Development of innovative calibration methodologies for particle number reduction on a highly boosted GDI engine during catalyst heating with an artificial neural network

Since particle number emissions are going to be one of the main concerns for automotive companies due to the new legislations, this thesis describe an innovative method to reduce this type of pollutant. To do that, an Artificial Neural Network have been developed and trained on a dataset acquired with Design of Experiment method. In the end, the results achieved have been used to propose an optimized calibration.