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Accession Number ADA562707
Title Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries.
Publication Date Oct 2010
Media Count 10p
Personal Author A. Saxena B. Saha J. Liu K. Goebel W. Wang
Abstract Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.
Keywords Adaptive recurrent neural network
Component reports
Life expectancy(Service life)
Lithium ion batteries
Predictions
Prognostics
Remaining useful life
Storage batteries
Symposia

 
Source Agency Non Paid ADAS
NTIS Subject Category 97M - Batteries & Components
Corporate Author National Aeronautics and Space Administration, Moffett Field, CA. Ames Research Center.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1225
Contract Number N/A

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