Fuel Cell Output Current Prediction with a Hybrid Intelligent System
A fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to pre...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/6317270 |
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| author | José-Luis Casteleiro-Roca Antonio Javier Barragán Francisca Segura José Luis Calvo-Rolle José Manuel Andújar |
| author_facet | José-Luis Casteleiro-Roca Antonio Javier Barragán Francisca Segura José Luis Calvo-Rolle José Manuel Andújar |
| author_sort | José-Luis Casteleiro-Roca |
| collection | DOAJ |
| description | A fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared error obtained for a global model of the fuel cell. |
| format | Article |
| id | doaj-art-e13b301ef4d34a3bac256ddfdcf75891 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e13b301ef4d34a3bac256ddfdcf758912025-02-03T01:22:59ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/63172706317270Fuel Cell Output Current Prediction with a Hybrid Intelligent SystemJosé-Luis Casteleiro-Roca0Antonio Javier Barragán1Francisca Segura2José Luis Calvo-Rolle3José Manuel Andújar4University of A Coruña, Department of Industrial Engineering, Avda. 19 de febrero s/n, 15495 Ferrol, A Coruña, SpainUniversity of Huelva, Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, 21071 Huelva, SpainUniversity of Huelva, Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, 21071 Huelva, SpainUniversity of A Coruña, Department of Industrial Engineering, Avda. 19 de febrero s/n, 15495 Ferrol, A Coruña, SpainUniversity of Huelva, Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, 21071 Huelva, SpainA fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared error obtained for a global model of the fuel cell.http://dx.doi.org/10.1155/2019/6317270 |
| spellingShingle | José-Luis Casteleiro-Roca Antonio Javier Barragán Francisca Segura José Luis Calvo-Rolle José Manuel Andújar Fuel Cell Output Current Prediction with a Hybrid Intelligent System Complexity |
| title | Fuel Cell Output Current Prediction with a Hybrid Intelligent System |
| title_full | Fuel Cell Output Current Prediction with a Hybrid Intelligent System |
| title_fullStr | Fuel Cell Output Current Prediction with a Hybrid Intelligent System |
| title_full_unstemmed | Fuel Cell Output Current Prediction with a Hybrid Intelligent System |
| title_short | Fuel Cell Output Current Prediction with a Hybrid Intelligent System |
| title_sort | fuel cell output current prediction with a hybrid intelligent system |
| url | http://dx.doi.org/10.1155/2019/6317270 |
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