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.
Compared to established empirical modeling strategies this approach separates dynamic from static effects by different model types, which are linked via thermodynamic characteristics. Thus a physical interpretation of the empirical model is still possible, since the thermodynamic characteristics are meant to describe the combustion process in a most general way. Inputs are (besides engine speed) ECU actuators only, allowing easy integration into various calibration environments, e.g. HiL systems.