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State of Charge (SOC) Estimation Based on Extended Exponential Weighted Moving Average H ∞ Filtering

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  • Shuaishuai Zhang

    (College Of Automation & College Of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Youhong Wan

    (College Of Automation & College Of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Jie Ding

    (College Of Automation & College Of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Yangyang Da

    (College Of Automation & College Of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

Abstract

When the classical H ∞ algorithm (HIF) is applied to estimate the state of charge (SOC) of a lithium battery, the influence of historical data is usually ignored, resulting in an increase in the estimation error. In order to improve the accuracy of SOC estimation, this paper proposes an extended exponential weighted moving average H ∞ algorithm (EE-HIF) in view of the influence of historical data. By designing the Gaussian function, the weighted distribution of the data at different times can effectively reduce the estimation error caused by the inaccuracy of the lithium battery model. In addition, when the system contains Gaussian white noise and alternating current input, the proposed method can achieve a faster convergence speed and better robustness. Simulation results show the advantages of the proposed algorithm, as compared to an HIF filtering algorithm and an exponentially weighted moving average H ∞ algorithm (EWMA).

Suggested Citation

  • Shuaishuai Zhang & Youhong Wan & Jie Ding & Yangyang Da, 2021. "State of Charge (SOC) Estimation Based on Extended Exponential Weighted Moving Average H ∞ Filtering," Energies, MDPI, vol. 14(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1655-:d:518628
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    References listed on IDEAS

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    Cited by:

    1. Xinghao Zhang & Yan Huang & Zhaowei Zhang & Huipin Lin & Yu Zeng & Mingyu Gao, 2022. "A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter," Energies, MDPI, vol. 15(18), pages 1-26, September.
    2. Li, Kangqun & Zhou, Fei & Chen, Xing & Yang, Wen & Shen, Junjie & Song, Zebin, 2023. "State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-th," Energy, Elsevier, vol. 263(PC).

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