Robust real-time optimization of a solid oxide fuel cell stack

Autores
Marchetti, Alejandro Gabriel; Gopalakrishnan, A.; Chachuat, B.; Bonvin, D.; Tsikonis, L.; Nakajo, A.; Wuillemin, Z.; Van Herle, J.
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
On-line control and optimization can improve the efficiency of fuel cell systems, whilst simultaneously ensuring that the operation remains within a safe region. Also, fuel cells are subject to frequent variations in their power demand. This paper investigates the realtime optimization (RTO) of a solid oxide fuel cell (SOFC) stack. An optimization problem maximizing the efficiency subject to operating constraints is defined. Due to inevitable model inaccuracies, the open-loop implementation of optimal inputs evaluated off-line may be suboptimal, or worse, infeasible. Infeasibility can be avoided by controlling the constrained quantities. However, the constraints that determine optimal operation might switch with varying power demand, thus requiring a change in the regulator structure. In this paper, a control strategy that can handle plant-model mismatch and changing constraints in the face of varying power demand is presented and illustrated. The strategy consists in the integration of RTO and model predictive control (MPC). A lumped model of the SOFC is utilized at the RTO level. The measurements are not used to re-estimate the parameters of the SOFC model at different operating points, but to simply adapt the constraints in the optimization problem. The optimal solution generated by RTO is implemented using MPC that uses a step-response model in this case. Simulation results show that near-optimality can be obtained, and constraints are respected despite model inaccuracies and large variations in the power demand.
Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Gopalakrishnan, A.. Ecole Polytechnique Federale de Lausanne; Suiza
Fil: Chachuat, B.. Imperial College London; Reino Unido
Fil: Bonvin, D.. Ecole Polytechnique Federale de Lausanne; Suiza
Fil: Tsikonis, L.. Laboratoire d; Suiza
Fil: Nakajo, A.. Laboratoire d; Suiza
Fil: Wuillemin, Z.. Laboratoire d; Suiza
Fil: Van Herle, J.. Laboratoire d; Suiza
Materia
Real Time Optimization
Constraint Adaptation
Model Predictive Control
Fuel Cells
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/15255
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/15255
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust real-time optimization of a solid oxide fuel cell stackMarchetti, Alejandro GabrielGopalakrishnan, A.Chachuat, B.Bonvin, D.Tsikonis, L.Nakajo, A.Wuillemin, Z.Van Herle, J.Real Time OptimizationConstraint AdaptationModel Predictive ControlFuel Cellshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2On-line control and optimization can improve the efficiency of fuel cell systems, whilst simultaneously ensuring that the operation remains within a safe region. Also, fuel cells are subject to frequent variations in their power demand. This paper investigates the realtime optimization (RTO) of a solid oxide fuel cell (SOFC) stack. An optimization problem maximizing the efficiency subject to operating constraints is defined. Due to inevitable model inaccuracies, the open-loop implementation of optimal inputs evaluated off-line may be suboptimal, or worse, infeasible. Infeasibility can be avoided by controlling the constrained quantities. However, the constraints that determine optimal operation might switch with varying power demand, thus requiring a change in the regulator structure. In this paper, a control strategy that can handle plant-model mismatch and changing constraints in the face of varying power demand is presented and illustrated. The strategy consists in the integration of RTO and model predictive control (MPC). A lumped model of the SOFC is utilized at the RTO level. The measurements are not used to re-estimate the parameters of the SOFC model at different operating points, but to simply adapt the constraints in the optimization problem. The optimal solution generated by RTO is implemented using MPC that uses a step-response model in this case. Simulation results show that near-optimality can be obtained, and constraints are respected despite model inaccuracies and large variations in the power demand.Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Gopalakrishnan, A.. Ecole Polytechnique Federale de Lausanne; SuizaFil: Chachuat, B.. Imperial College London; Reino UnidoFil: Bonvin, D.. Ecole Polytechnique Federale de Lausanne; SuizaFil: Tsikonis, L.. Laboratoire d; SuizaFil: Nakajo, A.. Laboratoire d; SuizaFil: Wuillemin, Z.. Laboratoire d; SuizaFil: Van Herle, J.. Laboratoire d; SuizaAmerican Society of Mechanical Engineers2011-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15255Marchetti, Alejandro Gabriel; Gopalakrishnan, A.; Chachuat, B.; Bonvin, D.; Tsikonis, L.; et al.; Robust real-time optimization of a solid oxide fuel cell stack; American Society of Mechanical Engineers; Journal of Fuel Cell Science and Technology; 8; 5; 10-2011; 1-112381-68722381-6910enginfo:eu-repo/semantics/altIdentifier/doi/10.1115/1.4003976info:eu-repo/semantics/altIdentifier/url/http://electrochemical.asmedigitalcollection.asme.org/article.aspx?articleid=1472319info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:44:08Zoai:ri.conicet.gov.ar:11336/15255instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:44:08.539CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust real-time optimization of a solid oxide fuel cell stack
title Robust real-time optimization of a solid oxide fuel cell stack
spellingShingle Robust real-time optimization of a solid oxide fuel cell stack
Marchetti, Alejandro Gabriel
Real Time Optimization
Constraint Adaptation
Model Predictive Control
Fuel Cells
title_short Robust real-time optimization of a solid oxide fuel cell stack
title_full Robust real-time optimization of a solid oxide fuel cell stack
title_fullStr Robust real-time optimization of a solid oxide fuel cell stack
title_full_unstemmed Robust real-time optimization of a solid oxide fuel cell stack
title_sort Robust real-time optimization of a solid oxide fuel cell stack
dc.creator.none.fl_str_mv Marchetti, Alejandro Gabriel
Gopalakrishnan, A.
Chachuat, B.
Bonvin, D.
Tsikonis, L.
Nakajo, A.
Wuillemin, Z.
Van Herle, J.
author Marchetti, Alejandro Gabriel
author_facet Marchetti, Alejandro Gabriel
Gopalakrishnan, A.
Chachuat, B.
Bonvin, D.
Tsikonis, L.
Nakajo, A.
Wuillemin, Z.
Van Herle, J.
author_role author
author2 Gopalakrishnan, A.
Chachuat, B.
Bonvin, D.
Tsikonis, L.
Nakajo, A.
Wuillemin, Z.
Van Herle, J.
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Real Time Optimization
Constraint Adaptation
Model Predictive Control
Fuel Cells
topic Real Time Optimization
Constraint Adaptation
Model Predictive Control
Fuel Cells
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv On-line control and optimization can improve the efficiency of fuel cell systems, whilst simultaneously ensuring that the operation remains within a safe region. Also, fuel cells are subject to frequent variations in their power demand. This paper investigates the realtime optimization (RTO) of a solid oxide fuel cell (SOFC) stack. An optimization problem maximizing the efficiency subject to operating constraints is defined. Due to inevitable model inaccuracies, the open-loop implementation of optimal inputs evaluated off-line may be suboptimal, or worse, infeasible. Infeasibility can be avoided by controlling the constrained quantities. However, the constraints that determine optimal operation might switch with varying power demand, thus requiring a change in the regulator structure. In this paper, a control strategy that can handle plant-model mismatch and changing constraints in the face of varying power demand is presented and illustrated. The strategy consists in the integration of RTO and model predictive control (MPC). A lumped model of the SOFC is utilized at the RTO level. The measurements are not used to re-estimate the parameters of the SOFC model at different operating points, but to simply adapt the constraints in the optimization problem. The optimal solution generated by RTO is implemented using MPC that uses a step-response model in this case. Simulation results show that near-optimality can be obtained, and constraints are respected despite model inaccuracies and large variations in the power demand.
Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Gopalakrishnan, A.. Ecole Polytechnique Federale de Lausanne; Suiza
Fil: Chachuat, B.. Imperial College London; Reino Unido
Fil: Bonvin, D.. Ecole Polytechnique Federale de Lausanne; Suiza
Fil: Tsikonis, L.. Laboratoire d; Suiza
Fil: Nakajo, A.. Laboratoire d; Suiza
Fil: Wuillemin, Z.. Laboratoire d; Suiza
Fil: Van Herle, J.. Laboratoire d; Suiza
description On-line control and optimization can improve the efficiency of fuel cell systems, whilst simultaneously ensuring that the operation remains within a safe region. Also, fuel cells are subject to frequent variations in their power demand. This paper investigates the realtime optimization (RTO) of a solid oxide fuel cell (SOFC) stack. An optimization problem maximizing the efficiency subject to operating constraints is defined. Due to inevitable model inaccuracies, the open-loop implementation of optimal inputs evaluated off-line may be suboptimal, or worse, infeasible. Infeasibility can be avoided by controlling the constrained quantities. However, the constraints that determine optimal operation might switch with varying power demand, thus requiring a change in the regulator structure. In this paper, a control strategy that can handle plant-model mismatch and changing constraints in the face of varying power demand is presented and illustrated. The strategy consists in the integration of RTO and model predictive control (MPC). A lumped model of the SOFC is utilized at the RTO level. The measurements are not used to re-estimate the parameters of the SOFC model at different operating points, but to simply adapt the constraints in the optimization problem. The optimal solution generated by RTO is implemented using MPC that uses a step-response model in this case. Simulation results show that near-optimality can be obtained, and constraints are respected despite model inaccuracies and large variations in the power demand.
publishDate 2011
dc.date.none.fl_str_mv 2011-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/15255
Marchetti, Alejandro Gabriel; Gopalakrishnan, A.; Chachuat, B.; Bonvin, D.; Tsikonis, L.; et al.; Robust real-time optimization of a solid oxide fuel cell stack; American Society of Mechanical Engineers; Journal of Fuel Cell Science and Technology; 8; 5; 10-2011; 1-11
2381-6872
2381-6910
url http://hdl.handle.net/11336/15255
identifier_str_mv Marchetti, Alejandro Gabriel; Gopalakrishnan, A.; Chachuat, B.; Bonvin, D.; Tsikonis, L.; et al.; Robust real-time optimization of a solid oxide fuel cell stack; American Society of Mechanical Engineers; Journal of Fuel Cell Science and Technology; 8; 5; 10-2011; 1-11
2381-6872
2381-6910
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1115/1.4003976
info:eu-repo/semantics/altIdentifier/url/http://electrochemical.asmedigitalcollection.asme.org/article.aspx?articleid=1472319
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Society of Mechanical Engineers
publisher.none.fl_str_mv American Society of Mechanical Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842268647995736064
score 13.13397

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