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A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer

Author

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  • Yigeng Huangfu

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Jiani Xu

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Dongdong Zhao

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Yuntian Liu

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Fei Gao

    (Institute of FEMTO-ST (UMR CNRS 6174), Energy Department, University of Bourgogne Franche-Comte, UTBM, 90010 Belfort, France)

Abstract

A novel method for Li-ion battery state of charge (SOC) estimation based on a super-twisting sliding mode observer (STSMO) is proposed in this paper. To design the STSMO, the state equation of a second-order RC equivalent circuit model (SRCECM) is derived to represent the dynamic behaviors of the Li-ion battery, and the model parameters are determined by the pulse current discharge approach. The convergence of the STSMO is proven by Lyapunov stability theory. The experiments under three different discharge profiles are conducted on the Li-ion battery. Through comparisons with a conventional sliding mode observer (CSMO) and adaptive extended Kalman filter (AEKF), the superiority of the proposed observer for SOC estimation is validated.

Suggested Citation

  • Yigeng Huangfu & Jiani Xu & Dongdong Zhao & Yuntian Liu & Fei Gao, 2018. "A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer," Energies, MDPI, vol. 11(5), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1211-:d:145451
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    References listed on IDEAS

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    Cited by:

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    3. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
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    5. 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.
    6. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
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