Search Results

Gasoline particulate filters (GPFs) are extremely effective at reducing tailpipe emissions of particulate mass and particulate number. Especially in the European and Chinese markets, where a particulate number standard is legislated, we see gasoline particulate filters being deployed in production on gasoline direct injected engines. Due to the high temperature in gasoline exhaust, most applications are expected to be passively regenerating without the help of an active regeneration strategy. However, for the few cases where a customer drive cycle has consistently low speed over a long time frame, an active regeneration strategy may be required. This involves increasing the exhaust temperature at the GPF up to around 600 degC so that soot can be combusted. We compare two different ways of achieving these temperatures, namely spark retard and air fuel ratio modulation. The former generates heat in the engine, the latter generates heat in one or more catalysts in the exhaust system.

The availability of connectivity and autonomy enabled resources, within the automotive sector, has primarily been considered for driver assist technologies and for extending the levels of vehicle autonomy. It is not a stretch to imagine that the additional information, available from connectivity and autonomy, may also be useful in further improving powertrain functions. Critical powertrain subsystems that must operate with limited or uncertain knowledge of their environment stand to benefit from such new information sources. Unfortunately, the adoption of this new information resource has been slow within the powertrain community and has typically been limited to the obvious problem choices such as battery charge management for electric vehicles and efforts related to fuel economy benefits from adaptive/coordinated cruise control. In this paper we discuss the application of connectivity resources in the management of an aftertreatment sub-system, the Diesel Particulate Filter (DPF).

Gasoline particulate filters (GPF) are used as an efficient solution to reduce particulate matter (PM) emissions on gasoline vehicles. GPFs are ceramic wall-flow filters and are normally located downstream of conventional three-way catalysts (TWC) [1]. The study in this paper is intended to evaluate the impact of drive cycle and ambient temperature on modelled GPF soot accumulation and regeneration. The test data were obtained through real road testing in Chinese cities including Nanjing, Hainan and Harbin. Five 2.0 L gasoline turbo direct-injection (GTDI) prototype vehicles from several China Stage 6 applications were employed for the road tests. The results of the testing indicated that a drive cycle with low engine speed and engine load, like a typical city road in rush hour traffic in Nanjing, had a low probability of generating high GPF temperatures (> 600 °C) and sufficient oxygen to regenerate the GPF.

While not commonly in production today, Gasoline Particulate Filters (GPFs) are likely to see widespread deployment to meet stringent EU6.2 and China particulate number (PN) standards. In many ways the operating conditions for GPFs are orthogonal to those of their diesel counterparts, and this leads to different and interesting requirements for the control strategy. We will present some generic system architectures for exhaust systems containing a GPF and will lay out an architecture for the GPF control strategy components which include: regeneration assist feature, soot estimation algorithm, GPF protection. The regeneration assist feature uses spark retard to increase exhaust temperature. The soot estimation algorithm describes how we can estimate soot from an open loop model or from a normalized pressure metric. The GPF protection feature controls oxygen flow to limit the soot burn rate. We will show validation data of the control strategy under different operating conditions.

Many excellent papers have been written about the subject of estimating engine-out NOx on diesel engines based on real-time available data. The claimed accuracy of these models is typically around 6-10% on validation data sets with known inputs. This reported accuracy typically ignores input uncertainties, thus arriving at an optimistic estimate of the model accuracy in a real-time application. In our paper we analyze the effect of input uncertainty on the accuracy of engine-out NOx estimates via a numerical Monte Carlo simulation and show that this effect can be significant. Even though our model is based on an in-cylinder pressure sensor, this sensor is limited in its capability to reduce the effect of other measured inputs on the model.