Remaining Useful Life Estimation of Li-ion Battery for Energy Storage System Using Markov Chain Monte Carlo Method

마코프체인 몬테카를로 방법을 이용한 에너지 저장 장치용 배터리의 잔존 수명 추정

  • Kim, Dongjin (School of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Kim, Seok Goo (School of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Choi, Jooho (School of Aerospace and Mechanical Engineering, Korea Aerospace Univ.) ;
  • Song, Hwa Seob (Hyosung Corporation) ;
  • Park, Sang Hui (Hyosung Corporation) ;
  • Lee, Jaewook (School of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
  • 김동진 (한국항공대학교 항공우주 및 기계공학과) ;
  • 김석구 (한국항공대학교 항공우주 및 기계공학과) ;
  • 최주호 (한국항공대학교 항공우주 및 기계공학과) ;
  • 송화섭 (효성 중공업) ;
  • 박상희 (효성 중공업) ;
  • 이재욱 (한국항공대학교 항공우주 및 기계공학과)
  • Received : 2016.05.18
  • Accepted : 2016.08.08
  • Published : 2016.10.01


Remaining useful life (RUL) estimation of the Li-ion battery has gained great interest because it is necessary for quality assurance, operation planning, and determination of the exchange period. This paper presents the RUL estimation of an Li-ion battery for an energy storage system using exponential function for the degradation model and Markov Chain Monte Carlo (MCMC) approach for parameter estimation. The MCMC approach is dependent upon information such as model initial parameters and input setting parameters which highly affect the estimation result. To overcome this difficulty, this paper offers a guideline for model initial parameters based on the regression result, and MCMC input parameters derived by comparisons with a thorough search of theoretical results.


Supported by : (주)효성


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