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C-Rate- and Temperature-Dependent State-of-Charge Estimation Method for Li-Ion Batteries in Electric Vehicles

Author

Listed:
  • Eyyup Aslan

    (Aurora Flight Sciences, a Boeing Company, Manassas, VA 20110, USA
    Electrical and Electronics Engineering Department, Bursa Technical University, 16310 Bursa, Turkey)

  • Yusuf Yasa

    (Electrical Engineering Department, Istanbul Technical University, 34485 Istanbul, Turkey)

Abstract

Li-ion batteries determine the lifespan of an electric vehicle. High power and energy density and extensive service time are crucial parameters in EV batteries. In terms of safe and effective usage, a precise cell model and SoC estimation algorithm are indispensable. To provide an accurate SoC estimation, a current- and temperature-dependent SoC estimation algorithm is proposed in this paper. The proposed SoC estimation algorithm and equivalent circuit model (ECM) of the cells include current and temperature effects to reflect real battery behavior and provide an accurate SoC estimation. For including current and temperature effects in the cell model, lookup tables have been used for each parameter of the model. Based on the proposed ECM, the unscented Kalman filter (UKF) approach is utilized for estimating SoC since this approach is satisfactory for nonlinear systems such as lithium-ion batteries. The experimental results reveal that the proposed approach provides superior accuracy when compared to conventional methods and it is promising in terms of meeting electric vehicle requirements.

Suggested Citation

  • Eyyup Aslan & Yusuf Yasa, 2024. "C-Rate- and Temperature-Dependent State-of-Charge Estimation Method for Li-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 17(13), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3187-:d:1424761
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    References listed on IDEAS

    as
    1. Jiang, Cong & Wang, Shunli & Wu, Bin & Fernandez, Carlos & Xiong, Xin & Coffie-Ken, James, 2021. "A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter," Energy, Elsevier, vol. 219(C).
    2. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    3. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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