A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods

Liyuan Shao, Yong Zhang (), Xiujuan Zheng, Xin He, Yufeng Zheng () and Zhiwei Liu ()
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Liyuan Shao: School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Yong Zhang: School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Xiujuan Zheng: School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Xin He: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yufeng Zheng: National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan 430079, China
Zhiwei Liu: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2023, vol. 16, issue 3, 1-22

Abstract: Lithium-ion batteries are a green and environmental energy storage component, which have become the first choice for energy storage due to their high energy density and good cycling performance. Lithium-ion batteries will experience an irreversible process during the charge and discharge cycles, which can cause continuous decay of battery capacity and eventually lead to battery failure. Accurate remaining useful life (RUL) prediction technology is important for the safe use and maintenance of energy storage components. This paper reviews the progress of domestic and international research on RUL prediction methods for energy storage components. Firstly, the failure mechanism of energy storage components is clarified, and then, RUL prediction method of the energy storage components represented by lithium-ion batteries are summarized. Next, the application of the data–model fusion-based method based on kalman filter and particle filter to RUL prediction of lithium-ion batteries are analyzed. The problems faced by RUL prediction of the energy storage components and the future research outlook are discussed.

Keywords: lithium-ion batteries; energy storage components; remaining useful life; kalman filter; particle filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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