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

Author

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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)

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.

Suggested Citation

  • Liyuan Shao & Yong Zhang & Xiujuan Zheng & Xin He & Yufeng Zheng & Zhiwei Liu, 2023. "A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods," Energies, MDPI, vol. 16(3), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1469-:d:1055327
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    References listed on IDEAS

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    Cited by:

    1. Pablo Carrasco Ortega & Pablo Durán Gómez & Julio César Mérida Sánchez & Fernando Echevarría Camarero & Ángel Á. Pardiñas, 2023. "Battery Energy Storage Systems for the New Electricity Market Landscape: Modeling, State Diagnostics, Management, and Viability—A Review," Energies, MDPI, vol. 16(17), pages 1-51, August.
    2. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    3. Lu Liu & Wei Sun & Chuanxu Yue & Yunhai Zhu & Weihuan Xia, 2024. "Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Small Sample Models," Energies, MDPI, vol. 17(19), pages 1-17, October.

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