A Review on Advancement in PEM Fuel Cell Diagnosis Based on Machine Learning Techniques

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dc.contributor.authorKahia, Hichem
dc.contributor.authorBoumerdassi, Selma
dc.contributor.authorBelmeguenai, Aissa
dc.contributor.authorHerbadji, Abderrahmane
dc.contributor.authorHerbadji, Djamel
dc.date.accessioned2025-12-08T09:05:13Z
dc.date.issued2025
dc.description.abstractProton exchange membrane (PEM) fuel cell has attracted much attention due to its high efficiency and environmental friendliness, whose only reaction product is water. The main obstacles to large-scale deployment are premature fuel cell failures that limit the PEM fuel cell lifetime, which brings us back to many difficulties. In this study, we have defined and discarded the main methodological approaches to identify and diagnose FC. In this context, we have discussed the possible existing PEM fuel cell diagnosis methods that provide insight into FC performance, with the aim of improving diagnostic techniques and to deepen the understanding of the diagnostic behavior of PEMFC
dc.identifier.isbn978-303200551-9
dc.identifier.issn03029743
dc.identifier.urihttps://dspace.univ-boumerdes.dz/handle/123456789/15843
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-032-00552-6_13
dc.language.isoen
dc.publisherSpringer Science and Business Media
dc.relation.ispartofseriesLecture Notes in Computer Science/vol.15540 ; pp. 180 - 194
dc.relation.ispartofseries7th International Conference on Machine Learning for Networking, MLN 2024
dc.subject(EIS)
dc.subject(SOH)
dc.subjectAI technology
dc.subjectNeural network model
dc.subjectPEM Fuel Cells
dc.titleA Review on Advancement in PEM Fuel Cell Diagnosis Based on Machine Learning Techniques
dc.typeArticle

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