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During series production of modern combustion engines a major challenge is to ensure the correct operation of every engine part. A common method is to test engines in end-of-line (EOL) cold test stations, where the engines are not fired but tugged by an electric motor. In this work we present a physically based 0D model for dynamic simulation of combustion engines under EOL test conditions. Our goals are the analysis of manufacturing faults regarding their detectability and the enhancement of test procedures under varying environmental conditions. Physical experiments are prohibitive in production environments, and the simulative approach reduces them to a minimum. This model is the first known to the authors exploring advanced engine test methods under production conditions. The model supports a wide range of manufacturing faults (with adjustable magnitude) as well as error-free production spread in engine components.

Predictive velocity control can be used to enable efficient driving regarding fuel efficiency and driving time. Commonly, velocity optimization algorithms only take static information, like road slope and curvature, into account and neglect dynamic information, like traffic lights and other traffic participants, although the information is available through sensors or could be made available by vehicle-tovehicle or vehicle-to-infrastructure communication. Thus, static optimization algorithms do not provide optimal solutions in dynamic environments, caused by driver or assistance systems intervention. Because the incorporation of dynamic information increases the complexity of the problem to find an optimal control policy, its use in real-time applications is often prohibited. An algorithm is presented which allows a fast computation of all optimal speed profiles with regard to time and fuel consumption.