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Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model

Author

Listed:
  • Zhiwei He

    (College of Electric Information, Hangzhou Dianzi University, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China)

  • Mingyu Gao

    (College of Electric Information, Hangzhou Dianzi University, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China)

  • Caisheng Wang

    (Department of Electrical and Computer Engineering, Wayne State University, 5050 Anthony Wayne Drive, Detroit, MI 48202, USA)

  • Leyi Wang

    (Department of Electrical and Computer Engineering, Wayne State University, 5050 Anthony Wayne Drive, Detroit, MI 48202, USA)

  • Yuanyuan Liu

    (College of Electric Information, Hangzhou Dianzi University, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China)

Abstract

Accurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.

Suggested Citation

  • Zhiwei He & Mingyu Gao & Caisheng Wang & Leyi Wang & Yuanyuan Liu, 2013. "Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model," Energies, MDPI, vol. 6(8), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:8:p:4134-4151:d:27921
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    References listed on IDEAS

    as
    1. 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.
    2. Yinjiao Xing & Eden W. M. Ma & Kwok L. Tsui & Michael Pecht, 2011. "Battery Management Systems in Electric and Hybrid Vehicles," Energies, MDPI, vol. 4(11), pages 1-18, October.
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