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Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification

Author

Listed:
  • Quan Ouyang

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Rui Ma

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Zhaoxiang Wu

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Guotuan Xu

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Zhisheng Wang

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model parameters need to be extracted through cumbersome prior experiments. To remedy such deficiency, a recursive least squares (RLS) algorithm is utilized for model parameter online identification, and an adaptive square-root unscented Kalman filter (SRUKF) is designed to estimate the battery’s SOC. As demonstrated in extensive experimental results, the designed adaptive SRUKF combined with RLS-based model identification is a promising SOC estimation approach. Compared with other commonly used Kalman filter-based methods, the proposed algorithm has higher precision in the SOC estimation.

Suggested Citation

  • Quan Ouyang & Rui Ma & Zhaoxiang Wu & Guotuan Xu & Zhisheng Wang, 2020. "Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification," Energies, MDPI, vol. 13(18), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4968-:d:417336
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    References listed on IDEAS

    as
    1. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    2. Shulin Liu & Naxin Cui & Chenghui Zhang, 2017. "An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-14, September.
    3. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
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    Cited by:

    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. João Pedro F. Trovão & Minh Cao Ta, 2022. "Electric Vehicle Efficient Power and Propulsion Systems," Energies, MDPI, vol. 15(11), pages 1-4, May.
    3. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    4. An, Qing & Peng, Jian, 2023. "Parameter identification of lithium battery pack based on novel cooperatively coevolving differential evolution algorithm," Renewable Energy, Elsevier, vol. 216(C).
    5. Areeb Khalid & Syed Abdul Rahman Kashif & Noor Ul Ain & Muhammad Awais & Majid Ali Smieee & Jorge El Mariachet Carreño & Juan C. Vasquez & Josep M. Guerrero & Baseem Khan, 2023. "Comparison of Kalman Filters for State Estimation Based on Computational Complexity of Li-Ion Cells," Energies, MDPI, vol. 16(6), pages 1-20, March.

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