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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.

Due to its high number of free parameters, the new generation of gasoline engines with direct injection require an efficient calibration process to handle the system complexity and to avoid a dramatic increase in calibration costs. This paper presents a concept of specific toolboxes within a standardized and automated calibration environment, supporting the complexity of GDI engines and establishing standard procedures for distributed development. The basic idea is the combination of a new and more efficient online DoE approach with the automatic and adaptive identification of the region of interest in the high dimensional parameter space. This guarantees efficient experimental designs even for highly non-linear systems with often irregularly shaped valid regions. As the main advantage for the calibration engineer, the new approach requires almost no pre-investigations and no specific statistical knowledge.