DOI码：10.1016/j.est.2023.108647

发表刊物：Journal of Energy Storage

摘要：State-of-health (SOH) estimation is critical in ensuring safe and reliable operation of Li-ion batteries. The first step in the estimation process is extracting features that reflect the SOH. This study proposes a novel method that utilizes both statistical and geometric features of Li-ion batteries to improve the accuracy of SOH estimation. Moreover, feature extraction is performed from the constant-voltage (CV) charging stage as it is unaffected by the randomness of charging onset point and does not require long resting after a full charge. Firstly, features are extracted from both statistical and geometric perspectives. Subsequently, these features are combined with the mean CV charging current to create a feature combination. Finally, the XGBoost algorithm is used to construct the SOH estimation model. The effectiveness of the proposed model is validated using three types of battery datasets. In all the experiments, the root mean square error and the mean absolute error of the proposed model are less than 1.3 % in the overall test set. Moreover, the proposed model achieves high accuracy for all three battery types and demonstrates good adaptability to different discharge current rates. Furthermore, the model achieves high accuracy, even with only the first 50 % of the CV charging data.

合写作者：Zikang Liang,Haoliang Yuan,Ling Yang,Fangyuan Xu,Yun Zhang

第一作者：Si-Zhe Chen

论文类型：期刊论文

论文编号：108647

学科门类：工学

一级学科：电气工程

文献类型：J

卷号：72:

页面范围：108647

是否译文：否

发表时间：2023-08-16

收录刊物：SCI、EI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X23020443