Lithium-ion battery life prediction based on mode decomposition and deep learning

doi:10.19799/j.cnki.2095-4239.2024.0983

Abstract

Abstract:

With the rapid growth in the electric vehicle (EV) adoption, accurately predicting the remaining useful life (RUL) of lithium-ion batteries has become critical for the sustained development of the EV industry. This paper proposes an innovative approach that integrates ensemble mode decomposition (EEMD) and deep learning to improve RUL prediction accuracy for lithium-ion batteries. The proposed method begins with EEMD, which performs multiscale decomposition of battery capacity data. This process separates the global degradation trend from local random fluctuation components. To mitigate the impact of noise on model prediction accuracy, a denoising autoencoder (DAE) is introduced to remove noise from the random fluctuation components. Subsequently, long short-term memory (LSTM) networks and the transformer model are applied to model the global degradation trend and the denoised random fluctuations, respectively. To further refine predictions, a random forest (RF) algorithm calculates the importance weights of each mode component, enabling a weighted reconstruction of the prediction results. Experiments were conducted on a public battery dataset provided by the National Aeronautics and Space Administration (NASA), leveraging 40% and 60% of the historical battery data. The results demonstrate that the proposed method outperforms existing approaches in both accuracy and effectiveness, validating its potential for application in lithium-ion battery RUL prediction.

Key words: lithium-ion battery, life prediction, deep learning, long short-term memory, random forest

CLC Number:

TM 912

Zuolin DONG, Jinyan SONG, Zidi MENG. Lithium-ion battery life prediction based on mode decomposition and deep learning[J]. Energy Storage Science and Technology, 2025, 14(4): 1645-1653.

Figures/Tables 10

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References 14

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电池编号	拟合优度	IMF1权重	IMF2权重	IMF3权重
B0005	0.85540	0.45478	0.30492	0.24029
B0006	0.91451	0.52218	0.27672	0.20109
B0007	0.86730	0.43125	0.29544	0.27330
B0018	0.86863	0.48244	0.29831	0.21925

参数	Transformer	LSTM
λ	0.001	0.001
s	64	256
ι	2	4
η	8	None
θ	Adan	Adan
б	32	6

电池	模型	RE	MAE	RMSE	MAPE
B0005	EEMD-LSTM	0.00878	0.00923	0.01223	0.00673
	DaTransformer	0.00824	0.00889	0.01322	0.00645
	EEMD-LSTM-DaTransformer	0.00719	0.00838	0.01074	0.00608
	Our model	0.00719	0.00714	0.00913	0.00519

B0006	EEMD-LSTM	0	0.01063	0.01468	0.00815
	DaTransformer	0.01639	0.01284	0.01485	0.00996
	EEMD-LSTM-DaTransformer	0	0.00878	0.01116	0.00670
	Our model	0.01869	0.00817	0.00997	0.00624

B0007	EEMD-LSTM	—	0.00846	0.01103	0.00512
	DaTransformer	—	0.00663	0.00939	0.00449
	EEMD-LSTM-DaTransformer	—	0.00731	0.00911	0.00493
	Our model	—	0.00617	0.00740	0.00416

B0018	EEMD-LSTM	0.05263	0.01194	0.01525	0.00852
	DaTransformer	1	0.01545	0.02520	0.01088
	EEMD-LSTM-DaTransformer	0.01053	0.01003	0.01460	0.00715
	Our model	0.01053	0.00754	0.01239	0.00583

电池	模型	RE	MAE	RMSE	MAPE
B0005	EEMD-LSTM	0.00813	0.00975	0.01442	0.00680
	DaTransformer	0	0.00996	0.01559	0.00685
	EEMD-LSTM-DaTransformer	0.00813	0.00786	0.01084	0.00548
	Our model	0.00813	0.00739	0.00990	0.00514

B0006	EEMD-LSTM	0.00935	0.01514	0.02213	0.01107
	DaTransformer	0.01802	0.01222	0.01998	0.00886
	EEMD-LSTM-DaTransformer	0.00935	0.01276	0.01605	0.00931
	Our model	0.00935	0.01090	0.01353	0.00795

B0007	EEMD-LSTM	—	0.00836	0.01358	0.00548
	DaTransformer	—	0.00770	0.01215	0.00510
	EEMD-LSTM-DaTransformer	—	0.00771	0.01098	0.00504
	Our model	—	0.00626	0.00897	0.00409

B0018	EEMD-LSTM	0.04211	0.01117	0.01611	0.00812
	DaTransformer	1	0.01788	0.02581	0.01214
	EEMD-LSTM-DaTransformer	0.01053	0.01153	0.01524	0.00795
	Our model	0.01053	0.01049	0.01436	0.00719

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