A Joint State of Health and Remaining Useful Life Estimation Approach for Lithium-ion Batteries Based on Health Factor Parameter

被引：0
|
作者：
Wang P. [1 ]
Fan L. [1 ]
Cheng Z. [1 ]
机构：
[1] School of Electrical and Information Engineering, Tianjin University, Nankai District, Tianjin
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2022年 / 42卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Gaussian process regression; Least squares support vector machine; Lithium-ion battery; Principle component analysis; Remaining useful life; State of health;
D O I：
10.13334/j.0258-8013.pcsee.202368
中图分类号：
学科分类号：
摘要：
Accurate estimation of state of health (SOH) and remaining useful life (RUL) of lithium batteries is crucial to ensure the safe and stable. operation of batteries. However, both of them are difficult to be directly measured. A SOH and RUL joint estimation approach based on gaussian process regression (GPR) was proposed in this paper. Health factor (HF) was extracted from the charging curve and indirect health factor (IHF) was obtained through principal component analysis (PCA). Then, an aging battery model based on GPR was established to estimate SOH. Furthermore, the least squares support vector machine (LS-SVM) was used to predict IHF in the future cycles, and the IHF obtained were combined with the established battery aging model to realize RUL estimation. Two battery data sets at different temperatures were utilized to verify the accuracy and adaptability of the algorithm. The results show high accuracy and robustness of the proposed method. © 2022 Chin. Soc. for Elec. Eng.
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页码：1523 / 1533
页数：10
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