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Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

Author

Listed:
  • Hao Wang

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Yanping Zheng

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Yang Yu

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

Suggested Citation

  • Hao Wang & Yanping Zheng & Yang Yu, 2021. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter," Mathematics, MDPI, vol. 9(15), pages 1-12, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1733-:d:599404
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    References listed on IDEAS

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    1. Jiandong Duan & Peng Wang & Wentao Ma & Xinyu Qiu & Xuan Tian & Shuai Fang, 2020. "State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter," Energies, MDPI, vol. 13(16), pages 1-18, August.
    2. Ye, Min & Guo, Hui & Cao, Binggang, 2017. "A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter," Applied Energy, Elsevier, vol. 190(C), pages 740-748.
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    Cited by:

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    3. Genbao Wang & Yejian Xue & Yafei Qiao & Chunyang Song & Qing Ming & Shuang Tian & Yonggao Xia, 2024. "Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network," Energies, MDPI, vol. 17(23), pages 1-16, December.
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    6. Pan, Yingjiu & Fang, Wenpeng & Ge, Zhenzhen & Li, Cheng & Wang, Caifeng & Guo, Baochang, 2024. "A hybrid on-line approach for predicting the energy consumption of electric buses based on vehicle dynamics and system identification," Energy, Elsevier, vol. 290(C).

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