Transactions of the Korean Society of Mechanical Engineers B (대한기계학회논문집B)

The Korean Society of Mechanical Engineers (대한기계학회)
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Remaining Useful Life Prediction of Li-Ion Battery Based on Charge Voltage Characteristics

충전 전압 특성을 이용한 리튬 이온 배터리의 잔존 수명 예측

  • Sim, Seong Heum (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Gang, Jin Hyuk (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • An, Dawn (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Kim, Sun Il (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Kim, Jin Young (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Choi, Joo Ho (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
  • 심성흠 (한국항공대학교 항공우주 및 기계공학과) ;
  • 강진혁 (한국항공대학교 항공우주 및 기계공학과) ;
  • 안다운 (한국항공대학교 항공우주 및 기계공학과) ;
  • 김선일 (한국항공대학교 항공우주 및 기계공학과) ;
  • 김진영 (한국항공대학교 항공우주 및 기계공학과) ;
  • 최주호 (한국항공대학교 항공우주 및 기계공학과)
  • Received : 2012.02.20
  • Accepted : 2013.01.07
  • Published : 2013.04.01
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Abstract

Batteries, which are being used as energy sources in various applications, tend to degrade, and their capacity declines with repeated charging and discharging cycles. A battery is considered to fail when it reaches 80% of its initial capacity. To predict this, prognosis techniques are attracting attention in recent years in the battery community. In this study, a method is proposed for estimating the battery health and predicting its remaining useful life (RUL) based on the slope of the charge voltage curve. During this process, a Bayesian framework is employed to manage various uncertainties, and a Particle Filter (PF) algorithm is applied to estimate the degradation of the model parameters and to predict the RUL in the form of a probability distribution. Two sets of test data-one from the NASA Ames Research Center and another from our own experiment-for an Li-ion battery are used for illustrating this technique. As a result of the study, it is concluded that the slope can be a good indicator of the battery health and PF is a useful tool for the reliable prediction of RUL.

배터리는 최근 여러 분야에서 중요한 에너지원 역할을 하고 있는데, 사용 중 충방전을 거듭하면 용량이 점차 저하되며 초기 대비 80% 이하로 떨어지면 고장으로 간주되므로, 이를 예측하기 위한 수명 예측 기법이 활발히 개발되고 있다. 본 연구에서는 사용중인 배터리에 대해 충전곡선 기울기를 이용하여 배터리의 용량을 평가하고 이를 바탕으로 잔존수명을 예측하는 새로운 방법을 제안하였다. 이 과정에서 발생하는 여러 불확실성을 고려하기 위해 베이지안 접근법에 기반한 파티클 필터 방법을 활용하였고 그 결과 잔존수명을 확률분포로 구하였다. 개발된 방법을 미국 NASA Ames 연구소와 본 연구실에서 직접 수행한 배터리 충방전 시험 데이터에 대해 각각 적용한 결과 충전곡선 기울기가 용량 열화를 잘 나타내며 파티클 필터로 예측된 잔존수명 신뢰구간은 실제 수명을 잘 포함함을 확인할 수 있었다.

Keywords

References

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