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Research Progress of Battery Life Prediction Methods Based on Physical Model

Author

Listed:
  • Xingxing Wang

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China
    School of Rail Transportation, Soochow University, Suzhou 215131, China)

  • Peilin Ye

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China)

  • Shengren Liu

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China)

  • Yu Zhu

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China)

  • Yelin Deng

    (School of Rail Transportation, Soochow University, Suzhou 215131, China)

  • Yinnan Yuan

    (School of Rail Transportation, Soochow University, Suzhou 215131, China)

  • Hongjun Ni

    (School of Zhang Jian, Nantong University, Nantong 226019, China)

Abstract

Remaining useful life prediction is of great significance for battery safety and maintenance. The remaining useful life prediction method, based on a physical model, has wide applicability and high prediction accuracy, which is the research hotspot of the next generation battery life prediction method. In this study, the prediction methods of battery life were compared and analyzed, and the prediction methods based on the physical model were summarized. The prediction methods were classified according to their different characteristics including the electrochemical model, equivalent circuit model, and empirical model. By analyzing the emphasis of electrochemical process simplification, different electrochemical models were classified including the P2D model, SP model, and electrochemical fusion model. The equivalent circuit model was divided into the Rint model, Thevenin model, PNGV model, and RC model for the change of electronic components in the model. According to the different mathematical expressions of constructing the empirical model, it can be divided into the exponential model, polynomial model, exponential and polynomial mixed model, and capacity degradation model. Through the collocation of different filtering methods, the different efficiency of the models is described in detail. The research progress of various prediction methods as well as the changes and characteristics of traditional models were compared and analyzed, and the future development of battery life prediction methods was prospected.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3858-:d:1137619
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    References listed on IDEAS

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    1. Chengcheng Fu & Cheng Gao & Weifang Zhang, 2024. "RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network," Mathematics, MDPI, vol. 12(8), pages 1-27, April.

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