An approach for remaining useful life prediction of power battery with improved grey wolf optimized GPR model

doi:10.19799/j.cnki.2095-4239.2024.0717

Abstract

Abstract:

Accurate and reliable prediction of remaining useful life (RUL) of power batteries is crucial for mitigating user concerns regarding mileage and safety. To improve the accuracy of RUL prediction, we propose an improved grey wolf algorithm to optimize the GPR model, based on the NASA dataset. This study focuses on three aspects. First, five indirect health factors were extracted based on battery charging and discharging data including charging voltage saturation interval (CVSI, HI1), charging peak temperature interval (CPTI, HI2), constant current charging interval (CCCI, HI3), discharging peak temperature interval (DPTI, HI4), and discharging constant current interval (DCCI, HI5). The grey correlation method was used to analyze the correlation between health factors and capacity. Second, GPR method was selected as the RUL prediction model for power batteries. In response to the problem of traditional model parameter identification falling into local optima, an improved grey wolf algorithm based on a differential algorithm is proposed to enhance the model's prediction ability. Finally, the proposed method was validated using the NASA dataset. The experimental results showed that the proposed algorithm can control the RUL prediction error within 2%.

Key words: power battery, remaining useful life, Gaussian process regression, grey wolf optimization algorithm

CLC Number:

TM 911

Xuzhi WU, Jian GUO. An approach for remaining useful life prediction of power battery with improved grey wolf optimized GPR model[J]. Energy Storage Science and Technology, 2025, 14(2): 728-736.

Figures/Tables 8

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GRA	电池5#	电池6#	电池7#
HI1	0.81	0.66	0.85
HI2	0.80	0.67	0.85
HI3	0.73	0.82	0.89
HI4	0.90	0.92	0.92
HI5	0.89	0.93	0.95

方法	电池	RMSE/%	MAE/%	MAPE/%
所提模型	5#	1.35	1.28	0.96
	6#	1.97	1.75	1.35
	7#	1.75	1.38	1.17

GWO-GPR	5#	1.89	1.54	1.28
	6#	2.95	2.31	1.77
	7#	2.55	1.93	1.45

GPR	5#	1.98	1.61	1.34
	6#	3.19	2.56	1.98
	7#	2.72	2.09	1.58

方法	电池	RMSE/%	MAE/%	MAPE/%
NN-GWO	5#	3.84	3.51	2.82
	6#	4.97	3.88	2.92
	7#	4.42	3.61	2.95

NARX-GWO	5#	2.76	2.35	1.73
	6#	4.15	3.29	2.63
	7#	3.85	2.77	2.25

LSSVM-GWO	5#	2.11	1.72	1.43
	6#	3.65	2.71	2.13
	7#	3.31	2.43	1.75

LSTM-GWO	5#	1.91	1.57	1.32
	6#	2.43	1.99	.1.96
	7#	2.19	1.69	1.55

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