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Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model

Author

Listed:
  • Lin Su

    (Shandong Provincial Key Laboratory of Automotive Electronics Technology, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China)

  • Guangxu Zhou

    (Shandong Provincial Key Laboratory of Automotive Electronics Technology, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China)

  • Dairong Hu

    (Shandong Provincial Key Laboratory of Automotive Electronics Technology, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China)

  • Yuan Liu

    (Shandong Provincial Key Laboratory of Automotive Electronics Technology, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China)

  • Yunhai Zhu

    (Science and Technology Service Platform, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China)

Abstract

Accurate estimation of the state of charge (SOC) of lithium batteries is paramount to ensuring consistent battery pack operation. To improve SOC estimation accuracy and suppress colored noise in the system, a fractional order model based on an unscented Kalman filter and an H-infinity filter (FOUHIF) estimation algorithm was proposed. Firstly, the discrete state equation of a lithium battery was derived, as per the theory of fractional calculus. Then, the HPPC experiment and the PSO algorithm were used to identify the internal parameters of the second order RC and fractional order models, respectively. As discovered during working tests, the parameters identified via the fractional order model proved to be more accurate. Furthermore, the feasibility of using the FOUHIF algorithm was evaluated under the conditions of NEDC and UDDS, with obvious colored noise. Compared with the fractional order unscented Kalman filter (FOUKF) and integer order unscented Kalman filter (UKF) algorithms, the FOUHIF algorithm showed significant improvement in both the accuracy and robustness of the estimation, with maximum errors of 1.86% and 1.61% under the two working conditions, and a terminal voltage prediction error of no more than 5.29 mV.

Suggested Citation

  • Lin Su & Guangxu Zhou & Dairong Hu & Yuan Liu & Yunhai Zhu, 2021. "Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model," Energies, MDPI, vol. 14(19), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6307-:d:649066
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    References listed on IDEAS

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    1. Zheng Liu & Xuanju Dang & Hanxu Sun, 2018. "Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-16, July.
    2. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.
    3. Ivan Pavlenko & Marek Ochowiak & Praveen Agarwal & Radosław Olszewski & Bernard Michałek & Andżelika Krupińska, 2021. "Improvement of Mathematical Model for Sedimentation Process," Energies, MDPI, vol. 14(15), pages 1-12, July.
    4. Yixing Chen & Deqing Huang & Qiao Zhu & Weiqun Liu & Congzhi Liu & Neng Xiong, 2017. "A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-19, September.
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    1. Yinfeng Jiang & Wenxiang Song & Hao Zhu & Yun Zhu & Yongzhi Du & Huichun Yin, 2022. "Extended Rauch–Tung–Striebel Smoother for the State of Charge Estimation of Lithium-Ion Batteries Based on an Enhanced Circuit Model," Energies, MDPI, vol. 15(3), pages 1-17, January.

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