Useful life prediction for lithium-ion batteries based on COA-LSTM and VMD

doi:10.19799/j.cnki.2095-4239.2024.0157

Abstract

Abstract:

Degradation of battery packs in electric vehicles is inevitable during their operational lifetime, making the estimated remaining useful life (RUL) a critical indicator of battery performance. This study proposes an optimized long short-term memory (LSTM) network-based RUL prediction model for EV lithium-ion batteries using the coyote optimization algorithm (COA). First, this study examines the capacity degradation characteristics of lithium-ion batteries. Indirect health indicators were extracted from the charge and discharge curves of the batteries, including constant current charging and discharging intervals and constant voltage holding time intervals. The correlations of these indicators were examined using the Pearson approach. Then, variational mode decomposition (VMD) was applied to decompose the health indicators into modal components. The LSTM model was used to predict the RUL of the battery pack. To address the issue of inaccurate LSTM model parameters affecting RUL prediction accuracy, COA was used to optimize these parameters and enhance the predictive capabilities of the model. The proposed method was validated using publicly available datasets from the NASA research center and compared with LSTM, VMD-LSTM, Gaussian process regression, and backpropagation neural network models. The experimental results indicate that the proposed approach can achieve RUL prediction errors of within 2%, demonstrating its ability to accurately predict RUL.

Key words: lithium-ion battery, remaining useful life, coyote optimization algorithm, long short-term memory

CLC Number:

TM 912

Zhonglin SUN, Jiabo LI, Di TIAN, Zhixuan WANG, Xiaojing XING. Useful life prediction for lithium-ion batteries based on COA-LSTM and VMD[J]. Energy Storage Science and Technology, 2024, 13(9): 3254-3265.

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Spearman	CCCT	CCDT	CVBT
B05	0.962	0.999	0.959
B06	0.967	0.999	0.965
B07	0.964	0.999	0.958
B18	0.956	0.999	0.954

电池	评价指标	LSTM	VMD-LSTM	VMD-COA-LSTM	GPR	BP
B05	RMSE	0.039581	0.034767	0.01410	0.019638	0.02653
	MAE	0.034842	0.031858	0.01102	0.018524	0.020902
	MAPE	0.025938	0.023772	0.00970	0.013363	0.016052

电池	评价指标	LSTM	VMD-LSTM	VMD-COA-LSTM	GPR	BP
B06	RMSE	0.074957	0.054723	0.01570	0.034601	0.043288
	MAE	0.062265	0.046706	0.01167	0.027603	0.034732
	MAPE	0.048457	0.036475	0.00978	0.020485	0.026712

电池	评价指标	LSTM	VMD-LSTM	VMD-COA-LSTM	GPR	BP
B07	RMSE	0.047274	0.037806	0.00983	0.022525	0.025216
	MAE	0.041957	0.033707	0.01641	0.022055	0.020375
	MAPE	0.028577	0.024875	0.01021	0.014722	0.013874

电池	评价指标	LSTM	VMD-LSTM	VMD-COA-LSTM	GPR	BP
B18	RMSE	0.02626	0.017482	0.01023	0.022467	0.022985
	MAE	0.02106	0.014850	0.01198	0.022462	0.020549
	MAPE	0.01512	0.01074	0.01134	0.015381	0.014672