An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries

Peter J. Liu, Abhinav Saxena, Kai Goebel, Bhaskar Saha, and Wilson Wang
Submission Type: 
Full Paper
Supporting Agencies (optional): 
UC Berkeley and NASA
AttachmentSizeTimestamp
phmc_10_065.pdf424.45 KBSeptember 28, 2010 - 1:59pm

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.

Publication Control Number: 
065
Submission Keywords: 
data driven prognostics
recurrent neural networks
Remaining useful Life
performance evaluation
battery health management
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