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The modern engine design process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive real-time simulation tools that represent the entire powertrain can likely contribute to improving the efficiency of the calibration process. Engine models, which are purely based on physical first principles, are usually not capable of real-time applications, especially if the simulation is focused on cold start and warm-up behavior. However, the initial data definition for the ECU using a Hardware-in-the-Loop (HiL)-Simulator requires a model with both real-time capability and sufficient accuracy. The use of artificial intelligence systems becomes necessary, e.g. neural networks. Methods, structures and the realization of a hybrid real-time model are presented in this paper, which combines physical and neural network models.

Since the CO2 emissions of passenger car traffic and their greenhouse potential are in the public interest, natural gas (CNG) is discussed as an attractive alternative fuel. The engine concepts that have been applied to date are mainly based upon common gasoline engine technology. In addition, in mono-fuel applications, it is made use of an increased compression ratio -thanks to the RON (Research Octane Number) potential of CNG-, which allows for thermodynamic benefits. This paper presents advanced engine concepts that make further use of the potentials linked to CNG. Above all, the improved knock tolerance, which can be particularly utilized in turbocharged engine concepts. For bi-fuel (CNG/gasoline) power trains, the realization of variable compression ratio is of special interest. Moreover, lean burn technology is a perfect match for CNG engines. Fuel economy and emission level are evaluated basing on test bench and vehicle investigations.