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The pre EGR valve pressure is an important measurement for the Diesel engine air handling system. It is commonly used for the EGR flow calculation during engine transient operation. Due to the erosive exhaust gas, an EGR pressure sensor will eventually have gold corrosion resulting in drive-ability issues. Therefore, a software replacement for the EGR pressure sensor is desirable. However, when the EGR valve is on the cold side of the EGR cooler, the accuracy of the EGR pressure estimation deteriorates because of the variability of the pressure drop across the EGR cooler due to EGR cooler fouling. In this paper, an adaptive scheme is developed to improve the accuracy of pre EGR valve pressure estimation in the presence of EGR cooler fouling for diesel engines. The pressure drop across the EGR cooler is shown to be proportional to the velocity pressure of the EGR flow through the cooler.

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.