이교범 임희성 윤진식 2022-02-01 1975-8359 https://aurora.ajou.ac.kr/handle/2018.oak/39657 https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002809273 This paper proposes a SOH Estimation of LiFePO4 battery management systems using a Linear Regression Analysis. Among the methods of machine learning, supervised learning learns the relationship between the input data(battery characteristic) and the output data(failure data) to find a model that is expressed as a rule or function. Unsupervised learning performs failure diagnosis and prediction by discovering patterns inherent in changing battery characteristics data during use. The algorithm estimates DCIR according to the input parameters using linear regression analysis of supervised learning, and clustering of data to confirm association with failure causes. The validity of the proposed machine learning algorithm is verified by experiment. kor 대한전기학회 선형회귀분석법을 이용한 리튬인산철 배터리의 상태추정 Article 372 2 366 전기학회논문지 71 전기학회논문지, Vol.71 No.2, pp.366-372 LiFePO4 LFP SOC SOH Linear Regression Battery BMS DCIR Article