State of Health Estimation of Lithium-ion Battery Based on Feature Optimization and Random Forest Algorithm

被引：0

作者：

Wu, Ji ^{[1
,2
]}

Fang, Leichao ^{[1
]}

Liu, Xingtao ^{[1
,2
]}

Chen, Jiajia ^{[1
,2
]}

Liu, Xiaojian ^{[1
]}

Lü, Bang ^{[1
]}

机构：

[1] School of Automobile and Transportation Engineering, Hefei University of Technology, Hefei

[2] Anhui Intelligent Vehicle Engineering Laboratory, Hefei

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2024年 / 60卷 / 12期

关键词：

feature selection; lithium-ion battery; random forest algorithm; state of health estimation;

D O I：

10.3901/JME.2024.12.335

中图分类号：

学科分类号：

摘要：

State of health(SOH) estimation plays a significant role in the battery management system for electric vehicles. Accurate estimation of the SOH is conducive to extending the lifespan of lithium-ion batteries and ensuring vehicles' safe and reliable operation. Aiming at the problem that the previous data-driven methods cannot balance the accuracy of SOH estimation and the cost of model calculation, a solution based on feature optimization is proposed. Firstly, several features are extracted based on the partial charging voltage curve and incremental capacity curve. Moreover, the importance of each feature is calculated by the Gini coefficient in the random forest algorithm. Then, the optimal feature subset is selected by considering the estimation accuracy of the model and the feature number of the selected subset. Finally, the random forest algorithm is employed to establish the battery aging model and estimate the SOH. The results show that the mean absolute error and root mean square error of the proposed SOH estimation method are within 0.4 % and 0.5 %, respectively. Here, the most relevant feature set can be selected by the developed feature optimization strategy. Hence, combined with the random forest algorithm, it can achieve higher SOH estimation accuracy while reducing the calculation cost of the model. © 2024 Chinese Mechanical Engineering Society. All rights reserved.

引用

页码：335 / 343

页数：8

共 24 条

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