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影响因子：9.4

DOI码：10.1016/j.est.2022.104472

发表刊物：Journal of Energy Storage

关键字：State of charge (SOC), Power lithium-ion battery, Online identification, Adaptive forgetting factor recursive augmented least squares (AFFRALS), Affine Iterative Adaptive Extended Kalman Filter (AIAEKF), Unknown SOC initial value

摘要：State of charge (SOC) is a very important parameter for power lithium-ion battery in battery operation, but it cannot be measured directly, so it needs to be accurately estimationed. Considering the time-varying characteristics of the model parameters of the power lithium-ion battery, an online identification algorithm called Adaptive Forgetting Factor Recursive Augmented Least Squares (AFFRALS) is proposed. It can obtain a more accurate model compared with the offline method (fixed model parameters) by considering that the noise of power lithium-ion battery system is commonly non-Gaussian white noise in practice, which is different from the existing equivalent circuit models. Then, an Affine Iterative Adaptive Extended Kalman Filter (AIAEKF) method is proposed to deal with the non-Gaussian white noise and accelerate the convergence rate of the estimated results when the initial SOC value is wrong. Experiments demonstrate the effective of the online identification method as well as the SOC estimation method. This method shows a faster convergence rate and better SOC estimation performance than the traditional Extend Kalman Filter (EKF) method. The RMSE of the SOC estimation results is less than 0.023 except for 0 °C, which rarely occurs in practice.

备注：中科院2区Top

合写作者：Linlin Qin,Gang Wu,Yusha Huang,Chun Shi

第一作者：Muyao Wu

论文类型：期刊论文

论文编号：104472

学科门类：工学

文献类型：J

卷号：51

ISSN号：2352-152X

是否译文：否

发表时间：2022-04-04

收录刊物：SCI、EI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X22004947