Prediction of the remaining useful life of lithium batteries based on Antlion optimization Gaussian process regression

doi:10.19799/j.cnki.2095-4239.2023.0865

Abstract

Abstract:

Rapidly obtaining accurate information about the remaining useful life (RUL) and health status of a lithium battery is critical to maintaining its reliability. To solve the problems of low prediction accuracy regarding the RUL of lithium batteries, unsatisfactory hyperparameter optimization results, and poor prediction effect of the traditional Gaussian process regression (GPR) model, in this study, the Antlion optimization algorithm was used to optimize the hyperparameters of Gaussian process regression (hereinafter referred to as "ALO-GPR") to accurately predict the RUL of lithium batteries. First, according to the cycle curve of battery voltage during battery charging, six parameters were extracted as the health factors of the battery; subsequently, the correlation between these factors and the battery capacity was verified by using the Pearson correlation coefficient. Finally, the following four parameters were selected as the health factors: the average discharge voltage, the amount of charge amount stored by the battery in the constant current charging stage, the amount of charge stored by the battery in the whole charging stage, and the discharge temperature in the time integral. Finally, support vector regression, GPR, and ALO-GPR were used to predict the RUL of lithium batteries, and various indicators were compared and analyzed. The model proposed in this study is compared with models proposed in other literatures. The effectiveness of the proposed model is verified by using the NASA lithium battery dataset. The experimental results show that the RUL prediction model of ALO-GPR has a small error; the root mean square error is controlled within 1%; and, the average absolute error is controlled within 0.65%. Thus, ALO-GRP shows strong generalization and a good application prospect regarding the prediction of RUL of lithium batteries.

Key words: lithium-ion battery, Gaussian process regression, ant lion optimized algorithm, remaining useful life

CLC Number:

TM 912

Nana FENG, Ming YANG, Zhouli HUI, Ruijie WANG, Hongyang NING. Prediction of the remaining useful life of lithium batteries based on Antlion optimization Gaussian process regression[J]. Energy Storage Science and Technology, 2024, 13(5): 1643-1652.

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健康因子	B0005	B0006	B0007
H₁	0.9824	0.9652	0.9611
H₂	0.9395	0.9524	0.8547
H₃	0.9082	0.9259	0.8668
H₄	0.9199	0.9361	0.9314
H₅	-0.4871	-0.4897	-0.3147
H₆	0.5863	0.9348	-0.0218

电池	R²			RMSE			MAE
电池	ALO-GPR	GPR	SVR	ALO-GPR	GPR	SVR	ALO-GPR	GPR	SVR
B0005	0.9976	0.9958	0.9881	0.0093	0.0146	0.0348	0.0060	0.0096	0.0226
B0006	0.9986	0.9953	0.9813	0.0097	0.0226	0.0379	0.0063	0.0141	0.0234
B0007	0.9985	0.9941	0.9804	0.0063	0.0151	0.0361	0.0044	0.0086	0.0240

电池	本工作	文献[22]	文献[23]	文献[24]
电池	ALO-GPR	DA-GPR	NN-GPR	IGWO-LSSVM	BP	PSO-ELM
B0005	0.0093	0.119	0.0290	0.0141	0.0330	0.0291
B0006	0.0097	0.022	0.0452	0.0220	0.0730	0.0344
B0007	0.0063	0.147	0.0259	0.0223	0.0365	0.0184

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