DocumentCode :
1755081
Title :
Intelligent Prognostics for Battery Health Monitoring Using the Mean Entropy and Relevance Vector Machine
Author :
Hong Li ; Donghui Pan ; Chen, C.L.P.
Author_Institution :
Sch. of Math. & Stat., Huazhong Univ. of Sci. & Technol., Wuhan, China
Volume :
44
Issue :
7
fYear :
2014
fDate :
41821
Firstpage :
851
Lastpage :
862
Abstract :
Battery prognostics aims to predict the remaining life of a battery and to perform necessary maintenance service if necessary using the past and current information. A reliable prognostic model should be able to accurately predict the future state of the battery such that the maintenance service could be scheduled in advance. In this paper, a multistep-ahead prediction model based on the mean entropy and relevance vector machine (RVM) is developed, and applied to state of health (SOH) and remaining life prediction of the battery. A wavelet denoising approach is introduced into the RVM model to reduce the uncertainty and to determine trend information. The mean entropy based method is then used to select the optimal embedding dimension for correct time series reconstruction. Finally, RVM is employed as a novel nonlinear time-series prediction model to predict the future SOH and the remaining life of the battery. As more data become available, the accuracy and precision of the prediction improve. The presented approach is validated through experimental data collected from Li-ion batteries. The experimental results demonstrate the effectiveness of the proposed approach, which can be effectively applied to battery monitoring and prognostics.
Keywords :
battery management systems; entropy; remaining life assessment; secondary cells; support vector machines; time series; wavelet transforms; Li-ion batteries; battery health monitoring; battery maintenance service; battery remaining life prediction; intelligent battery prognostics; mean entropy based method; multistep-ahead prediction model; nonlinear time-series prediction model; optimal embedding dimension; relevance vector machine; state of health; time series reconstruction; wavelet denoising approach; Batteries; Entropy; Monitoring; Predictive models; Support vector machines; Time series analysis; Vectors; Health monitoring; mean entropy; prognostics; relevance vector machine (RVM); remaining life; state-of-health (SOH);
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics: Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2168-2216
Type :
jour
DOI :
10.1109/TSMC.2013.2296276
Filename :
6731587
Link To Document :
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