Model uncertainty estimation of a solid oxide fuel cell using a Volterra-type model
The dynamic nature of solid oxide fuel cells (SOFC) shows that they can be conceived as multi-input multi-output nonlinear processes. Aiming at dynamic simulation and control, this work presents a modeling study of a SOFC stack following a gray-box modeling approach. For such purpose, a Modified Gen...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/11784 |
| Acceso en línea: | http://hdl.handle.net/11336/11784 |
| Access Level: | acceso abierto |
| Palabra clave: | Identification Uncertainty Estimation Solid Oxide Fuel Cell https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
| Sumario: | The dynamic nature of solid oxide fuel cells (SOFC) shows that they can be conceived as multi-input multi-output nonlinear processes. Aiming at dynamic simulation and control, this work presents a modeling study of a SOFC stack following a gray-box modeling approach. For such purpose, a Modified Generalized Memory Polynomial (MGMP) model is identified based only on input–output data of the system. Additionally, dedicated estimation is dealt with in order to cope with the presence of possible model uncertainty. Simulation results are given to illustrate the quality of the obtained model which is compared with other modeling approaches. |
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