自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 25    No. 2    February 2018

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Bayesian framework for satellite rechargeable lithium battery synthesizing bivariate degradation and lifetime data
ZHANG Yang(张洋), JIA Xiang(贾祥), GUO Bo(郭波)

College of Information Systems and Management, National University of Defense Technology,Changsha 410073, China

Abstract:Reliability and remaining useful life (RUL) estimation for a satellite rechargeable lithium battery (RLB) are significant for prognostic and health management (PHM). A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data, including bivariate degradation data and lifetime data. Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system. First, linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB’s temperature and discharge voltage. Next, the Bayesian method, in combination with Markov Chain Monte Carlo (MCMC) simulations, is provided to integrate limited bivariate degradation data with other congeneric RLBs’ lifetime data. Then reliability evaluation and RUL prediction are carried out for PHM. A simulation study demonstrates that due to the data fusion, parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes. Finally, a practical case study of a satellite RLB verifies the usability of the model.

 

Key words: rechargeable lithium battery; Bayesian framework; bivariate degradation; lifetime data; remaining useful life; reliability evaluation

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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