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Robust state-of-charge estimation for lithium-ion batteries based on an improved gas-liquid dynamics model

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  • Chen, Biao
  • Jiang, Haobin
  • Chen, Xijia
  • Li, Huanhuan

Abstract

The battery analytical model often plays an important role in accurately estimating the online state-of-charge of the battery. The improved gas-liquid dynamics battery model is proposed to simulate the physicochemical behaviors of a lithium-ion battery. The estimation equations of both iterative open-circuit voltage and terminal voltage are deduced according to this model. A strong robust state-of-charge estimation method is designed without coupling optimization algorithm based on the iterative open-circuit voltage estimation equation. Experimental results of the Li(NiMnCo)O2 batteries under the Dynamic Stress Test cycle, the New European Driving Cycle, the Federal Urban Driving Schedule cycle, the Urban Dynamometer Driving Schedule cycle and the constant current test confirm the efficacy of the proposed approach. Moreover, this method has excellent robustness against the initial error of 50% state-of-charge which is eliminated within 6 s under five working conditions and it provides a reliable state-of-charge estimation for the sampling data of different sampling periods between a second and 60 s.

Suggested Citation

  • Chen, Biao & Jiang, Haobin & Chen, Xijia & Li, Huanhuan, 2022. "Robust state-of-charge estimation for lithium-ion batteries based on an improved gas-liquid dynamics model," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022568
    DOI: 10.1016/j.energy.2021.122008
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    1. 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).
    2. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).
    3. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    4. Hou, Jiayang & Xu, Jun & Lin, Chuanping & Jiang, Delong & Mei, Xuesong, 2024. "State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method," Energy, Elsevier, vol. 290(C).

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