Application of Support Vector Machine to Obtain the Dynamic Model of Proton-Exchange Membrane Fuel Cell
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Archivos
Fecha
2022-11
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
MDPI
Nombre de Curso
Licencia CC
Atribución 4.0 Internacional (CC BY 4.0)
Licencia CC
https://creativecommons.org/licenses/by/4.0/deed.es
Resumen
An accurate model of a proton-exchange membrane fuel cell (PEMFC) is important for understanding this fuel cell’s dynamic process and behavior. Among different large-scale energy storage systems, fuel cell technology does not have geographical requirements. To provide an effective operation estimation of PEMFC, this paper proposes a support vector machine (SVM) based model. The advantages of the SVM, such as the ability to model nonlinear systems and provide accurate estimations when nonlinearities and noise appear in the system, are the main motivations to use the SVM method. This model can capture the static and dynamic voltage–current characteristics of the PEMFC system in the three operating regions. The validity of the proposed SVM model has been verified by comparing the estimated voltage with the real measurements from the Ballard Nexa® (Formula presented.) kW fuel cell (FC) power module. The obtained results have shown high accuracy between the proposed model and the experimental operation of the PEMFC. A statistical study is developed to evaluate the effectiveness and superiority of the proposed SVM model compared with the diffusive global (DG) model and the evolution strategy (ES)-based model. © 2022 by the authors.
Notas
Indexación: Scopus.
Palabras clave
Diffusive model, Evolution strategy, Proton-exchange membrane fuel cell, Regression model, Support vector machine, Voltage–current dynamic response
Citación
Membranes, Volume 12, Issue 11, November 2022, Article number 1058
DOI
10.3390/membranes12111058