Prognostics for remaining useful life estimation in proton exchange membrane fuel cell by dynamic recurrent neural networks

Prognostics for remaining useful life estimation in proton exchange membrane fuel cell by dynamic recurrent neural networks

This paper proposes a nonlinear dynamic recurrent neural network (DRNN) prognostics method for predicting the performance degradation trend and estimating the remaining useful life (RUL) of a proton exchange membrane fuel cell (PEMFC). The conducted DRNN prognostic methods are based on a nonlinear a...

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Bibliographic Details
Main Authors: Te-Jen Chang, Shan-Jen Cheng, Chang-Hung Hsu, Jr-Ming Miao, Shih-Feng Chen
Format: Article
Language:English
Published: Elsevier 2022-11-01
Series:Energy Reports
Subjects:
Prognostics
Proton exchange membrane fuel cell
Dynamic recurrent neural networks
Nonlinear autoregressive neural network
Nonlinear autoregressive with exogenous inputs neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484722012987

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http://www.sciencedirect.com/science/article/pii/S2352484722012987

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