A learning algorithm concept for updating look-up tables for automotive applications

Look-up tables are commonly used in the automotive field for handling operating point variations. However, constant maps cannot cope with systems variations and ageing. Methods, such as Kalman filter or Extended Kalman filter for non-linear cases, can be used for table adaptation providing an optima...

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Detalhes bibliográficos
Autores: Guardiola, Carlos|||0000-0002-3150-8566, Pla Moreno, Benjamín|||0000-0001-9238-2939, Blanco Rodríguez, David, Cabrera López, Pedro
Formato: artículo
Fecha de publicación:2013
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/50901
Acesso em linha:https://riunet.upv.es/handle/10251/50901
Access Level:acceso abierto
Palavra-chave:Kalman filter
Adaptive models
Maps
Look-up table
Automotive
Sensor
INGENIERIA AEROESPACIAL
MAQUINAS Y MOTORES TERMICOS
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repository_id_str
spelling A learning algorithm concept for updating look-up tables for automotive applicationsGuardiola, Carlos|||0000-0002-3150-8566Pla Moreno, Benjamín|||0000-0001-9238-2939Blanco Rodríguez, DavidCabrera López, PedroKalman filterAdaptive modelsMapsLook-up tableAutomotiveSensorINGENIERIA AEROESPACIALMAQUINAS Y MOTORES TERMICOSLook-up tables are commonly used in the automotive field for handling operating point variations. However, constant maps cannot cope with systems variations and ageing. Methods, such as Kalman filter or Extended Kalman filter for non-linear cases, can be used for table adaptation providing an optimal solution to the problem. But these methods are computationally intensive, making difficult to implement them on commercial engine control units. The current paper proposes a learning method for online updating of look-up tables or maps. This algorithm uses precalculated membership functions based on a standard Kalman filter observer for weighting the adaptation. The main contribution of the method is the derivation of a steady-state Kalman filter observer that lowers the calculation burden and simplifies the implementation against the standard Kalman filter implementation that requires higher computational cost. As far as table is updated online while engine runs, this allows correcting drift errors and the unit-to-unit dispersion. The method is illustrated for mapping engine variables such as λ−1 and NOx in a Diesel engine by using an adaptive look-up table, and its characteristics make it suitable for implementing in commercial engine electronic control units for online purposes.ElsevierDepartamento de Máquinas y Motores TérmicosEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialInstituto Universitario de Investigación CMT - Clean Mobility & ThermofluidsEscuela Técnica Superior de Ingeniería IndustrialRepositorio Institucional de la Universitat Politècnica de València Riunet20132013-04-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/50901reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/509012026-06-13T07:49:27Z
dc.title.none.fl_str_mv A learning algorithm concept for updating look-up tables for automotive applications
title A learning algorithm concept for updating look-up tables for automotive applications
spellingShingle A learning algorithm concept for updating look-up tables for automotive applications
Guardiola, Carlos|||0000-0002-3150-8566
Kalman filter
Adaptive models
Maps
Look-up table
Automotive
Sensor
INGENIERIA AEROESPACIAL
MAQUINAS Y MOTORES TERMICOS
title_short A learning algorithm concept for updating look-up tables for automotive applications
title_full A learning algorithm concept for updating look-up tables for automotive applications
title_fullStr A learning algorithm concept for updating look-up tables for automotive applications
title_full_unstemmed A learning algorithm concept for updating look-up tables for automotive applications
title_sort A learning algorithm concept for updating look-up tables for automotive applications
dc.creator.none.fl_str_mv Guardiola, Carlos|||0000-0002-3150-8566
Pla Moreno, Benjamín|||0000-0001-9238-2939
Blanco Rodríguez, David
Cabrera López, Pedro
author Guardiola, Carlos|||0000-0002-3150-8566
author_facet Guardiola, Carlos|||0000-0002-3150-8566
Pla Moreno, Benjamín|||0000-0001-9238-2939
Blanco Rodríguez, David
Cabrera López, Pedro
author_role author
author2 Pla Moreno, Benjamín|||0000-0001-9238-2939
Blanco Rodríguez, David
Cabrera López, Pedro
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Máquinas y Motores Térmicos
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Instituto Universitario de Investigación CMT - Clean Mobility & Thermofluids
Escuela Técnica Superior de Ingeniería Industrial
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Kalman filter
Adaptive models
Maps
Look-up table
Automotive
Sensor
INGENIERIA AEROESPACIAL
MAQUINAS Y MOTORES TERMICOS
topic Kalman filter
Adaptive models
Maps
Look-up table
Automotive
Sensor
INGENIERIA AEROESPACIAL
MAQUINAS Y MOTORES TERMICOS
description Look-up tables are commonly used in the automotive field for handling operating point variations. However, constant maps cannot cope with systems variations and ageing. Methods, such as Kalman filter or Extended Kalman filter for non-linear cases, can be used for table adaptation providing an optimal solution to the problem. But these methods are computationally intensive, making difficult to implement them on commercial engine control units. The current paper proposes a learning method for online updating of look-up tables or maps. This algorithm uses precalculated membership functions based on a standard Kalman filter observer for weighting the adaptation. The main contribution of the method is the derivation of a steady-state Kalman filter observer that lowers the calculation burden and simplifies the implementation against the standard Kalman filter implementation that requires higher computational cost. As far as table is updated online while engine runs, this allows correcting drift errors and the unit-to-unit dispersion. The method is illustrated for mapping engine variables such as λ−1 and NOx in a Diesel engine by using an adaptive look-up table, and its characteristics make it suitable for implementing in commercial engine electronic control units for online purposes.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-04-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/50901
url https://riunet.upv.es/handle/10251/50901
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
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
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
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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