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A switching gain adaptive sliding mode observer for SoC estimation of lithium-ion battery

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  • Qian, Wei
  • Li, Wan
  • Guo, Xiangwei
  • Wang, Haoyu

Abstract

A new state of charge (SoC) estimation method for lithium-ion battery that uses a Switching Gain Adaptive Sliding Mode Observer (SGASMO) is proposed. The purpose of SGASMO is to reduce the chattering of estimated results from the sliding mode observers (SMOs) and improve the estimation accuracy. First, the Dual Polarization (DP) equivalent circuit model is selected and its parameters are identified to provide a basis for the design of the new SMO. Second, based on the DP model, the nonlinear terminal sliding surface and continuous control law were introduced. And an improved switching gain equation was designed, which is adaptively adjusted according to the sliding mode surface equation. Thus, the SGASMO was realized, and the convergence of the proposed observer was proved by the Lyapunov stability theory. Finally, based on the test data of the self-built experimental platform, it is verified that the proposed SGASMO has less jitter in the estimated results and better estimation accuracy and robustness compared with the conventional SMOs and other types of mainstream improved SMOs.

Suggested Citation

  • Qian, Wei & Li, Wan & Guo, Xiangwei & Wang, Haoyu, 2024. "A switching gain adaptive sliding mode observer for SoC estimation of lithium-ion battery," Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224003578
    DOI: 10.1016/j.energy.2024.130585
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    References listed on IDEAS

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    Cited by:

    1. Da Li & Lu Liu & Chuanxu Yue & Xiaojin Gao & Yunhai Zhu, 2025. "Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range," Energies, MDPI, vol. 18(7), pages 1-19, April.
    2. Zou, Yuanru & Shi, Haotian & Cao, Wen & Wang, Shunli & Nie, Shiliang & Chen, Dan, 2025. "A high-speed recurrent state network with noise reduction for multi-temperature state of energy estimation of electric vehicles lithium-ion batteries," Energy, Elsevier, vol. 322(C).
    3. Vahid Behnamgol & Mohammad Asadi & Mohamed A. A. Mohamed & Sumeet S. Aphale & Mona Faraji Niri, 2024. "Comprehensive Review of Lithium-Ion Battery State of Charge Estimation by Sliding Mode Observers," Energies, MDPI, vol. 17(22), pages 1-39, November.
    4. Wang, Xiaoxuan & Yi, Yingmin & Yuan, Yiwei & Li, Xifei, 2025. "Enhanced state of charge estimation in lithium-ion batteries based on Time-Frequency-Net with time-domain and frequency-domain features," Energy, Elsevier, vol. 318(C).
    5. Zeng, Jiawei & Wang, Shunli & Cao, Wen & Zhou, Yifei & Fernandez, Carlos & Guerrero, Josep M., 2024. "Battery asynchronous fractional-order thermoelectric coupling modeling and state of charge estimation based on frequency characteristic separation at low temperatures," Energy, Elsevier, vol. 307(C).

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