The Transactions of the Korean Institute of Power Electronics (전력전자학회논문지)

The Korean Institute of Power Electronics (전력전자학회)
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Battery State-of-Health Estimation Method based on Deep-learning and Feature Engineering

딥러닝과 특징 추출 기반 배터리 노화 상태 추정 방법

  • Chang, Moon-Seok (Dept. of Electrical Engineering, Hanyang University) ;
  • Lee, Gang-Seok (Dept. of Electrical Engineering, Hanyang University) ;
  • Bae, Sungwoo (Dept. of Electrical Engineering, Hanyang University)
  • Received : 2021.10.10
  • Accepted : 2021.12.27
  • Published : 2022.08.20
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Abstract

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.

Keywords

Acknowledgement

본 연구는 2022년도 산업통상자원부 및 산업기술평가관리원(KEIT) 연구비 지원에 의한 연구임.(20011626)

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