基于多模型耦合的电动汽车三电系统安全性估计方法

doi:10.3901/JME.2023.12.354

机械工程学报 ›› 2023, Vol. 59 ›› Issue (12): 354-363.doi: 10.3901/JME.2023.12.354

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基于多模型耦合的电动汽车三电系统安全性估计方法

李达^1,2, 张普琛^1,2, 林倪^1,2, 张照生^1,2,3, 王震坡^1,2,3, 邓钧君^1,2

1. 北京理工大学电动车辆国家工程实验室北京 100081;
2. 北京理工大学电动车辆协同创新中心北京 100081;
3. 北京理工大学重庆创新中心重庆 401120

收稿日期:2022-08-02 修回日期:2023-01-15 出版日期:2023-06-20 发布日期:2023-08-15
通讯作者: 张照生(通信作者),男,1984年出生,副教授,博士研究生导师。主要研究方向为新能源汽车大数据分析,新能源汽车动力电池安全预警。E-mail:zhangzhaosheng@bit.edu.cn
作者简介:李达,男,1995年出生,博士研究生。主要研究方向为新能源汽车大数据分析,新能源汽车动力电池状态估计与故障诊断。E-mail:li_da_bit@126.com;张普琛,男,1997年出生,博士研究生。主要研究方向为新能源汽车大数据分析及应用。E-mail:zhang_puchen@163.com;王震坡,男,1976年出生,博士,教授,博士研究生导师。主要研究方向为动力电池成组理论与应用,新能源汽车大数据分析。E-mail:wangzhenpo@bit.edu.cn
基金资助:
国家重点研发计划(2019YFB1600800)，国家自然科学基金(52172398)和国家高技术船舶科研计划“船用电池动力系统工程化应用研究”资助项目。

Safety Estimation Method of Electric System in Electric Vehicles Based on Multiple Model Coupling

LI Da^1,2, ZHANG Puchen^1,2, LIN Ni^1,2, ZHANG Zhaosheng^1,2,3, WANG Zhenpo^1,2,3, DENG Junjun^1,2

1. National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081;
2. Collaborative Innovation Center for Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081;
3. Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 401120

Received:2022-08-02 Revised:2023-01-15 Online:2023-06-20 Published:2023-08-15

摘要/Abstract

摘要： 电动汽车动力电池、驱动电机和电控系统的安全性对于车辆的正常运行和乘员的生命财产安全至关重要。提出一种多模型耦合的电动汽车三电系统安全性估计方法。该方法仅需要实车传感器采集的稀疏数据作为输入，识别同车型中故障车辆。首先，构建三电系统安全性估计体系，该体系采用“自顶向下”的多层结构。之后，提出一种多模型耦合的方法，该方法由高斯混合、熵权计算和安全分数计算三部分组成，高斯混合得到安全性估计体系中各指标分布规律并输出概率密度，避免熵权重主观划分区间导致的误差；所提出的熵权计算基于概率密度确定各指标的权重，并根据安全性体系计算各车辆/系统的安全总指标，避免主观确定各指标的重要性；基于统计学与数据归一化，得到各车辆/系统的安全分数。最后，采用10辆电动汽车的实车数据对方法进行验证，包括整车安全性估计、三电系统安全性估计和不同季节鲁棒性。结果表明，所提出的方法识别故障车辆/系统与正常车辆/系统的准确率比层次分析法高40%/26.7%，且在不同季节不会对正常汽车产生误判。

关键词: 电动汽车, 动力电池, 驱动电机, 电控系统, 故障诊断

Abstract: The safety of power battery, drive motor and electronic control system is essential for the normal operation of electric vehicles and the safety of occupant's life and property. A novel safety estimation method for electric system in electric vehicles is proposed based on multiple model coupling. The method only needs sparse data collected by onboard sensors as input and can detect the fault vehicles with the same specification. Firstly, a safety estimation scheme of electric system is proposed, which is constructed by multiple layers “from top to bottom”. Then, a multi-model coupling method is proposed, consisting of gaussian mixture, entropy weight calculation and safety score computation. Gaussian mixture can obtain the distribution of safety indicators in safety estimation scheme and output the probability density. This can avoid the error caused by the subjective interval division of entropy weight; The proposed entropy weight calculation can determine the weight of each indicator based on probability density, and calculate the total safety indicator of each vehicle/system according to the safety estimation scheme. This can avoid the subjective determination of the importance of each indicator; The safety scores of each vehicle/system are then computed based on statistics and data normalization. Finally, the method is verified by the data of ten real-world electric vehicles, including vehicle safety estimation, electric system safety estimation and robustness in different seasons. The results show that the accuracies of the proposed method for normal and fault vehicle/electric system classification are 40%/26.7% higher than analytic hierarchy process, and it will not misjudge normal vehicles in different seasons.

Key words: electric vehicle, power battery, drive motor, electric control system, fault diagnosis

中图分类号:

U469

李达, 张普琛, 林倪, 张照生, 王震坡, 邓钧君. 基于多模型耦合的电动汽车三电系统安全性估计方法[J]. 机械工程学报, 2023, 59(12): 354-363.

LI Da, ZHANG Puchen, LIN Ni, ZHANG Zhaosheng, WANG Zhenpo, DENG Junjun. Safety Estimation Method of Electric System in Electric Vehicles Based on Multiple Model Coupling[J]. Journal of Mechanical Engineering, 2023, 59(12): 354-363.

参考文献

[1] 陈泽宇,熊瑞,李世杰,等. 电动载运工具锂离子电池低温极速加热方法研究[J]. 机械工程学报,2021,57(4):113-120. CHEN Zeyu,XIONG Rui,LI Shijie,et al. Extremely fast heating method of the lithium-ion battery at cold climate for electric vehicle[J]. Journal of Mechanical Engineering,2021,57(4):113-120.
[2] 江发潮,章方树,徐成善,等. 车用锂离子电池系统热蔓延试验与机理研究[J]. 机械工程学报,2021,57(14):23-31. JIANG Fachao,ZHANG Fangshu,XU Chengshan,et al. Experimental study on the mechanism of thermal runaway propagation in lithium-ion battery pack for electric vehicles[J]. Journal of Mechanical Engineering,2021,57(14):23-31.
[3] 彭忆强,芦文峰,邓鹏毅,等. 新能源汽车"三电"系统功能安全技术现状分析[J]. 西华大学学报,2018,37(1):54-61. PENG Yiqiang,LU Wenfeng,DENG Pengyi,et al. Analysis of state of the art for new energyvehicle functional safety technologies[J]. Journal of Xihua University,2018,37(1):54-61.
[4] 新浪汽车. 2020年电动汽车起火事故分析[EB/OL].[2021-02-18]. http://k.sina.com.cn/article_6394798159_17d28e04f02700vs5d.html. Sina Car. Analysis of electric vehicle fire accidents in 2020[EB/OL].[2021-02-18]. http://k.sina.com.cn/article_6394798159_17d28e04f02700vs5d.html.
[5] 虎扑新闻. 2021上半年新能源车起火事故排行[EB/OL].[2021-08-11]. https://bbs.hupu.com/44714044.html?ivk_sa=1024320u. Hupu News. Fire accident ranking of new energy vehicles in the first half of 2021[EB/OL].[2021-08-11]. https://bbs.hupu.com/44714044.html?ivk_sa=1024320u.
[6] FENG Xuning,OUYANG Minggao,LIU Xiang,et al. Thermal runaway mechanism of lithium ion battery for electric vehicles:A review[J]. Energy Storage Mater,2018,10:246-267.
[7] 孙振宇,王震坡,刘鹏,等. 新能源汽车动力电池系统故障诊断研究综述[J]. 机械工程学报,2021,57(14):87-104. SUN Zhenyu,WANG Zhenpo,LIU Peng,et al. Overview of fault diagnosis in new energy vehicle power battery system[J]. Journal of Mechanical Engineering,2021,57(14):87-104.
[8] FENG Xuning,YU Merla,WENG Caihao,et al. A reliable approach of differentiating discrete sampled-data for battery diagnosis[J]. eTransportation,2020(3):100051.
[9] FENG Xuning,HE Xu,LU Languang,et al. Analysis on the fault features for internal short circuit detection using an electrochemical-thermal coupled model[J]. Journal of the Electrochemical Society,2018,165:A155-A167.
[10] 佘承其,张照生,刘鹏,等. 大数据分析技术在新能源汽车行业的应用综述——基于新能源汽车运行大数据[J]. 机械工程学报,2019,55(20):3-16. SHE Chengqi,ZHANG Zhaosheng,LIU Peng,et al. Overview of the application of big data analysis technology in new energy vehicle industry:Based on operating big data of new energy vehicle[J]. Journal of Mechanical Engineering,2019,55(20):3-16.
[11] HONG Jichao,WANG Zhenpo,CHEN Wen,et al. Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles[J]. Journal of Energy Storage,2020,30:101459.
[12] HUANG Pengfei,CHEN Haodong,ANKIT V,et al. Non-dimensional analysis of the criticality of Li-ion battery thermal runaway behavior[J]. Journal of Hazardous Materials,2019,369:268-278.
[13] SHANG Yunlong,LU Gaopeng,KANG Yongzhe,et al. A multi-fault diagnosis method based on modified sample entropy for lithium-ion battery strings[J]. Journal of Power Sources,2020,446:227275.
[14] SANG B,NAEINI A,SHESHA J,et al. Test-based identification of stator insulation component with partial discharge activity for low voltage AC motors[J]. IEEE Transactions on Industry Applications,2020,56:2541-2549.
[15] ZHENG Dayong,ZHANG Pinjia. An online groundwall and phase-to-phase stator insulation monitoring method for inverter-fed machine[J]. IEEE Transactions on Industrial Electronics,2020,68:5303-5313.
[16] 闫军志. 永磁同步电机在线故障诊断策略研究[D]. 哈尔滨:哈尔滨工业大学,2020. YAN Zhijun. Research on on-line fault diagnosis strategy of permanent magnet synchronous motor[D]. Harbin:Harbin Institute of Technology,2020.
[17] 刘闯. 数据驱动的电机故障诊断与预测系统的设计与实现[D]. 青岛:青岛大学,2020. LIU Chuang. Design and implementation of data-driven motor fault diagnosis and prediction system[D]. Qingdao:Qingdao University,2020.
[18] BIBA D R,ANCUTI M C,IANOVICI A,et al. Power supply platform and functional safety concept proposals for a powertrain transmission electronic control unit[J]. Electronics,2020,9:1580.
[19] 姜久春,高洋,张彩萍,等. 电动汽车锂离子动力电池健康状态在线诊断方法[J]. 机械工程学报,2019,55(20):60-72. JIANG Jiuchun,GAO Yang,ZHANG Caiping,et al. Online diagnostic method for health status of lithium-ion battery in electric vehicl[J]. Journal of Mechanical Engineering,2019,55(20):60-72.
[20] LI Da,ZHANG Zhaosheng,LIU Peng,et al. Battery fault diagnosis for electric vehicles based on voltage abnormality by combining the long short-term memory neural network and the equivalent circuit model[J]. IEEE Transactions on Power Electronics,2021,36(2):1303-1315.
[21] HU Xiaosong,LI Shengbo,PENG Huei,et al. A comparative study of equivalent circuit models for Li-ion batteries[J]. Journal of Power Sources,2012,198:359-367.
[22] HE Hongwen,XIONG Rui,FAN Jinxin. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach[J]. Energies,2011,4(4):582-598.
[23] 罗勇,祁朋伟,黄欢,等. 基于容量修正的安时积分SOC估算方法研究[J]. 汽车工程,2020,42(5):681-687. LUO Yong,QI Pengwei,HUANG Huan,et al. Study on battery SOC estimation by ampere-hourintegral method with capacity correction[J]. Automotive Engineering,2020,42(5):681-687.
[24] 田乐. 电动汽车用永磁同步电机设计、分析与效率优化[D]. 杭州:浙江大学,2019. TIAN Le. Design,analysis and efficiency optimization of permanent magnet synchronous motors for electric vehicles[D]. Hangzhou:Zhejiang University,2019.
[25] WU Bulong,KIM D S,HAN Bongtae,et al. Hybrid approach to conduct failure prognostics of automotive electronic control unit using stress sensor as in situ load counter[J]. IEEE Transactions on Components,Packaging and Manufacturing Technology,2019,9(1):28-38.
[26] LIN J. Divergence measures based on the Shannon entropy[J]. IEEE Transactions on Information Theory,1991,37(1):145-151.
[27] BA-NGU V O,WING-KIN M A. The gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing,2006,54(11):4091-4104.
[28] SAATY R W. The analytic hierarchy process-What it is and how it is used[J]. Mathematical Modelling,1987,9(3):161-176.
[29] WANG Zhenpo,HONG Jichao,LIU Peng,et al. Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles[J]. Applied Energy,2017,196:289-302.
[30] 陈泽宇,熊瑞,孙逢春. 电动汽车电池安全事故分析与研究现状[J]. 机械工程学报,2019,55(24):93-104. CHEN Zeyu,XIONG Rui,SUN Fengchun. Research status and analysis for battery safety accidents in electric vehicles[J]. Journal of Mechanical Engineering,2019,55(24):93-104.

基于多模型耦合的电动汽车三电系统安全性估计方法

Safety Estimation Method of Electric System in Electric Vehicles Based on Multiple Model Coupling

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