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State of Charge Estimation of Lithium Battery Based on Integrated Kalman Filter Framework and Machine Learning Algorithm

Author

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  • Hongyuan Yuan

    (School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
    China Energy Construction Group Guangdong Electric Power Design and Research Institute Co., Ltd., Guangzhou 510700, China)

  • Jingan Liu

    (School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China)

  • Yu Zhou

    (China Energy Construction Group Guangdong Electric Power Design and Research Institute Co., Ltd., Guangzhou 510700, China)

  • Hailong Pei

    (School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China)

Abstract

Research on batteries’ State of Charge (SOC) estimation for equivalent circuit models based on the Kalman Filter (KF) framework and machine learning algorithms remains relatively limited. Most studies are focused on a few machine learning algorithms and do not present comprehensive analysis and comparison. Furthermore, most of them focus on obtaining the state space parameters of the Kalman filter frame algorithm models using machine learning algorithms and then substituting the state space parameters into the Kalman filter frame algorithm to estimate the SOC. Such algorithms are highly coupled, and present high complexity and low practicability. This study aims to integrate machine learning with the Kalman filter frame algorithm, and to estimate the final SOC by using different combinations of the input, output, and intermediate variable values of five Kalman filter frame algorithms as the input of the machine learning algorithms of six main streams. These are: linear regression, support vector Regression, XGBoost, AdaBoost, random forest, and LSTM; the algorithm coupling is lower for two-way parameter adjustment and is not applied between the machine learning and Kalman filtering framework algorithms. The results demonstrate that the integrated learning algorithm significantly improves the estimation accuracy when compared to the pure Kalman filter framework or the machine learning algorithms. Among the various integrated algorithms, the random forest and Kalman filter framework presents the highest estimation accuracy along with good real-time performance. Therefore, it can be implemented in various engineering applications.

Suggested Citation

  • Hongyuan Yuan & Jingan Liu & Yu Zhou & Hailong Pei, 2023. "State of Charge Estimation of Lithium Battery Based on Integrated Kalman Filter Framework and Machine Learning Algorithm," Energies, MDPI, vol. 16(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2155-:d:1077858
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    References listed on IDEAS

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    1. Ran Li & Hui Sun & Xue Wei & Weiwen Ta & Haiying Wang, 2022. "Lithium Battery State-of-Charge Estimation Based on AdaBoost.Rt-RNN," Energies, MDPI, vol. 15(16), pages 1-15, August.
    2. Alexandre Lucas & Ricardo Barranco & Nazir Refa, 2019. "EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions," Energies, MDPI, vol. 12(2), pages 1-17, January.
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    Cited by:

    1. Can Ding & Qing Guo & Lulu Zhang & Tao Wang, 2024. "Intelligent Learning Method for Capacity Estimation of Lithium-Ion Batteries Based on Partial Charging Curves," Energies, MDPI, vol. 17(11), pages 1-13, May.
    2. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.

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