Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System
dc.contributor.author | Derbeli, Mohamed | |
dc.contributor.author | Napole, Cristian | |
dc.contributor.author | Barambones Caramazana, Oscar | |
dc.date.accessioned | 2021-09-16T09:48:40Z | |
dc.date.available | 2021-09-16T09:48:40Z | |
dc.date.issued | 2021-08-26 | |
dc.identifier.citation | Mathematics 9(17) : (2021) // Article ID 2068 | es_ES |
dc.identifier.issn | 2227-7390, | |
dc.identifier.uri | http://hdl.handle.net/10810/53102 | |
dc.description.abstract | In recent years, machine learning (ML) has received growing attention and it has been used in a wide range of applications. However, the ML application in renewable energies systems such as fuel cells is still limited. In this paper, a prognostic framework based on artificial neural network (ANN) is designed to predict the performance of proton exchange membrane (PEM) fuel cell system, aiming to investigate the effect of temperature and humidity on the stack characteristics and on tracking control improvements. A large part of the experimental database for various operating conditions has been used in the training operation to achieve an accurate model. Extensive tests with various ANN parameters such as number of neurons, number of hidden layers, selection of training dataset, etc., are performed to obtain the best fit in terms of prediction accuracy. The effect of temperature and humidity based on the predicted model are investigated and compared to the ones obtained from real-time experiments. The control design based on the predicted model is performed to keep the stack operating point at an adequate power stage with high-performance tracking. Experimental results have demonstrated the effectiveness of the proposed model for performance improvements of PEM fuel cell system. | es_ES |
dc.description.sponsorship | This research was funded by the Basque Government, Diputación Foral de Álava and UPV/EHU, respectively, through the projects EKOHEGAZ (ELKARTEK KK-2021/00092), CONAVANTER and GIU20/063. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | machine learning | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | artificial neural network | es_ES |
dc.subject | ANN | es_ES |
dc.subject | PEM fuel cell | es_ES |
dc.subject | modeling | es_ES |
dc.subject | control | es_ES |
dc.title | Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2021-09-09T13:47:10Z | |
dc.rights.holder | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2227-7390/9/17/2068/htm | es_ES |
dc.identifier.doi | 10.3390/math9172068 | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika |
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Except where otherwise noted, this item's license is described as 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).