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Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms

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  • Jingyu Yan

    (Shenzhen Institutes of Advance Technology, the Chinese Academy of Science , Shenzhen, China
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Guoqing Xu

    (Shenzhen Institutes of Advance Technology, the Chinese Academy of Science , Shenzhen, China
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Huihuan Qian

    (Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Yangsheng Xu

    (Shenzhen Institutes of Advance Technology, the Chinese Academy of Science , Shenzhen, China
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

Abstract

State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-? nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H ? filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studiesin simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.

Suggested Citation

  • Jingyu Yan & Guoqing Xu & Huihuan Qian & Yangsheng Xu, 2010. "Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms," Energies, MDPI, vol. 3(10), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:3:y:2010:i:10:p:1654-1672:d:9764
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    References listed on IDEAS

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    1. 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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    Cited by:

    1. Taimoor Zahid & Weimin Li, 2016. "A Comparative Study Based on the Least Square Parameter Identification Method for State of Charge Estimation of a LiFePO 4 Battery Pack Using Three Model-Based Algorithms for Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-16, September.
    2. Kiarash Movassagh & Arif Raihan & Balakumar Balasingam & Krishna Pattipati, 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries," Energies, MDPI, vol. 14(14), pages 1-33, July.
    3. John Guirguis & Ryan Ahmed, 2024. "Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study," Energies, MDPI, vol. 17(14), pages 1-13, July.
    4. Da Xie & Haoxiang Chu & Yupu Lu & Chenghong Gu & Furong Li & Yu Zhang, 2015. "The Concept of EV’s Intelligent Integrated Station and Its Energy Flow," Energies, MDPI, vol. 8(5), pages 1-28, May.
    5. Marat Sadykov & Sam Haines & Mark Broadmeadow & Geoff Walker & David William Holmes, 2023. "Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-34, February.
    6. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
    7. Li, Zhirun & Xiong, Rui & Mu, Hao & He, Hongwen & Wang, Chun, 2017. "A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 363-371.
    8. Shifei Yuan & Hongjie Wu & Chengliang Yin, 2013. "State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model," Energies, MDPI, vol. 6(1), pages 1-27, January.

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