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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.

With the implementation of the “Worldwide harmonized Light duty Test Procedure” (WLTP) and the highly dynamic “Real Driving Emissions” (RDE) tests in Europe, different engineering methodologies from virtual calibration approaches to Engine-in-the-loop (EiL) methods have to be considered to define and calibrate efficient exhaust gas aftertreatment technologies without the availability of prototype vehicles in early project phases. Since different types of testing facilities can be used, the effects of test benches as well as real and virtual vehicle operators have to be determined. Moreover, in order to effectively reduce harmful emissions, the reproducibility of test cycles is essential for an accurate and efficient application of exhaust gas aftertreatment systems and the calibration of internal combustion engines.

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.

In order to allow continued production of the AM General Optimizer 6500 during MY 2007 through 2010 this IDI engine (Indirect Injection - swirl chamber) requires sophisticated aftertreatment controls while maintaining its fuel economy and durability. The main purpose of the development program was to retain the relatively inexpensive and simple base engine with distributor pump and waste-gated turbocharger, while adding hardware and software components that allow achievement of the phase-in emission standards for 2007 through 2010. The aftertreatment system consists of Diesel Oxidation Catalyst (DOC), NOx Adsorber Catalyst (or DeNOx Trap - DNT) and Diesel Particle Filter (DPF). In addition to the base hardware, an intake air throttle valve and an in-exhaust fuel injector were installed. The presented work will document the development process for a 2004 certified 6.5 l IDI heavy-duty diesel engine to comply with the 2007 heavy-duty emission standards.