An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
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DOI：
10.36001/phmconf.2010.v2i1.1896
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Jie Liu-;A. Saxena;K. Goebel;B. Saha;Wilson Q. Wang
中科院分区：
文献类型：
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作者：
Jie Liu-;A. Saxena;K. Goebel;B. Saha;Wilson Q. Wang
预测学是一门新兴的科学，根据当前和以前的系统状态预测系统（或其组件）的健康状况。一个可靠的预测器是非常有用的，广泛的行业，以预测系统的未来状态，以便在需要时可以提前安排维护服务。本文提出了一种用于系统动态状态预测的自适应递归神经网络（ARNN）。所开发的ARNN构造的自适应/递归神经网络架构的基础上，网络的权重自适应优化使用递归Levenberg-Marquardt（RLM）方法。通过在锂离子电池剩余寿命预测中的应用，证明了所提出的ARNN的有效性。
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.