An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Year: 2010
Type: conference-paper
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.
Institutions: Carleton University, Ames Research Center, Lakehead University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2126450366