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A structured, semi-automatic method for reducing a high-fidelity engine model to a fast running one has been developed. The principle of this method rests on the fact that, under certain assumptions, the computationally expensive components of the simulation can be substituted with simpler ones. Thus, the computation speed increases substantially while the physical representation of the engine is retained to a large extent. The resulting model is not only suitable for fast running simulations, but also usable and updatable in later stages of the development process. The thrust of the method is that the calibration of the fast running components is achieved by use of automatically selected neural networks. Two illustrative examples demonstrate the methodology. The results show that the methodology achieves substantial increase in computation speed and satisfactory accuracy.

Engines and vehicle systems are becoming increasing complex partly due to the incorporation of emission abatement components as well as control strategies that are technologically evolving and innovative to keep up with emissions requirements. This makes the testing and verification with actual prototypes prohibitively expensive and time-consuming. Consequently, there is an increasing reliance on Software-In-the-Loop (SIL) and Hardware-In-the-Loop (HIL) simulations for design evaluation of system concepts. This paper introduces a methodology in which detailed chemical kinetic models of catalytic converters are transformed into fast running models for control design, calibration or real time ECU validation. The proposed methodology is based on the use of a hybrid, structured, semi-automatic scheme for reducing high-fidelity models into fast running models.