Title :
A case study on battery life prediction using particle filtering
Author :
Xing, Yinjiao ; Ma, Eden W M ; Tsui, K.-L. ; Pecht, Michael
Author_Institution :
Centre for Prognostics & Syst. Health Manage., City Univ. of Hong Kong, Hong Kong, China
Abstract :
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 :
particle filtering (numerical methods); secondary cells; battery degradation models; battery life prediction; battery-powered systems; particle filtering; prognostic algorithms; Batteries; Impedance; Predictive models; Training data; RUL; SOH; degradation model; lithium-ion battery; particle filtering; prognostics;
Conference_Titel :
Prognostics and System Health Management (PHM), 2012 IEEE Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-1909-7
Electronic_ISBN :
2166-563X
DOI :
10.1109/PHM.2012.6228847
