Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements

This paper presents a non-intrusive approach for modeling a bidirectional DC-DC converter used in mild hybrid electric vehicles. A black-box identification methodology is proposed to find a model based on the data acquired from the input/output terminals. Measured data include the steady state and t...

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Autores: Rojas Dueñas, Gabriel, Riba Ruiz, Jordi-Roger|||0000-0001-8774-2389, Moreno Eguilaz, Juan Manuel|||0000-0001-6086-7034
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/350170
Acceso en línea:https://hdl.handle.net/2117/350170
https://dx.doi.org/10.1016/j.measurement.2021.109838
Access Level:acceso abierto
Palabra clave:DC-to-DC converters
DC-DC bidirectional converter
Mild hybrid electric vehicle
Deep learning
Modeling
Neural network
Convertidors continu-continu
Vehicles elèctrics híbrids
Àrees temàtiques de la UPC::Enginyeria elèctrica
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oai_identifier_str oai:upcommons.upc.edu:2117/350170
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repository_id_str
spelling Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurementsRojas Dueñas, GabrielRiba Ruiz, Jordi-Roger|||0000-0001-8774-2389Moreno Eguilaz, Juan Manuel|||0000-0001-6086-7034DC-to-DC convertersDC-DC bidirectional converterMild hybrid electric vehicleDeep learningModelingNeural networkConvertidors continu-continuVehicles elèctrics híbridsÀrees temàtiques de la UPC::Enginyeria elèctricaThis paper presents a non-intrusive approach for modeling a bidirectional DC-DC converter used in mild hybrid electric vehicles. A black-box identification methodology is proposed to find a model based on the data acquired from the input/output terminals. Measured data include the steady state and transient response, and different operating conditions of the DC-DC converter, including the buck and boost modes. A deep learning architecture based on a long-short-term memory neural network (LSTM-NN) is applied. The trained network is tested under a set of operating points different from those used during the training stage. The proposed method is compared with three black-box modeling techniques commonly used in power converters, proving its superior performance. Results presented in this paper indicate that the proposed model is able to replicate the behavior of the bidirectional converter without a priori knowledge of the converter circuitry. This approach can also be applied to other power devices.Peer Reviewed20212021-10-1320212021-07-27journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/350170https://dx.doi.org/10.1016/j.measurement.2021.109838reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3501702026-05-27T15:37:01Z
dc.title.none.fl_str_mv Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements
title Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements
spellingShingle Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements
Rojas Dueñas, Gabriel
DC-to-DC converters
DC-DC bidirectional converter
Mild hybrid electric vehicle
Deep learning
Modeling
Neural network
Convertidors continu-continu
Vehicles elèctrics híbrids
Àrees temàtiques de la UPC::Enginyeria elèctrica
title_short Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements
title_full Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements
title_fullStr Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements
title_full_unstemmed Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements
title_sort Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements
dc.creator.none.fl_str_mv Rojas Dueñas, Gabriel
Riba Ruiz, Jordi-Roger|||0000-0001-8774-2389
Moreno Eguilaz, Juan Manuel|||0000-0001-6086-7034
author Rojas Dueñas, Gabriel
author_facet Rojas Dueñas, Gabriel
Riba Ruiz, Jordi-Roger|||0000-0001-8774-2389
Moreno Eguilaz, Juan Manuel|||0000-0001-6086-7034
author_role author
author2 Riba Ruiz, Jordi-Roger|||0000-0001-8774-2389
Moreno Eguilaz, Juan Manuel|||0000-0001-6086-7034
author2_role author
author
dc.subject.none.fl_str_mv DC-to-DC converters
DC-DC bidirectional converter
Mild hybrid electric vehicle
Deep learning
Modeling
Neural network
Convertidors continu-continu
Vehicles elèctrics híbrids
Àrees temàtiques de la UPC::Enginyeria elèctrica
topic DC-to-DC converters
DC-DC bidirectional converter
Mild hybrid electric vehicle
Deep learning
Modeling
Neural network
Convertidors continu-continu
Vehicles elèctrics híbrids
Àrees temàtiques de la UPC::Enginyeria elèctrica
description This paper presents a non-intrusive approach for modeling a bidirectional DC-DC converter used in mild hybrid electric vehicles. A black-box identification methodology is proposed to find a model based on the data acquired from the input/output terminals. Measured data include the steady state and transient response, and different operating conditions of the DC-DC converter, including the buck and boost modes. A deep learning architecture based on a long-short-term memory neural network (LSTM-NN) is applied. The trained network is tested under a set of operating points different from those used during the training stage. The proposed method is compared with three black-box modeling techniques commonly used in power converters, proving its superior performance. Results presented in this paper indicate that the proposed model is able to replicate the behavior of the bidirectional converter without a priori knowledge of the converter circuitry. This approach can also be applied to other power devices.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-10-13
2021
2021-07-27
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/350170
https://dx.doi.org/10.1016/j.measurement.2021.109838
url https://hdl.handle.net/2117/350170
https://dx.doi.org/10.1016/j.measurement.2021.109838
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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