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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.

For diesel engines to meet current and future emissions levels, the amount of EGR required to reach these levels has increased dramatically. This increased EGR has posed big challenges for conventional turbocharger technology to meet the higher emissions requirements while maintaining or improving other vehicle attributes, to the extent that some OEMs resort to multiple turbocharger configurations. These configurations can include parallel, series sequential, or parallel - series turbocharger systems, which would inevitably run into other issues, such as cost, packaging, and thermal loss, etc. This study, as part of a U.S. Department of Energy (USDoE) sponsored research program, is focused on the experimental evaluation of the emission and performance of a modern diesel engine with an advanced single stage turbocharger.