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

被引：0
|
作者：
Hu J. [1 ,2 ,3 ]
Zhu X. [1 ,2 ,3 ]
He C. [1 ,2 ,3 ]
Yang G. [1 ,2 ,3 ]
机构：
[1] Wuhan University of Technology, Hubei Key Laboratory of Modern Auto Parts Technology, Wuhan
[2] Wuhan University of Technology, Auto Parts Technology Hubei Collaborative Innovation Center, Wuhan
[3] Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering, Wuhan
来源：
Qiche Gongcheng/Automotive Engineering | 2021年 / 43卷 / 09期
关键词：
Electric vehicle; Machine learning; SOH prediction;
D O I：
10.19562/j.chinasae.qcgc.2021.09.004
中图分类号：
学科分类号：
摘要：
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. © 2021, Society of Automotive Engineers of China. All right reserved.
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页码：1291 / 1299
页数：8
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