考虑温度及老化的储能用锂离子电池组荷电状态估算算法

doi:10.19595/j.cnki.1000-6753.tces.241201

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Abstract The traditional state of charge (SOC) estimation algorithm does not consider the ambient temperature and aging state of the battery changes when using the lithium-ion battery pack for energy storage. In practical applications, the adaptability is poor, and the estimation accuracy is low. In recent years, several SOC estimation algorithms for battery packs have been proposed, taking into account temperature or battery aging. However, most remain incomplete and exhibit high computational complexity. This paper proposes a SOC estimation algorithm for lithium-ion battery packs used in energy storage, which considers temperature and aging, to enhance the accuracy and robustness of SOC estimation in battery packs under various ambient temperatures and aging states.
First, the 100 A·h lithium iron phosphate battery produced by Zhongke De Fang (Hebei) New Energy Co., Ltd. is used as the research object, and its characteristics are tested under various temperatures and aging conditions. Depending on the characteristics of the selected battery, the second-order RC equivalent circuit model is chosen as the basic battery model. A nonlinear autoregressive neural network with external input (NARX) is chosen to replace the RC circuit in the second-order RC equivalent circuit model, and the NARX neural network structure is determined by training the model through actual working conditions data. Then, a battery model considering ambient temperature and battery aging is established. The working condition data is randomly selected from the enterprise database. SOC estimation is performed to verify the computation and accuracy of the optimized NARX neural network battery model. The experimental results show that although the convergence rate of the NARX neural network optimized battery model is slightly slower than that of the traditional second-order RC equivalent circuit model, the estimated error of the optimized battery model is much smaller than that of the conventional battery model. Therefore, the improved battery model performs better and is more suitable for SOC estimation of subsequent battery packs. Considering SOC and internal resistance differences, a battery pack mean difference model is proposed. An adaptive unscented Kalman filtering (AUKF) algorithm is selected to estimate the mean model and difference model, and the SOC of the difference model is modified to obtain the SOC of the single battery pack. A fuzzy control strategy determines the battery pack's SOC fusion weight.
Finally, a hardware-in-the-loop simulation platform is built to verify the SOC estimation algorithm for battery packs. The proposed algorithm is compared with the traditional extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms at ambient temperatures of -20℃, 0℃, and 50℃, and for aging states of the battery of 90% and 80%. Experimental results demonstrate that the proposed algorithm ensures an average estimation error of less than 3% under various temperatures and aging operating conditions. Under any working conditions, the average estimation error of the proposed algorithm is lower than that of traditional EKF and UKF algorithms.

Key words： Battery pack SOC estimation NARX neural network mean difference model AUKF algorithm fuzzy control

Received: 06 July 2024

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TM912.9

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.241201 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I17/5667

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