基于多尺度特征融合的锂离子电池早期剩余使用寿命预测

doi:10.19799/j.cnki.2095-4239.2025.0488

摘要/Abstract

摘要：

锂离子电池作为关键储能器件，在电动汽车等新能源领域具有广泛应用，其性能衰减与寿命预测对电池健康管理至关重要。早期预测不仅能够优化电池使用策略，更能为故障预警提供重要依据。针对锂离子电池早期数据质量差、退化特征微弱等挑战，本文提出了一种基于多尺度特征融合的锂离子电池早期剩余使用寿命预测的新方法。首先，通过改进蝗虫优化算法 (improved grasshopper optimization algorithm，IGOA) 自适应调整时变滤波经验模态分解 (time-varying filtered empirical mode decomposition，TVF-EMD) 的关键参数，优化早期信号的分解过程，有效提取早期退化特征；其次，通过K均值聚类算法将本征模态函数 (intrinsic mode functions, IMFs) 划分为表征容量退化的高频、中低频、趋势项三类分量，经加权融合后显著增强早期特征表达能力；进而，构建基于鲸鱼迁徙优化算法 (whale migrating algorithm, WMA) 优化的预测模型，对各频段IMFs进行独立预测最终通过多尺度预测结果的融合重构，实现锂电池全寿命周期的退化轨迹预测与剩余使用寿命的预测。基于CALCE和MIT数据集的实验表明，本方法较传统预测模型具有显著优势，均方根误差 (root mean square error, RMSE) 始终低于1.4%，为锂离子电池早期寿命预测提供了可靠解决方案。

关键词: 锂离子电池, 早期剩余使用寿命, 时变滤波经验模态分解, 多尺度预测

Abstract:

As a key energy storage device, lithium-ion batteries are widely used in new energy fields such as electric vehicles, and their performance attenuation and life prediction are crucial for battery health management. Early prediction can not only optimize battery usage strategies, but also provide an important basis for fault warning. In order to solve the challenges of poor early data quality and weak degradation characteristics of lithium-ion batteries, this paper proposes a new method for predicting the early remaining service life of lithium-ion batteries based on multi-scale feature fusion. Firstly, the decomposition process of the early signal was optimized by adaptively adjusting the key parameters of time-varying filtered empirical mode decomposition (TVF-EMD) by the improved grasshopper optimization algorithm (IGOA) to effectively extract the early degradation features. Secondly, the intrinsic mode functions (IMFs) were divided into three types of components: high-frequency, medium-low frequency and trend terms representing capacity degradation by K-means clustering algorithm, which significantly enhanced the early feature expression ability after weighted fusion. Furthermore, a prediction model based on whale migrating algorithm (WMA) optimization was constructed to independently predict IMFs in each frequency band, and finally through the fusion and reconstruction of multi-scale prediction results, the degradation trajectory prediction and remaining service life prediction of lithium battery life cycle were realized. Experiments based on CALCE and MIT datasets show that this method has significant advantages over traditional prediction models, and the root mean square error (RMSE) is always less than 1.4%, which provides a reliable solution for early life prediction of lithium-ion batteries.

Key words: lithium-ion batteries, early remaining useful life, time-varying filtered empirical mode decomposition, multi-scale prediction

中图分类号:

TM912

方枷云, 贾建芳. 基于多尺度特征融合的锂离子电池早期剩余使用寿命预测[J]. 储能科学与技术, doi: 10.19799/j.cnki.2095-4239.2025.0488.

Jiayun FANG, Jianfang JIA. Early Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Multi-Scale Feature Fusion[J]. Energy Storage Science and Technology, doi: 10.19799/j.cnki.2095-4239.2025.0488.

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参数	WMA-LSTM参数值(IMF-H)	WMA-LSTM参数值(IMF-M&L)	WMA-GRU参数值 (IMF-T)
Training set	9.5%	9.5%	9.5%
Test set	90.5%	90.5%	90.5%
Optimizer	Adam	Adam	Adam
MaxEpochs	1100	800	800
InitialLearnRate	0.001	0.001	0.001
NumHiddenUnits	78	80	77
LearnRateDropPeriod	0.9*MaxEpochs	0.9*MaxEpochs	0.8*MaxEpochs
LearnRateDropFactor	0.2	0.2	0.01
L2Regularization	0.0001	0.01	0.0001

电池名称	CS2-35	CS2-36	CS2-37	CS2-38
早期剩余使用寿命实际值	439	397	462	489
早期剩余使用寿命平均预测值	442	399	465	491
平均误差值	3	2	3	2

方法	RMSE	MAE	MAPE	计算时间/s
method1(本文)	0.012795	0.008655	1.48%	345.14
method2	0.017819	0.011417	2.03%	198.67
method3	0.021085	0.013243	2.20%	108.29
method4	0.040121	0.029786	5.25%	92.2

电池名称	误差指标	Proposed method	LSTM	BiLSTM	GRU	BiGRU	CNN
CS2-35	RMSE	0.012795	0.017819	0.04346	0.042771	0.051151	0.04906
	MAE	0.0086551	0.011417	0.028255	0.028971	0.035395	0.031632
	MAPE	1.4821%	2.0284%	5.4588%	5.4487%	6.5359%	6.1526%
CS2-36	RMSE	0.010392	0.017327	0.056927	0.043013	0.041721	0.2533
	MAE	0.0085821	0.010326	0.038947	0.031266	0.027494	0.17418
	MAPE	1.6543%	3.06%	9.9642%	7.5431%	7.3154%	44.401%
CS2-37	RMSE	0.012757	0.013615	0.039321	0.02109	0.032949	0.052894
	MAE	0.0084692	0.0062687	0.024614	0.015302	0.021141	0.034002
	MAPE	1.677%	1.5531%	5.0805%	2.8412%	4.2859%	6.7799%
CS2-38	RMSE	0.013085	0.019694	0.036283	0.023644	0.048999	0.078381
	MAE	0.010647	0.0095519	0.022347	0.016352	0.032896	0.057991
	MAPE	1.7069%	2.1601%	4.45%	3.0372%	6.2254%	10.3913%

误差指标	CH8	CH9	CH11	CH12
RMSE	0.0093619	0.0074526	0.0095817	0.0083431
MAE	0.0072235	0.0053415	0.0084304	0.0069983
MAPE	0.75174%	0.53872%	0.86675%	0.70607%