<div class="csl-bib-body">
<div class="csl-entry">Benaitier, A., Krainer, F., Jakubek, S., & Hametner, C. (2022). Robust physical quantities estimation for diesel engine emission reduction using sensor fusion. In <i>2022 IEEE Conference on Control Technology and Applications (CCTA)</i> (pp. 1080–1086). https://doi.org/10.1109/CCTA49430.2022.9966196</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/152754
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dc.description.abstract
Lowering heavy-duty emission thresholds towards ultra-low emission transportation systems constitute a seri-ous challenge. Indeed, the engine and exhaust aftertreatment systems have been complexified for decades to fulfill always stricter regulations. As a result, the powertrain represents a significant part of the final vehicle price. Therefore, advanced control methods are required to meet the upcoming emission thresholds without drastically increasing the system cost and complexity. Such control designs have already shown promising results but require accurate physical estimates - particularly the oxygen content in the intake and the exhaust manifold are essential information for a precise engine and aftertreatment systems control. Although observer methods exist for such accurate and robust estimations, they are not suitable for hardware implementation due to their complexity. This paper introduces an observer to estimate oxygen content-related physical signals. The proposed method takes advantage of all existing sensors and a simple dynamic model within a sensor fusion framework. The observer is implemented in an advanced simulation platform, where the observer estimates are shown to be unbiased and robust to noise and sensor inaccuracies. These results suggest that an advanced controller combined with the proposed observer method could reduce the engine-out transient emissions without supplementary hardware cost or complexity.
en
dc.language.iso
en
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dc.subject
air path observer
en
dc.subject
Diesel engine emissions
en
dc.subject
nonlinear Kalman filter
en
dc.subject
sensor fusion
en
dc.title
Robust physical quantities estimation for diesel engine emission reduction using sensor fusion
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dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
AVL list GmbH, Austria
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dc.description.startpage
1080
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dc.description.endpage
1086
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
2022 IEEE Conference on Control Technology and Applications (CCTA)
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tuw.peerreviewed
true
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tuw.researchTopic.id
E2
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tuw.researchTopic.id
C6
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tuw.researchTopic.id
C3
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tuw.researchTopic.name
Sustainable and Low Emission Mobility
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.name
Computational System Design
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tuw.researchTopic.value
30
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tuw.researchTopic.value
50
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tuw.researchTopic.value
20
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tuw.publication.orgunit
E325-04 - Forschungsbereich Regelungstechnik und Prozessautomatisierung