선형회귀 및 ARIMA 모델 이용한 배터리 사용자 패턴 변화 추적 연구

This paper addresses the safety concern that the SOH of batteries in electric vehicles decreases sharply when drivers change or their driving patterns change. Such a change can overload the battery, reduce the battery life and induce safety issues. This paper aims to present the SOH as it changes on a dashboard of an electric vehicle in real-time in response to user pattern changes. As part of the training process I used battery data among the datasets provided by NASA, and built models incorporating linear regression and ARIMA, and predicted new battery data that contained user changes based on previously trained models. As a result of the prediction, the linear regression is therefore better at predicting some changes in SOH based on the user's pattern change if we have more battery datasets with a wide range of independent values. The ARIMA model can instead be used if we only have battery datasets with SOH data.