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The Levenberg-Marquardt neural network model of the PEMFC's MEA

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dc.contributor.authorsDursun E., Kilic O.
dc.date.accessioned2022-03-15T01:58:46Z
dc.date.accessioned2026-01-11T10:41:25Z
dc.date.available2022-03-15T01:58:46Z
dc.date.issued2011
dc.description.abstractFuel cells are electrochemically complex, nonlinear, and dynamic energy conversion systems. Due to the dynamic characteristics of the fuel cell electrical performance models are used for system evaluation. In this study, Artificial Neural Network (ANN) technique is used as the modeling tool for internal structures of the fuel cells complex electrochemical reactions. The proton exchange membrane fuel cell (PEMFC) inputs are selected as anode flow, cathode flow, and cell temperature for the proposed Levenberg-Marquardt Neural Network model (LMNN). The outputs for the PEMFC model are current and voltage parameters. The model outputs are compared with the measured values and the maximum error is around 3%. The proposed ANN model is developed with MATLAB. © 2011 IEEE.
dc.identifier.doi10.1109/EEEIC.2011.5874597
dc.identifier.isbn9781424487820
dc.identifier.urihttps://hdl.handle.net/11424/247113
dc.language.isoeng
dc.relation.ispartof2011 10th International Conference on Environment and Electrical Engineering, EEEIC.EU 2011 - Conference Proceedings
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial neural networks
dc.subjectmembrane electrode assembly
dc.subjectPEMFC
dc.titleThe Levenberg-Marquardt neural network model of the PEMFC's MEA
dc.typeconferenceObject
dspace.entity.typePublication
oaire.citation.title2011 10th International Conference on Environment and Electrical Engineering, EEEIC.EU 2011 - Conference Proceedings

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