基于机器学习方法的锂电池剩余寿命预测研究进展

doi:10.19799/j.cnki.2095-4239.2024.0713

摘要/Abstract

摘要：

随着技术的不断进步和成本的逐步降低，锂电池在电动汽车、储能系统、便携式电子设备等多个领域实现了广泛应用，有效促进了清洁能源的普及和能源结构的优化。掌握锂电池衰变和剩余使用寿命(RUL)对于确保设备稳定运行、提高能源利用效率以及保障用户安全至关重要。通过优化电池设计和使用策略，可以延长锂电池的使用寿命，降低更换成本，进一步推动锂电池的规模化应用。锂电池的性能衰变是一个涉及多尺度化学、电化学反应的复杂过程，涉及其内部从材料、界面到多孔电极、器件等诸多因素影响。各种机器学习(ML)的方法正是建模处理复杂数据、寻找规律、反馈应用的重要手段。本文针对锂电池RUL建模研究的科学问题，综述了ML算法在预测电池RUL领域的最新进展，重点介绍数据驱动的电池管理、预测建模以及利用ML方法来提高电池性能和寿命方面的突破。最后，对当前领域内面临的关键问题进行了归纳总结，以期提供一个基于ML算法的电池RUL预测技术的全面视角，并展望其未来的发展趋势。

关键词: 电池管理系统, 电池剩余寿命, 寿命预测, 机器学习算法, 寿命延长

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

中图分类号:

TM 911

朱振威, 苗嘉伟, 祝夏雨, 王晓旭, 邱景义, 张浩. 基于机器学习方法的锂电池剩余寿命预测研究进展[J]. 储能科学与技术, 2024, 13(9): 3134-3149.

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.

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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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