Early Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Multi-Scale Feature Fusion

doi:10.19799/j.cnki.2095-4239.2025.0488

Abstract

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

CLC Number:

TM912

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%