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Observer based battery SOC estimation: Using multi-gain-switching approach

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  • Tang, Xiaopeng
  • Liu, Boyang
  • Lv, Zhou
  • Gao, Furong

Abstract

Sensor drifts and modelling mismatches are key factors that influence the accuracy of state of charge (SOC) estimation for LiFePO4 batteries. In this study, an observer robust to these factors is proposed. First, the causes of SOC errors, for example, modelling error and uncertain initial error, are studied. Second, a geometry classifier is designed to categorize these errors into different groups using the information of voltage error between model and measurements. Third, with the classifier, different types of errors are treated differently by switching the gains of the observer. Finally, the method is tested in comparison to the existing methods for both new and aged cells. The test results show that the proposed method can correctly categorize the error causes and take the corresponding countermeasures. The common problems encountered in SOC estimations, such as local model inaccuracy, current sensor drifting and data saturation, could be overcome. The computation time of the proposed method is close to that of the Luenberger observer, making it suitable for real embedded applications.

Suggested Citation

  • Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:1275-1283
    DOI: 10.1016/j.apenergy.2017.03.079
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    6. Li, Xiaoyu & Huang, Zhijia & Tian, Jindong & Tian, Yong, 2021. "State-of-charge estimation tolerant of battery aging based on a physics-based model and an adaptive cubature Kalman filter," Energy, Elsevier, vol. 220(C).
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    8. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    9. Takyi-Aninakwa, Paul & Wang, Shunli & Liu, Guangchen & Bage, Alhamdu Nuhu & Bobobee, Etse Dablu & Appiah, Emmanuel & Huang, Qi, 2024. "Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data," Applied Energy, Elsevier, vol. 363(C).
    10. Zhang, Xu & Wang, Yujie & Wu, Ji & Chen, Zonghai, 2018. "A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter," Applied Energy, Elsevier, vol. 216(C), pages 442-451.
    11. Tang, Xiaopeng & Gao, Furong & Zou, Changfu & Yao, Ke & Hu, Wengui & Wik, Torsten, 2019. "Load-responsive model switching estimation for state of charge of lithium-ion batteries," Applied Energy, Elsevier, vol. 238(C), pages 423-434.
    12. Tang, Xiaopeng & Zou, Changfu & Yao, Ke & Lu, Jingyi & Xia, Yongxiao & Gao, Furong, 2019. "Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method," Applied Energy, Elsevier, vol. 254(C).
    13. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    14. Xiaopeng Tang & Ke Yao & Boyang Liu & Wengui Hu & Furong Gao, 2018. "Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-16, January.
    15. Yang Guo & Ziguang Lu, 2022. "A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias," Energies, MDPI, vol. 15(4), pages 1-18, February.
    16. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).

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