Lithium-ion battery capacity estimation method combining Gaussian process regression and feature selection

Long Wang; Jian Xu; Yao Xie; Wenqin Chen

doi:10.1117/12.2628511

7 March 2022 Lithium-ion battery capacity estimation method combining Gaussian process regression and feature selection

Long Wang, Jian Xu, Yao Xie, Wenqin Chen

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 1216706 (2022) https://doi.org/10.1117/12.2628511
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

Due to the complex aging characteristics of lithium batteries, it is an unsolved problem to accurately predict the health of the battery. This problem has also largely affected the development of electrical energy storage. Battery aging involves chemical, physical and mechanical factors, and it is difficult to establish a unified standard for accurate prediction. This paper establishes a method for estimating the capacity of lithium batteries based on data-driven and Gaussian models. By analyzing the relationship model between battery power generation and charging, the effectiveness of the method proposed in this paper is determined through relevant data.

Citation Download Citation

Long Wang, Jian Xu, Yao Xie, and Wenqin Chen "Lithium-ion battery capacity estimation method combining Gaussian process regression and feature selection", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 1216706 (7 March 2022); https://doi.org/10.1117/12.2628511

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