Yinjiao Xing, Qiang Miao, K.-L. Tsui and Michael Pecht
Center for Prognostics and System Health Management, City University of Hong Kong, Hong Kong
Dept. of Manufacturing Engineering and Engineering Management, City University of Hong Kong Hong Kong
Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park Maryland, USA
Abstract:Health monitoring is used to analyze and predict the battery health status. However, no matter what health monitoring methods and parameters are, a major aim is to improve the battery reliability through surveillance and prognostics. Hence, the latest known methods of state estimation and life prediction based on battery health monitoring are discussed in this paper. Through comparing their characteristics respectively, a prognostics-based fusion technique is proposed that combines physics-of-failure (PoF) with data-driven technology. The fusion approach not only investigates battery failure mechanism caused by environmental and internal characteristics, but also assesses parameters with aid of real-time health monitoring. The specific method is presented to realize the estimation on remaining useful life (RUL) of batteries.
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