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A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters

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  • Guo, Feng
  • Hu, Guangdi
  • Xiang, Shun
  • Zhou, Pengkai
  • Hong, Ru
  • Xiong, Neng

Abstract

It is very important for the battery management system of electric vehicles to estimate the battery state of charge accurately and to achieve the on-line updating of the battery model parameters. In this paper, the estimation of the open circuit voltage is converted to the estimation of the open circuit voltage fitting parameters, the fast time-varying parameter open circuit voltage is converted into several slowly time-varying parameters. A multi-scale parameter adaptive method based on dual Kalman filters is developed. The multi-scale estimation of the battery state of charge and all parameters including open circuit voltage can be achieved. And the parameter adjustment method of dual extended Kalman filters in estimating multiple parameters is given. The experimental results show that the accuracy of the algorithm is improved by adding the estimation of the open circuit voltage. The proposed method can reduce the influence of the initial state error on the algorithm, and improve the robustness of the algorithm.

Suggested Citation

  • Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
    • Handle: RePEc:eee:energy:v:178:y:2019:i:c:p:79-88
    DOI: 10.1016/j.energy.2019.04.126
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    2. Chen, Liping & Wu, Xiaobo & Lopes, António M. & Yin, Lisheng & Li, Penghua, 2022. "Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter," Energy, Elsevier, vol. 252(C).
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    5. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    6. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    7. 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.
    8. Fang Liu & Jie Ma & Weixing Su & Hanning Chen & Maowei He, 2020. "Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation," Energies, MDPI, vol. 13(7), pages 1-19, April.
    9. Kuo Yang & Yugui Tang & Zhen Zhang, 2021. "Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter," Energies, MDPI, vol. 14(4), pages 1-15, February.
    10. Chuan-Xiang Yu & Yan-Min Xie & Zhao-Yu Sang & Shi-Ya Yang & Rui Huang, 2019. "State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation," Energies, MDPI, vol. 12(21), pages 1-19, October.
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    12. Hu, Xiaosong & Jiang, Haifu & Feng, Fei & Liu, Bo, 2020. "An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management," Applied Energy, Elsevier, vol. 257(C).

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