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With the introduction in Europe of drive cycles such as RDE and WLTC, transient emissions prediction is more challenging than before for passenger car applications. Transient predictions are used in the calibration optimization process to determine the cumulative cycle emissions for the purpose of meeting objectives and constraints. Predicting emissions such as soot accurately is the most difficult area, because soot emissions rise very steeply during certain transients. The method described in this paper is an evolution of prediction using a steady state global model. A dynamic model can provide the instantaneous prediction of boost and EGR that a static model cannot. Meanwhile, a static model is more accurate for steady state engine emissions. Combining these two model types allows more accurate prediction of emissions against time. A global dynamic model combines a dynamic model of the engine air path with a static DoE (Design of Experiment) emission model.

A full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality.