TY - JOUR A1 - Walter, Stefanie A1 - Schwanzer, Peter A1 - Hagen, Gunter A1 - Rabl, Hans-Peter A1 - Dietrich, Markus A1 - Moos, Ralf T1 - Soot Monitoring of Gasoline Particulate Filters Using a Radio-Frequency-Based Sensor JF - Sensors N2 - 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. KW - dielectric properties KW - engine test bench KW - gasoline particulate filter (GPF) KW - microwave cavity perturbation KW - radio-frequency (RF) KW - soot mass determination Y1 - 2023 U6 - https://doi.org/10.3390/s23187861 SN - 1424-8220 N1 - This research work was funded by the Bavarian Research Foundation (Bayerische Forschungsstiftung, BFS) as part of the project “Load Sensor for GPF” (AZ-1288-17). VL - 23 IS - 18 SP - 1 EP - 19 PB - MDPI ER - TY - JOUR A1 - Walter, Stefanie A1 - Schwanzer, Peter A1 - Hagen, Gunter A1 - Rabl, Hans-Peter A1 - Dietrich, Markus A1 - Moos, Ralf T1 - Combined Ash and Soot Monitoring for Gasoline Particulate Filters Using a Radio-Frequency-Based Sensor JF - Emission Control Science and Technology N2 - Increasingly stringent emission limits have made particulate filters necessary for gasoline engines. Similar to diesel applications, gasoline particulate filters (GPFs) can be monitored by differential pressure measurement or by the radio-frequency-based filter diagnosis (RF sensor). In addition to measuring the soot loading, ash detection is critical for monitoring the GPF over the entire vehicle lifetime. Because the RF sensor detects the filter loading through a change in the dielectric properties of the GPF, it can detect not only soot but also ash. In diesel applications, the RF sensor has already demonstrated its potential for ash detection. To verify the feasibility of simultaneous ash and soot monitoring for GPFs, filters were loaded with ash on an engine test bench and measured on a lab test bench under defined synthetic exhaust gas conditions. By evaluating resonant modes, soot and ash could be clearly distinguished, as ash mainly affects the resonant frequency, while soot also changes the quality factor due to its high dielectric losses. However, higher soot loadings could not be detected by the resonant parameters, but instead by a frequency-averaged transmission signal. While the presence of ash caused an offset in this signal, its sensitivity to soot was not affected. Thus, the influence of ash can be corrected if the signal in the soot-free filter state is known, e.g., from the behavior of the resonant parameters. Therefore, even with a continuously increasing ash loading over the lifetime of a vehicle, an accurate soot detection is possible with the RF sensor. KW - Gasoline particulate flter KW - Radio-frequency KW - Soot mass determination KW - Ash detection Y1 - 2023 U6 - https://doi.org/10.1007/s40825-023-00235-y PB - Springer ER -