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Non-Fragile H ∞ Nonlinear Observer for State of Charge Estimation of Lithium-Ion Battery Based on a Fractional-Order Model

Author

Listed:
  • Zhongwei Zhang

    (Institute of Energy Storage and New Materials Technology, Dongfang Electric Corporation Science and Technology Research Institute Co., Ltd., 18 Xixin Avenue High-Tech Zone West Park, Chengdu 611731, China
    These authors contributed equally to this work.)

  • Dan Zhou

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
    These authors contributed equally to this work.)

  • Neng Xiong

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
    These authors contributed equally to this work.)

  • Qiao Zhu

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

Abstract

This paper deals with the state of charge (SOC) estimation of lithium-ion battery (LIB) in electric vehicles (EVs). In order to accurately describe the dynamic behavior of the battery, a fractional 2nd-order RC model of the battery pack is established. The factional-order battery state equations are characterized by the continuous frequency distributed model. Then, in order to ensure the effective function of nonlinear function, Lipschitz condition and unilateral Lipschitz condition are proposed to solve the problem of nonlinear output equation in the process of observer design. Next, the linear matrix (LMIS) inequality based on Lyapunov’s stability theory and H ∞ method is presented as a description of the design criteria for non-fragile observer. Compared with the existing literature that adopts observers, the proposed method takes the advantages of fractional-order systems in modeling accuracy, the robustness of H ∞ method in restricting the unknown variables, and the non-fragile property for tolerating slow drifts on observer gain. Finally, The LiCoO 2 LIB module is utilized to verify the effectiveness of the proposed observer method in different operation conditions. Experimental results show that the maximum estimation accuracy of the proposed non-fragile observer under three different dynamic conditions is less than 2%.

Suggested Citation

  • Zhongwei Zhang & Dan Zhou & Neng Xiong & Qiao Zhu, 2021. "Non-Fragile H ∞ Nonlinear Observer for State of Charge Estimation of Lithium-Ion Battery Based on a Fractional-Order Model," Energies, MDPI, vol. 14(16), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4771-:d:609255
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    References listed on IDEAS

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    1. Qiao Zhu & Neng Xiong & Ming-Liang Yang & Rui-Sen Huang & Guang-Di Hu, 2017. "State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H ∞ Method," Energies, MDPI, vol. 10(5), pages 1-19, May.
    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.
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    4. 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.
    5. Renxin Xiao & Jiangwei Shen & Xiaoyu Li & Wensheng Yan & Erdong Pan & Zheng Chen, 2016. "Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods," Energies, MDPI, vol. 9(3), pages 1-15, March.
    6. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
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    8. 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.
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    Cited by:

    1. Omid Rezaei & Reza Habibifar & Zhanle Wang, 2022. "A Robust Kalman Filter-Based Approach for SoC Estimation of Lithium-Ion Batteries in Smart Homes," Energies, MDPI, vol. 15(10), pages 1-21, May.
    2. Ileana González & Antonio Sánchez-Squella & Diego Langarica-Cordoba & Fernando Yanine-Misleh & Victor Ramirez, 2021. "A PI + Sliding-Mode Controller Based on the Discontinuous Conduction Mode for an Unidirectional Buck–Boost Converter with Electric Vehicle Applications," Energies, MDPI, vol. 14(20), pages 1-15, October.

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