본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

IEEE transactions on instrumentation and measurement v.66 no.2, 2017년, pp.280 - 293   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-Ion Batteries

Liu, Zhenbao Sun, Gaoyuan Bu, Shuhui Han, Junwei Tang, Xiaojun Pecht, Michael
  • 초록  

    As an important part of prognostics and health management, accurate remaining useful life (RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to maintain the batteries in advance. This paper presents a novel method to predict the RUL of Li-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle degeneracy by resampling state particles first with considering the current measurement information and then propagating them. Meanwhile, PL is improved by adjusting the number of particles at each iteration adaptively to reduce the running time of the algorithm, which makes it suitable for online application. Furthermore, the kernel smoothing algorithm is fused into PL to keep the variance of parameter particles invariant during recursive propagation with the battery prediction model. This entire method is referred to as PLKS in this paper. The model can then be updated by the proposed method when new measurements are obtained. Future capacities are iteratively predicted with the updated prediction model until the predefined threshold value is triggered. The RUL is calculated according to these predicted capacities and the predefined threshold value. A series of case studies that demonstrate the proposed method is presented in the experiment.


 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • 원문이 없습니다.

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기