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影响因子：9.4

DOI码：10.1016/j.est.2021.102535

发表刊物：Journal of Energy Storage

关键字：State of charge (SOC)，Power lithium-ion battery，Unsymmetrical Thevenin model，Auto-tuning multiple forgetting factors recursive least squares，Adaptive time scale dual extend Kalman filtering， Sliding window forgetting factor approximate total recursive least squares

摘要：In this paper, we introduce the Unsymmetrical Thevenin model, an improved equivalent circuit model to obtain a more precise SOC estimation. We first propose an Auto-tuning Multiple Forgetting Factors Recursive Least Squares (AMFFRLS) for model parameter identification, then, we proposed an Adaptive Time Scale Dual Extend Kalman Filtering (ATSDEKF) to update the model parameters and Sliding Window Forgetting Factor Approximate Total Recursive Least Squares (SWFFATRLS) to update the maximum available capacity of a lithium-ion battery to obtain more accurate state of charge (SOC) estimation. Numerical experiments demonstrate that the proposed method can get better SOC estimation results compare to the traditional ones. Except for extreme temperatures, such as at 0 ℃, the root mean square error (RMSE) of the Unsymmetrical Thevenin model is below 1.2%, which is much smaller than the most common Thevenin model with fixed parameters based on Extend Kalman Filtering (EKF).

备注：中科院2区Top

合写作者：Linlin Qin,Gang Wu

第一作者：Muyao Wu

论文类型：期刊论文

论文编号：102535

学科门类：工学

文献类型：J

卷号：39

ISSN号：2352-152X

是否译文：否

发表时间：2021-05-02

收录刊物：SCI、EI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X21002814