Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model

Meng, Xianmeng and Cai, Cuicui and Wang, Yueqin and Wang, Qijian and Tan, Linglong (2022) Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model. Frontiers in Energy Research, 10. ISSN 2296-598X

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Abstract

The remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) is a crucial element of battery health management. The accurate prediction of RUL enables the maintenance and replacement of batteries with potential safety hazards, which ensures safe and stable battery operation. This paper develops a new method for the RUL prediction of LIBs, which is combined with complete ensemble empirical mode decomposition with adaptive noise (CEEDMAN), whale optimization algorithm (WOA), and support vector regression (SVR). Firstly, the CEEMDAN is employed to perform noise reduction in battery capacity data for prediction accuracy improvement. Then, an SVR model optimized by the WOA is proposed to predict the RUL. Finally, the public battery datasets are selected to validate the performance of the CEEMDAN-WOA-SVR method. The RUL prediction accuracy of the CEEMDAN-WOA-SVR method is better than the WOA-SVR method. In addition, a comparison is made between the proposed method and the existing methods (artificial bee colony algorithm-SVR method, ensemble empirical mode decomposition-gray wolf optimization-SVR method). The results show that the accurate prediction of the proposed method is superior to the two methods.

Item Type: Article
Subjects: Apsci Archives > Energy
Depositing User: Unnamed user with email support@apsciarchives.com
Date Deposited: 08 May 2023 04:59
Last Modified: 27 Jan 2024 04:12
URI: http://eprints.go2submission.com/id/eprint/943

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