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http://dx.doi.org/10.6113/TKPE.2022.27.3.265

Machine Learning-based Screening Algorithm for Energy Storage System Using Retired Lithium-ion Batteries  

Han, Eui-Seong (Dept. of Electrical and Computer Engineering, Sungkyunkwan University)
Lim, Je-Yeong (Dept. of Electrical and Computer Engineering, Sungkyunkwan University)
Lee, Hyeon-Ho (Dept. of Electrical and Computer Engineering, Sungkyunkwan University)
Kim, Dong-Hwan (Dept. of Electrical and Computer Engineering, Sungkyunkwan University)
Noh, Tae-Won (Dept. of Electrical and Computer Engineering, Sungkyunkwan University)
Lee, Byoung-Kuk (Dept. of Electrical Engineering, Sungkyunkwan University)
Publication Information
The Transactions of the Korean Institute of Power Electronics / v.27, no.3, 2022 , pp. 265-274 More about this Journal
Abstract
This paper proposes a machine learning-based screening algorithm to build the retired battery pack of the energy storage system. The proposed algorithm creates the dataset of various performance parameters of the retired battery, and this dataset is preprocessed through a principal component analysis to reduce the overfitting problem. The retried batteries with a large deviation are excluded in the dataset through a density-based spatial clustering of applications with noise, and the K-means clustering method is formulated to select the group of the retired batteries to satisfy the deviation requirement conditions. The performance of the proposed algorithm is verified based on NASA and Oxford datasets.
Keywords
ESS (Energy Storage System); Retired battery; Secondary usage; Clustering method;
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