荷电状态
电压
电流(流体)
控制理论(社会学)
锂离子电池
功率(物理)
锂(药物)
常量(计算机编程)
电力系统
材料科学
作者
Bin Xiao,Bing Xiao,Luoshi Liu
出处
期刊:Electronics
日期:2020-08-09
卷期号:9 (8): 1279-
被引量:9
标识
DOI:10.3390/electronics9081279
摘要
The state of health is an indicator of battery performance evaluation and service lifetime prediction, which is essential to ensure the reliability and safety of electric vehicles. Although a large number of capacity studies have emerged, there are few simple and effective methods suitable for engineering practice. Hence, a least square support vector regression model with polynomial kernel function is presented for battery capacity estimation. By the battery charging curve, the feature samples of battery health state are extracted. The grey relational analysis is employed for the feature selection, and the K-fold cross-validation is adopted to obtain hyper-parameters of the support vector regression estimation model. To validate this method, the support vector regression estimation model was trained and tested on the battery data sets provided by NASA Prognostics Center of Excellence. The experimental results show that the proposed method only needs some battery feature data, and can achieve high-precision capacity estimation, which indicates that the proposed method shows great efficiency and robustness.
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