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The modern power train calibration process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust aftertreatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive simulation tools that represent the complete power train can likely contribute to improving the efficiency of the calibration process using an integral model based workflow. Engine models, which are purely based on complex physical principles, are usually not capable of real-time applications, especially if the simulation is focused on transient emission optimization. Methods, structures and the realization of a global dynamic real-time model are presented in this paper, combining physical knowledge and experimental models and also static and dynamic sub-structures. Such a model, with physical a priori information embedded in the model structure, provides excellent generalization capability.

A calibration and validation workflow will be presented in this paper, which utilizes common static global models for fuel consumption, NOx and soot. Due to the applicability for warm-up tests, e. g. New European Driving Cycle (NEDC), the models need to predict the temperature influence and will be fitted with measuring data from a conditioned engine test bed. The applied model structure - consisting of a number of global data-based sub-models - is configured especially for the requirements of multi-injection strategies of common rail systems. Additionally common global models for several constant coolant water temperature levels are generated and the workflow tool supports the combination and segmentation of global nominal map with temperature correction maps for seamless and direct ECU setting.