Review of the remaining useful life prediction methods for lithium-ion batteries

doi:10.19799/j.cnki.2095-4239.2024.0098

Abstract

Abstract:

As the energy and power density of lithium-ion batteries have gradually increased in recent years, the safety performance and prediction of remaining service life have become increasingly crucial. This review offers a comprehensive analysis of the current research status of predicting the remaining useful life of lithium batteries. It systematically introduces the existing forecast algorithms, focusing on the application of machine learning methods in this field. Model-based methods encompass electrochemical, equivalent circuits, and empirical models. In contrast, data-driven methods involve machine learning techniques such as support vector machines, Gaussian process regression, extreme learning machines, convolutional neural networks, recurrent neural networks, and transformers. We meticulously examine the advantages and disadvantages of each method, emphasizing on the application and evolution of machine learning methods in feature extraction and fusion techniques. This study summarizes and analyzes current-voltage-temperature, IC, and EIS curves regarding feature extraction. It subdivides and analyzes fusion methods into model-model, data-model, and data-data fusion methods. Finally, addressing the existing research challenges, this review proposes research suggestions for predicting remaining service life from three perspectives: early, online, and multioperating condition predictions. These suggestions provide insights into enhancing the accuracy and practicability of remaining service life prediction algorithms for Li-ion batteries.

Key words: lithium-ion batteries, remaining useful life, data-driven, machine learning

CLC Number:

TM 912

Bingjin LI, Xiaoxia HAN, Wenjie ZHANG, Weiguo ZENG, Jinde WU. Review of the remaining useful life prediction methods for lithium-ion batteries[J]. Energy Storage Science and Technology, 2024, 13(4): 1266-1276.

Figures/Tables 5

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Table 1

Table 2

References 81

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预测方法	参考文献	输入特征	实验数据	评价指标	预测误差
SVM	[59]	等压降充电时间差、等压降发电时间差	NASA CALCE	RMSE	<2.5%
SVM	[46]	电压和温度曲线中的信号能量、曲率指数、凹凸指数、峰度指数	NASA	RMSE	0.357%

GPR	[60]	放电容量差	CALCE	MAE RMSE	<1% <1.3%
	[61]	最低放电电压对应时间、等压降放电时间、温度最高点对应时间	NASA	—	—
	[62]	恒流充电容量、电阻、峰值电压、电池单体与电池组的不一致性	实验数据	MAE RMSE	2 1

CNN	[63]	阻抗曲线	实验数据	RMSE	0.233%

RNN	[64]	温度、充电速率、SOC	实验数据	—	—
RNN	[65]	差分热伏安曲线波峰	NASA	RMSE	<1%

融合方法	参考文献	实验数据	评价指标	预测精度	特点
LSSVM+双UPF	[73]	NASA CALCE	RMSE	0.0980	使用相空间重构将二维数据映射到高维空间，挖掘数据中的隐藏信息
PF+LSTM	[74]	NASA	MAE	3.3309	构建从测量数据到系统状态的映射
PSO+ELM+RVM	[75]	NASA CALCE	循环次数	<9	可以间接预测具有置信区间的RUL
堆叠回归	[76]	NASA	RMSE MAE	0.01732	将多种回归方法堆叠，需要调整的参数较少
CNN+GPR+双指数模型	[77]	[78]	—	—	在早期预测中有优越性
双GPR+GRU	[79]	NASA CALCE	循环次数	<2	实际计算复杂度低
GPR+LSTM	[80]	NASA CALCE MIT-Stanford	RMSE MAE	0.01057 0.00659	在早期预测中有较强的泛化能力和不确定性管理能力
集成学习器	[81]	CALCE	RMSE	<0.0274	结合RVM、随机森林、弹性网络、自回归模型、LSTM，通过遗传算法确定每个学习器权重。具有较好的鲁棒性和泛化性

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