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Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters

Title data

Walter, Stefanie ; Schwanzer, Peter ; Hagen, Gunter ; Haft, Gerhard ; Rabl, Hans-Peter ; Dietrich, Markus ; Moos, Ralf:
Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters.
In: Sensors. Vol. 20 (2020) Issue 9 . - Art.Nr. 2659.
ISSN 1424-8220
DOI: https://doi.org/10.3390/s20092659

Project information

Project title:
Project's official titleProject's id
Load Sensor for GPFAZ-1288-17

Project financing: Bayerische Forschungsstiftung (BFS)

Abstract in another language

Gasoline particulate filters (GPFs) are an appropriate means to meet today’s emission standards. As for diesel applications, GPFs can be monitored via differential pressure sensors or using a radio-frequency approach (RF sensor). Due to largely differing soot properties and engine operating modes of gasoline compared to diesel engines (e.g., the possibility of incomplete regenerations), the behavior of both sensor systems must be investigated in detail. For this purpose, extensive measurements on engine test benches are usually required. To simplify the sensor development, a simulation model was developed using COMSOL Multiphysics® that not only allowed for calculating the loading and regeneration process of GPFs under different engine operating conditions but also determined the impact on both sensor systems. To simulate the regeneration behavior of gasoline soot accurately, an oxidation model was developed. To identify the influence of different engine operating points on the sensor behavior, various samples generated at an engine test bench were examined regarding their kinetic parameters using thermogravimetric analysis. Thus, this compared the accuracy of soot mass determination using the RF sensor with the differential pressure method. By simulating a typical driving condition with incomplete regenerations, the effects of the soot kinetics on sensor accuracy was demonstrated exemplarily. Thereby, the RF sensor showed an overall smaller mass determination error, as well as a lower dependence on the soot kinetics.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Engineering Science
Faculties > Faculty of Engineering Science > Chair Functional Materials > Chair Functional Materials - Univ.-Prof. Dr.-Ing. Ralf Moos
Profile Fields > Advanced Fields > Advanced Materials
Research Institutions > Research Centres > Bayreuth Center for Material Science and Engineering - BayMAT
Research Institutions > Research Units > BERC - Bayreuth Engine Research Center
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 12 May 2020 06:40
Last Modified: 12 May 2020 06:40
URI: https://eref.uni-bayreuth.de/id/eprint/55142