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In order to reduce the negative health effects associated with engine pollutants, environmental problems caused by combustion engine emissions and satisfy the current strict emission standards, it is essential to better understand and simulate the emission formation process. Further development of emission model, improves the accuracy of the model-based optimization approach, which is used as a decisive tool for combustion system development and engine-out emission reduction. The numerical approaches for emission simulation are closely coupled to the combustion model. Using a detailed emission model, considering the 3D mixture preparation simulation including, chemical reactions, demands high computational effort. Phenomenological combustion models, used in 1D approaches for model-based system optimization can deliver heat release rate, while using a two-zone approach can estimate the NOx emissions.

The future emission standards, including real driving emissions (RDE) measurements are big challenges for engine and after-treatment development. Also for development of a robust control system, in real driving emissions cycles under varied operating conditions and climate conditions, like low ambient temperature as well as high altitude are advanced physical-based algorithms beneficial in order to realize more precise, robust and efficient control concepts. A fast-running novel physical-based ignition delay model for diesel engine combustion simulation and additionally, for combustion control in the next generation of ECUs is presented and validated in this study. Detailed chemical reactions of the ignition processes are solved by a n-heptane mechanism which is coupled to the thermodynamic simulation of in-cylinder processes during the compression and autoignition phases.

The development and calibration of modern combustion engines is challenging in the area of continuously tightening emission limits and the necessity for meeting real driving emissions regulations. A focus is on the knowledge of the internal engine processes and the determination of pollutants formations in order to predict the engine emissions. A physical model-based development provides an insight into hardly measurable phenomena properties and is robust against changing input data. With increasing modeling depth the required computing capacities increase. As an alternative to physical modeling, data-driven machine learning methods can be used to enable high-performance modeling accuracy. However, these are dependent on the learned data. To combine the performance and robustness of both types of modeling a hybrid application of data-driven and physical models is developed in this paper as a grey box model for the exhaust emission prediction of a commercial vehicle diesel engine.