Investigation of Predictive Models for Application in Engine Cold-Start Behavior

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. The methodology for implementation of the real-time model into development programs is also examined. The neural network approximation is briefly outlined and a more detailed discussion can be found in [1]¹.