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

The Korean Institute of Power Electronics (전력전자학회)
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State of Health Estimation for Lithium-Ion Batteries Using Long-term Recurrent Convolutional Network

LRCN을 이용한 리튬 이온 배터리의 건강 상태 추정

  • Hong, Seon-Ri (Dept. of Electrical Engineering, Korea Institute of Energy Research) ;
  • Kang, Moses (Dept. of Electrical Engineering, Korea Institute of Energy Research) ;
  • Jeong, Hak-Geun (Dept. of Electrical Engineering, Korea Institute of Energy Research) ;
  • Baek, Jong-Bok (Dept. of Electrical Engineering, Korea Institute of Energy Research) ;
  • Kim, Jong-Hoon (Dept. of Electrical Engineering, Chungnam National University)
  • Received : 2020.10.21
  • Accepted : 2021.01.21
  • Published : 2021.06.20
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Abstract

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

Keywords

Acknowledgement

본 연구는 산업통상지원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구과제입니다. (No. 20182410105280)

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