Development of a Novel Gasoline Particulate Filter Loading Method Using a Burner Bench.

In view of the deliberations on new Euro 7 emission standards to be introduced by 2025, original equipment manufacturers (OEMs) are already hard at work to further minimise the pollutant emissions of their vehicles. A particular challenge in this context will be compliance with new particulate number (PN) limits. It is expected that these will be tightened significantly, especially by including particulates down to 10 nm. This will lead to a substantially increased effort in the calibration of gasoline particulate filter (GPF) control systems. Therefore, it is of great interest to implement advanced methods that enable shortened and at the same time more accurate GPF calibration techniques. In this context, this study presents an innovative GPF calibration procedure that can enable a uniquely efficient development process. In doing so, some calibration work packages involving GPF soot loading and regeneration are transferred to a modern burner test bench. This approach can minimise the costly and time-consuming use of engine test benches for GPF calibration tasks. Accurate characterisation of the particulate emissions produced after a cold start by the target engine in terms of size distribution, morphology, and the following exhaust gas backpressure and burn-off rates of the soot inside the GPF provides the basis for a precise reproduction and validation process on the burner test bench. The burner test bench presented enables the generation of particulates with a geometric mean diameter (GMD) of 35 nm, exactly as they were measured in the exhaust gas of the engine. The elemental composition of the burner particulates also shows strong similarities to the particulates produced by the gasoline engine, which is further confirmed by matching burn-off rates. Furthermore, the exhaust backpressure behaviour can accurately be reproduced over the entire loading range of the GPF. By shifting GPF-related calibration tasks to the burner test bench, total filter loading times can be reduced by up to 93%. [ABSTRACT FROM AUTHOR]