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http://dx.doi.org/10.7471/ikeee.2022.26.4.531

Machine Learning Based State of Health Prediction Algorithm for Batteries Using Entropy Index  

Sangjin, Kim (Hankook Electric Power Information Co.)
Hyun-Keun, Lim (Hankook Electric Power Information Co.)
Byunghoon, Chang (Hankook Electric Power Information Co.)
Sung-Min, Woo (Chungbuk Technopark)
Publication Information
Journal of IKEEE / v.26, no.4, 2022 , pp. 531-536 More about this Journal
Abstract
In order to efficeintly manage a battery, it is important to accurately estimate and manage the SOH(State of Health) and RUL(Remaining Useful Life) of the batteries. Even if the batteries are of the same type, the characteristics such as facility capacity and voltage are different, and when the battery for the training model and the battery for prediction through the model are different, there is a limit to measuring the accuracy. In this paper, We proposed the entropy index using voltage distribution and discharge time is generalized, and four batteries are defined as a training set and a test set alternately one by one to predict the health status of batteries through linear regression analysis of machine learning. The proposed method showed a high accuracy of more than 95% using the MAPE(Mean Absolute Percentage Error).
Keywords
Entropy; Battery; SOH; Voltage Distribution; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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