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Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method

Author

Listed:
  • Peng Guo

    (College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

  • Xiaobo Wu

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • António M. Lopes

    (LAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal)

  • Anyu Cheng

    (College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

  • Yang Xu

    (College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

  • Liping Chen

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

Abstract

This paper proposes a fractional order (FO) impedance model for lithium-ion batteries and a method for model parameter identification. The model is established based on electrochemical impedance spectroscopy (EIS). A new hybrid genetic–fractional beetle swarm optimization (HGA-FBSO) scheme is derived for parameter identification, which combines the advantages of genetic algorithms (GA) and beetle swarm optimization (BSO). The approach leads to an equivalent circuit model being able to describe accurately the dynamic behavior of the lithium-ion battery. Experimental results illustrate the effectiveness of the proposed method, yielding voltage estimation root-mean-squared error (RMSE) of 10.5 mV and mean absolute error (MAE) of 0.6058%. This corresponds to accuracy improvements of 32.26% and 7.89% for the RMSE, and 43.83% and 13.67% for the MAE, when comparing the results of the new approach to those obtained with the GA and the FBSO methods, respectively.

Suggested Citation

  • Peng Guo & Xiaobo Wu & António M. Lopes & Anyu Cheng & Yang Xu & Liping Chen, 2022. "Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method," Mathematics, MDPI, vol. 10(17), pages 1-11, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3056-:d:896740
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    References listed on IDEAS

    as
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