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Reduction of the CO2 greenhouse gas emissions is one major challenge the automotive industry as a part of the transportation sector is facing. Hydrogen is regarded as one of the key energy solutions for CO2 reduction in the future transportation sector. First, a hydrogen-powered single-cylinder test rig for 2 liter heavy-duty engine will be introduced. Followed by a discussion of experimental results including variations of engine speed, torque, ignition strategy, air-fuel ratio, etc. In addition, the paper proposes a new phenomenological model for the prediction of hydrogen combustion. The model is based on the well-known two-zone Entrainment approach, supported by newly developed hydrogen-specific submodels for the calculation of the laminar flame speed and auto-ignition in the unburned mass zone. The developed physical-based combustion model is extensively validated based on the experimental single-cylinder results.

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.