Paper Info
Title
Prognostics of Lithium ion battery using functional principal component analysis
Abstract
Lithium ion batteries are widely used for energy storage. Its capacity degradation modeling and cycle to failure estimation has become very significant. Repeated capacity measurements over the whole life of batteries, i.e., the longitudinal data, are investigated to understand the degradation process and the cycle to failure behavior. This paper proposes a method for prognostics of lithium ion batteries based on the functional principal component analysis. The observed degradation signal is decomposed into the mean function and variance-covariance function. The local quadratic smoothing method is used to estimate the mean function. Functional principal components of the variance-covariance function are represented and modeled through eigenfunctions, which are further approximated and estimated using a combination of B-Splines. For the battery capacity prognostics, three eigenfunctions explained 99.98% of the total variation. Capacity prediction and cycle to failure distribution are also analyzed and evaluated based on the proposed method.
Year
DOI
Venue
2017
10.1109/ICPHM.2017.7998299
2017 IEEE International Conference on Prognostics and Health Management (ICPHM)
Keywords
Field
DocType
Lithium ion battery,capacity,degradation,cycle to failure,functional data analysis,eigenfunctions,functional principal components
Energy storage,Functional principal component analysis,Mathematical optimization,Biological system,Prognostics,Quadratic equation,Smoothing,Engineering,Lithium-ion battery,Lithium,Principal component analysis
Conference
ISBN
Citations 
PageRank 
978-1-5090-5711-5
0
0.34
References 
Authors
4
2
Name
Order
Citations
PageRank
Jian Guo152.46
Zhaojun Li2788.58