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A deep learning method based on CNN-BiGRU and attention mechanism for proton exchange membrane fuel cell performance degradation prediction
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References 41 · Cited by 119
Research works printed but not linked: 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
- ORCID
- 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
- Licence
- 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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