Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (3): 156-165.doi: 10.6040/j.issn.1672-3961.0.2025.089

• Electrical Engineering • Previous Articles     Next Articles

Model-data hybrid driven estimation method of the state of charge for the lithium battery

LI Wei1, REN Qiwen1, CAO Yongji2*, YU Sen3, LI Changgang4, KAN Rui1, LIU Ziqi1   

  1. LI Wei1, REN Qiwen1, CAO Yongji2*, YU Sen3, LI Changgang4, KAN Rui1, LIU Ziqi1(1. Shandong Electric Power Engineering Consulting Institute Co., Ltd., Jinan 250013, Shandong, China;
    2. Academy of Intelligent Innovation, Shandong University, Jinan 250101, Shandong, China;
    3. School of Integrated Circuits, Shandong University, Jinan 250101, Shandong, China;
    4. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University), Jinan 250061, Shandong, China
  • Published:2026-06-09

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Abstract: To address the issues of insufficient estimation accuracy in model-driven methods and susceptibility of data-driven methods to the quality of training data, a model-data hybrid driven estimation method of the state of charge(SOC)for the lithium battery was proposed, which improved the estimation accuracy of SOC by combining the prior knowledge of physical models with the nonlinear fitting capabilities of data-driven methods. Considering the dynamic characteristics of the lithium battery, a second-order resistor-capacitance(RC)equivalent circuit model was used to establish the state-space equations of the battery system. The extended Kalman filter(EKF)was employed to preliminarily estimate the SOC of the lithium battery. The estimation results from the EKF, along with the terminal current and terminal voltage of the lithium battery, were used as inputs to an improved Transformer encoder to achieve a refined estimation of the SOC. The results of the case study showed that the proposed method achieved a mean absolute error of less than 0.7% under various operating conditions and temperatures, which could effectively improve the accuracy and robustness of the SOC identification method.

Key words: state of charge, lithium battery energy storage, extended Kalman filter, Transformer, model-data hybrid driven

CLC Number: 

  • TM71
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