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Future heavy-duty (HD) concepts should fulfill very tight tail-pipe NOx emissions and simultaneously fulfill the fuel efficiency targets. In current HD Euro VII discussions, real working cycles become key to ensure emission conformity. For instance, cold start and cold ambient conditions during testing with low load profiles starting from 0% payload, require external heating measures. Knowing the trade-off between fuel consumption and tail-pipe NOx emissions a holistic engine and EAT system optimization with innovative thermal management is required. Towards a carbon neutral mobility, Hydrogen combustion engines are one of the key solutions. Advanced combustion system development enables maximal usage of lean burning as the major advantage of the Hydrogen fuel for efficiency improvement and NOx reduction.

One challenge for the development of commercial vehicles is the reduction of CO2 greenhouse, where hydrogen can help to reduce the fleet CO2. For instance, in Europe a drop in fleet consumption of 15% and 30% is set as target by the regulation until 2025 and 2030. Another challenge is EURO VII in EU or even already approved CARB HD Low NOx Regulation in USA, not only for Diesel but also for hydrogen combustion engines. In this study, first the requirements for the combustion and after-treatment system of a hydrogen engine are defined based on future emission regulations. The major advantages regarded to hydrogen combustion are due to the wide range of flammability and very high flame speed numbers compared to other fossil based fuels. Thus, it can be well used for lean burn combustion with much better fuel efficiency and very low NOx emissions with an ultra lean combustion. A comprehensive experimental investigation is performed on a HD 2 L single-cylinder engine.

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.

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.