Kahia, HichemBoumerdassi, SelmaBelmeguenai, AissaHerbadji, AbderrahmaneHerbadji, Djamel2025-12-082025978-303200551-903029743https://dspace.univ-boumerdes.dz/handle/123456789/15843https://link.springer.com/chapter/10.1007/978-3-032-00552-6_13Proton 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 PEMFCen(EIS)(SOH)AI technologyNeural network modelPEM Fuel CellsArticle