Cited by 119Fuel Cells and Related MaterialsElectric and Hybrid Vehicle TechnologiesElectrocatalysts for Energy Conversion

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A deep learning method based on CNN-BiGRU and attention mechanism for proton exchange membrane fuel cell performance degradation prediction

Overview

Integrity signals

Registry

Crossref · 10.1016/j.ijhydene.2024.11.127

Cites

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References 41 · Cited by 119

Research works printed but not linked: 1

  1. Gouriveau, 2014, IEEE PHM 2014 data challenge: outline, experiments, scoring of results, winners, Proc IEEE Conf Prognostics Health Manage, 1

Metadata

Authors
Zhou, J., Shu, X., Zhang, J., Yi, F., Jia, C., Zhang, C., Kong, X., Zhang, J., & Wu, G.
Affiliations
  • Weifang University of Science and Technology (CN)
  • Shandong Jiaotong University (CN)
  • Beijing Institute of Technology (CN)
  • Chongqing University (CN)
Corresponding author
  • Yi, Fengyan
  • Jia, Chunchun
Venue
International Journal of Hydrogen Energy
Publisher
Elsevier BV
Published
2024-12
Volume
94
Pages
394-405
ISSN
0360-3199 (ISSN-L)
OpenAlex
W4404281436
Cited by
119
References
41
Citations by year
2026: 64 · 2025: 53 · 2024: 2
FWCI
12.32
Type
journal article
Language
en
Open access
closed
Locations
International Journal of Hydrogen Energy (publishedVersion)
Funders
  • National Natural Science Foundation of China (52472380)
  • Doctoral Foundation of Shandong Province (SDCX-ZG-202203102)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Fuel Cells and Related Materials
  • Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
Keywords
proton exchange membrane fuel cells, performance degradation prediction, fuel cell durability, bidirectional gated recurrent unit, attention mechanism, convolutional neural networks, steady-state operation, long-term operation

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