콘텐츠 본문
논문 해외 국제전문학술지(SCI급) Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2020-11
- 저자명 정의림공동(참여),이동헌,이융,홍준기
- 학술지명 APPLIED ENERGY
- 발행처명 ELSEVIER SCI LTD
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 4
논문 초록 (Abstract)
This paper presents the first full end-to-end deep learning framework for the swift prediction of lithium-ion battery remaining useful life. While lithium-ion batteries offer advantages of high efficiency and low cost, their instability and varying lifetimes remain challenges. To prevent the sudden failure of lithium-ion batteries, researchers have worked to develop ways of predicting the remaining useful life of lithium-ion batteries, especially using data-driven approaches. In this study, we sought a higher resolution of inter-cycle aging for faster and more accurate predictions, by considering temporal patterns and cross-data correlations in the raw data, specifically, terminal voltage, current, and cell temperature. We took an in-depth analysis of the deep learning models using the uncertainty metric, t-SNE of features, and various battery related tasks. The proposed framework significantly boosted the remaining useful life prediction (25X faster) and resulted in a 10.6% mean absolute error rate.
