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dc.contributor.authorCasteleiro Roca, José Luis
dc.contributor.authorBarragán Piña, Antonio Javier 
dc.contributor.authorSegura Manzano, Francisca 
dc.contributor.authorCalvo Rolle, José Luis
dc.contributor.authorAndújar Márquez, José Manuel
dc.identifier.citationCasteleiro Roca, J. L., Barragán Piña, A. J., Segura Manzano, F., Calvo Rolle, J. L., Andújar Márquez, J. M. (2019). Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand. Electronics, 8(11), 1325. DOI:
dc.description.abstractHydrogen-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.sponsorshipThis work has been funded by the Spanish Ministry of Economy Industry and Competitiveness through the H2SMART- m GRID (DPI2017-85540-R) project
dc.relation.isversionofPublisher’s versión
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subject.otherFuel celles_ES
dc.subject.otherHydrogen energyes_ES
dc.subject.otherIntelligent systemses_ES
dc.subject.otherHybrid systemses_ES
dc.subject.otherArtificial neural networkses_ES
dc.subject.otherPower managementes_ES
dc.titleFuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demandes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/Spanish Ministry of Economy Industry and Competitiveness [DPI2017-85540-R]

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