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Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles

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  • Lin, Cheng
  • Tang, Aihua
  • Xing, Jilei

Abstract

Real-time and accurate state-of-charge (SoC) estimation of lithium-ion batteries is a critical issue for efficient monitoring, control and utilization of advanced battery management systems (BMS) in electric vehicles (EVs). The electrochemical mechanism model can accurately describe the spatially distributed behavior of the internal states of the battery, but the model is complex and computationally huge, which is difficult to simulation in vehicle BMS. To solve these problems, it is necessary to simplify the battery mechanism model and study the model-based SoC estimation approaches. In this paper, two order-reduced models including an average-electrode model (AEM) and a single particle model (SPM) are first proposed. Additionally, the reduced-models combined with algorithms, including an extended Kalman filter (EKF), a sliding-mode observer (SMO) with a uniform reaching law (URL) and an SMO with an exponential reaching law (ERL), are employed to design battery SoC observers. To achieve an optimal trade-off between the tracking accuracy and convergence ability, the performances of these approaches are compared under an Urban Dynamometer Driving Schedule (UDDS) test. The comparison results indicate that the SPM-EKF approach can obtain a reliable battery voltage response and a more accurate SoC estimation than other approaches.

Suggested Citation

  • Lin, Cheng & Tang, Aihua & Xing, Jilei, 2017. "Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 394-404.
    • Handle: RePEc:eee:appene:v:207:y:2017:i:c:p:394-404
    DOI: 10.1016/j.apenergy.2017.05.109
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    References listed on IDEAS

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    Cited by:

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    2. Huang, Haichi & Bian, Chong & Wu, Mengdan & An, Dong & Yang, Shunkun, 2024. "A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries," Energy, Elsevier, vol. 288(C).
    3. Bizhong Xia & Zheng Zhang & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 11(6), pages 1-20, June.
    4. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    5. 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).
    6. Longxing Wu & Kai Liu & Hui Pang & Jiamin Jin, 2021. "Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias," Energies, MDPI, vol. 14(17), pages 1-12, August.
    7. Gao, Yizhao & Liu, Chenghao & Chen, Shun & Zhang, Xi & Fan, Guodong & Zhu, Chong, 2022. "Development and parameterization of a control-oriented electrochemical model of lithium-ion batteries for battery-management-systems applications," Applied Energy, Elsevier, vol. 309(C).
    8. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    9. Yi, Yahui & Xia, Chengyu & Shi, Lei & Meng, Leifeng & Chi, Qifu & Qian, Liqin & Ma, Tiancai & Chen, Siqi, 2024. "Lithium-ion battery expansion mechanism and Gaussian process regression based state of charge estimation with expansion characteristics," Energy, Elsevier, vol. 292(C).

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