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Health State Assessment of Lithium-Ion Batteries Based on Multi-Health Feature Fusion and Improved Informer Modeling

Author

Listed:
  • Jun He

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China)

  • Xinyu Liu

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China)

  • Wentao Huang

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China)

  • Bohan Zhang

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China)

  • Zuoming Zhang

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China)

  • Zirui Shao

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China)

  • Zimu Mao

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China)

Abstract

Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%.

Suggested Citation

  • Jun He & Xinyu Liu & Wentao Huang & Bohan Zhang & Zuoming Zhang & Zirui Shao & Zimu Mao, 2024. "Health State Assessment of Lithium-Ion Batteries Based on Multi-Health Feature Fusion and Improved Informer Modeling," Energies, MDPI, vol. 17(9), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2154-:d:1387010
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    References listed on IDEAS

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