Lecture Notes in Electrical Engineering 638

Limin Jia · Yong Qin · Baoming Liu ·Zhigang Liu · Lijun Diao · Min An ·Editors

Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019Novel Traction Drive Technologies of Rail Transportation

Lecture Notes in Electrical Engineering

Volume 638

Series Editors

Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of NapoliFederico II, Naples, ItalyMarco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán,MexicoBijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, IndiaSamarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, GermanyJiming Chen, Zhejiang University, Hangzhou, Zhejiang, ChinaShanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, ChinaTan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore,Singapore, SingaporeRüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology,Karlsruhe, GermanyHaibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, ChinaGianluigi Ferrari, Università di Parma, Parma, ItalyManuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid,Madrid, SpainSandra Hirche, Department of Electrical Engineering and Information Science, Technische UniversitätMünchen, Munich, GermanyFaryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA,USALimin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaJanusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, PolandAlaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, EgyptTorsten Kroeger, Stanford University, Stanford, CA, USAQilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USAFerran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra,Barcelona, SpainTan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, SingaporeWolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, GermanyPradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USASebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, GermanySubhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University,Palmerston North, Manawatu-Wanganui, New ZealandCun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USAToyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, JapanFederica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, ItalyYong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaGan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University,Singapore, SingaporeJoachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, GermanyGermano Veiga, Campus da FEUP, INESC Porto, Porto, PortugalHaitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, ChinaJunjie James Zhang, Charlotte, NC, USA

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Limin Jia • Yong Qin • Baoming Liu •

Zhigang Liu • Lijun Diao • Min AnEditors

Proceedings of the4th International Conferenceon Electrical and InformationTechnologies for RailTransportation (EITRT) 2019Novel Traction Drive Technologies of RailTransportation

123

EditorsLimin JiaState Key Laboratory of Rail TrafficControl and SafetyBeijing Jiaotong UniversityBeijing, China

Yong QinState Key Laboratory of Rail TrafficControl and SafetyBeijing Jiaotong UniversityBeijing, China

Baoming LiuNational Innovation Centerof High Speed TrainQingdao, China

Zhigang LiuBeijing Jiaotong UniversityBeijing, China

Lijun DiaoBeijing Jiaotong UniversityBeijing, China

Min AnSchool of Science, Engineeringand EnvironmentUniversity of SalfordSalford, UK

ISSN 1876-1100 ISSN 1876-1119 (electronic)Lecture Notes in Electrical EngineeringISBN 978-981-15-2861-3 ISBN 978-981-15-2862-0 (eBook)https://doi.org/10.1007/978-981-15-2862-0

© Springer Nature Singapore Pte Ltd. 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof 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 transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe 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 thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

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Contents

Study on Ball Screw Wear Model Considering the Influenceof Abrasive Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Qiujin Li, Ying Liu, Genbao Zhang and Yan Ran

Electrical Transformation to Improve Energy-Stored ExperimentalCapability of Rail Transit Test Line of Tongji University . . . . . . . . . . . 13Liwei Dong, Haiquan Liang, Jingtai Hu and Yufei Chen

E-Field Simulation and Voltage Withstand Test Analysis of SolidInsulators for AC GIS Used in High-Speed Railway TractionSubstation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Shiling Zhang

Research on Train Energy-Saving Optimization Based on ParallelImmune Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 33Shibo Li, Wang Dai, Lichao Fang, Yong Zhang and Zongyi Xing

Research on Comprehensive Evaluation Method of OptimizedCharging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Liye Wang, Lifang Wang, Wenjie Zhang and Yuwang Zhang

Study on Key Metrics of Maintenance of Cascade-Utilized BatteryStorage System’s Consistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Na Li, Kai Bai, Kairang Wang, Yinchi Shao, Yu Gong, Jia Diaoand Yaqi Zhao

Reverse Characteristic Model of SiC MPS Diode . . . . . . . . . . . . . . . . . . 71Qiwen Du and Xuehui Tao

Stochastic Simulation of Traction Load for Urban Rail TransportBased on Dynamic Train Headway . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Zhenhai Sun, Hui Liu, Jinping Fang and Shaobing Yang

v

Transient Direct Current Control of Single-Phase Cascaded H-BridgeRectifier Without Phase-Locked Loop . . . . . . . . . . . . . . . . . . . . . . . . . . 93Juxia Ding and Qunzhan Li

A Design of Ground Auto-Passing Neutral Section LogicalControl System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Lintong Li and Baodi Xiao

Parallel Control of Auxiliary Inverter Based on VirtualSynchronous Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117Lei Lu, Xuefei Li, Yang Kuang, Jie Chen and Ruichang Qiu

Application of Improved PSO Algorithms in Train EnergyConsumption Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Binhui Yang, Jing Zhang, Yong Zhang and Zongyi Xing

Design of LRC Filter for High-Power Inverters . . . . . . . . . . . . . . . . . . . 135Zongju Cai, Jie Yang and Zhijie Zhang

Research on a Secondary Pulsation Active Power Filterfor Single-Phase PWM Rectifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Yuxi Zhang, Fujun Peng, Qiyang Xu, Lailai Shen and Jie Chen

An Improved Shunt Hybrid Active Power Filter Based on CascadedH-Bridge Converter and Its Generalized Inverse SystemDecoupling Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Xin Li, Ruoqiong Li and Bo Li

Research on Motor Bearing Fault Detection Method Basedon Cyclic Autocorrelation Function Analysis . . . . . . . . . . . . . . . . . . . . . 163Peng Wang, Chidong Qiu, Xinbo Wu and Zhengyu Xue

Double-Loop Control Strategy for Buck/Boost-CLLLC Two-StageBidirectional DC/DC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171Linghang Huang, Jie Chen, Ruichang Qiu and Zhigang Liu

Research on Motor Bearing Fault Detection Method Basedon Teager–Kaiser Energy Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183Yuan Gao, Chidong Qiu, Changqing Xu and Zhengyu Xue

Research on Active Power Factor Correction Technology . . . . . . . . . . . 191Yuan Sun, Jie Chen, Fei Meng, Ruichang Qiu and Zhigang Liu

Fault Diagnosis of Induction Motor Bearing Based on MultitaperSpectrum and Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . 199Xinchao Zheng, Zhengyu Xue and Chidong Qiu

vi Contents

A Simplified Method Based on LM Algorithmfor Electrochemical–Thermal Coupling Model of TramSupercapacitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209Xueting Fu, Chaohua Dai and Yun Du

Review and Research Progress of Energy BidirectionalFeed ICPT System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221Junjie Wang, Ruoqiong Li and Xin Li

Sensorless Control for IPM Synchronous MotorUsing Disturbance Observer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233Yikun Wang, Xizheng Guo and Donghui Cao

A U2P-Miner-Based Method to Identify Critical Energy-ConsumingParts of Urban Rail Operation System . . . . . . . . . . . . . . . . . . . . . . . . . . 245Mingtao Wu, Yanhui Wang, Shuai Lin, Boyan Hao and Pengfei Sun

Cascade–Parallel Structure Application of Convertersfor AC–DC–AC Traction Substation . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Linwei Li and Mingli Wu

Influence of Air Gap on Electric Field of the EMU RoofCable Terminal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277JiXing Sun, QingYun Zhi, Jin Li and Qi Dai

Research on Reactive Power Optimization Scheme of Metro MediumVoltage Power Supply Network Based on the Medium Voltage EnergyFeeding Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285Lingmin Meng, Dawei Song, Jie Chen, Fuqiang Mu and Zhigang Liu

Research and Application on Electric Vehicle ChargingCommunication Protocols Compatibility Detection . . . . . . . . . . . . . . . . . 295Sixiang Zhao, Zhenyu Jiang, Di Han, Hanji Ju, Yachao Wangand Chenwei Zhang

Hybrid Energy Storage Trolley System Configuration OptimizationMethod Taking into Account the Whole Life Cycle Costof the Whole Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305Qi An, Fangli Shi, Beiyu Liu, Chaohua Dai and Weirong Chen

Review of Energy-Efficient Train Trajectory Optimizationand Intelligent Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317Jie Yang, Biao Wang, Limin Jia and Kuan Zhu

Influence Analysis on the CRH2 EMUs Vehicle Bodies VoltageFluctuation Considering Isolated Rail Joint Discharge . . . . . . . . . . . . . . 327Ying Lu, Ying Wang, Yanlong Shen, Xiaoqiang Chen and Yanming Lu

Contents vii

Hydrogen Consumption Minimization for Fuel Cell Trains Basedon Speed Trajectory Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335Zheng Huang, Chaoxian Wu, Shaofeng Lu and Fei Xue

Simulation of Permanent Magnet Synchronous Motor Basedon Dual PWM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347Huawei Wang, Xilian Wang, Daqiang Bi and Yanhua Li

Analysis and Implementation of High Efficiency High Power DensityBattery Charger for Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . 357Jie Yang, Zhijie Zhang, Zongju Cai, Yu Yao, Fei Li and Wei Yang

Simulation of Power Supply System Considering Inverter FeedbackDevice in Urban Rail Transit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367Liping Zhao, Feng Wang, Sheng Lin and Xiaohong Lin

Study on Stray Current Distribution of the Metro Depot . . . . . . . . . . . . 377Liping Zhao, Junyi Li, Sheng Lin and Aimin Wang

Active Fault-Tolerant Control Strategy of ANPC Three-Level InverterBased on Sliding Mode Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387Qinyue Zhu, Zhaoyang Li, Xitang Tan, Wei Dai, Dabo Xie and Sha Liu

A Survey: Learning-Based Energy Management Strategyfor Heterogeneous Multi-energy Drive System . . . . . . . . . . . . . . . . . . . . 399Xiaoyu Ren, Xinyue Zhang, Dangwei Duan and Lijun Diao

Feed-Forward Compensation Strategy for Suppressing BeatPhenomenon in Railway Traction System . . . . . . . . . . . . . . . . . . . . . . . 407Xueyan Ma, Dongyi Meng, Yaru Xue and Lijun Diao

Dead-Band Optimization for ZVS in Inductive PowerTransfer System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417Bing Li, Daomeng Cai, Xuemei Diao, Lei Wang and Lijun Diao

Research on the Improved Fault-Tolerant Control of ANPCThree-Level Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425Qinyue Zhu, Chen Wang, Xitang Tan, Pengcheng Zhang and Qingwen Fan

Reliability Analysis of Metro Traction Substation Basedon Bayesian Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Guangtao Cong, Wen Xu and Zongyi Xing

Study on Steady-State Output Characteristics of Three-LevelBoost Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451Chengwei Kang, Wei Wang, Guoqin Zhang and Huiqing Du

Optimization of Magnetic Coupling for Small and Medium PowerWireless Charging System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461Xuemei Diao, Chunhui Miao, Bing Li, Weiyao Mei and Lijun Diao

viii Contents

Dynamic Programming in Energy Management Strategyfor Optimizing Multi-energy Drive System . . . . . . . . . . . . . . . . . . . . . . . 471Xinyue Zhang, Xiaoyu Ren, Caihui Zheng, Yue Yuan and Lijun Diao

Research on Principle and Control Method of Totem-Pole PFC . . . . . . 481Chunjiang Liu, Chao Li, Weiyao Mei, Chunmei Xu and Lijun Diao

Pure Electric Brake of the Train with Speed SensorlessVector Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489Qiya Wu, Shaobo Yin, Dangwei Duan, Chunmei Xu and Lijun Diao

Rule-Based Energy Management Strategy for Multi-energyDrive System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499Yue Yuan, Rongjia He, Haibo Zhao, Enlong Li, Xuekun Liand Chunmei Xu

Vector Control Optimization of Traction Motors Based on OnlineParameter Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507Xitang Tan, Dabo Xie, Qinyue Zhu, Zhaoyang Li, Wei Daiand Quanpeng Wu

T-Type Equivalent Circuit of Double-Sided Long Primary LinearInduction Motor Considering the Backward Traveling Wave . . . . . . . . 519Jiefang Ma, Huijuan Liu and Qian Zhang

Optimal Matching of High-Speed Pantograph ParametersConsidering Catenary Suspension Irregular Pointand Wind Damping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527Yulong Liu, Ying Wang, Li Cao, Xiaoqiang Chen and Zhengqing Hao

Dynamic Power Threshold Control Strategy of Wayside HybridEnergy Storage System Based on Battery SOC Tracking . . . . . . . . . . . . 537Mingcheng Ai, Zhongping Yang, Fei Lin and Qiangqiang Qin

Research on Key Technologies of Energy Storagein Photovoltaic/Battery MicroGrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547Jun Zhou, Tao Yang, Wen Xuan Wang, Yalou Chen and ZhaoRui He

Performance Evaluations of DCAT Position for the Floating DCATSystem in DC Railways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557Miao Wang, Xiaofeng Yang, Trillion Q. Zheng, Menghan Ni and Wei Guo

Negative Impedance Converter for Reducing Rail Potentialin Urban Rail Transit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569Wei Guo, Xiaofeng Yang, Jingda Gu and Trillion Q. Zheng

Distributed Cooperative Absorption Method of Braking Energyfor Urban Rail Transit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579Shenyi Liu, Lei Wang, Ming Li, Yanqin Wang and Lei Zhang

Contents ix

Lifetime Prediction Model of IGBT Modules in EMU . . . . . . . . . . . . . . 591Benchao Zhu, Lei Wang, Lei Zhang, Ming Li and Yanqin Wang

Research on Medium Voltage MMC Fault-Tolerant OptimizedControl Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601Qinyue Zhu, Qingwen Fan, Xitang Tan, Zhaoyang Li, Wei Daiand Dabo Xie

Energy Storage System Design for Catenary Free Modern Trams . . . . . 613Li Wang, Qinghua Zhao, Hongliang Duan and Changqing Liu

Modeling and Simulation of Multi-Vehicle DC Traction Power SupplySystem Based on MATLAB/Simulink . . . . . . . . . . . . . . . . . . . . . . . . . . . 623Hui Liu, Zhenhai Sun, Jinping Fang and Zihui Hao

Research on Eddy Current Braking Control Strategy of 600 km/hHigh-Speed Maglev Train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635Hongfeng Qi, Wenjin Hao and Jianqiang Liu

Extended Kalman Filter Observer Based on the Induction MotorModel Considering Iron Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645Leiting Zhao, Chengguang Huang, Shaobo Yin, Dongyi Mengand Kan Dong

Online Fault Protection Method and Its Implementation Basedon Control Bus for Electromechanical Composite TransmissionBench Test System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655Lei Guo, Gen Zeng and Yaoheng Li

Research on Transformer Core Loosening Fault Diagnosis Basedon VMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671Yuzhi Liu, Kuankuan Zhai, Xiaorui Kang, Wei Guo and Xinyu Zhang

Braking Energy Harvesting Strategy of High-SpeedMaglev System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683Dong Yang, Rong Wei and Pengkun Sun

Research on the Improved Time-Sharing Control Strategywith MPPT Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691Yi Fan, Fan Lu, Tao Yang, Wenxuan Wang and Mingxiang Liu

Research on Power Conversion System Based on MultipleDC/DC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701Wenxuan Wang, Tao Yang, Jun Zhou, Yi Fan and Zhaorui He

Simulation Analysis and Experimental Verification of High-SpeedTrain Electromagnetic Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711Jun Yu

x Contents

Research on a PMSM Control Method Based on ModelPrediction Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721Yalou Chen, Jun Zhou, Wenxuan Wang, Tao Yang and Zhaorui He

Research of the Three-Phase Input High Efficiency and LowHarmonic Battery Charging Power Supply . . . . . . . . . . . . . . . . . . . . . . 731Chunhui Miao, Jie Yang, Sen Yang, Yu Yao, Zhijie Zhang, Fei Liand Wei Yang

Analysis and Suppression of Bearing Electric Erosion in the MetroTraction Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741Erle Yang, Ruifang Liu, Qincong Zhao and Zhiwei Zheng

Hardware-in-the-Loop Real-Time Simulation Platform Designfor Electric Rail Traction System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751Zhen Zhao, Jialu Liu, Leiting Zhao and Weizhi Liu

Control Strategy of Rail Transit High-Power Permanent MagnetSynchronous Motor and Verification of Semi-physical Platform . . . . . . 761Zhenzhong Liang, Qubo Xie and Xuepu Li

Research on PWM Modulation Strategy of High-Power PermanentMagnet Synchronous Traction System . . . . . . . . . . . . . . . . . . . . . . . . . . 769Qubo Xie, Zhenzhong Liang and Wen Wang

Optimization Control of Energy-Efficient Driving for Trains in UrbanRail Transit Based on GA-PSO Algorithm . . . . . . . . . . . . . . . . . . . . . . . 777Lidan Zhao, Junqin Peng, Jiaxing Wang and Yonghua Zhou

Impact of Inverter’s DC Virtual Resistance on Braking Energy Flowin Urban Rail Transit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787Dongsheng Xu, Gang Zhang, Fengjie Hao and Yong Wang

Grey Prediction Method for Cylinder Wear Extentof Automobile Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797Zheng-Wei Fan

Modeling Analysis and Control of Bidirectional DC/DC Converterin Hybrid EMU System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805Wang Li, Miaoxin Jin and Libing Fan

Research on Energy Configuration and Characteristics of HybridEMU Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819Yunxin Fan, Wang Li, Miaoxin Jin and Qingmin Qin

Dynamic Imitation Real Platform of Urban Rail Transit TractionPower Supply System for PHM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829Jianmin Wen, Kaikang Wang, Fei Ye, Bin He and Jiangtao Wu

Contents xi

Design Method of Fuzzy Controller for Speed Controlof Heavy-haul Train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839Jiao Dong, Youneng Huang and Huazhen Yu

Design of Wireless Data Transmission System for Vehicle EnergyStorage Components Based on STM32 . . . . . . . . . . . . . . . . . . . . . . . . . . 849An Zhang, Yan kun Li, Yan ru Zhang and Ding hong Chen

The Charging Control Scheme of On-board Battery Energy StorageSystem in Tram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859Dangwei Duan, Caihui Zheng, Zhanguo Wang and Fulai An

SOC Estimation of All-Vanadium Redox Flow Battery via ParametersIdentification and UKF Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867Guobin Sun, Yufu Hao, Zhenghao Li, Li Wang and Kun Fang

The Control Strategy of Hybrid Energy Storage System of TramcarBased on Parallel Interleaving Bidirectional DC/DC Converter . . . . . . . 877Zhexu Zhang, Yang Cui, Qinkun Zhao, Bin Zhou and Zhenghao Li

Vehicle Propulsion System of New MBTA Orange Line Trains . . . . . . . 891Caihui Zheng, Hang Yin, Haifeng Hong and Changqing Liu

xii Contents

Study on Ball Screw Wear ModelConsidering the Influenceof Abrasive Particles

Qiujin Li, Ying Liu, Genbao Zhang and Yan Ran

Abstract Wear is the main cause of the failure of the ball screw pair, whichseriously affects its accuracy and service life. Therefore, it is necessary to build andanalyze the wear model of ball screw pairs based on analyzing wear mechanism indepth. At present, the study of ball screw wear model mostly adopts the theory ofadhesion wear. It is believed that the wear amount during normal wear period isproportional to time. But in fact, this conclusion cannot express the change of thewhole wear process. Based on the wear mechanism of the ball screw pair, this paperdeeply analyzes the influence of abrasive wear on the ball screw during the entirelife cycle. And according to the Archard wear theory, a wear model considering theimpact of abrasive particles is established. In this model, the wear of the ball screwpair is exponentially related to time. The experiments verify the applicability andaccuracy of the model set out in this paper, which provides a theoretical foundationfor the following research on the accuracy maintenance of ball screw pairs.

Keywords Ball screw � Abrasive wear � Adhesive wear � Wear model

1 Introduction

Ball screw pairs play an important part in CNC machine tools and other mechanicalequipment, but domestic ball screw pairs always have problems such as pooraccuracy retention and fast wear failure. These problems are due to wear and tearcaused by the relative movement of the ball with the screw and nut, respectively. Atpresent, ball screw pairs are developing toward high speed and high precision, so it

Q. Li � Y. Liu � G. Zhang � Y. Ran (&)School of Mechanical Engineering, Chongqing University, Chongqing 400044,People’s Republic of Chinae-mail: ranyan@cqu.edu.cn

G. ZhangSchool of Mechanical and Electrical Engineering, Chongqing Universityof Arts and Sciences, Chongqing 402160, People’s Republic of China

© Springer Nature Singapore Pte Ltd. 2020L. Jia et al. (eds.), Proceedings of the 4th International Conference on Electricaland Information Technologies for Rail Transportation (EITRT) 2019, Lecture Notesin Electrical Engineering 638, https://doi.org/10.1007/978-981-15-2862-0_1

1

is important to study the wear law of ball screw pairs. The wear analysis of ballscrew pairs needs to be based on the surface contact friction model. Early scholarssuch as Greenwood [1] put forward a random model assuming that the contactsurface was composed of hemispheres, which reflects the contact condition of thefriction surface. Based on of volume conservation of plastic deformation, Chang [2]proposed a micro-model of rough contact surface, which was further extended byHorng [3] to make it suitable for various contact surfaces. However, the calculationof these wear models is rather complicated. Scholars have proposed using Archardmodel to study the wear of ball screw pairs. For example, Zhong [4] used Archardwear theory to analyze the wear process of ball linear guide pair and established acalculation model of slider displacement to predict the wear amount of ball linearguide pair. Xu [5] established the wear model of ball screw pair based on theincremental form of Archard model. The wear law of ball screw pair during normalwear period was obtained by numerical method and verified by experiment. Liu [6]also used Archard model to find out the friction coefficients of ball screw pairs bythe same material under different loads and sliding speeds. However, these studieshave their own emphases and seldom considering the effect of abrasive particles onthe whole wear process. Ball screw pair is a closed friction pair. The number ofabrasive particles in the ball screw pair will accumulate over time. A large numberof abrasive particles will aggravate the wear condition. Ignoring the influence ofabrasive particles in the raceway will inevitably lead to errors between theoreticalcalculation and actual results. Olofsson’s [7] tests on spherical roller bearings alsoshowed that the wear amount is obviously affected by the wear particles falling offthe contact surface over a long period of time.

At present, the calculation formulas of life and dynamic load rating of ball screwpairs used in the national standard are based on the mechanical model of ballbearing. The calculation results themselves have deviation. Moreover, the wearresearch of ball screw pairs focuses on fatigue wear and adhesion wear, which is notconducive to the rapid development of ball screw pairs to high speed and highprecision. Therefore, it is necessary to study the effect of abrasive particles on thewear of ball screw pairs. In this paper, the wear mechanism of ball screw pair isdeeply analyzed, and according to the actual wear condition of ball screw pair, thewear model considering the effect of abrasive particles is established. The cor-rectness of the model is verified by an example. The relationship between weardepth and time of ball screw raceway determined by this model can be helpful forthe study of precision degradation and life prediction of ball screw pairs.

2 Wear Mechanism and Model

Wear is a complicated failure mode of surface damage. Domestic scholars classifiedwear into four categories: adhesive wear, abrasive wear, fatigue wear, and corrosivewear to the different failure mechanism and wear characteristics of the frictionsurface [8]. In the actual wear phenomenon, there are usually several forms of wear

2 Q. Li et al.

at the same time, and one kind of wear often induces other forms. For example,wear debris generated by fatigue wear can cause abrasive wear. Under differentworking conditions, the main and secondary wear modes are different.

The phenomena of adhesive wear are as follows. In contact area, the materialsunder contact load form adhesion point due to adhesion effect. When relativesliding occurs, shear fracture happens at the adhesion point, and the sheared surfacematerial migrates from one surface to another or falls off into abrasive debris.Archard [9] did a lot of research on adhesion wear, and on this basis, a calculationmodel of adhesion wear was proposed. Archard considered that the wear volume ofmaterials is proportional to the slip distance and normal contact load, but inverselyproportional to the yield limit of materials. Therefore, the formula for calculatingthe adhesive wear volume is as follows:

W ¼ KNsH

ð1Þ

In the formula, W is the wear volume; K is the wear coefficient, the probability ofproducing abrasive particles; N is the normal load; s is the sliding distance; H is thehardness of the weaker material. The formula is widely used as the basic formulafor studying material wear.

Abrasive wear is materials migration or shedding caused by hard particles orhard bumps on the friction surface during the friction process. Rabinovich [10]proposed a quantitative formula for abrasive wear based on micro-cutting theory.The formula shows the relationship between abrasive sharpness and wear rate:

dWds

¼ Ptan hp � H ð2Þ

In the formula, dW is the increment of wear volume; ds is the increment ofsliding distance; P is the load on the abrasive grain; h is the angle between theconical surface of the abrasive grain and the horizontal surface of the metal.Archard also gave a formula for calculating abrasive wear [11]:

dWds

¼ KNH

ð3Þ

K refers to abrasive wear constant, which is determined by factors such asabrasive hardness, shape, and number of abrasive particles. The diagrams ofadhesion wear and abrasive wear are shown in Fig. 1.

Compared with the first two wear forms, the quantitative formulas of the othertwo wear forms are seldom studied. Fatigue wear is pitting or spalling on thefriction surface caused by plastic deformation and fatigue of surface material underalternating contact stress. Bayer [12] also believed that the fatigue of materials is thebasic factor causing the wear of materials under sliding friction. Archard directlygave the same formula of fatigue wear as adhesive wear, in which K was given anew meaning; 1/K was equal to the number of stress cycles that cause fatigue

Study on Ball Screw Wear Model Considering the Influence … 3

failure. Corrosion wear is the surface damage caused by chemical or electro-chemical reaction between metal and surrounding medium. Corrosion wear isgreatly affected by environmental factors, such as acid, alkali, and salt. Archardgave the same formula for calculating corrosion wear, in which K represents thecoefficient related to the thickness of corrosion film.

At present, most of the wear formula of ball screw pairs is based on Archardadhesive wear model. Although the calculation formulas of various wear models arethe same, the values of coefficient K have different meanings. From the perspectiveof adhesive wear alone, the corresponding K values of different wear forms andoperating environments are also different [6, 13]. Therefore, the adhesive wearmodel is difficult to accurately represent the wear condition of the ball screw.

3 Wear Model of Ball Screw Pair

Macroscopically, the whole wear process of ball screw pair includes three stages:running-in wear period, normal wear period, and severe wear period. During therunning-in wear period, the surface defects of wear pairs caused by processing andassembling lead to too excessive peak pressure on the contact surface that abrasionis intense and irregular. After that, the convex peak of the wear pair is flattened, thecontact area increases, and the wear rate decreases sharply, thus entering a stablewear period. Previous research suggests that, in the normal wear period, the wearrate is constant [8], and the wear rate is approximately proportional to the timewhen other operating conditions are determined. But for closed wear pairs such asball screw pairs, there are two main types of wear during the stable wear period,adhesive wear and abrasive wear. With the increase of abrasive particles, abrasivewear becomes more serious, the wear rate should have increased. Through a largenumber of experiments, SKF Bearing Manufacturing Company concluded that the

Fig. 1 a Adhesive wear diagram and b abrasive wear diagram

4 Q. Li et al.

fatigue life of rolling bearings can be prolonged to 10–50 times by removing 2–5micron solid particles in lubricating oil [14]. Therefore, the influence of the numberof abrasive particles should not be neglected when considering the wear model.

There are two main wear forms of ball screw pairs in the process of low loadwear, which should be considered in two parts when calculating wear amount. Thefirst part is adhesive wear, which can directly adopt the commonly used adhesivewear formula of ball screw [5]:

dWds

¼ ksFN

3rsð4Þ

In the formula, ks is dimensionless wear constant, and take 10−9 when lubrica-tion is good. FN is the normal force acting on a single bonding point. rs is thecompressive yield limit of softer material between rolling element and raceway.

The second part is abrasive wear. From the microscopic point of view, theinfluence of abrasive particles on contact surface has two parts. One is the largeabrasive particles which are directly squeezed into the softer material, which causesthe friction surface to produce wear marks and new abrasive particles under thefurrow action; the other is the small particles which are directly attached to thefrictional surface to form a peak. The role of this part of abrasive particles is similarto adhesion wear, which also produces grooves and more debris. For closed wearpairs, this is a vicious cycle process, which means the wear increment per unit timeshould be positively correlated with the number of abrasive particles involved in thewear process. Therefore, the abrasive wear formula under closed condition can bewritten as follows:

dWds

¼ kFN

3rs�WV

ð5Þ

In the formula, V is the average volume of debris produced, W=V is the numberof abrasive grains, and k is a dimensionless constant, which is related to the pro-portion, hardness, and shape of abrasive particles actually involved in the wearprocess. Combining these two parts, the wear model of ball screw pair can beobtained:

dWds

¼ ksFN

3rsþ k

FN

3rs�WV

ð6Þ

Assuming that the area of the contact surface between two friction pairs is DA,the wear depth on the contact area is Dh; then, dW/DA = Dh, and Dt is unit time,and then, the relative sliding velocity of ball and raceway is v = ds/Dt. Divide thetwo sides of Eq. (6) by DA and Dt at the same time; then, the relationship betweenwear depth and time is obtained as follows:

Study on Ball Screw Wear Model Considering the Influence … 5

dhDt

¼ ksFN � v3rsDA

� 1þ kks� DAV

� h� �

ð7Þ

Formula (7) is a differential equation, and the initial condition is t = 0 and h = 0.An approximate model for calculating the wear depth of ball screw can be obtainedby solving the differential equation:

h ¼ ksVkDA

� exp kFN � v3rsV

� t� �

� ksVkDA

ð8Þ

Compared with the wear model of ball screw in literature [5]:

dhDt

¼ ksFN � v3rsDA

ð9Þ

When the operating conditions such as load and lubrication conditions aredetermined, the right side of Eq. (9) is a constant. This shows the wear depth of theoriginal model has a linear relationship with the running time. However, the newmodel is exponential. The accuracy of the new model will be verified by theexperimental data in reference [5], and the estimation of the correlation coefficientwill be obtained.

4 Verification of Ball Screw Wear Model

In reference [5], the wear data of ball screw pair under general conditions wereobtained by testing a certain type of ball screw pair made in China. The experimentwas conducted on a vertical test stand, and the axial load was simulated by means ofthe counterweight applied under the test bench. The structural parameters andoperating conditions of the ball screw pair are given in Table 1.

In the initial of the experiment, the sample is in the running-in period and themeasured value changes greatly. Therefore, the ball screw pair will run for 50 h toenter a stable wear period. During the test, the measurement was made every 50 h,and the initial measurement value was taken as the reference value. Then, thedifference between the measured value and the initial value was taken as themeasurement value of wear. In order to eliminate the interference of human factors,the average values of the data obtained from the three groups of experiments weretaken as the final experimental results. The experimental data are shown in Table 2.

The parameters k=V related to abrasive wear coefficient and average volume ofdebris in the new model cannot be measured. Therefore, the correctness of themodel cannot be proved directly by comparing the calculated data of the new modelwith the experimental data. However, according to the trend of test data and con-sidering the possible actual relationship between wear rate and time, the

6 Q. Li et al.

proportional model, power function model, and exponential model are selected to fitthe test data. The fitting results obtained by the least square method using Origin areshown in Fig. 2.

The coefficient R2 of goodness of fit for proportional relation, power relation, andexponential relation is 0.4718, 0.9747, and 0.9936, respectively. The results showthe fitness of the exponential model is the highest. Therefore, when the confidencelevel is 0.05, there is a 99.36% probability that the relationship between wear rateand wear time is exponential. This means the new model is in the right form. Andthe exponential fitting model of test data is as follows:

y ¼ A1 � exp x=t1ð Þþ y0 ð10Þ

Table 1 Structural parameters and operating conditions of ball screw pairs

Parameter Unit Value

Lead angle u ° 3.64

Pitch diameter of lead screw Dpw mm 40

Ball diameter Dw mm 4.763

Curvature radius of screw raceway rs mm 2.477

Curvature radius of nut raceway rn mm 2.477

Initial contact angle a0 ° 45

Total effective balls N 100

Modulus of elasticity E1, E2 GPa 207

Poisson ratio l1, l2 0.3

Yield limit of bearing steel under compression rs MPa 1617

The sum of preload and axial load Fa N 6000

Speed of screw n r/min 100

Running time Dt h 310

Studio temperature °C 20 ± 1

Lubricating oil type L-FC 32

Table 2 Measured value of wear amount of ball screw pairs

Running time (h) Effective displacement variation (lm) Relative wear depth (lm)

50 8.64 0.00

100 8.97 0.33

150 9.20 0.56

200 9.42 0.78

250 9.70 1.06

300 10.98 2.34

350 18.96 10.32

Study on Ball Screw Wear Model Considering the Influence … 7

To further verify the correctness of the new model theory, the unknownparameters in the new model can be estimated by using the coefficient 1/t1 of shapedetermination in the fitting equation. After the estimated values of unknownparameters are obtained, the theoretical values of wear can be calculated by the newmodel. Then, the theoretical calculation values of the original model and the newmodel are compared with the experimental measurements, respectively. Comparingthe new model with the fitting equation, the relationship between the parameters canbe obtained as follows:

1t1

¼ kFN � v3rsV

ð11Þ

Therefore, the formula for calculating unknown parameters is as follows:

k

V¼ 3rs

FN � v � t1 ð12Þ

Then, according to Hertz elastic contact characteristics and kinematics analysis,the deformation, contact angle, and relative sliding speed of the contact points

Fig. 2 Fitting results: a proportional relationship, b power relationship, and c exponentialrelationship

8 Q. Li et al.

between ball and screw raceway, ball and nut raceway can be calculated. Theconcrete calculation formula can be referred to reference [5]. The calculation resultsare directly used here. The wear amount of the new model corresponding to thetime can be calculated by adding the calculated parameters into Eq. (8). The cal-culation data of the original model and the new model are compared, as shown inTables 3 and Fig. 3.

According to the formula of correlation coefficient test method:

R2 ¼Xn

i¼1ðXi� XÞ Yi� Y

� �h i2� Xn

i¼1ðXi� XÞ2

Xn

i¼1ðYi� YÞ2

h ið13Þ

The correlation coefficients of the original model and the new model areR12 = 0.6973 and R2

2 = 0.9962, respectively. Therefore, we can draw the following

Table 3 Measured values of wear of ball screw pairs and calculated data of original model

Runningtime (h)

Measuredrelative wear(lm)

Calculating relative wearamount by original model(lm)

Calculating relative wearamount by new model (lm)

50 0.00 0.00 0.0000

100 0.33 0.29 0.0034

150 0.56 0.58 0.0197

200 0.78 0.87 0.0984

250 1.06 1.16 0.4772

300 2.34 1.45 2.3016

350 10.32 1.96 11.0884

Fig. 3 Comparison of experimental data with model data

Study on Ball Screw Wear Model Considering the Influence … 9

conclusions: (1) The estimated values of unknown parameters in the new model arereasonable, and the theory of the new model is correct. (2) The new model is betterthan the adhesive wear model in expressing the relationship between wear rate andtime. However, there are some deviations between the calculated data of the newmodel and the experimental data in the early stage of operation. This is because theirregular wear in the running-in stage produces a certain amount of debris, and theinitial wear value is not zero. On the whole, the new model considering the effect ofabrasive particles can better reflect the wear condition of ball screw during thewhole operation process.

5 Conclusion

Based on the traditional study of ball screw pair wear, this paper analyzes the wearmechanism of closed pairs and builds a wear model considering the effect ofabrasive particles. The model shows that the debris generated by the closed wearpair during operation will aggravate the wear process, and the wear amount andtime are exponentially related. The rationality and accuracy of the model are ver-ified by wear experiments of ball screw pairs. The model provides a theoreticalbasis for predicting the precision degradation law and service life of ball screwpairs. At the same time, the model proposed in this paper can also be applied toother closed wear pairs such as bearings. However, the model needs to solve thefollowing problems. One is to determine the effective value of the average volumeof debris produced in the wear process. The other is to determine the value ofabrasive wear coefficient k. These parameters need a lot of experiments to deter-mine, which also point out the direction for the future work.

Acknowledgements This work is supported by the National Natural Science Foundation, China(No. 51835001; 51705048); the National Major Scientific and Technological Special Project for“High-grade CNC and Basic Manufacturing Equipment,” China (2018ZX04032-001).

References

1. Greenwood JA, Williamson JBPP (1996) Contact of nominally flat surfaces. Proc Roy Soc AMath Phys Eng Sci 295(1442):300–319

2. Chang WR, Etsion I, Bogy DB (1987) An elastic-plastic model for the contact of roughsurfaces. ASME J Tribol 109(2):257–263

3. Horng JH (1998) An ellipticity elastic-plastic asperity microcontact model for rough surfaces.ASME J Tribol 120:82–88

4. Zhong Y, Tao WJ, Han J (2003) Research on precision loss model of ball linear guide pair.Modular Mach Tools Autom Process Technol (12):33–36 (2003) (in Chinese)

5. Xu XH, Tang WH, Yu T (2016) Ball screw wear prediction based on Archard theory.Combination Mach Tools Autom Process Technol 2:54–59 (in Chinese)

10 Q. Li et al.

6. Liu CH, Chen XY, Gu JM (2009) High-speed wear life-time analysis of instrument ballbearings. Proc Inst Mech Eng Part J: J Eng Tribol 223(3):497–510

7. Olofsson U (1997) Characterization of wear in boundary lubricated spherical roller thrustbearings. Wear 208(1–2):194–203

8. Wen SZ, Ping H (2012) Principles of tribology (2nd edn). Tsinghua University Press, Beijing,pp 301–340 (in Chinese)

9. Archard JF (1953) Contact and rubbing of flat surfaces. J Appl Phys 24(8):981–98810. Rabinowicz E, Dunn LA, Russell PG (1961) A study of abrasive wear under three-body

conditions. Wear 4(5):345–35511. Gui CL (1990) Archard’s wear design calculation model and its application method. Lubr Eng

1:12–21 (in Chinese)12. Bayer RG, Clinton WC, Sirico JL (1964) A note on the application of the stress dependency

of wear in the wear analysis of an electrical contact. Wear 7(3):282–28913. Gao HQ, Cai HJ, Cai M (2018) Finite element analysis of angular contact ball bearing wear

based on archard modified model. Mach Tool Hydrau Pressure 46(15):159–164 (in Chinese)14. Wang N (2008) Physical characteristics of wear faults of low-speed bearings based on

fractal-wavelet analysis. Northeast University (in Chinese)

Study on Ball Screw Wear Model Considering the Influence … 11

Electrical Transformation to ImproveEnergy-Stored Experimental Capabilityof Rail Transit Test Line of TongjiUniversity

Liwei Dong, Haiquan Liang, Jingtai Hu and Yufei Chen

Abstract The application of regenerative braking energy storage system is one ofthe development directions of rail transit energy traction technology, which con-forms to the concept of energy saving and environmental protection. In order tocarry out relevant research and experiments, the electrical transformation of the railtransit test line of Tongji University was carried out. Based on the analysis of themain wiring of traction substation, a scheme of adding a new DC feeder cabinet onthe DC side is proposed, and the electrical components in the cabinet are designed.At last, a simulation model of lithium-ion supercapacitor energy storage system isestablished based on the transformed test line. The simulation results show that thetransformed rail transit test line has a good ability of energy-storage-relatedexperiments, and the expected goal has been achieved.

Keywords Rail transit � Traction substation � DC feeder cabinet � Energy storagesystem � Supercapacitor

1 Introduction

Due to the short distance between urban rail transit stations, the vehicles start andstop frequently, and considerable braking energy is generated during the brakingprocess. The supercapacitors have the advantages of high power density, fast chargeand discharge speed, long cycle life, and high efficiency. Therefore, they are verysuitable for urban rail transit braking energy recovery system [1, 2].

L. Dong � H. Liang (&) � J. HuInstitute of Rail Transit, Tongji University, No. 4800 Cao’an Highway,Jiading District, Shanghai, Chinae-mail: lhq@tongji.edu.cn

Y. ChenCollege of Electronic and Information Engineering, Tongji University,No. 4800 Cao’an Highway, Jiading District, Shanghai, China

© Springer Nature Singapore Pte Ltd. 2020L. Jia et al. (eds.), Proceedings of the 4th International Conference on Electricaland Information Technologies for Rail Transportation (EITRT) 2019, Lecture Notesin Electrical Engineering 638, https://doi.org/10.1007/978-981-15-2862-0_2

13

To improve the performance of traditional supercapacitors, scholars and expertsin the industry mainly improve their energy density and monomer voltage throughchemical research on electrode materials and electrolyte materials of supercapaci-tors [3], thus deriving a series of more excellent supercapacitors, includinglithium-ion supercapacitors. In addition to materials, the research of supercapacitormodel [4, 5], management system [6, 7], and mixed use with other energy storagecomponents [8, 9] are also the hotspots in the application of supercapacitors.

Lithium-ion supercapacitor combines the advantages of supercapacitor andlithium-ion battery and has higher energy density and monomer voltage. It isnecessary to conduct research on the application of lithium-ion supercapacitor inurban rail transit. In order to build a lithium-ion supercapacitor energy recoverysystem, research on regenerative braking with energy stored and related experi-mental preparations are carried out. On the one hand, a transformation scheme isproposed based on the analysis of existing traction substation. A new DC feedercabinet is reasonably designed and installed to ensure the safe operation of theenergy recovery system. On the other, energy recovery system is simulated basedon test line parameters, and the simulation results show that energy-stored exper-imental capability of test line is effectively improved.

2 Research Methods

2.1 Overview of Traction Substation

There is a traction substation in the rail transit test line. The traction substationobtains electric energy from the regional substation and transforms it into therequired voltage level of the test vehicle by step-down and rectification. Figure 1shows the main wiring of the traction substation. Electric energy is fed through10 kV GIS incoming cabinet and is sent to two sets of 750 V 12-pulse rectifier unitsthrough the autotransformer. The autotransformer can adjust the 10 kV voltagethrough the voltage regulating switch to meet the relevant experimental require-ments. The two sets of rectifier units switch the connection state through a pair ofself-locking isolating switches. Equivalent 750 V voltage is output in parallel, and1500 V voltage is output in series. The DC traction electric energy is input to thepositive bus through the rectifier, and then flows to the catenary through the DCfeeder cabinet; the running test vehicle is subjected to the electric bow to obtainelectric energy from the catenary, and the generated current flows back to thenegative bus through the rail and the return line, thereby completing one completetraction reflow process.

14 L. Dong et al.

2.2 Electrical Transformation

Our school undertakes the project funded by Shanghai Science and TechnologyCommission. The project content is the engineering demonstration of lithium-ionsupercapacitor energy recovery system, namely realizing the intermodulation oflithium-ion supercapacitor system, DC-DC converter, and the network terminal onthe rail transit test line, and verifying the feedback response of energy recoverysystem including in overload protection, short-circuit protection, and other workingconditions by connecting with the existing real-time signal detection system. In thisproject, the stationary supercapacitor energy storage system is selected, andlithium-ion supercapacitor bank is needed to be connected in traction substation. Atthe same time, corresponding protection should be provided for energy storageequipment and DC feeder section. Based on this requirement, the traction substationshould be transformed. A new 1500 V DC feeder cabinet is added to the DC part inthe dotted frame, as shown in Fig. 2. The negative of it is introduced into the DC

Fig. 1 Main wiring diagram of traction substation

Electrical Transformation to Improve Energy-Stored … 15

switch cabinet, and the new feeder cabinet is connected with the energy storageequipment through the disconnector.

The traction power supply system can be equivalent to the circuit shown inFig. 3.

When the switch K is on, short-circuit fault of DC feeder circuit can be simu-lated. The mathematical model of short-circuit system is as shown in (1).

Ldidt

þ Req þR1� �

Ik ¼ Us ð1Þ

where L is equivalent inductance of DC feeder circuit, Req and R1 are equivalentinternal resistance of traction substation and resistance of DC feeder circuit,respectively, Ik is short-circuit current, and Us is an ideal voltage source equivalentto traction substation.

Fig. 2 Scheme of main circuit of DC feeder cabinet

Fig. 3 Equivalent circuit of traction power supply system

16 L. Dong et al.

The expression of Ik can be obtained by solving (1):

Ik ¼ Us

Req þR11� e�

Req þR1L t

� �¼ Us

Req þR1� Us

Req þR1e�

Req þR1L t ¼ Ip � Iap ð2Þ

Ik consists of Ip and Iap. Ip is the component of steady-state short-circuit current. Iapis the component of transient short-circuit current, and it decreases exponentiallywith time.

According to (2), the maximum value of Ik is Ip. And Ip is inversely proportionalto the sum of Req and R1. The maximum short-circuit current Ik may appear near thetraction substation, and R1 can be seen as zero at this time. So Ikmax can be cal-culated as shown in (3).

Ikmax ¼ Us

Reqð3Þ

The relationship between ideal voltage source and rated voltage of traction sub-station is shown in (4).

Req ¼ Us � Unð Þ=In ð4Þ

where Un is the rated voltage of DC side and In is rated current of tractionsubstation.

Considering that two sets of rectifiers in series or in parallel correspond to DCvoltage output of 1500 V or 750 V, respectively, DC feeder cabinet and equipmentin cabinet should meet the requirements of two voltage modes at the same time. Theinternal insulation level of DC feeder cabinet is designed according to DC 1500 Vsystem. The maximum short-circuit current of two sets of rectifier units operating inparallel with the output voltage of 750 V is much larger than that in series, so thebreaking capacity of circuit breaker should be designed according to the workingcondition of 750 V in parallel. The equivalent internal resistance Req of tractionsubstation is calculated as shown in (5).

Req ¼ KrUd%

100� U2

n

0:9nSTð5Þ

where Kr is internal resistance coefficient, Ud% is the ratio of short-circuit impe-dance voltage to rated voltage of rectifier transformer, n is the number of tractionrectifier unit, and ST is the capacity of rectifier transformer.

From (3), (4), and (5), it can be estimated that the maximum short-circuit currentIkmax which may occur in the circuit connecting the energy storage equipment is62.485 kV.

The model of DC feeder cabinet is selected as WDQ-1500. Considering thepossible maximum short-circuit current, the rated switching current of DC circuit

Electrical Transformation to Improve Energy-Stored … 17

breaker is selected as 80 kV, and the model of it is selected as WLDS1-2500.During the selection process, the rated current of DC disconnector should be largerthan that of circuit breaker. Otherwise, it may occur that when the circuit breakerdoes not reach its maximum load current and is not disconnected, the disconnectorwill be burnt because of the excess of the maximum current, and then, the arc willproduce overvoltage, which will endanger other equipment. Therefore, the model ofDC disconnector is selected as HD18-3000. The main electrical components in theDC feeder cabinet are shown in Table 1.

The setting range of tripping current of selected circuit breaker is 3200–8000 A.The tripping current setting is selected as 8000 A to verify the operation time ofcircuit breaker.

When one set of 750 V 12-pulse rectifier units works alone, short-circuit currentat the end of DC feeder circuit is calculated as (6).

Ik1 ¼ Us

Req þR11� e�

Req þR1L t

� �¼ Us

Req þ rd1� e�

Req þ rdld t

� �

¼ 783:4350:0251þ 0:03� 0:1

1� e�0:0251þ 0:03�0:12:5�10�3�0:1

t� �

¼ 27; 880 1� e�112:4t� � ð6Þ

where r is unit resistance of DC feeder circuit and the value is 0.03 X/km. l is theunit inductance of DC feeder circuit, and the value is 2.5 mH/km. d is length ofcable in DC feeder circuit, and the value is 0.1 km.

When two sets of 750 V 12-pulse rectifier units work in parallel, short-circuitcurrent at the end of DC feeder circuit is calculated as (7).

Ik2 ¼ 783:4350:0125þ 0:03� 0:1

1� e�0:0125þ 0:03�0:12:5�10�3�0:1

t� �

¼ 50; 544 1� e�62t� � ð7Þ

When two sets of 750 V 12-pulse rectifier units work in series, short-circuitcurrent at the end of DC feeder circuit is calculated as (8).

Table 1 Main electrical components of DC switch cabinet

Electrical components Model Number

DC circuit breaker WLDS1-2500 1

DC disconnector HD18-3000 1

Microcomputer protection device ST700 1

Splitter FL-2 3000 A/60 mV 1

Voltage isolation transmitter P42001D3 1

Current isolation transmitter P41066D1 1

Fault detection device WL-JC 1

DC voltmeter 6C2-V 1

DC ammeter 6C2-A 1

18 L. Dong et al.