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Machine Learning Based State of Health Prediction Algorithm for Batteries Using Entropy Index
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엔트로피 지수를 이용한 기계학습 기반의 배터리의 건강 상태 예측 알고리즘

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Type
Academic journal
Author
Sangjin Kim (Hankook Electric Power Information) Hyun-Keun Lim (Hankook Electric Power Information) Byunghoon Chang (Hankook Electric Power Information) Sung-Min Woo (Chungbuk Technopark)
Journal
Institute of Korean Electrical and Electronics Engineers Journal of IKEEE Vol.26 No.4 KCI Accredited Journals
Published
2022.12
Pages
8 - 13 (6page)

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Machine Learning Based State of Health Prediction Algorithm for Batteries Using Entropy Index
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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).
#Entropy #Battery #SOH #Voltage Distribution #Machine Learning

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Abstract
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Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 결론
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