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

The Korean Institute of Power Electronics (전력전자학회)
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Remaining Useful Life Prediction for Litium-Ion Batteries Using EMD-CNN-LSTM Hybrid Method

EMD-CNN-LSTM을 이용한 하이브리드 방식의 리튬 이온 배터리 잔여 수명 예측

  • Lim, Je-Yeong (Dept. of Electrical & Computer Engineering, Sungkyunkwan University) ;
  • Kim, Dong-Hwan (Dept. of Electrical & Computer Engineering, Sungkyunkwan University) ;
  • Noh, Tae-Won (Dept. of Electrical & Computer Engineering, Sungkyunkwan University) ;
  • Lee, Byoung-Kuk (Dept. of Electrical Engineering, Sungkyunkwan University)
  • Received : 2021.10.08
  • Accepted : 2021.12.27
  • Published : 2022.02.20
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Abstract

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2020R1A2C2003445).

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