Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction.

Cao, Jie; Wang, Shunli; Fernandez, Carlos

Abstract

Remaining useful life (RUL) of lithium-ion batteries is an important indicator for battery health management, and accurate prediction can promote reliable battery system design, as well as safety and effectiveness of practical use. Therefore, we extract the health factor during charging and a multi-kernel support vector regression (MKSVR) RUL prediction model to achieve high accuracy estimation of the RUL of lithium-ion batteries. Firstly, based on the current, voltage, and temperature data during charging, seven characteristic parameters that can reflect the battery capacity decay are extracted, and then, three health factors (HF) that are highly correlated with the capacity decay are screened out using the Pearson coefficients. Secondly, the Gray wolf cuckoo search optimization (GWOCS) model is used to realize the intelligent optimization search of the kernel function parameter combinations of the multi-kernel support vector regression, and then the RUL prediction model of the multi-kernel support vector regression is established. Finally, the validation analysis is performed based on the NASA battery aging data set. The results show that the improved multi-kernel support vector regression has higher prediction accuracy compared with the single-kernel support vector regression, and its RUL prediction errors are all less than 5 cycles, and the maximum root mean square error is all less than 0.028.

Citation

CAO, J., WANG, S. and FERNANDEZ, C. 2023. Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction. Journal of solid state electrochemistry [online], Online First. Available from: https://doi.org/10.1007/s10008-023-05650-3

• Journal Article Type: Article
• Acceptance Date: Aug 20, 2023
• Online Publication Date: Aug 30, 2023
• Deposit Date: Oct 17, 2023
• Publicly Available Date: Aug 31, 2024
• Journal: Journal of solid state electrochemistry
• Print ISSN: 1432-8488
• Electronic ISSN: 1433-0768
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s10008-023-05650-3
• Keywords: Lithium-ion battery; Health factor; Gray wolf cuckoo search model; Multi-kernel support vector regression
• Public URL: https://rgu-repository.worktribe.com/output/2072176