Please use this identifier to cite or link to this item: http://archives.univ-biskra.dz/handle/123456789/29625
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dc.contributor.authorHichem_kahia-
dc.date.accessioned2024-11-20T12:00:29Z-
dc.date.available2024-11-20T12:00:29Z-
dc.date.issued2023-
dc.identifier.urihttp://archives.univ-biskra.dz/handle/123456789/29625-
dc.descriptiongénie électriqueen_US
dc.description.abstractIn favor low emissions and high efficiency of fuel cell (FC), Fuel cell is regarded as next generation power devices in smart cities and sustainable mobility. Fuel cells convert the chemical energy stored in fuels to electricity in an electrochemically way. A suitable diagnostic is required to identify the different faults that may occur in fuel cell systems. The water management issue is particularly important in PEMFC. The role of the humidification circuit is to humidify the gases entering the fuel cell, generally from the water produced by the cell, recovered by means of a condenser. Drying or overwetting the membrane decreases electrical energy production and limits FC life. To ensure proper operation (yield, FC safety, response time, mechanical constraints, etc.), it is necessary to have a global control system that acts on understanding, detection, diagnosis and isolation of each of these failure modes in the PEMFC. This work focuses in finding a suitable, effective, and easy to use method, to avoid the frequent mistakes that are presented by the poor flow of water inside the fuel cell during its operation.This study demonstrated that past and present relative humidity correlates with the electrochemical impedance spectroscopy parameters (EIS), and an ANN control model is effective in health estimation of PEMFC and diagnosing water management related problems that cause performance deterioration, durability. The presented methods in this study provides many advantages compared to other techniques that require a large number of database and instruments, and this justified by the analysis in term of fast accurate prognostic, quick to implement and low cost.en_US
dc.language.isoenen_US
dc.publisherUniversité Mohamed Khider-Biskraen_US
dc.subjectProton exchange membrane fuel cells (PEMFCen_US
dc.subjectArtificial neural network, State of health (SOH), Artificial intelligence (AI).en_US
dc.subjectPiles à combustible à membrane échangeuse de protons (PEMFC),en_US
dc.subjectRéseau de neurones artificiels, État de santé (SOH), Intelligence artificielle (IA).en_US
dc.titleContribution à l'étude du diagnostic du vieillissement d'une pile à combustible PEMFC.en_US
dc.typeThesisen_US
Appears in Collections:Département de Génie Electrique

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