A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell-Reference-Cited by-同舟云学术

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A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell

Published:2018-07 Issue:27 Volume:43 Page:12314-12327
ISSN:0360-3199
Container-title:International Journal of Hydrogen Energy
language:en
Short-container-title:International Journal of Hydrogen Energy

Author:

Cheng Yujie,Zerhouni Noureddine,Lu Chen^ORCID

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Condensed Matter Physics,Fuel Technology,Renewable Energy, Sustainability and the Environment

Reference23 articles.

1. Prognostics and health management of PEMFC–state of the art and remaining challenges;Jouin;Int J Hydrogen Energy,2013

2. Data-based short-term prognostics for proton exchange membrane fuel cells;Liu;Int J Hydrogen Energy,2017

3. New approach for PEMFC diagnostics based on quantitative description of quasi-periodic oscillations;Nigmatullin;Int J Hydrogen Energy,2016

4. Degradations analysis and aging modeling for health assessment and prognostics of PEMFC;Jouin;Reliab Eng Syst Saf,2016

5. Accelerated stress test procedures for PEM fuel cells under actual load constraints: state-of-art and proposals;Petrone;Int J Hydrogen Energy,2015

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1. Degradation prediction of proton exchange membrane fuel cell using a novel neuron-fuzzy model based on light spectrum optimizer;Renewable Energy;2024-11

2. Hybrid Method for Remaining Useful Life Prediction of Power IGBT Modules in High-Speed Trains;IEEE Transactions on Power Electronics;2024-11

3. High accuracy and adaptability of PEMFC degradation interval prediction with Informer-GPR under dynamic conditions;Energy;2024-10

4. Degradation prediction of PEMFC based on BiTCN-BiGRU-ELM fusion prognostic method;International Journal of Hydrogen Energy;2024-10

5. Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells;Energy and AI;2024-09

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