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Investigation of optimized temporal convolutional network based on GKSO algorithm for lithium battery state of charge estimation

Author

Listed:
  • Zhou, Jintao
  • Liu, Kaimin
  • Pei, Zhongwen
  • Chen, Xiaofei
  • Feng, Xiaopeng
  • Huang, Chengxiang
  • Jiang, Zhi
  • Liao, Penghong

Abstract

To address the limitations of traditional state of charge (SOC) estimation methods, this study presents a novel composite algorithm that integrates the Genghis Khan Shark Optimization (GKSO) algorithm with a temporal convolutional network (TCN) model. This approach enhances the accuracy of SOC estimation in battery management systems (BMSs) for electric vehicles (EVs) by providing online compensation for the estimation errors of the forgetting factor recursive least squares-extended Kalman filter (FFRLS-EKF). A second-order RC model is utilized to accurately characterize battery behavior, while a high-precision ninth-order polynomial effectively models the SOC-Open Circuit Voltage (OCV) relationship. The FFRLS is utilized to determine the parameters of the battery model and the EKF is used to estimate the SOC. Meanwhile, the TCN model, trained offline, serves as an error correction mechanism that dynamically adjusts the SOC estimates generated by the FFRLS-EKF algorithm in real-time, thereby enhancing the robustness and adaptability of the estimation process under complex operating conditions. Simulation results across four distinct scenarios demonstrate that the optimized TCN model achieves remarkable improvements in estimation accuracy, with an average reduction of 48.4 % in root mean square error (RMSE) at 0 °C, 42.3 % at 25 °C, and 49.0 % at 45 °C.

Suggested Citation

  • Zhou, Jintao & Liu, Kaimin & Pei, Zhongwen & Chen, Xiaofei & Feng, Xiaopeng & Huang, Chengxiang & Jiang, Zhi & Liao, Penghong, 2025. "Investigation of optimized temporal convolutional network based on GKSO algorithm for lithium battery state of charge estimation," Energy, Elsevier, vol. 341(C).
    • Handle: RePEc:eee:energy:v:341:y:2025:i:c:s0360544225051333
    DOI: 10.1016/j.energy.2025.139491
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    References listed on IDEAS

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