State of charge estimation of LiFePO4 batteries based on a transferable force-electric dual-observation model

Chun Chang, Bingrui Qu, Haijun Yu, Yang Gao, Li Liao, Lu Lv, Tiezhou Wu and Jiuchun Jiang

Energy, 2025, vol. 331, issue C

Abstract: In the background of the rapid development of electric vehicles, accurate estimation of the State of charge (SOC) is essential to achieve battery management. However, there is a plateau problem with the voltage of LiFePO4 batteries. This creates challenges for state estimation. To address these problems. A SOC estimation method for LiFePO4 batteries based on a transportable force-electric dual-observation model is presented in this paper. In this paper, the expansion force signal and voltage signal are combined. The force-electric dual-observation model is obtained by training using a least squares support vector machine (LSSVM). The force-electric dual observation model is used as an observation equation for the adaptive untraceable Kalman filter algorithm (AUKF). The optimal weights for the force-electric dual-observation model are obtained by pre-experimentation. In addition, the force-electric dual-observation model for 12AH and 5AH LiFePO4 batteries was obtained using transfer learning (TL). Finally, the proposed method is fully evaluated by experiments under different constraints. The experimental results show that the average absolute errors of SOC estimation for the three batteries with different capacities are all within 1.2 %. The root mean square errors are all within 1.8 %. The robustness and accuracy of the proposed method are confirmed.

Keywords: LiFePO4 battery; SOC estimation; Force-electric dual observation model; Least squares support vector machine; Adaptive untraceable Kalman filter algorithm; Transfer learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:331:y:2025:i:c:s0360544225026441

DOI: 10.1016/j.energy.2025.137002

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