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A framework for state-of-charge and remaining discharge time prediction using unscented particle filter

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  • Wang, Yujie
  • Chen, Zonghai

Abstract

As a typical complex system, the lithium-ion battery system is characterized by strong coupling and nonlinearity, which brings great challenges to its modeling, state estimation, and control. The modeling and state estimation especially the state-of-charge and remaining discharge time are key issues for the battery management system. This paper details a framework for observation of the battery state-of-charge and remaining discharge time by using the unscented particle filter. First, an equivalent circuit model considering hysteresis is presented and verified at different temperatures. Then the framework for observation of the battery state-of-charge and remaining discharge time is proposed using the unscented particle filter in order to improve the observation accuracy. The recursive method is employed to predict the probable future current considering the historical data. In addition, the prediction results of the probable future current with different forgetting factors are compared and analyzed in order to select the optimal parameter for the remaining discharge time prediction. Finally, experiments under different dynamic driving cycles at different temperatures are carried out to verify the proposed method. The performance of the unscented particle filter and the extended Kalman filter are compared and analyzed. The experimental results indicate that the proposed unscented particle filter method has high accuracy and fast convergence under dynamic driving cycles.

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  • Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919320112
    DOI: 10.1016/j.apenergy.2019.114324
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    15. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    16. Zhao, Xinze & Sun, Bingxiang & Zhang, Weige & He, Xitian & Ma, Shichang & Zhang, Junwei & Liu, Xiaopeng, 2024. "Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries," Applied Energy, Elsevier, vol. 353(PA).
    17. Tian, Jiaqiang & Fan, Yuan & Pan, Tianhong & Zhang, Xu & Yin, Jianning & Zhang, Qingping, 2024. "A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    18. He, Lin & Hu, Xingwen & Yin, Guangwei & Shao, Xingguo & Liu, Jichao & Shi, Qin, 2023. "A voltage dynamics model of lithium-ion battery for state-of-charge estimation by proportional-integral observer," Applied Energy, Elsevier, vol. 351(C).
    19. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    20. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    21. Yue Ren & Chunhua Jin & Shu Fang & Li Yang & Zixuan Wu & Ziyang Wang & Rui Peng & Kaiye Gao, 2023. "A Comprehensive Review of Key Technologies for Enhancing the Reliability of Lithium-Ion Power Batteries," Energies, MDPI, vol. 16(17), pages 1-38, August.
    22. Chen, Lei & Wang, Shunli & Jiang, Hong & Fernandez, Carlos, 2024. "A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range," Applied Energy, Elsevier, vol. 355(C).
    23. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    24. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    25. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

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