truck frame is proposed based on VPG. Three key technologies, high precision laser scanning of pave, tire parameter identification and full vehicle modeling are discussed. The dynamic model of a truck with flexible frame is established. Based on the durability test criterion of the proving ground, the dynamic load decomposition of the frame is completed, and then the fatigue life evaluation of the frame is carried out, which verifies the feasibility of this new technical scheme. Keywords Virtual proving ground · Flexible frame · Fatigue analysis · Pave scanning · Tire parameters identification

1 Introduction In the early stage of automotive development, it is necessary to obtain accurate dynamic response of the full vehicle for evaluating the riding and durability performance of the vehicle. The existing technical schemes require the load test of the prototype on the proving ground or actual road. Due to the limitation of prototype and road/pave test, it is impossible to evaluate the design scheme at the early stage of design without prototype. Meanwhile the repeated pave test caused by the change of the state of the prototype would take a long time and high cost. The establishment of Virtual Proving Ground (VPG) technical capability can reduce the dependence on physical prototype. In the early stage of automotive development, virtual road load C. Fang · T. Li · H. Wang · X. Sun (B) Wisdplat Automotive Technology Co., Ltd., Shanghai 201204, China e-mail: [email protected] X. Sun Shanghai Institute of Technology, Shanghai 201418, China © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_4

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data acquisition via VPG can be collected to complete the fatigue life, riding comfort and NVH prediction without prototype. That can shorten the test time and reduce the development cost. VPG technology can obtain the loads of body and chassis components in a fully virtual environment. At present, VPG technology has been applied in GM, Nissan, Mira Technology Center, Chrysler, Opel and other foreign automobile enterprises [1–5]. Since 2015, Wisdplat had cooperated with foreign advanced automotive consulting companies, and provided VPG related technical services to more than ten Chinese automotive OEMs, covering the fields of road scanning, tire parameter identification, load decomposition and fatigue analysis. With the rapid development of VPG technology in China, a large number of automobile OEMs and research institutes are gradually building their technological capabilities. FAW, SAIC passenger cars, PATAC, GWM and CAERI have published relevant papers [6–10]. However, the research and application of the virtual proving ground are mainly concentrated in the field of passenger vehicles. The application of VPG technology has been restricted as the complex modeling of leaf spring and the large flexible file of truck frame. In this paper, a flexible frame dynamic model of light truck based on plot method is presented. The dynamic load of the frame is obtained by VPG technology, and then the fatigue life of the frame is evaluated. The feasibility of VPG technology in the field of commercial vehicles is verified.

2 Constructing VPG Environment for Truck The core contents of VPG include road surface modeling via laser scanning, advanced tire model and precise vehicle dynamic model. The pave surface modeling uses laser scanning system to obtain high-precision grid data (up to 5 * 5 mm); the tire obtains characteristic parameters through lab experiments to identify and generate flexible ring FTIRE model; the vehicle model uses refined modeling to verify the geometric and characteristic responses of key components, taking into account the flexibility of components to form a more accurate vehicle model.

2.1 Creating Durability Pave Model In the process of road digitization, the vehicle-mounted road scanning system is used to obtain the coordinates of the point cloud of the road based on the principle of laser reflection ranging with a high resolution (up to 2 million points per second) laser scanner, and then to process and form a grid road file. The test system integrates a variety of high-precision sensors and is eventually installed on the vehicle. It is convenient for mobile transportation and has high measurement efficiency. The

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Fig. 1 Digital CRG model of cobble stone

digital grid pavement adopts CRG format with 5 * 5 mm in mesh size, and the open virtual pave standard designated by the working team composed of Audi, BMW, Daimler and other vehicle R&D centers in 2008. Figure 1 is a typical CRG files diagram of cobble stone pave.

2.2 Modeling of Tires Tires have a direct impact on the traction, braking, stability, ride comfort, crosscountry performance and fuel economy of automobiles. According to the mechanical characteristics of tire, physical structure model is used to replace tire structure, and physical structure deformation is used as tire deformation. Typically, Ftire and CDTire are used to simulate the ride comfort and durability of tire. The general practice in vehicle development is to send tires to a professional tire testing laboratory, obtain the original tire performance data through a series of tests, and then identify the tire model which is close to the physical structure. In this paper, the Ftire tire model is adopted. It is completely nonlinear and has a frequency response in 120–150 Hz. Figure 2 shows the Ftire parameter identification results of a truck tire. From the comparison curves between the simulation and the test data, it can be seen that when the FTire tire model rolls over the drum at different rotating speeds under different tire pressures and vertical loads, the longitudinal force and vertical force characteristics in the time domain are in good consistent with the test data, and the frequency response of the model simulation in the frequency domain is very high. And it has high simulation accuracy, which can fully meet the simulation requirements of vehicle ride comfort simulation and other vibration conditions, and ensure the accuracy of simulation results. From the simulation and test curves, it can be seen that the identification accuracy of all directions is relatively high except the aligning torque. Meanwhile, the tire positive moment is relatively small, integrated FTIR is sufficient to meet the VPG simulation requirements.

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a)Vertical stiffness

b) Longitudinal stiffness

b)Lateral stiffness

d) Aligning torque

Fig. 2 Tire parameter identification of Ftire

2.3 Multi-body System Model of Truck There are many factors affecting the calculation of load spectrum of commercial vehicles, including tires, leaf springs, cab suspension, shock absorber characteristics, flexibility of frame and chassis parts, etc. The results show that the leaf spring, shock absorber characteristics and the flexibility of the frame have great influence on the vehicle dynamics modeling and simulation. Leaf spring plays the role of body support and shock absorption. On the durability road surface, the leaf spring will inevitably have a larger displacement. Therefore, it is necessary to ensure the accuracy of leaf spring modeling. At present, there are three methods for leaf spring modeling: leaf spring modeling based on FEM(Finite Element Method), SAE three-bar method and discrete beam simulation method. FEM method has high accuracy and can accurately reflect the dynamic and kinematic characteristics of leaf spring. However, the model is too complex and the degree of freedom is too much, which results too long simulation time to be used in VPG simulation. SAE three-bar method can establish leaf spring model with reduced parameters, and has certain simulation accuracy, which reduces

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the running time of vehicle dynamics simulation. However, it is difficult to accurately reflect the leaf spring characteristics with gradual stiffness or conforming stiffness, especially the siding torque in VPG simulation. Beam discrete modeling method can effectively reduce the degree of freedom of vehicle model and the amount of calculation, but the degree of freedom is more than the three-bar leaf spring model. So the discrete beam method is adopted in this paper. The discrete beam method divides each spring into a certain number of small mass blocks. The adjacent small mass blocks are constrained by mechanical elements without mass. The single mass block and mechanical element are shown in formula (1). ⎤ ⎡ K 11 Fx ⎢F ⎥ ⎢ 0 ⎢ y⎥ ⎢ ⎢ ⎥ ⎢ ⎢ Fz ⎥ ⎢ 0 ⎢ ⎥=⎢ ⎢ Tx ⎥ ⎢ 0 ⎢ ⎥ ⎢ ⎣ Ty ⎦ ⎣ 0 Tz 0 ⎡

0 K 22 0 0 0 0

0 0 K 33 0 0 0

0 0 0 K 44 0 0

0 0 0 0 K 55 0

⎤⎡ ⎤ ⎡ x −l C11 0 ⎢ ⎥ ⎢ 0 ⎥ ⎥⎢ y ⎥ ⎢ C21 ⎥⎢ ⎥ ⎢ 0 ⎥⎢ z ⎥ ⎢ C31 ⎥⎢ ⎥−⎢ 0 ⎥⎢ A x ⎥ ⎢ C41 ⎥⎢ ⎥ ⎢ 0 ⎦⎣ A y ⎦ ⎣ C51 Az C61 K 66

C12 C22 C32 C42 C52 C62

C13 C23 C33 C43 C53 C63

C14 C24 C34 C44 C54 C64

C15 C25 C35 C45 C55 C65

⎤ C16 C26 ⎥ ⎥ ⎥ C36 ⎥ ⎥ C46 ⎥ ⎥ C56 ⎦ C66

(1) Force element connection is a kind of flexible connection. Block and block can have relative displacement. This small displacement can simulate leaf spring deformation. The leaf spring model is shown in Fig. 3. The frame of commercial vehicle is the main bearing component, which plays an important role in the force analysis of frame and driving axle. The stiffness and mass distribution of the flexible body model directly determine the load distribution characteristics. Therefore, the flexible body of the FE model MNF mainly must ensure the flexural and torsional stiffness of the frame. In this case, the frame is modeled by stiffness/strength analysis, as shown in Fig. 4. The bolt connection is simulated by rbe2 and beam elements, as shown in Fig. 5, and the solder joints are simulated by rbe3 and hexa elements, as shown in Fig. 6. The mesh size of frame model unit is 10 mm, and the number of units is about 150,000. The leaf spring model of commercial vehicle VPG is simulated by beam element, which brings some difficulties to simulate. At the same time, Ftire tire model is also a non-linear model. The simulation would be very slow and cost too much time. If the MNF file (1.7G) is used directly, the file is too large to simulate the whole vehicle.

Fig. 3 Model of leaf spring

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Fig. 4 Model of truck frame

Fig. 5 Model of bolt

Fig. 6 Model of wielding point

Therefore, a suitable scheme for VPG technology is needed to shorten the size of flexible frame without reducing the precision. The modal values of the two methods are shown in Fig. 7. The comparison shows that there is almost no difference between the two methods.

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Fig. 7 Comparison of modal (left: original, right: new)

2.4 Model Benchmark In order to improve the accuracy of load decomposition simulation, it is necessary to adjust the vehicle dynamics model before simulation to ensure the accuracy of the model. The adjustment includes static adjustment and dynamic adjustment, among which the static adjustment is chassis K&C verification. Typical K&C simulation and test conditions are selected to mark, and the parameters of typical elastic components such as stiffness and clearance of buffer block, bushing stiffness, spring stiffness and free length are adjusted. The simulation and measurement of steering and bump stability and ride comfort conditions are calibrated as the dynamic adjustment, and the vehicle mass, centroid parameters, steering parameters, elastic component damping and dynamic stiffness of bushing are also adjusted.

3 Dynamic Load Decomposition via VPG Based on digital road surface, Ftire tire model and flexible frame, the VPG simulation environment of the whole light truck dynamics model is built as shown in Fig. 8. At present, more than ten paves are involved in the vehicle durability test. Dynamic load decomposition simulation based on VPG not only has different simulation speed and time for each pavement, but also takes a lot of time to extract load for each pave. Therefore, engineers need to spend a lot of time to set the parameters, wait for simulation results and data post-processing. Wisdplat independently developed SimCar_VPG software. One hand, it can simulate and extract load report in batch mode, which would greatly improve the work efficiency and save the time; the other hand, it can encrypt and protect the core data such as digital tire and road model. For the authorized users can normally use the core data, but they can’t obtain or modify. Data sharing in different departments or even with the third parties would be necessary for this function. At present, the software has obtained the copyright

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Fig. 8 VPG simulation of truck

a Main interface

b

Multi-condition setting interface

Fig. 9 SimCar_VPG GUI

of the China national software. The user interface of the software is shown in Fig. 9. The results of the vertical dynamic load decomposition of the wheel center under two typical durability conditions of Belgian road and pothole are shown in Fig. 10.

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Fig. 10 Part dynamic response result via VPG simulation

Fig. 11 Fatigue analysis result of truck frame

4 Fatigue Analysis of Truck Frame Based on the vehicle multi-body dynamic model, the dynamic simulation of the road surface of the durability specification is carried out by VPG technology, and the dynamic loads of all the attachment points of the frame are obtained for the calculation of fatigue analysis. In this analysis, quasi-static linear superposition method is used to calculate fatigue. Firstly, unit loads in six directions are applied to the attachment points of the frame and chassis, and the stress response results under unit loads are obtained by inertial release methods. Then the fatigue analysis of the frame is carried out. The fatigue damage results of the frame are calculated considering the effects of stress gradient, average stress, elastic-plastic deformation, wield and wield joint. According to the analysis results, the maximum damage of the frame meets the reliability fatigue durability target, as shown in Fig. 11.

5 Summary and Outlook This paper summarizes the process and analysis method of truck using VPG, solves the problem of truck frame flexibility, and achieves the purpose of evaluating the

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durability of truck in the early development stage of the project without porotype, and it is great significance for guiding the strength and fatigue life design of truck components. Limited by research time and funds, the related frame fatigue field test verification has not been implemented; the problems of flexible shock absorber and bushing dynamic stiffness involved in improving the precision of truck using VPG have not been discussed, and these further research and verification will be carried out in the future work. Funding The author(s) received the support of Shanghai Alliance Program (LM201739) for the research.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References 1. Schudt JA, Kodali R, Shah M et al (2011) Virtual road load data acquisition in practice at general motors. In: SAE 2011 World Congress & Exhibition 2. Hong HJ, Strumpfer SD (2011) Virtual road load data acquisition for twist axle rear suspension. In: SAE 2011 World Congress & Exhibition 3. Roy N, Villaire M (2013) Virtual road load data acquisition using full vehicle simulations. SAE Techn Pap 2. https://doi.org/10.4271/2013-01-1189 4. Tasci M, Tebbe JC, Davis JD et al (2011) Development of 3-D digital proving ground profiles for use in virtual prediction of vehicle system/sub-system loads. In: SAE 2011 World Congress & Exhibition 5. More R, Vachhani D, Raval C (2015) Durability prediction of rear engine bus using virtual proving ground road loads. SAE Techn Pap 2015-26-0237. https://doi.org/10.4271/2015-260237 6. Zhenglin C, Jun L, Konghui G (2012) Research on passenger car suspension durability using virtual proving ground. J Mech Eng 48(10):122–127 (in Chinese) 7. Ligang L (2015) Load prediction based on virtual pavement. Shanghai Auto 1:22–27 (in Chinese) 8. Xinxin X, Feng S, Ruifeng W (2016) Strength analysis on rear steering knuckle based on virtual proving ground based on motionview. Comput Aided Eng 25(6):46–50 (in Chinese) 9. Sun C, Duan X, Weng Y et al (2017) A study on the evaluation method of vehicle durability performance based on 3D digital road. Automot Eng 2017(10):1211–1216. (in Chinese) 10. Bing R, Pan X, Jianwen Z (2018) A fatigue comparative analysis of chassis based on simulated road load spectrum and measured road load spectrum. J Vib Shock 37(12):179–186 (in Chinese)

A Digital Road Construction Method Applied in Virtual Proving Ground Technique Shuai Zhou, Yunping Zhou, Jian Shao, Hua Wang, and Zhongling Jiang

Abstract In recent years, virtual proving ground (VPG) technique has been widely used in vehicle multi-body dynamics analysis. This paper mainly focuses on the method to construct the digital roads incorporated in the virtual proving ground. At first, the Mobile Road Scanning System was introduced to acquire point clouds of the physical test roads of an actual proving ground located in southwest of China. Then, considering both road characteristics and development requirements, a CRG road surface model construction methodology was proposed. In the end, digital surface models of the physical test roads were constructed and coupled in full-vehicle simulations. As a result, the simulation based on the proposed method can predict the external loads of wheel parts under various loading cases, by comparing with load data acquired by experiments. In consequence, our research will assist in providing an efficient tool for the analysis of vehicle system/sub-system loads. Keywords VPG · Digital road · Road surface model · Road load data

1 Introduction The integration of digital road, vehicle and driver within a MBD analyzing environment, which is usually called virtual proving ground, offers an innovative and effective way to predict vehicle system/sub-system loads for detailed analysis of durability as well as comfort & ride. Due to its advantages of earlier appearance in the development process, low cost and time-saving et al., it has gradually replaced conventional physical test [1–3]. High-precision road surface model that can accurately retain all the unevenness and irregularity of the real road surface, is a prerequisite in the development of virtual proving ground. Based on the Inertial Reference Road Profile Detection System, which is made up of 5 laser sensors and vertical acceleration sensors, a number of real road surfaces in China were reproduced by Zhu et al. [4], Yan et al. [5], Rong et al. [6] S. Zhou (B) · Y. Zhou · J. Shao · H. Wang · Z. Jiang Chongqing Changan Automobile Co., Ltd., Chongqing 400023, China e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_5

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through mathematical methods including AR model and Spline Interpolation;Using the General Motor’s Road Scanner, a quasi-static measuring device, Mine Tasci et al. [7] digitized several ride ad durability roads of Milford Proving Ground and further studied the simulation difference between 2-D profile and 3-D profile. Above research efforts only discussed construction methods of road surface model within specific conditions, which to some extent has limitations in broader application scenarios. Therefore, the authors present a more adaptive digital road construction method from the perspective of practical development of virtual proving ground.

2 Scanning of Proving Ground 2.1 Mobile Road Scanning System The Mobile Road Scanning System shown in Fig. 1 is comprised of laser scanning sub-system, positioning sub-system, cabinet sub-system and generator sub-system. All the equipment of above sub-systems are integrated in an aluminum frame that rigidly connected to the vehicle through roof rack and tail hook. PPS 90 pavement scanner shown in Fig. 2 is the core instrument of the laser scanning sub-system. The distance between road surface and scanner can be measured in real time as the scanning vehicle moves forward. The specifications of the laser scanning sub-system are presented in Table 1. Since the valid sampling angle is 70°, the scanner has to be placed at a position of 3 m above the ground, as illustrated in Fig. 1, to meet the development requirement of 4 m of road width. The Inertially-Aided Real-Time Kinematic technology is used by the POS LV positioning sub-system to determine the position of the scanning vehicle in case of Fig. 1 Mobile road scanning system

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rough GPS signal reception conditions. Its specifications are presented in Table 2. In order to stabilize the dynamic positioning data, 4 Distance Measurement Indicators are installed at the wheel centers of the scanning vehicle, and a GNSS reference station is installed on the top of a building close to the proving ground as well (Fig. 3). The cabinet sub-system that integrates servers, disk arrays, processors, time card is arranged inside the scanning car and have been especially fastened to reduce vibration. The generator sub-system is fixed in the middle of the aluminum alloy frame that on top of the scanning vehicle to ensure the power supply safety of the whole road scanning system (Fig. 4). Fig. 2 Laser scanning sub-system

Table 1 Specs of laser scanning sub-system

Table 2 Specs of POS LV positioning sub-system

Fig. 3 Distance measurement indicators & GNSS reference station

Scanning frequency/Hz

800

Sampling angle/°

70

Distance/m

1.3–5

Wave length/μm

1.5

Height resolution/mm

0.2

Laser output/mW

>100

X position/cm

2

Roll angle/°

0.015

Y position/cm

2

Pitch angle/°

0.015

Z position/cm

5

Heading angle/°

0.02

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2.2 System Calibration Several devices of the scanning system have been calibrated to ensure the validity of the acquired data: 1) Positioning sub-system calibration; 2) Laser scanning sub-system calibration; 3) Timestamp check for all measurements sensors. In order to calibrate the POS LV positioning system, all the lever arms in the positioning system, including lever arms to distance measurement indicators, lever arms to the GPS antennas et al., are calibrated individually to determine the reference center of the scanning vehicle so that the coordinates of measurements could be corrected. It is required to calibrate the inner and outer systematic errors to improve accuracy of the laser scanning sub-system. The inner errors are calculated with a special metal beam, as illustrated in Fig. 5. Several characteristics of the metal beam have been measured with a high-precision instrument in advance to correct the inner errors induced by the laser scanning system itself. The outer errors of coordinate differences between laser scanning system and POS LV positioning system are calibrated to ensure data consistency of the whole system.

Fig. 4 Cabinet sub-system & generator sub-system

Fig. 5 A special metal beam

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Time synchronization of each sensor was performed by the processors in the cabinet system to obtain a unified time base.

2.3 Clean-Up of Proving Ground The proving ground has been well cleaned up before the scan to avoid interference that may be caused by superficial obstacles such as weeds and gravel.

3 Construction of Digital Roads The Curved Regular Grid road surface model jointly developed by Daimler AG, BMW AG et al. [8], is a preferable option of road surface model in the development of virtual proving ground due to its abilities of accurately representing road elevation, slope and inclination angle in a manageable size.

3.1 Principle of CRG Road Surface Model The principle of CRG road surface model is mainly comprised of two parts: one curved reference line and the other curved regular grid, as shown in Fig. 6. The centerline of the real road surface is usually a smooth curve in three-dimensional space XYZ. Its projection in Z direction then becomes a two-dimensional curve on the XY plane, which is the so called curved reference line. Any position on the XY projective plane can be conveniently described by a curvilinear coordinate system UV that constructed by the curved reference line. The U-axis positively takes the Fig. 6 Principle of CRG road surface model

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direction of advancement of the curved reference line, and the V-axis is perpendicular to the U-axis all the time. The U-axis and the V-axis are both segmented in fixed increments to construct the curved regular grid lattice on the UV plane. The direction of advancement of each segment on the U axis is determined by azimuth angle ϕ. Given the starting point of the reference line, the end point is determined by integration of azimuth angles of all the segments.

3.2 Construction of Curved Regular Grid The position and shape of the curved reference line are determined before building the curved regular grid. Since the Inertially-Aided Real-Time Kinematic technology has been utilized by the mobile road scanning system to obtain inertia-corrected coordinates in the earth coordinate system, the curved reference line to be constructed is only a two-dimensional curve. By curve fitting, several control points are integrated to approximate the real road centerline, since the curvature is relatively small. A zoom view of the point cloud of the vehicle body torsion road (TOR) is illustrated in Fig. 7. It can be seen that the trapezoidal obstacles that forcing the vehicle body to twist are staggered on each side of the road, and the centerline and boundary of the road are clear. Judging from such kind of characteristics, a control point is manually picked up on the centerline every 4 m. A total of 12 control points were picked up, as shown in Fig. 8. The work flow for construction of curved regular grid points is illustrated in Fig. 9. The real road centerline with above 12 control points is fitted with a thirdorder polynomial function. The road centerline is segmented by a distance of 5 mm, and then the X, Y coordinates of each segment are calculated by the fitting curve. Given the coordinates (xi−1 , yi−1 ), (xi , yi ) of the ends of segment i, the azimuth angle ϕi can be calculated by Eq. 1. Simultaneously, other X, Y coordinates of the grid points on that cross section can be calculated by the normal line equation of plane. Fig. 7 Zoom view of the point cloud of TOR

Fig. 8 Control points of TOR

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Fig. 9 Flow chart of construction of curved regular grid points

Above steps were repeated for cross sections at each node along the curved reference line to obtain the plane coordinates of all curved regular grid points.    ϕi = arctan (yi − yi−1 ) (xi − xi−1 )

(1)

Figure 10 shows a comparison between the constructed curved regular grid points and the original point cloud. It can be seen that the outline of the grid points is basically within the scope of original point cloud, and the curved reference line coincides well with the road centerline.

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Fig. 10 Comparison between curved regular grid points and point cloud

Fig. 11 Distance between scanned points

3.3 Interpolation of Elevation of Grid Points The grid points constructed are generally at different locations from the scanned points, hence, interpolation has to be performed to obtain the elevations of the grid points. As shown in Fig. 11, the distance between scanned points is generally about 3 mm, and the target precision of the road surface model is 5 × 5 mm mentioned before, hence, the interpolation process will not decrease data precision. There are many well-established algorithms for the interpolation of discrete data points, one of which is the scatteredinterpolant class in the numerical computing language MATLAB [9]. It contains several common discrete data interpolation algorithms, including linear interpolation, nearest neighbor interpolation, natural neighbor interpolation et al. The point cloud of the vehicle body torsion road TOR shown in Fig. 7, which has a number of 20.7 million of scanning points, is interpolated by different algorithms, and the results are presented in Table 3. The mean and standard deviation in the table refer to the overall distance between constructed CRG grid points and the scanned points. As shown in Table 3, the mean value of the overall distance of the three methods is about 3 mm, and the standard deviation is less than 1 mm. Such a statistical performance is close to the precision of the experimental scanning system of the point cloud. The natural neighbor interpolation algorithm is slightly more time-consuming than other two algorithms, but its precision is the highest. In order to obtain the best interpolation effect as much as possible, the natural neighbor interpolation algorithm is finally selected to build the road surface model, as shown in Fig. 12.

A Digital Road Construction Method Applied in VPG Technique Table 3 Results of 3 interpolation algorithms

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Algorithm

Time/s

Mean/mm

Std deviation/mm

Linear interpolation

170

3.04

0.80

Nearest neighbor interpolation

172

3.05

0.89

Natural neighbor interpolation

183

3.04

0.76

Fig. 12 Interpolation results of TOR

Fig. 13 Transitional road

3.4 Construction of Transitional Road The starting section of the constructed road surface model usually has a certain inclination angle, and it tends to result in static convergence problem in the multibody dynamics simulation. As a solution, a 10 m straight transitional section is constructed in front of the starting section of the original road, enabling the vehicle smoothly travels from the static balance position to original road. As shown in Fig. 13, the transitional road is composed of two parts: one flat road and the other ramp road. The elevations of all the grid points on the flat road are 0 in order to solve the problem of static misconvergence. The elevation of each longitudinal profile of the ramp road linearly changes from 0 to the height of the original road, ensuring smooth connections of the three parts.

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4 Validation of the Constructed CRG Road Surface Model Based on the constructed CRG road surface model, the target virtual proving ground is finally realized with complements of FTire flexible ring tire model [10] and rigidflexible coupled vehicle MBD model in the environment of Adams/Car [11]. In order to validate it, a road load data acquisition test was carried out in the actual proving ground. Road spectrum of multiple load cases was obtained, including vehicle body torsion road (TOR), pothole road (CHK), curb road (DRW), stationary stochastic road (BLG), et al.

4.1 Inspection of the CRG Road Surface Model The physical test roads and CRG road surface models are shown in Figs. 14, 15, 16 and 17, and three local features of each road are marked for comparison,as shown in Table 4. Notice that parameter a, b, and c in the DRW road represent the spacing, height and length of the rails, respectively, while in the Belgian road, they are the gap, length and width of the belgian block, respectively. In Table 4, a, b and c are the data of real roads, and a’, b’ and c’ are the data of CRG models. It can be seen that the error of each local feature ranges from 2 mm to 6 mm, which is in the same level as the accuracy of 5 × 5 mm of the curved regular grid. Hence, the constructed CRG model well retains the detailed feature of real road surface, and lays a good foundation for subsequent benchmarking of road load data. Fig. 14 Vehicle body torsion road TOR

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Fig. 15 Pothole road (CHK)

Fig. 16 Curb road DRW

4.2 Validation of the Virtual Proving Ground A 6-component force transducer is installed at each wheel center of the test vehicle, as shown in Fig. 18. It enables acquisition of force and torque time histories of the wheel parts in directions of X, Y, and Z. Before the benchmarking, the multi-body dynamics model of the full vehicle was calibrated according to K&C test results to

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Fig. 17 Stationary stochastic road (BLG)

Table 4 Comparison of local features Road

a/mm

a’/mm

b/mm

b’/mm

c/mm

c’/mm

TOR

999

993

993

994

122

124

CHK

1414

1411

87

89

585

585

DRW

1362

1358

25

27

51

52

BLG

20

20

170

167

200

200

Fig. 18 Vehicle of road load data acquisition test

ensure a consistency of K&C characteristics. The driving behavior of the simulated vehicle is controlled according to the speed and trajectory signal in the road spectrum test, preventing from interference of subjective factors to the validation of the virtual proving ground.

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Changes in the elevation of road surface have more influence on the force in the vertical direction, hence, the vertical forces at the center of the left front wheel and right rear wheel are selected for comparison. Due to space limitations, only benchmarking results of CHK road and BLG road are shown in Figs. 19, 20, 21 and 22. In the CHK road, though load levels of the two wheel parts range drastically from 0 to 10 kN, the peak values, valley values and phases of the time history curves agree well, which indicates that the accuracy of the constructed virtual proving ground is acceptable in time domain. It can be seen from the frequency domain that the excitation of CHK road is mainly below 20 Hz. As for the stationary stochastic road BLG, characteristic of high frequency can be seen from the time history curve, and the amplitude is relatively close. In the aspect of peak values in frequency domain, Fig. 19 Comparison in time domain, TOR

Fig. 20 Comparison in frequency domain, TOR

Fig. 21 Comparison in time domain, BLG

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Fig. 22 Comparison in frequency domain, BLG

the simulation results reach a good consistence with the experimental data, which indicates that the accuracy of this virtual proving ground is acceptable in frequency domain as well.

5 Conclusions An actual proving ground located in southwest of China was scanned by the mobile road scanning system to obtain three-dimensional point clouds of real road surfaces. According to the principle of CRG road surface model, a method of constructing digital roads by scanned point cloud was proposed, and with complements of FTire high-precision tire model and vehicle multi-body dynamics model, the corresponding virtual proving ground was finally realized. Comparison between simulation results and experiments shows that the precision of constructed virtual proving ground is acceptable, which indicates that the constructed CRG model is capable of preserving unevenness ad irregularity of real road surface as well.

References 1. Li J, Song K, Cheng A et al (2011) Fatigue analysis of T joint in auto-body based on virtual proving ground. Automot Eng 5(33):422–427 2. Wu Y, Zhou Y, Wang J et al (2017) Application of virtual proving ground simulation technology in impact fatigue analysis of knuckle. In: Proceedings of 2017 SAE-China Congress & Exhibition. SAE-China, Shanghai, pp 1539–1542 3. Xing R, Liu Y (2018) Method of the dynamic load cascading based on virtual durable road. J Shenyang Aerosp Univ 6(35):39–49 4. Zhu M, Yan J, Wang G et al (2010) Reproduction of three-dimensional virtual road based on ADAMS. Mach Des Manuf 06:171–173 5. Yan J, Zhang K, Xie F et al (2011) Reconstruction and validation of non-straight road model based on ADAMS. Automot Eng 11(33):985–989 6. Bing R, Pan X, Jianwen Z et al (2017) Reconstruction and comparative analysis on 3D virtual intensified road in a proving ground. Automot Eng 2(39):214–231

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7. Tasci M, Tebbe JC, Davis JD et al (2011) Development of 3-D Digital Proving Ground Profiles for Use in Virtual Prediction of Vehicle System/Sub-System Loads. SAE Technical Paper 2011-01-0189. https://doi.org/10.4271/2011-01-0189 8. VIRES Simulationstechnologie Gmbh, Gemany. OpenCRG – Home [EB/OL]. http://www.ope ncrg.org. Accessed 18 Apr 2019 9. The MathWorks, Inc. Interpolate 2-D or 3-D scattered data – MATLAB [EB/OL]. https:// www.mathworks.com/help/matlab/ref/scatteredinterpolant.html?s_tid=doc_ta. Accessed 18 Apr 2019 10. Gipser M (2007) FTire – the tire simulation model for all applications related to vehicle dynamics. Veh Syst Dyn 45(sup1):139–151 11. MSC Software, Inc. Adams – The Multibody Dynamics Simulation Solution [EB/OL]. https:// www.mscsoftware.com/product/adams. Accessed 18 Apr 2019

Research on Evaluation Methodology of Durability of the Plastic Tray System for Battery Junjie Duan, Hailong Mei, Guangyao Wang, Chengzhi Sun, and Shizhan Zhang

Abstract We study the evaluation method of durability of the plastic tray system for battery mainly based on the background of the battery tray gradually transforming from metal parts to plastic parts. Durability of the plastic tray system for battery refers to the ability to maintain good quality for a long time which is usually characterized by service life. After the impact of vehicle load and climatic factors such as temperature and humidity repeatedly, the performance index of battery plastic system will gradually decrease. The paper proposes that the modal is used as the main index to evaluate the durability of the plastic tray system for battery based on the study of acceleration power spectrum density and resonance mechanism of typical vehicles, and we compare the CAE analysis results with the local modal experimental results of the plastic tray system. Finally, the standardized modeling method of the plastic tray system is formed, and the modal target of durability of the plastic tray system for battery is preliminarily determined. Keywords Plastic tray system · Modal · Durability · Modeling method

1 Introduction Lightweight technology has always been a concern of the automotive industry. Composite materials are also applied transforming from vehicle body and external parts to functional parts. With the progress of long glass fiber reinforced plastic technology and being driven by cost pressure, long glass fiber reinforced PP material is gradually replacing metal components and some short fiber reinforced PA materials used in automobile structure [1]. Long glass fiber reinforced thermoplastic composites (abbreviated as LFT) are more prominent in the application of functional parts, of which long glass fiber reinforced polypropylene (LGF-PP) is one of the new varieties of attention. As a vehicle module carrier material, this material can not only effectively improve the stiffness, impact strength, creep resistance and dimensional J. Duan (B) · H. Mei · G. Wang · C. Sun · S. Zhang Technical Center, SAIC Motor Corporation Limited Passenger Vehicle Company, Shanghai, China e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_6

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stability of the product, but also can make more complex automotive module products [2]. The battery tray is usually made of steel plate with thickness of 1.2 mm and material for soft steel. The tray is fixed by the pressure plate, two pull rods, nuts and the battery. It is called “bundled” connection. This fixed way involves many parts, which has a heavy overall quality. In recent years, the fixed mode has been improved slightly, which mainly adopts A tray with a fixed side plate and a small pressure plate are fixed to the bottom of the battery. This fixed method is simple and reliable, but the overall weight is still relatively heavy because of the tray material of steel. So the battery tray is gradually transformed from metal material to composite material, and the long glass fiber reinforced polypropylene material has also been widely used in the battery tray in the process of a project development. Durability of the plastic tray system for battery refers to the ability to maintain good quality for a long time which is usually characterized by service life. After the impact of vehicle load and climatic factors such as temperature and humidity repeatedly, the performance index of battery plastic system will gradually decrease. If the life of the plastic tray system is not reached, the damage of the plastic tray system indicates that durability of the plastic tray system do not satisfy the demand of design requirements. So we need the designer to meet its durability standard and reach its service life. A mathematical model is proposed to calculate chloride ingress profiles in fire damaged concrete, so as to explore the service life prediction of the structure [3]. It is carried out to evaluate the effectiveness of surface treatments on the durability of concrete and suggests a number of different evaluation methodologies for assessing the performance of various surface treatments [4].

2 Performance Requirements of the Battery Tray In order to reasonably evaluate the durability of the plastic tray system for battery, it is necessary to analyze and understand the use requirements, conditions and environment of the battery plastic tray. (1) The battery tray is generally located in the front compartment of the vehicle, which is fixed on the front longitudinal beam through the battery bracket. (2) The battery tray can be loaded according to different vehicle configurations, and the battery weight can reach up to 20 kg. It needs to bear the vertical force of long time bumpy motion and lateral force of acceleration, deceleration or turning in the course of vehicle running. (3) The maximum temperature of the working environment of the front cabin is about 120 °C, and the lowest is generally set to −40°C. Under normal operating conditions, the maximum temperature of the measured battery surface is about 80 °C. It has a great influence on the material’s properties of the plastic battery tray to work in a high temperature environment for a long time.

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(4) The battery tray is easy to contact with other media such as water or oil. So attention should be paid to anti-corrosion performance. (5) For the design principles of automotive parts, we need to consider the requirements of lightweight, recyclability and low cost as much as possible [5].

3 Durability Evaluation Method of the Plastic Tray System for Battery 3.1 Durability Evaluation Index of the Plastic Tray System for Battery 3.1.1

Durability Evaluation Method of the Tray System for Battery

Presently, there are two main methods for evaluating the durability of the tray system for battery in OEMs. One is equivalent to quasi-static evaluation method, and the other is vibration fatigue analysis method based on acceleration spectrum. Comparing with quasi-static evaluation method, vibration fatigue analysis method can evaluate the damage value more accurately, but it needs accurate fatigue curve in the evaluation process, so it is more suitable for metal materials at present, while for non-metallic materials (such as LGF-PP) which are in anisotropic state, and fatigue curves are difficult to obtain in current test conditions, so vibration fatigue analysis method can’t be used to evaluate non-metallic materials. Based on the theory of vibration fatigue, Sanliturk et al. proposed a vibration fatigue life estimation method based on frequency response function, which considered not only the influence of structural elasticity on fatigue life, but also the influence of structural inertia and damping factor on fatigue life. Bishop carried out random fatigue life analysis in frequency domain, expressed fatigue load and realized fatigue damage calculation by power spectral density function; Hanna analyzed the vibration fatigue life of electronic control unit of automobile brake system by using Bishop’s method [6]. Yao Qihang et al. have been carrying out scientific research on structural vibration fatigue since the end of the 20th century [7]. Assuming that the power spectral density of a random excitation is shown in Fig. 1, it is decomposed into Figs. 1(a) and 1(b) according to the superposition property of the power spectrum [7]. If the unique response frequency response function M(i f ) of a single-degree-offreedom system  M(i f ) = 1/ 1 −



f fn

2

  f + i2ζ fn

(1)

In the formula, f n is the resonance frequency and ζ is the damping coefficient.

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Fig. 1 Spectrum density distribution of a random excitation (a): Limited bandwidth flat spectrum distribution (b): Narrow band peak spectrum distribution

If a finite bandwidth flat spectrum in Fig. 1(a) is taken as excitation HE ( f ) 

HE ( f ) = HE , f 1 ≤ f ≤ f 2 HE ( f ) = 0, others

(2)

Crandall [7] pointed out that the mean square response σ of a single-degree-offreedom system in this case ∞

+∞

0

0

σ 2 = ∫ |M(i f )|2 HE d f = ∫

     f2 π f n HE f1 C ,ζ − I ,ζ 4ζ fn fn

(3)

In the formula, ⎤ ⎡



 ⎤ f 2 f + 1 + 2 1 − ζ 2ζ fn fn ζ ⎥ 1 −1 ⎢ ⎥ ⎢ ln⎣ 2 C= ⎦ + tg ⎣



2 ⎦ (4) π f f f 2π 1 − ζ2 − 2 1 − ζ2 fn + 1 1 − fn fn ⎡ 2 f fn

If the narrowband peak function N ( f )

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⎤ ⎡    2 2  2 Af f f ⎦ + /⎣ 1 − f0 g2 fn f0 g

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(5)

If the motivation 

HE ( f ) = N ( f ), f 1 ≤ f ≤ f 2 HE ( f ) = 0, others g = f 0 Δf

(6) (7)

In the formula, f 0 is the peak frequency of the narrow band peak, A is the peak spectral density of the narrow band peak, Δf is the bandwidth between two half peaks (A/2) and g is the sharpness factor. Then f2

σ 2 = ∫ |M(i f )|2 N ( f )d f

(8)

f1

After the quadrature and approximate processing, if f n is close to f 0 , then σ2 ≈

π fn A P 2 2(P + g)

(9)

In the formula, P = 1/2ζ, which is the resonance amplification multiple of a single degree of freedom system. If f n is far from f 0 , its response can be neglected. It can be seen that the system response has a great correlation with the natural frequency of the system.

3.1.2

Energy Transfer Path and Input Stimulation Test of the Plastic Tray System for Battery

For the traditional fuel vehicle, the external excitation of the vehicle is mainly provided by the engine and the pavement during the driving process. For the electric vehicle, the external excitation of the vehicle is mainly provided by the driving motor and the pavement. For the plastic tray system for battery, the input excitation can be considered to be the response of the front longitudinal beam, and Fig. 2 is a schematic diagram of the transfer path. According to the actual transmission path, the acceleration sensors are arranged at the front longitudinal beam for different models in different durability road test pavement, and the data are collected and converted. Figure 3 shows the position map of the acceleration sensor for the left front longitudinal beam of a certain type of vehicle.

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Fig. 2 A schematic diagram of the transfer path Fig. 3 The position map of the acceleration sensor for the left front longitudinal beam of a certain vehicle

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Durability Evaluation Index of the Plastic Tray System for Battery

Figure 4 is the distribution map of acceleration power spectrum density after the data acquisition of various vehicle models. The abscissa is the frequency, the unit of which is Hz. The ordinate is the power spectral density, the unit of which is g2 /Hz, and the unit of g is m/s2 . In the course of vehicle driving, the resonance phenomenon of the vehicle subsystem is easy to occur, that is, the amplitude of the system increases significantly when the frequency of the excitation of the vehicle subsystem is close to the natural frequency of the system. It can be seen in Fig. 4 that the power spectral density of each collection model at some different frequency bands has a sharp peak, that is, the power spectral density at the peak is relatively high, and it can be seen that the vibration energy of the frequency section is relatively high. At the same time, we analyzed the stress level of each part of the battery tray under extreme conditions. The results show that the stress level is relatively low. The stress level is far less than the UTS (Ultimate Tensile Strength) of the tray itself. It can be concluded that the fundamental cause of the damage is not the strength problem, but the damage caused by resonance. So we think that the mode can be used as an evaluation index for durability of the plastic tray system for battery. Therefore, in order to avoid the resonance phenomenon, the mode [8] can be changed to avoid the relatively high frequency of the vibration energy, thus the durability of the plastic tray system for battery can be ensured in the course of road Fig. 4 The distribution map of acceleration power spectrum density

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tests. In addition, the mode is related to the mass and stiffness of the system. The higher the mode, the greater the stiffness is when the system quality is the same.

3.2 Research on the Local Mode Target Setting and Analysis Modeling Method of the Plastic Tray System for Battery 3.2.1

The Local Mode Target Setting of the Plastic Tray System for Battery

The state of road tests is divided into two kinds of normal and damaged states according to the different conditions of road tests of the existing plastic tray systems, that is, according to the damage phenomenon of the plastic tray systems. And three typical road test models are selected for the local modal test of the plastic tray system. The test is mainly based on the static measurement of the local mode of the plastic tray system by hammering method, as shown in Fig. 5. The Bode diagrams below show the results of the partial model tests of the plastic pallet system, which is normal of two typical cases of road tests. It can be seen that the main modal frequency of the plastic tray system of the model 1 is 28 Hz, and the main modal frequency of the plastic tray system of the model 2 is 27.4 Hz in Fig. 6. The Bode diagram below shows the result of the partial model test of the plastic pallet system, which is damaged of a typical case of the road test. It can be seen that the main modal frequency of the plastic tray system of the model 3 is 24.4 Hz in Fig. 7. Thus, the modal target value of durability of the plastic pallet system for battery can be determined to be 28 Hz. Fig. 5 The schematic diagram of hammering method

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Fig. 6 The Bode diagrams of the partial model tests of the plastic pallet system which is normal of two typical cases for road tests

Fig. 7 The Bode diagram of the partial model test of the plastic pallet system which is damaged of a typical case for a road test

3.2.2

Research on the Modeling Method of Local Modal Analysis of the Plastic Pallet System for Battery

There are no analytical modeling methods for the local mode of the plastic tray system for battery, and the different modeling methods have great influence on the simulation results, so it is also necessary to study the modeling method. After a series of analysis and research, the finite element model [9] of the plastic pallet system for battery is built. The following aspects should be considered. (1) The performance parameters of the theory material and the CAE analysis material are considered at the same time because of the self-structural complexity and the manufacturing process of the composite material. The performance parameters of the CAE material provided by the supplier are suggested. (2) The half body in BIW is adopted in the finite element model, which basically does not affect the calculation results, but also saves computing resources and improves computing efficiency.

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Table 1 The benchmark between CAE modal simulation results and experimental modal results State of the road test

Vehicle

Simulation results (Hz)

Test results (Hz)

Range of error

Normal

Vehicle 1

26.9

28

4.1%

Vehicle 2

26.1

27.4

5.0%

Damaged

Vehicle 3

22.9

24.4

6.7%

(3) The battery tray bracket and the tray itself are gridding. The metal bracket is divided by the shell unit of 4 mm, the plastic tray is divided by the body unit of 2 mm, and the outer shell of the body unit is 0.1 mm. (4) Simplify the battery body to the quality point and grab it with RBE3. At the same time, the moment of inertia of the battery body should be considered. (5) Grasp the contact points between battery and plastic pallet evenly. (6) The inserts at the installation points of the plastic pallets need to be modeled. 3.2.3

The Benchmark Between CAE Modal Simulation Results and Experimental Modal Results

Using the above standard modeling method, the body unit uses the first order unit and uses the Nastran solver to calculate the local modal simulation results of the plastic tray system of the three typical road tests and the error of test results is below 10%. The specific data are shown in the following form (Table 1):

3.3 The Verification of the Evaluation Standard The local mode of the plastic tray system for battery of a new model is 30.2 Hz. The durability performance of the plastic tray system meets the requirements by the road test. It shows that the modeling standard and evaluation method of the local mode of the plastic tray system for battery are feasible.

4 Conclusions Long glass fiber reinforced polypropylene material has been used in many independent brand models for its light weight, noise reduction, corrosion resistance and low cost. At present, there is no evaluation method for durability of the plastic tray system for battery. Therefore, it is very necessary to study the durability evaluation method of the battery plastic tray system. This paper presents an evaluation method for durability of the plastic pallet system for battery. The concrete conclusions are as follows.

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(1) The acceleration power spectrum density map and the resonance mechanism of the typical models are studied, and the modal is put forward as the main index of the durability evaluation of the battery plastic tray system, which can be more convenient and quick to evaluate its durability. (2) Comparing the modal simulation analysis and road test results of the battery plastic tray system, the modal target value of the durability of the plastic tray system is determined to be 28 Hz. When the first order mode is larger than 28 Hz, the risk of durability is lower, which provides the basis for the durability assessment of the plastic tray system for battery. (3) To compare the modal simulation analysis and test results of the plastic tray system for battery, the analytical modeling method is studied to improve the accuracy of the simulation analysis and to predict the durability risk more accurately before the road test.

References 1. Stauber R, Vollrath L (2011) External Application of Plastics for Automotive Engineering. Chemical Industry Press, Beijing 2. Jing W (1995) Research progress in long glass fiber reinforced thermoplastic composites. Chem Prog 2:1–4 3. Jin W, Zhang Y (2007) Fire’s effect on chloride ingress related durability of concrete structure. J Zhejiang Univ-SCI A (Appl Phys Eng) 8(5):675–681 4. Zhao Y, Du P, Jin W (2010) Evaluation of the performance of surface treatments on concrete durability. J Zhejiang Univ-SCI A (Appl Phys Eng) 11(5):349–355 5. Shengjun W (2013) Long glass fiber reinforced PP material used in automotive battery tray applications. Automot Technol Mater 9:45–49 6. Liu W, Chen G et al (2012) A review of research on vibration fatigue of structures. J Eng Des 19(1):1–8 7. Qihang Y, Xueqin Y et al (1998) Handbook for Acoustic Fatigue Design of Aircraft Structures. Aviation Industry Press, Beijing 8. Debao L (1987) Understanding of some problems in modal analysis. J Tsinghua Univ (Nat Sci Edn) 5:86–94 9. Altair HyperWorks 12.0 Reference Guide (2013)

Test and Analysis on Distance Measurement Accuracy of Commercial Vehicle Forward Collision Warning System Chengyong Niu, Zhanling Su, Kunlun Wu, Xiong Hu, and Jianxun Xu

Abstract The distance measurement accuracy is directly related to the safety margin of forward collision warning system (FCWS) warning time, which affects the safety of vehicles and pedestrians. Distance measurement accuracy of a commercial vehicle equipped with different FCWS sensing schemes was tested and analyzed according to relevant regulations and standards on the basic of the analysis of the distance measurement principle of FCWS sensor scheme. The test results show that the distance measurement accuracy of millimeter wave radar and camera information fusion has the preferable performance. Besides, we pointed out some problems existing in the test and evaluation of distance measurement accuracy for commercial vehicle FCWS and put forward some feasible suggestions. Keywords Commercial vehicle · Forward Collision Warning System (FCWS) · Distance measurement accuracy · Test · Analysis

1 Introduction Automobile forward collision warning system (FCWS) can monitor the vehicle in front through the vision or radar perception system in real time to judge the distance, orientation and relative speed [1]. When the system detects the potential collision danger in the front area, it timely reminds and warns the driver to take corresponding measures to avoid the risk [2, 3]. However, the key technology of FCWS is distance C. Niu (B) · Z. Su · K. Wu · X. Hu · J. Xu National Bus and Coach Quality Supervision and Test Center, Chongqing Vehicle Test & Research Institute Co., Ltd, Chongqing, China e-mail: [email protected] Automotive Active Safety Testing Technology, Chongqing Key Laboratory of Industry and Information Technology, Chongqing, China C. Niu Autonomous Drive System and Intelligent Connected Vehicle Technology, Chongqing Test and Research Engineering Center, Chongqing, China © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_7

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measurement, which can provide depth information support and guarantee driving safety [4]. With the implementation of transportation industry standards, Commercial Vehicle Driving Dangerous Warning System Technical Requirements and Test Procedures (JT/T 883-2014) [5] and the national standard, Intelligent Transportation Systems— Forward Vehicle Collision Systems—Performance Requirements and Test Procedures (GB/T 33577-2017) [6], a large quantity of FCWS equipment in different sensor types are equipped on the commercial vehicle. Therefore, it is necessary to compare and analyze the performance of FCWS products of various domestic suppliers, especially the distance measurement accuracy, so as to provide technical reference for domestic major vehicle manufacturers to understand the distance measurement performance of FCWS and related parts suppliers to better develop FCWS products. In addition, to point out the existing problems in the measurement and evaluation of commercial vehicle FCWS distance measurement accuracy, and put forward relevant feasible suggestions, providing reference for further revision and improvement of the standard.

2 Evaluation Method of Commercial Vehicle FCWS Distance Measurement Accuracy 2.1 Types and Characteristics of Commercial Vehicle FCWS Sensors At present, the FCWS distance measurement methods widely used in commercial vehicle mainly include millimeter wave radar, vision (camera) and millimeter wave radar and camera information fusion distance measurement. The distance measurement principle of the three sensing schemes is introduced and analyzed.

2.1.1

Millimeter Wave Radar Distance Measurement

Radar distance measurement is to detect the position of obstacles in front, including distance, orientation and relative speed through the reflection of electromagnetic waves. At present, pulse radar distance measurement and continuous wave radar distance measurement are the two main distance measurement methods of millimeter wave radar. Figure 1 shows the pulse radar distance measurement schematic diagram. It mainly uses the time difference T between electromagnetic wave emission and reception to calculate the distance L. Although the principle of distance measurement is relatively simple, the pulse distance measurement method has some difficulties in the specific technical implementation: complex hardware structure and high cost.

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Fig. 1 Schematic diagram of pulse radar distance measurement

Fig. 2 Schematic diagram of continuous wave radar distance measurement

The continuous wave radar distance measurement uses frequency modulation continuous wave (FMCW) distance measurement method: (1) the radar antenna sends continuous frequency modulation signals to the front detection area, and when the target is detected, the echo will be generated after a certain delay; (2) mixing the transmitted signal with the received signal to extract the target distance information from the signal spectrum; (3) process and calculate the relative distance with the target according to the difference between beat signals as shown in Fig. 2. In the figure, the central frequency of the transmitted wave is set as f 0 , frequency band width as B, sweep frequency period as T, modulation signal as triangular wave, c as the speed of light, V and R as the relative velocity and relative distance of the target. The structure of the distance measurement radar is relatively simple, small in size, lightweight and low in cost.

2.1.2

Vision Distance Measurement

Due to the high accuracy of vision distance measurement technology and the ability to obtain abundant road environment information, it is widely used in the field of ADAS. At present, there are two main methods of vision distance measurement used in commercial vehicle: monocular distance measurement and binocular distance measurement [7].

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(1) Monocular distance measurement Monocular distance measurement is to use a camera to collect a single image and estimate depth information through internal and external parameters of the camera or use feature matching and optical flow technology to estimate 3D parameters from the image sequence [8]. Advantages of monocular distance measurement: a. simple structure and fast operation speed; b. low cost; c. there is no image registration problem; d. good real-time, which can meet the actual application requirements. (2) Binocular distance measurement Binocular distance measurement is to use the method of parallax sensing distance to simulate the human vision system through two cameras to obtain the same target in different positions of the image and stereo matching to obtain the parallax map, so as to achieve the distance measurement. The key of binocular distance measurement lies in camera calibration (binocular correction) and image pair matching (binocular matching). However, in the process of projecting a 3D scene into a 2D image, there are inevitably some problems such as distortion, noise, loss of depth and invisible part of information, which greatly increases the difficulty of binocular matching. Besides, the adoption of multiple cameras makes it more difficult to install the system and collect images synchronously. 2.1.3

Millimeter Wave Radar and Camera Information Fusion Distance Measurement

Principle of information fusion distance measurement: (1) millimeter wave radar and camera respectively collect data information of the target obstacle in real time; (2) feature extraction and pattern recognition processing of output data of each sensor, and accurate correlation of targets according to categories; (3) use the fusion algorithm to integrate all sensors data of the same target in order to reach a consistent conclusion about “target threat” [9, 10]. Compared with single vision or single millimeter wave radar, the information fusion scheme has more advantages in system resolution, data reliability, system robustness and stability.

2.2 Test Procedures According to the requirements of national standard Intelligent Transportation Systems—Forward Vehicle Collision Systems—Performance Requirements and Test Procedures (GB/T 33577-2017) and transportation industry standards, Commercial Vehicle Driving Dangerous Warning System Technical Requirements and Test Procedures (JT/T 883-2014), the warning distance measurement accuracy of commercial vehicle FCWS is carried out on the basis of the following procedures:

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Fig. 3 Test method of warning distance measurement accuracy

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The test is carried out while the vehicle is running, and the target vehicle should be in the detection area. The test vehicle is travelling towards the target vehicle at a speed of V = 72 km/h and the target vehicle is stationary, as shown in Fig. 3. The warning distance measured is compared with the warning distance set by the manufacturer and the warning distance measurement accuracy should meet the standard requirement: the relative error of the warning distance should not exceed 1 m or 5% according to JT/T 883-2014 and 2 m or 15% is the limit value of GB/T 33577-2017. Where, the formula for calculating the “relative error” of warning distance is σ=

dactual distance − dsystem detection distance × 100% dactual distance

The “absolute error” is δ = dactual distance − dsystem detection distance . In addition, we conducted supplementary tests based on our testing experience, as shown in Fig. 4. The supplementary test includes two test scenarios: a. CCRm: slow moving target vehicle at two different speeds; b. CCRb: braking target vehicle at three different deceleration. According to the requirements of regulations and standards, we built the test scenario as shown in Fig. 5 for FCWS distance measurement accuracy test. Fig. 4 Supplementary test method of warning distance measurement accuracy

(a) CCRm test scenario

(b) CCRb test scenario

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Fig. 5 Test scenario of FCWS distance measurement accuracy

3 Measurement and Result Analysis of Commercial Vehicle FCWS Distance Measurement Accuracy 3.1 Test Equipment The test equipment required by the distance measurement accuracy test of commercial vehicle FCWS mainly includes the ABD SR60 and SR150 driving robot imported from UK, the Oxford RT3002 inertial navigation system +GPS base station, the DEWE-501 data acquisition system, and the target balloon car manufactured in Germany certified by Euro-NCAP, as shown in Fig. 6. Test equipment accuracy: (1) speed accuracy: 0.05 km/h; (2) acceleration accuracy: 0.01 m/s2 ; (3) positioning accuracy: 0.02 m.

3.2 Test Vehicle FCWS Sensor Schemes FCWS sensor schemes for a commercial vehicle are listed in Table 1. Among them, millimeter wave radar B1 and B2 adopt foreign and domestic radars respectively, and monocular cameras are all produced by domestic suppliers.

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ABD SR150 steering robot (test vehicle) 

ABD SR60 steering robot (target vehicle)

brake accelerator combined robot 

Oxford RT 3002

target balloon car

RT base station

data acquisition system

Fig. 6 Test equipment of FCWS distance measurement accuracy Table 1 FCWS sensor schemes

Sensor schemes

Code name

The camera

A1

Millimeter wave radar

B1

A2 B2 Camera and millimeter wave radar information fusion

C

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3.3 Test Result Analysis of Commercial Vehicle FCWS Distance Measurement Accuracy 3.3.1

FCWS Based on Monocular Camera Scheme

(1) Description of the monocular camera distance measurement algorithm: a. The distance measurement algorithm of monocular camera A1 adopts “target pixel contour size”, and the distance measurement curve is fitted according to the image size, area and camera installation position of the target on the camera plane to determine the distance of the target. b. The distance measurement algorithm of monocular camera A2 adopts the “distance of the shadow at the bottom of the target vehicle”, which takes the shadow at the bottom of the target vehicle as interested region, obtains the position of the shadow on the road, reproduces the width of the vehicle and other information, and fits the distance measurement curve through the camera calibration. (2) Analysis of test results: the distance measurement accuracy of the two algorithms is shown in Figs. 7 and 8. It can be seen from the above figure that the “relative error” and “absolute error” of the FCWS warning distance based on monocular cameras A1 and A2 are relatively larger: σ A1 = −11.52% (−7.76 m) and σ A2 = −23.34% (−13.82 m). In addition, the imaging effect of monocular camera is not ideal, resulting in poor recognition effect of shadow at the bottom of the vehicle, and the distance measurement algorithm is not as good as the “target pixel contour size” algorithm. Meanwhile, the distance measurement accuracy of domestic monocular camera is not high and the error of distance measurement is even as high as ten meters, which can’t meet the requirements of JT/T 883-2014. However, the product A1 can meet the requirements of GB/T 33577-2017, the specific reasons are as follows. a. Low recognition accuracy of monocular vision: according to the principle of monocular vision distance measurement mentioned above, the monocular camera Fig. 7 Distance measurement accuracy comparison curve of monocular camera A1

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Fig. 8 Distance measurement accuracy comparison curve of monocular camera A2

adopts “pixel distance fitting space distance curve” distance measurement method rather than directly measure the relative distance between the vehicle and the target vehicle. Therefore, its distance measurement accuracy is poor. b. Low recognition efficiency of monocular vision: the monocular camera needs to identify the target obstacles before measuring distance. In other words, the monocular camera must identify the obstacle is a person, vehicle or other objects in the first place. In view of this, the natural disadvantage of monocular cameras is that they require large databases, which are constantly updated, maintained and optimized. c. Problem of fixed focus monocular camera: generally speaking, monocular camera has far detecting distance in long focus, and short focus means short detection distance. Therefore, the target detection in different range needs to be constantly zoom to ensure the distance measurement accuracy. However, monocular cameras currently on the market have a fixed focus. 3.3.2

FCWS Based on Millimeter Wave Radar Scheme

(1) Description of millimeter wave radar distance measurement method: both foreign-made millimeter wave radar B1 and domestic millimeter wave radar B2 adopt FMCW distance measurement method. (2) Test results are shown in Figs. 9 and 10. As can be seen from the figure above, the “relative error” and “absolute error” of the FCWS warning distance based on millimeter wave radar B1 and B2 are relatively smaller: σ A1 = −3.39% (−1.86 m) and σ A2 = −4.03% (−2.55 m). It can be seen that the distance measurement accuracy of imported millimeter wave radar is high, which meets the requirements of regulations and standards. However, compared with B1, domestic millimeter wave radar B2 has poor distance measurement performance, resulting in target loss and “blindness” occasionally. The reason is that the localization of millimeter wave radar is lagging behind, and radar chip is the key constraint. It should be pointed out that international Tier 1 chip suppliers such as Infineon and Freescale have not fully released the supply of 77 GHz radar chips to China

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Fig. 9 Distance measurement accuracy comparison curve of millimeter wave radar B1

Fig. 10 Distance measurement accuracy comparison curve of millimeter wave radar B2

due to many factors such as intellectual property and supply chain. As a result, the development of domestic 77 GHz millimeter wave radar is restricted to a large extent and the progress is relatively slow.

3.3.3

FCWS Based on Camera and Millimeter Wave Radar Information Fusion Scheme

(1) Description of millimeter wave radar and camera fusion scheme: the camera and millimeter wave radar are responsible for detecting target appearance and measuring distance, respectively. Distance measurement process: a. measuring distance through vision and radar at the same time; b. combining with measured distance between the two sensors to determine whether there is a false identification, screening out the threat of the target obstacles; c. the distance measured by millimeter wave radar is taken as the actual measured distance. (2) As can be seen from Fig. 11, the relative error of FCWS warning distance based on the 77 GHz millimeter wave radar and camera information fusion scheme C is a relatively much smaller: σC = −2.72% (−1.75 m), which also meets the requirements of regulations and standards.

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Fig. 11 Distance measurement accuracy comparison curve of information fusion C

Obviously, compared with monocular vision or single millimeter wave radar, the information fusion scheme has more advantages in system resolution, data reliability, system robustness and stability.

3.3.4

Supplementary Test Result Analysis T of Monocular Camera A1

In order to fully analyze the FCWS sensor distance measurement accuracy and find a more appropriate measurement method, the camera scheme A1 was tested in accordance with the test scenarios shown in Fig. 4 by referring to relevant test methods and combining with the test experience. (1) When the speed of target vehicle is 10 and 32 km/h, the test result is σA1 = −5.00% (−2.71 m) and σA1 = −3.47% (−1.57 m), respectively. As can be seen from the test result, the smaller the relative speed between the test vehicle and target vehicle, the higher the distance measurement accuracy. Besides, there is not much difference between the “relative error” and “absolute error” of the FCWS warning distance by this test method. In other words, there is a big difference between the two evaluation indexes through previous test method, namely, in general, the “relative error” can meet the requirements of regulations and standards and the “absolute error” can’t (Fig. 12). (2) When the deceleration of target vehicle is 0.1, 0.3 and 0.5 g, the test results are σA1 = −4.48% (−1.06 m), σA1 = −11.89% (−2.76 m) and σA1 = −45.15% (−4.33 m), respectively. As can be seen from the test result, the smaller the deceleration of target vehicle, the higher the distance measurement accuracy (Fig. 13).

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Fig. 12 Distance measurement accuracy comparison curve of A1 (CCRm: supplementary test)

Fig. 13 Distance measurement accuracy comparison curve of A1 (CCRb: supplementary test)

3.4 Analysis and Suggestion on the Evaluation of Distance Measurement Accuracy of FCWS (1) With the “bottleneck” restrictions of monocular vision distance measurement principle, FCWS distance measurement accuracy based on vision is sensitive to the relative velocity, the target vehicle deceleration, relative distance and other parameters, mainly affected by the distance measurement algorithm and data computing delay. The distance measurement accuracy on the basic of monocular vision can’t meet JT/T 883-2014 standard requirement, and most of the FCWS products can meet the requirements of GB/T 33577-2017. It is suggested to adjust the distance measurement accuracy requirements in the standard according to different FCWS sensing schemes. At the same time, the FCWS suppliers need to constantly optimize the distance measurement algorithm to improve the accuracy and reliability of the system distance measurement. (2) Problems of test method of FCWS distance measurement accuracy: at present, only one test method is specified in the standard, that is, the front target vehicle is stationary, and the test vehicle approaches at a speed of 72 km/h. The test method may not be able to effectively evaluate the sensor distance measurement accuracy. Therefore, it may be considered to increase the test method to simulate the actual driving conditions such as the driving conditions of target vehicle moving at a low speed. (3) Problems of the distance measurement accuracy evaluation index: there are differences in the consistency of distance measurement accuracy requirements in the standards GB/T 33577-2017 and JT/T 883-2014, that is, the “absolute error” requirements (1 or 2 m) are significantly higher than the “relative error”

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(5 or 15%). For example, the test result of monocular camera A1 is 11.52% (7.76 m). The “absolute error” is 7.76 m, which is not satisfied. On the contrary, the “relative error” is 11.52%, which meets the requirements of standards. In view of this, we hope to revise the evaluation index in the standard and form a unified evaluation standard.

4 Conclusion Based on the analysis of the distance measurement principle of FCWS sensor scheme, distance measurement accuracy of one commercial vehicle equipped with different FCWS sensing schemes was tested and evaluated in accordance with the traffic industry standard JT/T 883-2014 and national standard GB/T 33577-2017. Furthermore, the supplementary tests were carried out and the factors affecting the distance measurement accuracy were explained. Meanwhile, the existing problems in the test and evaluation of the distance measurement accuracy of commercial vehicle FCWS are pointed out and relevant feasible suggestions are proposed.

References 1. Niu C, Xu J, Zeng J et al (2018) Test and evaluation on performance of bus coach forward collision warning system. Automob Technol 49(5):16–19 2. Tseng D-C, Huang C-C (2017) Vision-based vehicle detection for a forward collision warning system. World J Eng Technol 05(03):81 3. Cicchino JB (2016) Effectiveness of forward collision warning and autonomous emergency braking systems in reducing front-to-rear crash rates. Accid Anal Prev 99(Pt A):142–152 4. Niu C, Zeng J, Xu J et al (2018) Test and evaluation on performance of vehicle with automatic emergency braking system. J Hebei Univ Technol 47(05):75–81 5. Transportation Industry Standard of the People’s Republic of China (2014) JT/T 883-2014. Commercial vehicle driving dangerous warning system technical requirements and test procedures. China Communications Press, Beijing 6. National Standard of the People’s Republic of China (2017) GB/T 33577-2017. Intelligent transportation systems—forward vehicle collision systems—performance requirements and test procedures. Standards Press of China, Beijing 7. Wu Z (2016) Monocular vehicle detection for forward collision warning system. Hunan University 8. Chen X (2016) Study on vehicle detection using vision and radar. Jilin University, Changchun 9. Cheng L (2016) Study on vehicle detection using vision and radar. Jilin University, Changchun 10. Lu D (2009) The obtaining technology of front vehicle information in foggy weather based on millimeter-wave radar. Wuhan University, Wuhan

Car Body Durability Analysis Based on Modal Superposition Method Zichun Zhang, Changpeng Wu, Zhaoming Wu, and Yanbing Lei

Abstract Common car durability developing methods are firstly introduced, and then focus on simulation methods. Generally, there are two simulation methods to get structural stress in time-domain: quasi-static method and modal superposition method. The former cannot consider structural dynamic response while the latter can do, which is used to calculate car body stress and damage with fatigue curves. According to comparing results between test and simulation, results of modal superposition method has better consistency with test results than quasi-static method where local modes predominated. Furthermore, it is easy to use modal superposition method to search countermeasures without extra software and calculation resources, which has good application prospect. Keywords Car body · Durability analysis · Quasi-static method · Modal superposition method

1 Introduction In recent years, people’s purchasing power for cars has been gradually increasing with the improvement of national economy and manufacturing level. At this time, people put forward higher requirements for cars, among which durability becomes one of the important indicators. The monocoque body is widely used in modern car design, which endures complex loads during driving. It has to bear not only inertial loads such as drive and brake, but also complex load of powertrain vibration and road excitation. How to consider practical situation in the new car design stage becomes a critical part. The methods of new car durability development mainly include test method and simulation method, which as shown in Fig. 1. Test method includes road test, proving ground test and bench test. The road test is confirmed by real road test, and the results are the most accurate. However, it is Z. Zhang (B) · C. Wu · Z. Wu · Y. Lei Dongfeng Nissan Technical Center, Guangzhou, Guangdong, China e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_8

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PVG Test Bench Test

Durability Development Simulation Method

Limited Conditions

Potholes, 3g Bumper, Kerbing, 28 Cases

Frequency Domain

Based on Dirlik hypothesis, with PSD as input

Time Domain

Quasi-static Method: based on multi-channel linear superposition Transient Dynamics Method: based on modal superposition

Fig. 1 Development means of new car durability

costly, time consuming and cannot be carried out in digital phase of development. Accelerated aging test can be carried out on proving ground and bench, which is time-efficient. However, much time and money are still needed for mule car building and road spectrum acquisition [1]. With the development of simulation and hardware, car durability can be predicted with finite element method. We can check some limited conditions cases (such as potholes, 3g bumper, kerbing and 28 cases) [2–4] to ensure durability. This method is simple and direct, but it has a large deviation from the actual fatigue life. Sometimes it is easy to cause excessive performance. The more accurate simulation method is based on structural stress, applying fatigue analysis theory and combining with fatigue curves to make prediction. The methods of obtaining structural stress can be divided into frequency domain and time domain. The advantage of the frequency domain method is that it is usually easier to obtain stress PSD from the frequency response function than from the time domain signal. The time domain method is based on the actual load, and the analysis process and results are easy to understand. Classified by stress calculation method, the time domain method can be divided into quasi-static superposition method and transient response method. The former cannot consider dynamic performance of the body structure. It is suitable for the structure whose natural frequency is much higher than the load frequency, such as chassis durability. The latter can consider the dynamic performance. It is suitable for structure whose natural frequency is close to or overlaps with the load frequency, such as durability of car body. Fatigue life of key parts of car body has been calculated by using the frequency domain method with transfer function based on the PSD load. The results were in good agreement with the fatigue test [5]. Based on the combination of actual road load spectrum and fatigue simulation, the fatigue failure was predicted and improved by using quasi-static method. Finally, car durability test passed at one time, which is a good practice [6]. The fatigue of high-speed railway train was studied with transient dynamic method. Compare results with quasi-static method, fatigue life of some parts have significant difference [7].

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At present, the frequency domain method and the time domain method based on quasi-static superposition are widely used in car fatigue simulation. The time domain method based on modal superposition is mainly applied to railway vehicles, but its application on cars is rarely reported. This paper will discuss application of modal superposition method in car body durability analysis.

2 Transient Response Analysis Transient response analysis is used to determine the dynamic response of a structure under arbitrary time-varying loads. The input load can be a random combination of steady-state load, transient load and harmonic load. The output results can be structural time-varying displacement, stress, etc. In car body durability analysis, transient response analysis is used to calculate stress distribution of the car body as one of the inputs for durability analysis. The dynamic equation of motion is + [K ]{u(t)} = { f (t)} ˙ + [C]{u(t)} ¨ [M]{u(t)}

(1)

where [M] = mass matrix, [C] = damping matrix, [K ] = stiffness matrix, {u(t)} = displacement vector, { f (t)} = external incentive vector. There are two ways to solve the equation above: direct method and modal superposition method.

2.1 Direct Method Structural response is computed by solving a set of coupled equations using direct numerical integration. The fundamental structural response is solved at discrete times, typically with a fixed integration time step. According to the central difference method, the velocity and acceleration at a certain time can be calculated from the displacement results at the time before and after: 1 {u n+1 − u n−1 } 2t

(2)

1 {u n+1 − 2u n + u n−1 } t 2

(3)

{u˙ n } = {u¨ n } =

Upon substituting Eqs. (2) and (3) into Eq. (1), we obtain

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[A1 ]{u n+1 } = [A2 ] + [A3 ]{u n } + [A4 ]{u n−1 }

(4)

where  K C M 1 + + [A2 ] = { f n+1 + f n + f n−1 } 2 t 2t 3 3     K K C 2M M − − + = − ] [A3 ] = [A 4 t 2 3 t 2 2t 3 

[A1 ] =

After getting the displacement of the node, the strain and stress can be calculated. When the mass matrix is diagonal matrix, the damping matrix can also be diagonalized. Then matrix inversion can be avoided and nonlinearity can be taken in to account. However, when applied to durability analysis, time domain is usually divided into very small intervals to ensure the accuracy of rain-flow counting. Considering calculation time span is large, finite element model is huge, computing time is long, which makes it difficult to use.

2.2 Modal Superposition Method Modal superposition method uses mode shapes of structure to uncouple equations of motion. After obtaining modal displacement, superimpose contribution of each mode to get structural response. This method makes the numerical integration more efficient. The calculation is divided into three steps: firstly, the modes of the finite element model are calculated, and the dynamic equation is decoupled to obtain the modal stress for each order; secondly, the modal stress is recovered and the modal participation factors are obtained; finally, the system response is obtained by superimposing each modal response. Convert physical coordinates to modal coordinates by {u(t)} = [φ]{ξ (t)}

(5)

where [φ] = mode shapes of each mode, {ξ (t)} = modal coordinate vectors. Upon substituting Eq. (5) into Eq. (1), in order to uncouple motion equation, premultiply by [φ]T to obtain + [φ]T [C][φ]{u(t)} + [φ]T [K ][φ]{u(t)} = [φ]T { f (t)} ¨ ˙ [φ]T [M][φ]{u(t)}

(6)

where [φ]T [M][φ] = modal stress matrix, [φ]T [K ][φ] = modal stiffness matrix, both = modal damping matrix, which is ˙ are uncoupled diagonal matrix; [φ]T [C][φ]{u(t)} coupled off-diagonal matrix. Then use central difference method to calculate, which is similar to the direct method except that they are in terms of modal coordinates.

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Thus, each coefficient in Eq. (4) can be expressed as 

 M K C 1 + + [A1 ] = [φ] [φ], [A2 ] = [φ]T { f n+1 + f n + f n−1 }, t 2 2t 3 3     M K K C T 2M T − − [A3 ] = [φ] [φ], [A4 ] = [φ] − 2 + [φ]. t 2 3 t 2t 3 T

Then the dynamic equation can be decoupled by applying each order damping to each order mode m i ξ¨i (t) + ci ξ˙i (t) + ki ξi (t) = f i (t)

(7)

or ξ¨i (t) + 2ζi ωi ξ˙i (t) + ωi2 ξi (t) =

1 f i (t) mi

(8)

where ζi = 2mbiiωi , known as modal damping ratio; ωi2 = mkii , known as modal frequency. After obtaining modal coordinate vectors and modal stress of each mode, dynamic stress response of car body can be calculated by superposition method {σ (t)} =

n i=1

ξi (t){σi }

(9)

Since modal orders (considering modal truncation) are much less than calculation steps of the direct method, modal superposition method has higher efficiency.

2.3 Modal Truncation and Residual Modes Theoretically, the number of modes is equal to the number of DOFs. However, in practice, the modal response of the same point excitation decreases exponentially with the increase of modal frequency [8]. Therefore, the dynamic response of car body is mainly affected by the lower order mode. In order to save computational consumption, modal truncation is necessary. If the truncation frequency is set too low, some important modes will be missed, which will affect the calculation accuracy; if the truncation frequency is set too high, this will increase computational consumption dramatically. Generally, the frequency is determined by actual excitation frequency. However, those points that connect body with suspension have large dynamic stiffness. Their local modes need very high frequencies to excite, which are often truncated. This will cause a small response near these points, which is inconsistent with the actual situation. Therefore, it is necessary to take the part of the residual modal as the static modal to compensate the response analysis.

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Crack site

Crack site

Bumper

(a)

(b)

(c)

Fig. 2 Durability test results of a new car. a Crack area of test; b Schematic diagram of crack location; c Simulation results of quasi-static method

3 Body Durability Analysis A car body was found cracked at left-front bracket of bumper after experiencing an equivalent load of 216,000 km, as shown in Fig. 2. The results of quasi-static method shows that damage here is small. It was not indicated as a risk position in the design phase. The crack site is far away from the loading points of the car body. The quasi-static method uses inertia release method, which only considers the static inertial mass force of the local structure. This results in a small local stress and a small damage of the cracking site. Since the front bumper is connected here, it vibrates violently during the durability test. The influence of dynamic response should be considered, and transient response analysis should be used to obtain stresses. Then damage will be calculated.

3.1 The Finite Element Model In order to keep consistent with the real car, TB model calibrated by NVH is adopted as the finite element model. It includes meshed assemblies (such as BIP, closures, seats, etc.), trimmed mass (such as wire harness, interior trim parts, etc.) and durable counterweights. Sheet metals are meshed with 2D elements; plastic injection parts are meshed with 3D tetrahedral elements. Bolts are modelled with rigid elements; glues are modelled with ACM elements; adhesives are modelled with spring elements; welds are modelled with fine elements if they are concerned. The general size of sheet metal is 10 mm, while the crack site is refined with 2 mm. The final finite element model contains about 1.8 million elements and 1.6 million nodes, which is shown in Fig. 3.

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Whole model

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Local refined model

Fig. 3 Finite element model of car

Le Front

Right Front

Le Rear

Right Rear

Fig. 4 Load PSD of front and rear shock tower

3.2 Normal Modal Analysis Normal modal analysis is the basis of modal transient response analysis, which can obtain modal stresses of each mode. Modal extraction should be considered with modal truncation. By analysing the z-direction load PSD of front and rear shock tower (Fig. 4), it can be seen that signal energy is mainly distributed below 30 Hz. Consider local mode influence in medium frequency on the crack site, the mode truncation frequency is set to 50 Hz. The result of normal modes shows that there are 134 natural modes (including free modes) and 84 static modes extracted within 50 Hz. Main bone modal frequencies and modes are shown in Table 1 and Fig. 5. Modal stress of each mode is also obtained.

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Table 1 TB modes result Natural modes

Static modes

Order

Freq./Hz

Remark

Order

Freq./Hz

1

0.0

Free modes

135

69.0

2

0.0

136

74.0

3

0.0

137

76.0

4

0.0

138

77.4

5

0.0

139

83.1

6

0.0

140

86.0

7

6.1

141

87.6

8

8.2

142

94.7

……

……

143

98.1

40

26.6

41

26.9

42

……

……

211

1495.9

27.1

212

1510.2

……

……

213

1520.3

76

36.1

214

1802.2

77

36.3

215

1914.0

Bending

Torsion

78

36.5

216

2153.6

……

……

217

2190.3

134

49.6

218

4002.6

3.3 Modal Transient Response Analysis The time domain history of modal coordinate vector can be obtained by the modal transient response analysis. Typically, there are 14 connecting points between body and suspension. They are front and rear suspension installation point, engine mount point, shock absorber and spring installation point, etc. (Fig. 6). Time-domain dynamic load, which is decomposed from multi-body model, is applied to the connecting points. In order to avoid stress distortion in resonance, empirical damping coefficient should be specified. In order to avoid missing stress peak, the calculation frequency is required to be more than 10 times of the signal frequency. Excessive frequency cause an increase in computing time. After calculation, the time domain history of modal coordinate vectors of each order is obtained. The first 12 orders of non-rigid body modal coordinate vectors are shown in Fig. 7. As can be seen, the amplitude of modal coordinates decreases rapidly with the increase of modal order. This shows that stress contribution is dominated by lower modes.

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First-order transverse bending

First-order torsional

Fig. 5 Main mode of body

Fig. 6 Connecting points for body durability analysis

3.4 Durability Analysis The modal stress and modal coordinate vectors are imported into the fatigue analysis software FEMFAT, which can perform uniaxial and multi-axial fatigue calculation (BASIC module), consider plastic deformation and complex load (PLAST module),

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Fig. 7 Modal vectors of first 12 orders (without rigid body mode)

Quasi-stac method

Modal superposion method

Fig. 8 Damage result of car body

also support multi-channel loading (ChannelMAX module). Firstly, modal displacement and modal response stress are matched. Then, transient response stress of the structure is calculated by removing the modal result of rigid body. Finally, the rain flow method is used to calculate the fatigue damage of the car body, which is shown in Fig. 8. Compared result with quasi-static method (Fig. 8), the results of modal superposition method have following characteristics:

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1. Damage distribution of the two is basically consistent (with same scale and similar colors indicate similar damages). Most damages are in the same order of magnitude, indicating good consistency of the results. 2. The damage value calculated by quasi-static method is greater than that calculated by modal superposition method at connecting points between body and suspension, which is shown in Fig. 9. This is due to the modal truncation. 3. The difference is obvious at areas far away from loading point, where have great mass (e.g., fuel tank mounting) and pronounced local mode (e.g., roof). Figure 10 shows some typical areas. Since the modal superposition method considers dynamic characteristics of the car body, which is closer to reality.

Quasi-stac method

Modal superposion method

Fig. 9 Damage result at rear shock absorber tower

Quasi-stac method Fig. 10 Damage result at fuel tank mounting and roof

Modal superposion method

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4 Results Analysis and Discussion The analytical result of the crack site is shown in Fig. 11, where damage is calculated based on equivalent load of 1 km. The result shows that damage calculated by modal superposition method is two orders of magnitude higher than that calculated by quasistatic method, which means this area is greatly affected by dynamic characteristics. For the No.3 site, the model superposition method predicts a life of 280,000 km, which is close to the test result of 216,000 km. Figure 12 shows the ratio of modal stress to modal damage contribution. It can be seen that the 93rd modal stress reaches the maximum value of 268.23 MPa, while modal damage contribution is mainly in the first two modal stress peaks: order 21 (56.5%) and order 32 (32.7%). This indicates that lower modes have great influence on the durability.

Quasi-stac method NO.

Damage Quasi-stac 2.38E-10 Modal superposion 2.44E-08

Modal superposion method

1 Life(km) 4.20E+09 4.10E+07

Fig. 11 Damage result at cracking site

Fig. 12 Modal results at cracking site

2 Damage 3.46E-11 2.51E-08

Life(km) 2.89E+10 3.98E+06

3 Damage 2.99E-08 3.57E-06

Life(km) 3.34E+07 2.80E+05

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Fig. 13 Modal shapes of 21st and 32nd order at cracking site

The local modes of the 21st and the 32nd order are shown in Fig. 13, which is mainly bending vibration forward and backward. If countermeasures are needed, local stiffness can be improved by increasing flange height, optimizing boss layout and increasing plate thickness. It can increase local stiffness, reduces dynamic responses, and improve durability.

5 Summary By comparing results of transient response (modal superposition) method with results of quasi-static method, the following findings are made: 1. Transient response method can consider the influence of both load frequency and body natural frequency. In the site with local modes, the modal superposition method is closer to the reality than the quasi-static method. 2. Lower order modes have major effects on car durability. 3. Compared with direct response method, modal superposition method does not need to output huge time-domain result of dynamic stress of car body, which makes the transient response analysis feasible. 4. Refer to the damage where modes dominated, countermeasures can easily be made. 5. Because of the computational cost, modal superposition calculation needs modal truncation. If the truncated mode has a great impact on the result, a large error will be caused. When the simulation and test cannot match, quasi-static method or frequency domain method can be considered for verification.

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References 1. Dong Q (2014) Contribution analysis of different proving ground road fatigue damage on the car body. Heibei University of Engineering 2. Wu T, Mao H, Dai T (2012) Vehicle body fatigue analysis based on frequency domain of road spectrum. Comput Aided Eng 21(2):50–52, 83 3. Wang D, Basch R (2007) Effects of braking on suspension loads in potholes. SAE technical paper, 2007-01-1647 4. Specification for 261 powertrain mounts: Body-frame-integral subsystems GMW14116. North American Engineering Standards, December 2006 5. Wu L, Chen C (2007) Durability study on BIW based on random vibration method. Beijing Automotive Engineering, no 5, pp 19–22 6. Cheng Y, Qiu R, Luo M (2012) Study on improving fatigue life of vehicle body based on road load spectrum. Shanghai Auto (6):26–30 7. Xie N (2015) Analysis of vibration modal influence on fatigue life for high-speed train carbody. Southwest Jiaotong University 8. Xu X, Wang H et al (2012) Effects analyses of modal cut-off on vehicle hull structural dynamic response. Mech Sci Technol Aerosp Eng 31(3):492–497

Virtual Proving Ground Simulation in Practice for Vehicle Durability and Ride Comfort Performance Jian Shao, Yunping Zhou, Hua Wang, Shuai Zhou, Zhongling Jiang, and Wenjuan Wang

Abstract A procedure of virtual proving ground simulation in practice for vehicle durability and ride performance is introduced in this paper. Digital road models including of durability and ride performance roads are created by a way of 3D laser scanning on proving ground. Then a high-accuracy full vehicle multi-body dynamic model is built, using complete vehicle design parameters. Meanwhile, due to its well-known ability to deal with medium and high frequency dynamic problems, FTire is incorporated in building the full vehicle model. Based on the road load data acquisition, driver control programs are developed to make the MBD model traveling with same track and speed as the test car, and then VPG simulation and validation are fulfilled. Comparing with test results, simulation results make a good prediction on wheel center forces, spring displacements, damper forces, and so on. Hence, the VPG simulation technology provides a powerful tool to meet the demand of vehicle durability and ride comfort analysis. Keywords Virtual proving ground · Road model · Tire model · Vehicle MBD model · Validation

1 Introduction Vehicle durability and ride comfort performances are concerned by customers as two key automotive qualities, and they are also important research contents in the process of automotive product development. During the development of vehicle durability, the traditional method of fatigue life investigation is the road test in proving ground, which is most direct and accurate. However, there are many disadvantages of road test, such as long test period and high costs due to changes in parts and components. Therefore, it has become an important work to predict the fatigue life in the early stage of vehicle development [1, 2]. If the real road load inputs can be obtained in the early stage, then the fatigue CAE J. Shao (B) · Y. Zhou · H. Wang · S. Zhou · Z. Jiang · W. Wang Changan Auto R&D Center, Changan Automobile Co., Ltd., Chongqing 401120, China e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_9

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analysis can solve these problems. For a long time, the multi-body dynamic load cascading based on road load data acquisition has been widely used in automobile OEMs to obtain load inputs of vehicle body and chassis components. But it also has some problems, such as long acquisition period, high quality requirements for physical prototype, poor adaptability and so on. It is difficult to meet the needs of fast development, design changes and future vehicle development [3]. In the field of ride comfort, for a long time, the primary and secondary ride comfort has been evaluated mainly in a subjective way, and supplemented by objective testing. CAE analysis is difficult to play a role in the early design and late optimization [4]. One of the most important reasons for this situation is the lack of real and accurate road input in ride comfort simulation, so that it is impossible to analyze all kinds of subjective evaluation results. Therefore, it is of great significance to develop virtual proving ground simulation technology, which enables the engineers to predict the fatigue load of the whole vehicle [5] and evaluate the ride comfort at the early stage of the project. Through this technology, it will reduce the number of mule cars, support easily dealing with various design changes and the development of the next generation cars, and help realizing the integrated analysis and optimization of vehicle performance. Finally, it can shorten the period of automobile product development.

2 Workflow of VPG Research At first, digital road models are derived from 3D laser road scanning technology, and FTire models, which were proved to be suitable for durability and ride analysis, are fitted by a professional team after a series of tire tests. Meanwhile, based on a complete set of design data of a sedan, a multi-body dynamic model of the full vehicle is built and checked by system level and full vehicle level tests. And then the road load data acquisition is implemented to obtain real vehicle response signals. At last, road models, tire models and vehicle MBD model are integrated, and the virtual proving ground simulation is carried out. The workflow of the research is shown in Fig. 1.

3 The Digital Road Models A mobile vehicle is used to collect measurement data of the ground surface. The whole measuring system, which mainly includes pavement laser scanner, positioning and orientation system, digital camera system, is installed on the vehicle, shown in Fig. 2. The laser scanner is required to be high enough so that the laser cover more than 4 m of road width. The speed of the scanning vehicle is required to be as low and constant as possible to achieve enough data points.

Virtual Proving Ground Simulation … Fig. 1 Flowchart of the VPG research procedure

Fig. 2 Measurement vehicle for scanning

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Fig. 3 Belguim road in proving ground

Fig. 4 Belguim road CRG model

After scanning of the proving ground, point cloud of the road profile is generated. The point cloud data needs to be carefully checked to deal with the missing area caused by a lack of laser or the abnormal area caused by weeds and pebbles. Then the data is export to OpenCRG. The curved regular grid file can accurately reflect the elevation, slope, inclination angle and other information of the road surface. It is commonly used in virtual proving ground simulation in the past few years. In this work, a total of more than 60 digital road models have been obtained, which can meet the needs of durability and ride comfort simulation analysis at the same time (Among them, a typical Belgian road surface and its digital road model are shown in Figs. 3 and 4).

4 The Tire Models FTire which was known as being able to deal with medium and high frequency dynamic problems is used as the tire model in the full vehicle model. The FTire model is a nonlinear 3D dynamic tire model based on physical structure, and the in-plane and out-of-plane simulation frequency can reach between 150–200 Hz [6]. It has been widely used in vehicle dynamic analysis for a long time. FTire measuring requirements on test rig for passenger car tires are shown in Table 1.

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In order to apply the VPG technology more widely and faster, more than 10 tires are tested. The tire width covers the 185–255 mm range, and the rim diameter covers the 15–20 in. range. Because of the complexity and non-publicity of modeling, the FTire models involved in this research are fitted by a professional technical service provider in the USA. The fitting reports show the accuracy of in-plane test is higher than that of out-of-plane test, while the accuracy of vertical force is higher than that of longitudinal force, and the accuracy of lateral force is the lowest. Figures 5 and 6 show one vertical force and one longitudinal force fitting results of a tire of size 215/50R17. The following simulations in this paper are all based on the model of this tire. Table 1 FTire measuring requirements Classification

Measurements

Base data

Inflation pressures, mass and inertia, outer contour, tread thickness, inflated maximum radius, dynamic rolling circumference, shore a stiffness

Footprint

Footprint

Static stiffness

Stiffness on flat surface, stiffness on cleat

Dynamic stiffness Vertical dynamic stiffness Handling

Cornering, drive and brake

Cleat

Transversal cleat, oblique cleat

Fig. 5 Vertical force fitting result of In-plane test

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Fig. 6 Longitudinal force fitting result of In-plane test

Fig. 7 The full vehicle MBD model

5 The Full Vehicle Model The object vehicle in this research is a sedan with Macpherson front suspension and twist beam rear suspension. The MBD model of the full vehicle is composed of front suspension, rear suspension, steering system, body, powertrain, braking system and tires, shown in Fig. 7. In order to build this model, except for tire test, a large number of full vehicle level tests, system level tests and component tests need to be done, including of handling test, K&C test, damping test of shock absorber, etc.

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The suspension stroke has an important influence on durability load analysis, because it determines the peak value of the impact force. K&C test helps us check the suspension stroke by the wheel rate curves, shown in Figs. 8 and 9. The two figures show that rear suspension stroke is almost the same with K&C test result, but there is a slight difference in the end of the rebound stroke of front suspension. The comparison of other K&C characteristics is shown in Table 2, which shows an extremely high accuracy of the front and rear suspension MBD models.

Fig. 8 Front wheel rate curve

Fig. 9 Rear wheel rate curve

−0.189

−0.236

Longitudinal toe compliance/deg · kN−1

Lateral compliance

1.70

0.204 0.0288

Lateral wheel center compliance/mm · kN−1

Lateral toe compliance/deg · kN−1

0.0279

0.212

15.8

1.595

Longitudinal wheel center compliance/mm · kN−1

15.4

Steering ratio/

Long. compliance

1154

−10.9

Steering geometry

1102

−10.5

Bump spin/deg · m−1

−13.5

−4.57

26.3

Total roll stiffness/Nm · deg−1

−15.9

−3.62

25.9

3.1%

3.9%

19.9%

6.6%

2.6%

4.7%

3.8%

15.1%

26.2%

1.5%

-0.009

1.2

0.042

0.38

\

674

141

−2.545

1.93

23.28

-0.009

1.3

0.048

0.42

\

649

142.7

−2.9

2.2

22.3

Simulation

Rear suspension Test

Relative deviation

Test

Simulation

Front suspension

Bump camber/deg · m−1

Bump steer/deg ·

m−1

Wheel rate/N · mm−1

Suspension system requirements

Roll

Vertical bounce

Test events

Table 2 Suspension K&C metrics

0.0%

8.3%

14.3%

10.5%

\

3.7%

1.2%

13.9%

14.0%

4.2%

Relative deviation

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After checking of K&C characteristics, the full vehicle model is adjusted and verified. The total mass and inertia of the model, wheel load distribution, the height of CG. are adjusted to be consistent with the real vehicle. Finally, several standard handling performance analysis has been done. The results present a high accuracy of the full vehicle model. Figures 10 and 11 show the yaw rate correlation in slalom and on-center test. It can be seen that the curves of simulation are in good agreement with that of test.

Fig. 10 Yaw rate correlation in slalom test

Fig. 11 Yaw rate correlation in on-center test

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Fig. 12 Wheel force transducer (left) and acceleration transducer (right)

6 VPG Simulation and Validation 6.1 Road Load Data Acquisition for Validation Along with the application of virtual proving ground simulation technology, the road load data acquisition will be gradually reduced or even be canceled. However, in this research, a RLDA with a large number of signal channels is carried out to validate the accuracy of the VPG simulation. The wheel forces are obtained by wheel force transducers fixed in a tailored wheel, and the body and chassis accelerations are obtained by acceleration transducers, shown in Fig. 12. The chassis components loads and spring displacements are obtained by strain transducers. The test vehicle is loaded to half-load and full-load respectively.

6.2 Vehicle Driving Control Different driving track or driving speed must generate different response. For the most roads, vehicle drives along with centerline of the road surface. But the driving speed is not constant, sometimes more than 20% variation from the desired speed. Thus, for better correlation, the target speed in simulation is defined as the measured speed from the test vehicle. In Adams/Car, a PID controller is used to minimize the lateral position error and longitudinal speed error. So the analysis event file (*.xml) matched to each digital road model is created. In the event files, the PID gains for speed and lateral position are adjusted to achieve the best control effect. As shown in Fig. 13, there is only a slight difference between the speed of VPG simulation and the test.

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Fig. 13 VPG versus test speed

Fig. 14 Front left wheel center longitudinal force under the braking behavior

6.3 Simulation and Validation In this study, a total of nearly 30 kinds of digital roads are simulated and validated. The following content shows the results of three typical durable roads and one ride comfort road.

6.3.1

Braking at 70 km/h on the Concrete Road

The event of braking on concrete road mainly investigate the influence of longitudinal load and vertical load transfer under different braking levels. Wheel center longitudinal forces and wheel center vertical forces are chosen as the target channels for validation, because they reflect the most important characteristics of brake event. As shown in Figs. 14, 15, 16 and 17, VPG simulation results are in good agreement with the test results.

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Fig. 15 Rear right wheel center longitudinal force under the braking behavior

Fig. 16 Front left wheel center vertical force under the braking behavior

Fig. 17 Rear right wheel center vertical force under the braking behavior

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6.3.2

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Driving Through the Chuckhole B Road

Chuckhole B road is one of the most important durable roads. There are two rectangular holes on the left and right of the road, and they are anti-phase. Passing through the Chuckhole road, vehicle mainly suffers vertical and longitudinal loads. Wheel center longitudinal forces, wheel center vertical forces, spring displacements and damper forces are chosen as the target channels for validation. As shown in Figs. 18, 19, 20, 21, 22 and 23, it can be seen that the peak values of the signal from each channel can be well matched. Besides, the accuracy of wheel center longitudinal force is a little lower than that of wheel center vertical force. On the whole, the results meet the demand of durability analysis.

Fig. 18 Front left wheel center longitudinal force on Chuckhole B road

Fig. 19 Rear right wheel center longitudinal force on Chuckhole B road

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Fig. 20 Front left wheel center vertical force on Chuckhole B road

Fig. 21 Rear right wheel center vertical force on Chuckhole B road

Fig. 22 Front left spring displacement on Chuckhole B road

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Fig. 23 Front left damper force on Chuckhole B road

Fig. 24 Front left wheel center vertical force on Resonance road

6.3.3

Driving Through the Resonance Road

Resonance road is also one of the most important durable roads. The distance between the convex platform and the specified driving speed excite an 11 Hz vibration response on the vehicle. Wheel center vertical forces, Wheel center vertical accelerations and body vertical accelerations are chosen as the target channels for validation. The time domain curves are shown in Figs. 24, 25 and 26, and the frequency domain curves are shown in Figs. 27, 28 and 29, which present that the resonance response derived from the road are well reproduced. But, it can be seen that body acceleration of VPG simulation is smaller than that of test above 20 Hz. This problem may be inferred to the absence of a flexible body model.

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Fig. 25 Front left wheel center vertical acceleration on Resonance road

Fig. 26 Front left body vertical acceleration on Resonance road

Fig. 27 Front left wheel center vertical force PSD on Resonance road

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Fig. 28 Front left wheel center vertical acceleration PSD on Resonance road

Fig. 29 Front left body vertical acceleration PSD on Resonance road

6.3.4

Driving at 80 km/h on the Smooth Bitumen Road

Smooth bitumen road is one of the ride comfort roads used for subjective evaluation and objective testing. This road represents most of the good roads driving daily. Usually, the root mean square value of acceleration is defined as one of the evaluation targets of this kind of road. The RMS value can be calculated by power spectral density function, as shown in formula (1). The vertical acceleration PSD is shown in Fig. 30 and band-limited RMS values are shown in Table 3. 

fu

a= fi

1/2 G a ( f )d f

(1)

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Fig. 30 Vertical acceleration PSD at driver’s seat track on the smooth bitumen road

Table 3 Band-limited RMS values

Test (m · s−2 )

Simulation (m · s−2 )

a, 0.7–4 Hz

0.232

0.197

a, 4–8 Hz

0.198

0.180

a, 8–16 Hz

0.223

0.196

a, 16–50 Hz

0.132

0.050

where f i and f u is the low and up limit of the frequency bandwidth, and G a ( f ) is PSD function. From Fig. 30 and Table 3, it can be seen that the RMS values of VPG simulation are in good agreement below 16 Hz, but much smaller than test results above 16 Hz. This problem can also be inferred to that the flexible body model is not used in the full vehicle model. So at present, VPG simulation has an enough accuracy for ride comfort in low frequency range.

7 Conclusion The accuracy of virtual proving ground simulation mainly depends on the accuracy of road model, tire model and vehicle MBD model. In order to investigate their influence on the test results, it is necessary to create full vehicle analysis event files, which make MBD model running in the same way as the test vehicle. The comparison between simulation and test data shows that the simulation can accurately predict wheel force, wheel acceleration, spring displacement, damper force, and so on. The VPG simulation can reproduce all kinds of driving events of the durability test and Ride Comfort test in a short time, and output the load and acceleration of each key interface point. If the design parameters change, one only needs to modify

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the vehicle model and rerun the simulation, and then the new load and acceleration will come out. Therefore, there is no doubt that VPG simulation makes it possible for early performance evaluation, and decreases the risk of later performance validation of a vehicle project. Finally, it can significantly reduce the test cost and shorten the development cycle.

References 1. Liu L (2015) A method of load prediction based on virtual road profile. Shanghai Auto 01(33):22– 27 2. Wu Y, Zhou Y, Wang J et al (2017) Application of virtual proving ground simulation technology in impact fatigue analysis of knuckle. In: Proceedings of 2017 SAE-China congress & exhibition. SAE-China, Shanghai, pp 1539–1542 3. Xing R, Liu Y (2018) Method of the dynamic load cascading based on virtual durable road. J Shenyang Aerosp Univ 6(35):39–49 4. Cheng K, Gao J, Lv Z (2010) VPG-based simulation and analysis on vehicle driving comfort. Chin J Constr Mach 2(8):208–212 5. Schudt JA, Kodali R, Shah M et al (2011) Virtual road load data acquisition in practice at general motors. SAE technical paper 2011-01-0025. https://doi.org/10.4271/2011-01-0025 6. Zhang H (2016) Comparative study on the SWIFT model and the FTire model. Jilin University

Research on Optimization Analysis Method of Exhaust System Under Multiple Loading Conditions Yan Qiao, XinTian Qu, GuiQi Yu, ShuangXi Zhan, and WenJun Kang

Abstract The exhaust system is connected to the engine and mounted on the bodyin-white with lifting lugs and rubbers. The vibration of the engine can be transmitted to the exhaust system through the bellows, which affects the NVH performance of the vehicle. Optimal design of the vibration isolation, the stiffness of the lifting lugs, the strength and other performance parameters should be taken into account. The exhaust system has many variable parameters, including the stiffness of the bushing, the shape of structure, the thickness of the exhaust pipes, and so on. It is difficult to find the optimal parameters of the exhaust system based on conventional method. In order to solve the problem, In order to solve the problem, the HyperWorks software was used to simulate the exhaust system. With HyperStudy, the DOE design method is used to create a response fitting approximate model, and the optimal solution is obtained by genetic algorithm. Then in the finite element model of the exhaust system, the size and shape optimization design were carried out to obtain the global optimal solution. The results revealed that this analytical method is practical and suggestive for multi-objective optimization analysis of exhaust system. Keywords Exhaust system · Multiple loading conditions · Optimal parameters · DOE · Fitting model · Optimization

1 Introduction With rapid development of the automotive industry, people have increasingly demanding of comfortable ride and NVH performance. The exhaust system is connected to the engine and mounted on the body-in-white. If the local structure or the stiffness of the bushing is not reasonable, it may cause problems such as the resonance, the fracture of the lugs, and noise. Therefore, avoiding the engine idle speed excitation frequency, controlling the transmission force of the lifting lugs are important goals of the exhaust system vibration control [3, 4]. Y. Qiao (B) · X. Qu · G. Yu · S. Zhan · W. Kang Dongfeng Motor Company Technology Center, Wuhan 430058, China e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 China Society of Automotive Engineers (ed.), Proceedings of China SAE Congress 2019: Selected Papers, Lecture Notes in Electrical Engineering 646, https://doi.org/10.1007/978-981-15-7945-5_10

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132

Y. Qiao et al. Original finite element model

1) Set reasonable design variables 2) Create concern system responses 3) Determine the optimization goal

DOE analysis 1) Screening main effects 2) Accumulate data points for fitting

1) Using optimal solution based on fitting approximate model as the initial design point 2) Combining finite element model

Response fitting approximate model 1)Create accurate response surfaces for different system responses

Optimal solution based on fitting approximate model

Multi-conditions optimization method

Fig. 1 Optimization analysis method under multiple loading conditions

In this paper, the design variables were exhaust pipe wall thickness T, stiffness of four exhaust lifting lugs rubber bushing in X\Y\Z three directions, bellows axial stiffness and radial stiffness, and six shape variables. It is extremely difficult to adjust parameters in a wide range of variables to achieve design requirements. In this paper, the Hamersley test design was carried out for screening the design variables that affect the performance of the exhaust system. The screened out main effect factors were taken as design variables. By constructing a moving least squares fit approximation model, multi-objective genetic algorithm was used for optimization analysis. Finally, the global optimal solution was obtained in addition with finite element model. Under the multiple loading conditions, the performance requirements for the exhaust system are achieved. The Optimization analysis method of the exhaust system is shown in Fig. 1.

2 Establishment of Exhaust System Finite Element Model In order to reduce the difficulty of modeling and ensure the efficiency of calculation, the model has been simplified. The finite element model of the exhaust system includes the powertrain, the powertrain suspension, the exhaust system, lifting lugs, and rubber bushings. As follows in Fig. 2:

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(1) The engine was simplified as a mass point element with moment of inertia arranged at the center of mass. The suspension brackets were simulated by CBUSH elements with stiffness. Rigid element was used for connecting the engine and the suspension bracket. The parameters of the engine and suspension are shown in Table 1 below: (2) The rubber bushings and bellows were simulated by CBUSH elements. The stiffness of rubber bushings includes three directions stiffness X, Y, and Z. The bellows contains axial stiffness and radial stiffness. The initial stiffness is shown in Table 2 below: (3) The catalyst, the muffler, and the exhaust pipe were simulated by shell elements. The material is steel, the modulus of elasticity is 2.1 * 105 Mpa, the Poisson’s ratio is 0.3, and the density is 7.9 * 10−9 t/mm3 . Right suspension bracket

Rubber bushing1 LeŌ suspension bracket Rubber bushing2 Rubber bushing3

The mass of engine

211.77kg

Rubber bushing4

Fig. 2 Finite element model of the exhaust system

Table 1 System parameters Coordinate X

Stiffness Y

Z

X (N/mm)

Y (N/mm)

Z (N/mm)

LH

−200

−393

370

50

200

200

RH

−280

473

430

80

80

120

T/ROD

86.2

−63.5

−51.7

100

10

10

Powertrain centroid

−233.93

24.88

214.11

–

–

–

Powertrain moment of inertia (kg · m)

Ixx

Iyy

Izz

Ixy

Iyz

Izx

8

4

6

−0.2

1

−0.6

Powertrain Suspension bushing

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3 The Finite Element Analysis Results of the Exhaust System 3.1 Constrained Modal Analysis Results The exhaust system is connected to the engine. In order to prevent resonance with the engine, the modal of the exhaust system should avoid the second-order idle excitation frequency of the engine. The formula of Engine idle speed calculation is: f=

n∗i 2∗n∗i = 60 ∗ τ 30 ∗ τ

(1)

f is the frequency, n is the engine rotating speed, the unit is r/min, i is the number of cylinders, and τ is the number of strokes. The engine idle speed is 850 r/min. From formula 1, it can be calculated that the second-order idle excitation frequency of the engine is 28.3 Hz. Because there is a certain range of idle speed, the gap range to avoid resonance is set to 2 Hz. The constrained modal avoidance range is 26.3–30.3 Hz. The concerned modal of the exhaust system above 20 Hz is extracted, as shown in Table 3 below (Fig. 3). The engine idle frequency range is 26.3–30.3 Hz. The 17th modal is 29.8 Hz, and it falls within the engine idle frequency range and has the risk of resonance. Theoretically the 15th and 16th modals should be less than 26.3 Hz, and the 17th and 18th modals should be greater than 30.3 Hz to achieve the frequency avoidance effect. Table 2 Bushings and bellow stiffness parameters

Table 3 Constrained modal

Stiffness

X (N/mm)

Y (N/mm)

Bushing 1

20

20

20

Bushing 2

7.8

11.2

7.8

Bushing 3

7.8

11.2

7.8

Bushing 4

7.8

11.2

7.8

Bellows

10

2.6

2.6

Modal order

Frequency (Hz)

15

21.8

16

23.1

17

29.8

18

34.5

Z (N/mm)

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Fig. 3 Modal analysis results

Table 4 Lifting lugs displacement and reaction force Lifting lugs number

Displacement (mm)

Displacement target (mm)

Reaction force (N) Reaction force target (N)

1

2.32

0, the counter value will be updated (Cnt = Cnt + StepDe); otherwise, the counter value will remain unchanged. The condition of ax > 0 is designed for avoid false identification due to the imprecise estimation of resistance in case of decelerating to stop on sand. If the updated counter value is reduced to 0, the current road is identified as non-sand; otherwise, the current road identification result is kept as sand.

3.2 Counter Step Length Updated Algorithm by Fuzzy Rules Counter step length is the key to determine algorithm performance. In order to obtain the stability and rapidity of the algorithm. The identification result should not change when the road features are not obvious and should change as soon as possible when the road features are obvious. Thus, StepIn and StepDe should be nonlinear function of rolling resistance Ff and wheel speed fluctuation index Δ. Because of the fuzziness of the determination of driving characteristics, the updating algorithm of step length is designed by fuzzy logic.

3.2.1

Calculation of Incremental Step Length

The inputs of the fuzzy rule for incremental step length are rolling resistance Ff and wheel speed fluctuation index Δ, its output is incremental step length StepIn. The vehicle test shows that the maximum rolling resistance of a car can be more than 10 kN when driving on sand, and the sand characteristics when the rolling resistance exceeding 10 kN are very obvious, so the range of rolling resistance is set as [0, 10]

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(unit kN), and 10 kN is used when Ff exceeds 10 kN. Similarly, the range of wheel speed fluctuation index is set as [0, 2] (unit m/s). In order to adapt the increasing step length of output to different degrees of sand features, the range is set as [0, 50]. When the vehicle driving characteristics and sand features are more consistent, the increasing step size will be larger. The variable set of rolling resistance contains seven language variables, which can be expressed as: {S, MON, MTW, MTH, BON, BTW, BTH} The variable set of wheel speed fluctuation index contains eight language variables, which can be expressed as: {SON, STW, STH, MON, MTW, MTH, BON, BTW} Triangular and trapezoidal distribution membership are selected, and the membership functions of language variables of rolling resistance are shown in Fig. 2. S fuzzy subset corresponds to a small driving resistance with obvious non-sand characteristics, while BTH subset corresponds to a large driving resistance with obvious sand characteristics, both use trapezoidal membership function. The triangular membership function is evenly distributed in the middle range. Figure 3 shows the membership function of wheel speed fluctuation index. The fuzzy subset BTW corresponds to the working condition where wheel speed fluctuation phenomenon is obvious and the membership function chooses trapezoidal distribution, the rest subsets choose triangular distribution. Counter incremental step length variable set contains six language variables, which can be expressed as: {ZR, S, M, BON, BTW, BTH} Fig. 2 Membership of rolling resistance

MON MTW MTH BON BTW

S

1

BTH

Membership

0.8

0.6

0.4

0.2

0

0

2

4

6

Rolling resistance [kN]

8

10

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Fig. 3 Membership of wheel speed fluctuation index

STH

MON

MTW

MTH

BON

BTW

Membership

0.8

0.6

0.4

0.2

0

0

1

0.5

2

1.5

Wheel speed fluctuation index [m/s]

Fig. 4 Membership of increasing step length

1

ZR

S

M

BON

10

20

BTW

BTH

Membership

0.8

0.6

0.4

0.2

0 0

30

40

50

StepIn

The membership function of incremental step length output is shown in Fig. 4. According to these rules, the fuzzy rules are designed and shown in Table 2. The fuzzy reasoning model used here is Mamdani model, and the output surface of fuzzy rules is shown in Fig. 5.

3.2.2

Calculation of Decremental Step Length

The inputs of the fuzzy rule for decremental step length are also rolling resistance Ff and wheel speed fluctuation index Δ, its output is decremental step length StepDe. The range of rolling resistance is set as [0, 4] (unit kN), the range of wheel speed fluctuation index is set as [0, 0.6] (unit m/s). In order to prevent frequent changes of

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Table 2 Fuzzy rules of incremental step length 

Ff S

MON

MTW

MTH

BON

BTW

BTH

SON

ZR

ZR

ZR

ZR

ZR

ZR

ZR

STW

ZR

S

S

M

BON

BTW

BTH

STH

ZR

M

BON

BTW

BTH

BTH

BTH

MON

ZR

BON

BTW

BTH

BTH

BTH

BTH

MTW

ZR

BON

BTW

BTH

BTH

BTH

BTH

MTH

ZR

BON

BTW

BTH

BTH

BTH

BTH

BON

ZR

BON

BTW

BTH

BTH

BTH

BTH

BTW

ZR

BON

BTW

BTH

BTH

BTH

BTH

50 40

StepIn

30 20 10 2 10 1

[m/s]

5 0

0

Rolling resistance [kN]

Fig. 5 Output surface of incremental step length

the recognition results which influence the driving stability, two case two situations need to be considered when setting the range of decremental step length StepDe. First, when the running characteristics show obvious non-sand characteristics, the counter step length will be set as a negative value, so that the counter value will decrease to 0 and thereby triggering the identification of non-sand; Second, when the running characteristics show some sand characteristics, the counter step length will be set as a positive value and the counter value will increase, which reduces the possibility of recognition result from sand to non-sand. According to these considerations, the range of StepDe is set to [−5, 2]. The more inconsistent the vehicle driving characteristics and sand features are, the closer the decremental step size is to −5, and the faster the counter value decreases.

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The variable set of rolling resistance contains eight language variables, which can be expressed as: {SON, STW, STH, MON, MTW, MTH, MFO, B} The variable set of wheel speed fluctuation index contains five language variables, which can be expressed as: {SON, STW, STH, MON, MTW} the membership functions of language variables of rolling resistance are shown in Figs. 6 and 7 shows the membership function of wheel speed fluctuation index. Counter decremental step length variable set contains four language variables, which can be expressed as: Fig. 6 Membership of rolling resistance

1

SON

STW STH MON

MTW

MTH

B

MFO

Membership

0.8

0.6

0.4

0.2

0

2

1

0

4

3

Rolling resistance[kN]

Fig. 7 Membership of wheel speed fluctuation index

STW STW

1

STH

MTW

MON

Membership

0.8

0.6

0.4

0.2

0

0

0.1

0.2

0.3

Wheel speed fluctuation index[m/s]

0.4

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{NB, NM, NS, PS} The membership function of incremental step length output is shown in Fig. 8. The design principles of membership function are similar to those in Sect. 3.2.1 and will not be repeated here. According to these rules, the fuzzy rules are designed and shown in Table 3 and the output surface of fuzzy rules is shown in Fig. 9. NB

1

NM

NS

PS

Membership

0.8

0.6

0.4

0.2

0 -5

-4

-2

-3

-1

1

0

2

StepDe

Fig. 8 Membership of decremental step length

Table 3 Fuzzy rules of decremental step length 

Ff SON

STW

STH

MON

MTW

MTH

MFO

B

SON

NB

NB

NB

NB

NB

NM

NS

PS

STW

NB

NB

NB

NM

NM

NM

PS

PS

STH

NB

NB

NM

NS

NS

PS

PS

PS

MON

NB

NM

NS

PS

PS

PS

PS

PS

MTW

NM

NS

NS

PS

PS

PS

PS

PS

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0

StepDe

-1 -2 -3 -4 4 0.4 2

[m/s]

0.2 0

0

WhlSpdFluct

Fig. 9 Output surface of decremental step length

Fig. 10 Test field

4 Verification of the Identification Strategy 4.1 Test Field and Test System The field tests were carried out in a sand mine of TongLiao. The sand and soil inside the mine are uniform and the terrain is flat, which is shown in Fig. 10. The test system is shown in Fig. 11, which includes the test vehicle, inertial navigation system, CAN bus signal acquisition tool, Logitech HD camera used to collect road image information and host installed with data acquisition software.

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Fig. 11 Schematic diagram of real car test system

8

f

F [kN]

6 4 2

[m/s]

0 0.6 0.4 0.2 0 Identification

4 Result

Real road 2

0 0

10

20

30 Time [s]

Fig. 12 Identification results of driving on sand

40

50

60

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4.2 Test Result (a) Identification results of driving on sand The test results of driving on sand are shown in Fig. 12. It can be seen that rolling resistance quickly reaches around 8000 N after starting, and the fluctuation level of wheel speed also increases greatly. According to the identification logic designed in this paper, the increment step of the counter is very large at this time, and the value of counter quickly increases and exceeds the threshold, so the algorithm recognizes the current road surface as sand, slightly lagging behind the change of the real road surface type. Resistance and wheel speed fluctuation indexes both decreased significantly at about 33 s, the counter value decreased to 0 and the recognition algorithm judged that the current road surface is non-sand, which is very close to the time point of driving away from the sand. (b) Identification results of driving on non-sand The identification algorithm proposed in this paper is designed based on rolling resistance and fluctuation index of wheel speed. When a certain feature is large, false identification may occur. In order to verify the accuracy of the algorithm, verification of driving freely on asphalt road, driving with trailer and driving on bumpy road is carried out.

f

[kN]

1

0.5

[m/s]

0 0.4

0.2

0 Identification

4 Result

Real road 2

0 0

20

40 Time [s]

Fig. 13 Identification results of driving freely on asphalt

60

80

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8

F [kN]

6

f

4 2 0

[m/s]

0.3 0.2 0.1 0 Identification

4 Result

Real road 2

0 25

30

35

40

45

Time [s]

Fig. 14 Identification results of driving with trailer

Figures 13 and 14 are the identification results of driving on asphalt. The former is driving freely, while the latter is driving with trailer. It can be seen that rolling resistance and wheel speed fluctuation are both very low when driving freely, and no false identification occurred. When the trailer was added, the rolling resistance increased significantly to nearly 8000 N, but the fluctuation index of wheel speed remained at a low level, the identification result remained non-sand, and no false identification occurred. Figure 15 shows the recognition results of driving on bumpy road. High road roughness causes wheel speed fluctuation index to reach the upper limit. However, the resistance hardly increases compared with flat asphalt road, so it still does not have the driving characteristics of sand, the identification result remains non-sand.

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f

[kN]

1 0.5

[m/s]

0 2

1

0 Identification

Result

4

Real road 2

0 240

260

280

300

320

340

Time [s]

Fig. 15 Identification results of driving on bumpy road

5 Conclusion This paper designs an online sand identification strategy for SUV, taking vehicle driving resistance and wheel speed fluctuation index as identification characteristics. The real car test system was built and verified by road test. The results show that the algorithm can effectively identify sand and non-sand, and in the experiment of driving with trailer on asphalt road and bumpy road, the algorithm does not misidentify. An online identification strategy of sand terrain for SUV is proposed by using rolling resistance and wheel speed fluctuation as the identification characteristics. A recognition logic based on the counter threshold and a calculator based on fuzzy rules of increasing and decreasing step size is designed to ensure the stability and rapidity of the algorithm. The real car test system is established, and the strategy is verified by road test. The results show that the strategy can effectively identify sand and non-sand, and no false identification occurred in the experiment of driving with trailer on asphalt road and bumpy road.

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References 1. Wei F (2016) Test system design of vehicle’s ground trafficability. Southeast University, Nanjing 2. Rybansky M (2015) Soil trafficability analysis. In: International conference on military technologies (ICMT) 2015, pp 1–5. IEEE 3. Taghavifar H, Mardani A (2013) Investigating the effect of velocity, inflation pressure, and vertical load on rolling resistance of a radial ply tire. J Terrramech 50(2):99–106 4. Liu J, Gao H, Deng Z et al (2008) Effect of slip on tractive performance of small rigid wheel on loose sand. In: International conference on intelligent robotics and applications, pp 1109–1116. Springer, Heidelberg 5. Halatci I, Brooks CA, Iagnemma K (2007) Terrain classification and classifier fusion for planetary exploration rovers. In: 2007 IEEE aerospace conference, pp 1–11. IEEE 6. Khan YN, Komma P, Bohlmann K et al (2011) Grid-based visual terrain classification for outdoor robots using local features. In: 2011 IEEE symposium on computational intelligence in vehicles and transportation systems (CIVTS) proceedings, pp 16–22. IEEE 7. Chen C (2014) Study on terrain recognition and obstacle avoidance for off-road mobile robot. Shandong University, Jinan 8. Zhu X (2012) Research on image processing and analyzing for autonomous navigation of mobile robot in outdoor environments. National University of Defense Technology, Changsha 9. Sadhukhan D, Moore C, Collins E (2004) Terrain estimation using internal sensors. In: Proceedings of the IASTED international conference on robotics and applications 10. Brooks CA, Iagnemma K (2005) Vibration-based terrain classification for planetary exploration rovers. IEEE Trans Rob 21(6):1185–1191 11. Yang F (2016) Research on key techniques of vehicle trafficability base on wheel force test. Southeast University, Nanjing 12. Xue K, Li Q, Xu H et al (2013) Vibration-based terrain classification for robots using k-nearest neighbors algorithm. J Vib Meas Diagn 33(01):88–92+167–168 13. Han Y, Meng G, Huang C et al (2014) Identification of soft roads by real-time monitoring of rolling resistance. Trans Chin Soc Agric Eng 30(11):45–52 14. Yu Z (2009) Automobile theory. China Machine Press, Beijing