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Li-ion dynamics and state of charge estimation

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  • Li, Mingheng

Abstract

This paper focuses on real-time estimation of Li-ion State of Charge (SoC). A first-principles model validated by experimental data from literature is chosen to mimic a real Li-ion cell. Its impedance responses at different SoCs are studied by a simulated electrochemical impedance spectroscopy (EIS). An equivalent circuit model is developed for estimator design in which the parameters (including lumped series resistances R1, lumped interfacial resistances R2 and time constant τ) are derived from system identification and compared with the EIS results. The estimator is designed using extended Kalman filtering (EKF) and is implemented in the first-principles model. It is demonstrated by computer simulation that the SoC during charge/discharge cycles can be estimated with a relative error <3%. The accuracy of SoC tracking is improved if it is jointly estimated along with either R1 or R2 given that these model parameters vary with SoC as revealed by EIS.

Suggested Citation

  • Li, Mingheng, 2017. "Li-ion dynamics and state of charge estimation," Renewable Energy, Elsevier, vol. 100(C), pages 44-52.
    • Handle: RePEc:eee:renene:v:100:y:2017:i:c:p:44-52
    DOI: 10.1016/j.renene.2016.06.009
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    References listed on IDEAS

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    Cited by:

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    2. Yun Zhang & Yunlong Shang & Naxin Cui & Chenghui Zhang, 2017. "Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 11(1), pages 1-15, December.
    3. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    4. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).

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