基于实车数据的电动汽车电池健康状态预测

doi:10.19562/j.chinasae.qcgc.2021.09.004

Abstract

Abstract:

In view of that most of the existing battery state of health （SOH） prediction schemes are based on the experimental data obtained in the laboratory with limited conditions， and the poor accuracy of single indicator prediction， a machine learning model is constructed based on the analysis on real vehicle operating data and the extraction of battery health state factors， with the battery capacity， internal resistance and cell consistency as its features， to achieve accurate prediction of battery SOH with multiple indicators. Aiming at the problems of the incomplete interval of real vehicle data and the large interval of segments， an adaptive state estimation method is proposed. Non-dominated sorting genetic algorithm （NSGA?II） is used to conduct a multi-objective （accuracy and efficiency） optimization， with the optimal voltage interval obtained and the accuracy of variable interval estimation of battery capacity enhanced. The results show that the method proposed can effectively achieve the accurate prediction of battery SOH based on real vehicle data， with the maximum mean absolute error of test set less than 2% when using 5?fold cross validation.

Key words: electric vehicle, SOH prediction, machine learning

Jie Hu,Xueling Zhu,Chen He,Guangyu Yang. Prediction on Battery State of Health of Electric Vehicles Based on Real Vehicle Data[J].Automotive Engineering, 2021, 43(9): 1291-1299.

Figures/Tables 26

References 18

1	刘树林. 电动汽车用锂离子动力电池建模与状态估计研究［D］. 济南：山东大学， 2017.
	LIU S L. Research on modeling and state estimation of lithium ion power battery for electric vehicle ［D］. Jinan：Shandong University， 2017.
2	徐兴，徐琪凌，王峰，等. 车用锂离子动力电池电化学模型修正方法［J］. 机械工程学报， 2019， 55（12）：128-136.
	XU X ， XU Q L， WANG F，et al. Electrochemical model modification method for lithium⁃ion power battery for vehicle ［J］. Journal of Mechanical Engineering， 2019，55 （12）：128-136.
3	胡晓松，唐小林. 电动车辆锂离子动力电池建模方法综述［J］. 机械工程学报， 2017， 53（16）：20-31.
	HU X S， TANG X L. Summary of modeling methods for lithium ion power battery for electric vehicles ［J］. Journal of Mechanical Engineering， 2017， 53（16）：20-31.
4	周頔. 电动汽车锂电池健康状态在线估算方法研究［D］. 哈尔滨：哈尔滨工业大学， 2020.
	ZHOU D. Research on online estimation method of electric vehicle lithium battery health status ［D］. Harbin ：Harbin Institute of Technology， 2020.
5	王常虹，董汉成，凌明祥，等. 车用锂离子电池剩余使用寿命预测方法［J］. 汽车工程， 2015， 37（4）：476-479.
	WANG C H， DONG H C， LING M X，et al. Vehicle lithium⁃ion battery residual service life prediction method ［J］. Automotive Engineering， 2015， 37（4）：476-479.
6	YU J. State of health prediction of lithium⁃ion batteries： multiscale logic regression and Gaussian process regression ensemble［J］. Reliability Engineering & System Safety， 2018， 174（jun.）：82-95.
7	刘中财，严晓，余维，等. 锂离子电池健康状态新型测定方法［J］. 电源技术， 2019， 43（1）：74-76，157.
	LIU Z C，YAN X， YU W，et al. A new method for measuring the health status of lithium ion battery ［J］. Power Supply Technology， 2019， 43（1）：74-76，157.
8	梁培维，张彦会. 基于欧姆内阻对锂电池健康状态的估算［J］. 电源技术， 2019， 43（10）：1623-1625，1704.
	LIANG P W， ZHANG Y H. Estimation of lithium battery health state based on ohm internal resistance ［J］. Power Supply Technology， 2019， 43（10）：1623-1625，1704.
9	XU Jun， MEI Xuesong， WANG Xiao， et al. Wavelet based relative state of health estimation for lithium-ion batteries［J］. Energy Procedia， 2019， 158.
10	徐文静. 纯电动汽车锂动力电池健康状态估算方法研究［D］. 长春：吉林大学， 2012.
	XUN W J. Research on health state estimation method of lithium power battery for pure electric vehicle ［D］. Changchun：Jilin University， 2012.
11	CHANG Cheng， LIU Zhengyu， HUANG Yewei ，et al. Estimation of battery state of health using back propagation neural network［J］. Computer Aided Drafting，Design and Manufacturing， 2014， 24（1）：60-63.
12	郝雪玲. 锂离子电池健康状态多指标融合和剩余寿命预测方法研究［D］. 哈尔滨：哈尔滨理工大学， 2019.
	HAO X L. Research on health status and residual life prediction of lithium⁃ion battery ［D］. Harbin ：Harbin University of Science and Technology， 2019.
13	YANG D，WANG Y，PAN R，et al. State⁃of⁃health estimation forthe lithium⁃ion battery based on support vector regression［J］. Applied Energy， 2018， 227（S1）：273-283.
14	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 Y， QI P W， HUANG H，et al. Research on estimation method of ampere⁃hour integral SOC based on capacity correction ［J］. Automotive Engineering， 2020， 42（5）：681-687.
15	刘轶鑫，张頔，李雪，等. 基于SOC-OCV曲线特征的SOH估计方法研究［J］. 汽车工程， 2019， 41（10）： 1158-1163.
	LIU Y X， ZHANG D， LI X，et al. Research on SOH estimation method based on SOC⁃OCV curve characteristics ［J］. Automotive Engineering， 2019， 41（10）：1158-1163.
16	颜湘武，郭琪，杨漾，等. 动力电池组健康状态评价方法的研究［J］.湖南大学学报（自然科学版）， 2015， 42（2）：93-99.
	YAN X W， GUO Q， YANG Y， et al. Study on evaluation method of power battery's health condition ［J］. Journal of Hunan University （Natural Science Edition）， 2015， 42（2）：93-99.
17	齐腾飞. 基于多因素分析的锂离子电池寿命研究［D］. 长春：吉林大学，2017.
	QI T F. Lithium⁃ion battery life research based on multi⁃factor analysis ［D］. Changchun：Jilin University， 2017.
18	郑路路. 基于实车行驶数据的动力电池容量衰减性研究［D］. 上海：上海交通大学， 2017.
	ZHENG L L. Research on capacity decay of lithium⁃ion battery based on real vehicle driving data ［D］. Shanghai ：Shanghai Jiao Tong University， 2017.

[1]	LI Huan, HUANG Ying, ZHANG Fu-Jun, ZHAO Yu, GE Yan-Wu. [J]. , 2016, 38(12): 1420 -1426 .
[2]	HAO Han, WANG Si-南, LI Xiao, LIU Zong-Wei, ZHAO Fu-Quan. [J]. , 2017, 39(1): 1 -8 .
[3]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[4]	ZHANG Yi-Bing, 吕Jian-Jun , YUAN Guang-Hui. [J]. , 2016, 38(12): 1467 -1470 .
[5]	ZHANG Lei, ZHANG Jin-Qiu, LUO Tao, BI Zhan-Dong, YAO Jun. [J]. , 2016, 38(12): 1494 -1499 .
[6]	CUI An, LI Bin, WANG Xue-Liang, ZHANG Shi-Zhan. [J]. , 2016, 38(12): 1521 -1525 .
[7]	CHU Liang, ZHAO Di, LI Wen-Hui. [J]. , 2017, 39(1): 61 -65 .
[8]	FENG Chong, ZHANG Dong-Hao, LUO Yu-Gong, LI Ke-Qiang. [J]. , 2017, 39(1): 66 -72 .
[9]	HAN Xiao-Mei, LIN Xue-Dong, LI De-Gang. [J]. , 2017, 39(1): 23 -27 .
[10]	YUAN Zhi-Qun, GU Zheng-Qi, YANG Ming-Zhi, PENG Qian, LIU Xian-Gui. [J]. , 2017, 39(1): 28 -34 .

数据类型	数据字段名称	数据说明
时空信息	time	数据采集时间
时空信息	location	车辆位置
车辆信息	vid	车辆编号
	speed	车速/（km·h^-1）
	mileage	累计里程/km
	t_volt	总电压/V
	t_current	总电流/A
电池信息	soc	电池荷电状态/%
	max_cell_volt	电池单体电压最高值/V
	min_cell_volt	电池单体电压最低值/V
	max_temp	最高温度值/℃
	min_temp	最低温度值/℃

数据采集时间	…	总电流/A	最高温度值/℃
2019-04-05 09：55：39	…	0	18
2019-04-05 09：55：39	…	0	18
2019-04-05 09：55：39	…	47
2019-04-05 09：55：39	…	13	15

误差	基于自适应变区间的三指标预测	基于多目标优化的容量法预测	基于多目标优化的三指标预测
平均绝对误差	1.978	1.729	1.535
均方误差	0.060	0.047	0.032
平均绝对百分比误差	2.130	1.911	1.709

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Prediction on Battery State of Health of Electric Vehicles Based on Real Vehicle Data

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