Soot Monitoring of Gasoline Particulate Filters Using a Radio-Frequency-Based Sensor.
dielectric properties
engine test bench
gasoline particulate filter (GPF)
microwave cavity perturbation
radio-frequency (RF)
soot mass determination
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
13 Sep 2023
13 Sep 2023
Historique:
received:
14
07
2023
revised:
11
08
2023
accepted:
14
08
2023
medline:
28
9
2023
pubmed:
28
9
2023
entrez:
28
9
2023
Statut:
epublish
Résumé
Owing to increasingly stringent emission limits, particulate filters have become mandatory for gasoline-engine vehicles. Monitoring their soot loading is necessary for error-free operation. The state-of-the-art differential pressure sensors suffer from inaccuracies due to small amounts of stored soot combined with exhaust gas conditions that lead to partial regeneration. As an alternative approach, radio-frequency-based (RF) sensors can accurately measure the soot loading, even under these conditions, by detecting soot through its dielectric properties. However, they face a different challenge as their sensitivity may depend on the engine operation conditions during soot formation. In this article, this influence is evaluated in more detail. Various soot samples were generated on an engine test bench. Their dielectric properties were measured using the microwave cavity perturbation (MCP) method and compared with the corresponding sensitivity of the RF sensor determined on a lab test bench. Both showed similar behavior. The values for the soot samples themselves, however, differed significantly from each other. A way to correct for this cross-sensitivity was found in the influence of exhaust gas humidity on the RF sensor, which can be correlated with the engine load. By evaluating this influence during significant humidity changes, such as fuel cuts, it could be used to correct the influence of the engineon the RF sensor.
Identifiants
pubmed: 37765917
pii: s23187861
doi: 10.3390/s23187861
pmc: PMC10536291
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Bavarian Research Foundation
ID : AZ-1288-17
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