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dc.contributor.authorCasteleiro-Roca, José-Luis
dc.contributor.authorBarragán, Antonio Javier
dc.contributor.authorSegura Manzano, Francisca
dc.contributor.authorCalvo-Rolle, José Luis
dc.contributor.authorAndújar-Márquez, José Manuel
dc.date.accessioned2019-12-09T15:40:33Z
dc.date.available2019-12-09T15:40:33Z
dc.date.issued2019-11-10
dc.identifier.citationCasteleiro-Roca, J.-L.; Barragán, A.J.; Manzano, F.S.; Calvo-Rolle, J.L.; Andújar, J.M. Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand. Electronics 2019, 8, 1325.es_ES
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/2183/24448
dc.description.abstract[Abstract]: Hydrogen-based energy storage and generation is an increasingly used technology, especially in renewable systems because they are non-polluting devices. Fuel cells are complex nonlinear systems, so a good model is required to establish efficient control strategies. This paper presents a hybrid model to predict the variation of H2 flow of a hydrogen fuel cell. This model combining clusters’ techniques to get multiple Artificial Neural Networks models whose results are merged by Polynomial Regression algorithms to obtain a more accurate estimate. The model proposed in this article use the power generated by the fuel cell, the hydrogen inlet flow, and the desired power variation, to predict the necessary variation of the hydrogen flow that allows the stack to reach the desired working point. The proposed algorithm has been tested on a real proton exchange membrane fuel cell, and the results show a great precision of the model, so that it can be very useful to improve the efficiency of the fuel cell system.es_ES
dc.description.sponsorshipMinisterio de Economía, Industria y Competitividad; H2SMART-mGRID (DPI2017-85540-R)es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.urihttps://doi.org/10.3390/electronics8111325es_ES
dc.rightsCreative Commons Attribution (CC BY 4.0)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectFuel Celles_ES
dc.subjectHydrogen energyes_ES
dc.subjectIntelligent systemses_ES
dc.subjectHybrid systemses_ES
dc.subjectArtificial neural networkses_ES
dc.subjectPower managementes_ES
dc.subjectPila de combustiblees_ES
dc.subjectEnergía del hidrógenoes_ES
dc.subjectSistemas inteligenteses_ES
dc.subjectSistemas híbridoses_ES
dc.subjectRedes neuronales artificialeses_ES
dc.subjectGestión de potenciaes_ES
dc.titleFuel cell hybrid model for predicting hydrogen inflow through energy demandes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleElectronicses_ES
UDC.volume8es_ES
UDC.issue11es_ES


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