Data-driven proton exchange membrane fuel cell degradation predication through deep learning method-Reference-Cited by-同舟云学术

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Data-driven proton exchange membrane fuel cell degradation predication through deep learning method

Published:2018-12 Issue: Volume:231 Page:102-115
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Ma Rui,Yang Tao,Breaz Elena,Li Zhongliang,Briois Pascal,Gao Fei

Funder

European Commission H2020

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference40 articles.

1. Performance and degradation of proton exchange membrane fuel cells: state of the art in modeling from atomistic to system scale;Jahnke;J Power Sources,2016

2. Breaz E, Gao F, Miraoui A, Tirnovan R. A short review of aging mechanism modeling of proton exchange membrane fuel cell in transportation applications. In: Proc. IEEE Ind. Electron. Soc.; 2014. p. 3941–3947.

3. Main factors affecting the lifetime of Proton Exchange Membrane fuel cells in vehicle applications: a review;Pei;Appl Energy,2014

4. Membrane degradation in PEM fuel cells: from experimental results to semi-empirical degradation laws;Chandesris;Int J Hydrogen Energy,2017

5. A reconstructed fuel cell life-prediction model for a fuel cell hybrid city bus;Hu;Energy Convers Manage,2018

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