Research progress in lithium-ion battery remaining useful life prediction based on machine learning

doi:10.19799/j.cnki.2095-4239.2024.0713

Abstract

Abstract:

The performance degradation of lithium-ion batteries encompasses detailed processes at multiple scales, ranging from materials, interfaces, and porous electrodes to devices and involving complex chemical and electrochemical reactions. In recent years, informatics has emerged as a vibrant new field for the study of the degradation of lithium-ion batteries, and it intersects data science with battery materials science. These developments promise to accelerate the resolution of complex issues such as battery state modeling, performance management, and lifetime prediction. Various machine learning (ML) methods serve as crucial tools for modeling complex data, discovering patterns, and informing applications. This study focuses on modeling the remaining useful life (RUL) of lithium-ion batteries by reviewing the latest advancements for predicting the RUL of lithium-ion batteries based on ML. It presents breakthroughs by ML methods for data-driven battery management, predictive modeling, and enhanced battery performance and lifespan. Despite numerous achievements in this field, several key challenges hinder its further development. Finally, this study summarizes the primary problem within the field at present, with the intent to provide a comprehensive perspective on ML-based battery RUL prediction and an outlook for future trends and directions.

Key words: battery management system, remaining useful life, life prediction, machine learning algorithms, lifespan extension

CLC Number:

TM 911

Zhenwei ZHU, Jiawei MIAO, Xiayu ZHU, Xiaoxu WANG, Jingyi QIU, Hao ZHANG. Research progress in lithium-ion battery remaining useful life prediction based on machine learning[J]. Energy Storage Science and Technology, 2024, 13(9): 3134-3149.

Figures/Tables 4

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年份	机器学习方法	参考文献	预测性能	电池类型	精度	备注
2021	RNN	[94]	容量	LCO/石墨(NASA 5#)	0.0030 RMSE	较好的泛化性能
2021	ESN	[95]	容量	LCO/石墨(NASA 5#)	3周	预测精度高，稳定
2021	LSTM	[27]	SOH	CALCE CS2-34#	0.0017 RMSE(1周)	不同电池不同工况下误差小，精度高
2021	GRU	[96]	容量	LCO/石墨(NASA 5#)	0.0156 RMSE	效率和精度都较高
2022	SVR-PSO	[97]	电压对时间的积分	LCO/石墨(NASA 5#)	0.0133 RMSE	高精度在线预测
2022	CNN	[54]	自定义	LFP/石墨(MIT)	6.46%MAPE	只需要一圈循环数据即可以实现预测
2022	CTC-ELM	[68]	容量	LCO/石墨(NASA，Oxford)	0.000036MES(NASA)，0.000001MES(Oxford)	高精度预测
2021	RVM	[76]	容量	LCO/graphite (NASA 5#)	0.0105 RMSE	具有长预测能力，预测稳定性高
2021	GPR	[87]	峰位置、峰高、峰面积	CALCE CS2-35#	5周	高精度，适配不同电池，预测实际变化曲线表现良好

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