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影响因子：9.4

DOI码：10.1016/j.est.2023.108359

发表刊物：Journal of Energy Storage

关键字：LiFePO4 power battery, Forgetting Factor Recursive Least Squares, Data supervisory mechanism, Online modeling method

摘要：The establishment of the accurate lithium-ion power battery model is an important basis to realize the reliable state estimation of the lithium-ion power battery, and also a necessary work to develop the battery management system. However, the existing modeling algorithms lack the data supervision mechanism for the online modeling, and cannot guarantee the stability and clear physical meaning of the model parameters used in the Battery Management System (BMS), which may lead to the breakdown of the BMS state estimation algorithm and major security risks. Therefore, the data supervisory mechanism is designed to solve the problem and a lithium-ion power battery online modeling method based on it is proposed in this paper. Experimental results on the LiFePO4 power battery demonstrate the effective of the proposed online modeling method and lay a foundation for the subsequent state estimation of the LiFePO4 power battery. As the ambient temperature increases from 10℃ to 40℃, the average value and the standard deviation of the LiFePO4 power battery ohmic internal resistance decrease 25.88% and 83.33% respectively. Meanwhile, the Mean Absolutely Error (MAE), the Root Mean Square Error (RMSE) and the Maximum Absolute Error (Max-AE) of the terminal voltage decrease 31.91%, 30.43% and 58.06% respectively.

备注：中科院2区Top

合写作者：Ji Wu

第一作者：Muyao Wu

论文类型：期刊论文

通讯作者：Li Wang

论文编号：108359

学科门类：工学

文献类型：J

卷号：72

ISSN号：2352-152X

是否译文：否

发表时间：2023-07-14

收录刊物：SCI、EI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X23017565