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In terms of vehicle dynamics, four-wheel drive exhibits great capabilities as a power transmission system for high-performance passenger cars. There are now numerous different concepts for four-wheel drive configurations. To provide an overview of the different systems and their characteristics, this report first defines the design requirements for four-wheel drive systems. It then compares the alternative concepts in schematic form. Finally, the results of simulations and measurements are given to describe the characteristics of the quattro system developed by Audi in the various stages of its development.

Nowadays, the engine charging practice is widely adopted in the automotive field in relation to the “downsizing” technology: the reduction of the displacement and the adoption of a higher boost pressure, through a charging system, allow shifting the engine operating point in a zone of higher efficiency for a given engine torque. On the other hand, given a certain displacement, a supercharger can be adopted to increase the performance of the engine. The objective of this work is to provide a detailed analysis about the feasibility of the implementation of a charged engine to a motorbike, with main focus on the possibility to achieve a challenging performance target: in a first stage, several engine architectures (In-line, V-configuration, Boxer) together with different charging concepts (centrifugal or volumetric compressor, with mechanical or fluid-dynamic connection to the engine) have been analyzed from the point of view of packaging.

In the last decades the operational requirements to control the gas exchange of an internal combustion engine increased immensely. The gas exchange is mainly controlled by the valve train system affecting the combustion behavior and power output of the engine. The necessary valve lift curve must be ensured within the whole operation range of the engine. Moreover, a minimum friction has to be achieved. On the one hand engine speeds and loads of the valve train components have increased and the weights of the components have decreased, on the other hand the demands regarding durability increased as well. The engine development department has tried to find out optimization potentials of valve train dynamics to get along with these challenges. During an engine development process analysis tools together with measurements of the dynamic valve train behavior on motored cylinder heads using Laser Doppler Vibrometry are applied.

The continually increasing stringent requirements in terms of emissions and performance lead to the demand for further development of gasoline engines, in order to satisfy the regulations and to be competitive in the market. One of the main limitations in simultaneously improving the efficiency and performance of SI engines is the knock behaviour. This phenomenon limits either the possibility to adopt a higher compression ratio, which would be beneficial for the engine efficiency, or it causes a poor combustion timing which leads to a higher fuel consumption and a lag in performance. As a result, having the possibility to judge the risk of knock during the design phase can be beneficial to increase the potentials of the engine. In this work, a methodology for the prediction of the knock tendency in spark ignition engines using a 3D-CFD software has been developed.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.