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The future worldwide emission regulations will request a drastic decrease of Diesel engine tailpipe emissions. Depending on the planned application and the real official regulations, a further strong decrease of engine out emissions is necessary, even though the utilized exhaust after-treatment systems are very powerful. To reduce NOx emissions internally, the external exhaust gas recirculation (EGR) is known as the most effective way. Due to the continuously increasing requirements regarding specific power, dynamic behavior and low emissions, future air path systems have to fulfill higher requirements and, consequently, become more and more complex, e.g. arrangements with a 2-stage turbo charging or 2-stage EGR system with different stages of cooling performance.

The modern engine development 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. It is expected that predictive simulation tools that encompass the entire powertrain can potentially improve the efficiency of the calibration process. The testing of an ECU using a HiL system requires a real-time model. Additionally, if the initial parameters of the ECU are to be defined and tested, the model has to be more accurate than is typical for ECU functional testing. It is possible to enhance the generalization capability of the simulation, with neuronal network sub-models embedded into the architecture of a physical model, while still maintaining real-time execution. This paper emphasizes the experimental investigation and physical modeling of the port fuel injected SI engine.