2012 Prognostics & System Health Management Conference
(PHM-2012 Beijing)

A Case Study on Battery Life Prediction Using Particle Filtering

Yinjiao Xing1, Eden W. M. Ma1, K.L. Tsui2 & Michael Pecht3

1Center for Prognostics and System Health Management, City University of Hong Kong, Hong Kong

2Dept. of Systems Engineering and Engineering Management, City University of Hong Kong,
Hong Kong

3Center for Advanced Life Cycle Engineering (CALCE)
University of Maryland
College Park, MD 20742



Batteries play a critical role for the reliability of battery-powered systems. The prognostics in batteries provide warning to the advent of failure, which requires timely maintenance and replacement of batteries. This paper reviews current research on battery degradation models and focuses on the online implementation of prognostic algorithms. The particle filtering approach is utilized to track battery performance based on two degradation models that are highly efficient for online applications. An experimental demonstration of this method is provided. Through a comparison of the prognostic results, the problems of the models and the algorithm are discussed.

Keywords:prognostics; lithium-ion battery; SOH; RUL; particle filtering; degradation model

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