리튬이온 배터리의 랜덤 포레스트 기반 잔여 용량 추정 및 중요 전압 구간 선정 연구

Abstract

This paper proposes random forest regression, which is an ensemble model for estimating the remaining battery capacity. The partial capacity of the set charging voltage range and the remaining capacity are used as the training data for the random forest model. The training data can be used directly without any pre-processing. When the partial capacity of a specific voltage range is entered into the trained random forest model, it estimates the remaining capacity. Furthermore, the important voltage range is extracted by selecting the voltage section that is critical for estimating the remaining capacity based on the permutation importance. The mean absolute error and coefficient of determination were used to validate the important voltage range. Validation results show that the proposed model can estimate the remaining capacity with a low error. Therefore, the proposed model can be used for online battery capacity estimation using a narrow voltage range.