Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher elect...

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Autores: Lopez-Sanz, Jorge, Ocampo-Martínez, Carlos, Alvarez-Florez, Jesus, Moreno-Eguilaz, Manuel, Ruiz-Mansilla, Rafael, Kalmus, Julian, Gräeber, Manuel, Lux, Gerhard
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2016
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/166363
Acceso en línea:http://hdl.handle.net/10261/166363
Access Level:acceso abierto
Palabra clave:Thermal management
Plug-in hybrid electric vehicles (PHEV)
Li-ion battery cooling
Nonlinear model predictive control (NMPC)
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spelling Nonlinear model predictive control for thermal management in plug-in hybrid electric vehiclesLopez-Sanz, JorgeOcampo-Martínez, CarlosAlvarez-Florez, JesusMoreno-Eguilaz, ManuelRuiz-Mansilla, RafaelKalmus, JulianGräeber, ManuelLux, GerhardThermal managementPlug-in hybrid electric vehicles (PHEV)Li-ion battery coolingNonlinear model predictive control (NMPC)A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input-Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi-domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.The authors wish to acknowledge financial support from the Generalitat de Catalunya (GRC MCIA, Grant n SGR 2014-101). This work was supported by the catalan Government: la Generalitat de Catalunya.Peer ReviewedInstitute of Electrical and Electronics EngineersGeneralitat de CatalunyaConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2018201820162018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/166363reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1109/TVT.2016.2597242Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1663632026-05-22T06:33:51Z
dc.title.none.fl_str_mv Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
title Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
spellingShingle Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
Lopez-Sanz, Jorge
Thermal management
Plug-in hybrid electric vehicles (PHEV)
Li-ion battery cooling
Nonlinear model predictive control (NMPC)
title_short Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
title_full Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
title_fullStr Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
title_full_unstemmed Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
title_sort Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
dc.creator.none.fl_str_mv Lopez-Sanz, Jorge
Ocampo-Martínez, Carlos
Alvarez-Florez, Jesus
Moreno-Eguilaz, Manuel
Ruiz-Mansilla, Rafael
Kalmus, Julian
Gräeber, Manuel
Lux, Gerhard
author Lopez-Sanz, Jorge
author_facet Lopez-Sanz, Jorge
Ocampo-Martínez, Carlos
Alvarez-Florez, Jesus
Moreno-Eguilaz, Manuel
Ruiz-Mansilla, Rafael
Kalmus, Julian
Gräeber, Manuel
Lux, Gerhard
author_role author
author2 Ocampo-Martínez, Carlos
Alvarez-Florez, Jesus
Moreno-Eguilaz, Manuel
Ruiz-Mansilla, Rafael
Kalmus, Julian
Gräeber, Manuel
Lux, Gerhard
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Generalitat de Catalunya
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Thermal management
Plug-in hybrid electric vehicles (PHEV)
Li-ion battery cooling
Nonlinear model predictive control (NMPC)
topic Thermal management
Plug-in hybrid electric vehicles (PHEV)
Li-ion battery cooling
Nonlinear model predictive control (NMPC)
description A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input-Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi-domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.
publishDate 2016
dc.date.none.fl_str_mv 2016
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/166363
url http://hdl.handle.net/10261/166363
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1109/TVT.2016.2597242
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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