State of health estimation method for lithium⁃ion battery based on curve compression and extreme gradient boosting

被引：0
|
作者：
Liu X.-T. [1 ,2 ]
Liu X.-J. [1 ]
Wu J. [1 ,2 ]
He Y. [3 ]
Liu X.-T. [1 ,2 ]
机构：
[1] School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei
[2] Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province, Hefei University of Technology, Hefei
[3] Automotive Research Institute, Hefei University of Technology, Hefei
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2022年 / 52卷 / 06期
关键词：
Automotive engineering; Douglas-Pucker algorithm; Extreme gradient boosting algorithm; Lithium-ion battery; State of health estimation;
D O I：
10.13229/j.cnki.jdxbgxb20210020
中图分类号：
学科分类号：
摘要：
In order to accurately estimate the State of Health (SOH) of the lithium-ion battery, a method based on Douglas-Puck algorithm and Extreme Gradient Boosting (XGBoost) algorithm is proposed. Firstly, each set of voltage data is preprocessed, and the Douglas-Puck algorithm is used to vectorize the constant current charging voltage curve of each cycle. On the basis of this data, the XGBoost algorithm is applied to establish a lithium-ion battery degradation model and estimate the SOH. The results of comparative experiments show that the proposed method can effectively compress the battery voltage curve and reduce the dimension of network training data. At the same time, the developed method also has a higher prediction accuracy and faster running speed, and can realize the fast and accurate estimation of the lithium-ion battery SOH. © 2022, Jilin University Press. All right reserved.
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页码：1273 / 1280
页数：7
相关论文
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