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The number of electronic control units (ECU) in modern automobiles has steadily increased lately. Joining them in a vehicle network causes complex bus structures, in which ECU's of different suppliers have to cooperate. In order to keep the development effort as small as possible, the automobile industry would rather have the possibility of testing these bus structures in an early state of development. This was the reason why we created a test system for the network management of a CAN-Low-Speed network, which supports the test of an electronic control system in many states of development. This test system is able to check different network structures and to search errors in the ECU's by checking the network management. This is done by an automatic test that generates an evaluation and a report.

Within the scope of a current research project at the Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS), the potential for an estimation of vehicle side slip angle and yaw rate arising from online measurement of tire forces is evaluated. Investigations focus on how the vehicle state can be determined, if in addition to wheel speeds and steering angle the tire forces currently acting on the vehicle are known. Different estimation procedures based on inverse tire models, direct integration of vehicle accelerations and closed-loop-observer are discussed. The performance is tested with data from vehicle dynamics simulation.

This paper presents an integrated numerical and experimental approach to take best possible advantage of the common development tools at hand (1D, CFD and wind tunnel) to determine the cooling air mass flow at the different vehicle development stages. 1D tools can be used early in development when neither 3D data nor wind tunnel models with detailed underhood flow are available. A problem that has to be resolved is the dependency on input data. In particular, the pressure coefficients on the outer surface (i.e. at the air inlet and outlet region) and the pressure loss data of single components are of great importance since the amount of cooling air flow is directly linked to these variables. The pressure coefficients at the air inlet and outlet are not only a function of vehicle configuration but also of driving velocity and fan operation. Both, static and total pressure coefficient, yield different advantages and disadvantages and can therefore both be used as boundary conditions.

In recent years, driving simulators have become a valuable tool in the automotive design and testing process. Yet, in the field of vehicle dynamics, most decisions are still based on test drives in real cars. One reason for this situation can be found in the fact that many driving simulators do not allow the driver to evaluate the handling qualities of a simulated vehicle. In a driving simulator, the motion cueing algorithm tries to represent the vehicle motion within the constrained motion envelope of the motion platform. By nature, this process leads to so called false cues where the motion of the platform is not in phase or moving in a different direction with respect to the vehicle motion. In a driving simulator with classical filter-based motion cueing, false cues make it considerably more difficult for the driver to rate vehicle dynamics.

In addition to the analysis of human driving behavior or the development of new advanced driver assistance systems, the high simulation quality of today’s driving simulators enables investigations of selected topics pertaining to driving dynamics. With high reproducibility and fast generation of vehicle variants the subjective evaluation process leads to a better system understanding in the early development stages. The transfer of the original on-road test run to the virtual reality of the driving simulator includes the full flexibility of the vehicle model, the maneuver and the test track, which allows new possibilities of investigation. With the opportunity of a realistic whole-vehicle simulation provided by the Stuttgart Driving Simulator new analysis of the human’s thresholds of perception are carried out.