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

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Jie Liu Abhinav Saxena Kai Goebel Bhaskar Saha Wilson 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.

How to Cite

Liu, J., Saxena, A. ., Goebel, K. ., Saha, B. ., & Wang, W. . (2010). An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1896
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Keywords

data driven prognostics, recurrent neural networks, Remaining useful Life, performance evaluation, battery health management

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Technical Papers

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