Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Deep Learning Approaches to RUL Prediction of Lithium-ion Batteries

딥러닝을 이용한 리튬이온 배터리 잔여 유효수명 예측

  • Jung, Sang-Jin (Department of Mechanical System Engineering(Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology) ;
  • Hur, Jang-Wook (Department of Mechanical System Engineering(Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology)
  • 정상진 (금오공과대학교 기계시스템공학과 (항공기계전자융합전공)) ;
  • 허장욱 (금오공과대학교 기계시스템공학과 (항공기계전자융합전공))
  • Received : 2020.06.09
  • Accepted : 2020.08.07
  • Published : 2020.12.31
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Abstract

Lithium-ion batteries are the heart of energy-storing devices and electric vehicles. Owing to their superior qualities, such as high capacity and energy efficiency, they have become quite popular, resulting in an increased demand for failure/damage prevention and useable life maximization. To prevent failure in Lithium-ion batteries, improve their reliability, and ensure productivity, prognosticative measures such as condition monitoring through sensors, condition assessment for failure detection, and remaining useful life prediction through data-driven prognostics and health management approaches have become important topics for research. In this study, the residual useful life of Lithium-ion batteries was predicted using two efficient artificial recurrent neural networks-ong short-term memory (LSTM) and gated recurrent unit (GRU). The proposed approaches were compared for prognostics accuracy and cost-efficiency. It was determined that LSTM showed slightly higher accuracy, whereas GRUs have a computational advantage.

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References

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