This HTML5 document contains 44 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n17http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n9http://purl.org/net/nknouf/ns/bibtex#
n19http://linked.opendata.cz/resource/domain/vavai/projekt/
n20http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n8http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F18627757%3A_____%2F14%3A%230000040%21RIV15-TA0-18627757/
n21http://linked.opendata.cz/ontology/domain/vavai/
shttp://schema.org/
rdfshttp://www.w3.org/2000/01/rdf-schema#
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n7http://bibframe.org/vocab/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n16http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n18http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n12http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n10http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n11http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F18627757%3A_____%2F14%3A%230000040%21RIV15-TA0-18627757
rdf:type
skos:Concept n21:Vysledek
rdfs:seeAlso
http://papers.sae.org/2014-01-1165/
dcterms:description
This paper presents the application of model predictive control (MPC) to DOC temperature control during DPF regeneration. The model predictive control approach is selected for its advantage - using a model to optimize control moves over horizon while handling constraints. Due to the slow thermal dynamics of the DOC and DPF, computational bandwidth is not an issue, allowing for more complex calculations in each control loop. The control problem is formulated such that all the engine control actions, other than far post injection, are performed by the existing production engine controller, whereas far post injection is selected as the MPC manipulated variable and DOC outlet temperature as the controlled variable. The Honeywell OnRAMP Design Suite (model predictive control software) is used for model identification, control design and calibration. The paper includes description of the DPF regeneration process, model identification and validation results, control design and trade-off analysis and experimental validation of the controller on a Ford Superduty diesel truck. This paper presents the application of model predictive control (MPC) to DOC temperature control during DPF regeneration. The model predictive control approach is selected for its advantage - using a model to optimize control moves over horizon while handling constraints. Due to the slow thermal dynamics of the DOC and DPF, computational bandwidth is not an issue, allowing for more complex calculations in each control loop. The control problem is formulated such that all the engine control actions, other than far post injection, are performed by the existing production engine controller, whereas far post injection is selected as the MPC manipulated variable and DOC outlet temperature as the controlled variable. The Honeywell OnRAMP Design Suite (model predictive control software) is used for model identification, control design and calibration. The paper includes description of the DPF regeneration process, model identification and validation results, control design and trade-off analysis and experimental validation of the controller on a Ford Superduty diesel truck.
dcterms:title
Model Predictive Control of DOC Temperature during DPF Regeneration Model Predictive Control of DOC Temperature during DPF Regeneration
skos:prefLabel
Model Predictive Control of DOC Temperature during DPF Regeneration Model Predictive Control of DOC Temperature during DPF Regeneration
skos:notation
RIV/18627757:_____/14:#0000040!RIV15-TA0-18627757
n3:aktivita
n12:P
n3:aktivity
P(TA01030170)
n3:dodaniDat
n11:2015
n3:domaciTvurceVysledku
n20:4337034
n3:druhVysledku
n5:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
29754
n3:idVysledku
RIV/18627757:_____/14:#0000040
n3:jazykVysledku
n18:eng
n3:klicovaSlova
Model Predictive Control; DPF Regeneration; DOC Model; Catalyst; Particulate Filters
n3:klicoveSlovo
n4:DOC%20Model n4:Catalyst n4:DPF%20Regeneration n4:Model%20Predictive%20Control n4:Particulate%20Filters
n3:kontrolniKodProRIV
[DC15EFC0467E]
n3:mistoKonaniAkce
Detroit, USA
n3:nazevZdroje
SAE 2014 World Congress
n3:obor
n10:JB
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
4
n3:projekt
n19:TA01030170
n3:rokUplatneniVysledku
n11:2014
n3:tvurceVysledku
Van Nieuwstadt, Michiel Pekař, Jaroslav Stewart, Greg Kim, Yong-Wha
n3:typAkce
n17:WRD
n3:zahajeniAkce
2014-01-01+01:00
s:issn
0148-7191
s:numberOfPages
8
n7:doi
10.4271/2014-01-1165
n9:hasPublisher
Neuveden