Due to high range and continious power requirements in electric and hybrid cars the efficiency of electrical machines is one key parameter. In this paper ac copper loss effects, mainly circulating currents, are investigated. A permanent magnet machine used for electric and hybrid cars showing 6 pole pairs, a maximum power of 80kW and a maximum speed of 12000 rpm is used for this analysis. In general parallel strands of small diameter are used to facilitate handling tasks and to minimize skin and proximity effects. But in the design process feasible circulating currents due to unsuitable strand distribution are often not considered. Furthermore the wire distribution is very often unkown due to automatic winding technology. So the main topic of this study is to analyze the impact of the parallel wires' placement based on the described machine and to propose a measurement technique which could be used later to judge automatically wound machines containing unknown strand placement. For this study a good and bad case machine is built up by hand winding to clarify the influence. To realize high quality measurements both machines use exactly the same rotor, about 60 thermocouples and a specific current measurement system in each parallel strand of one phase. It is shown that the measured losses differ for more than 3kW, meaning an additional loss amount of 65% at 11000 rpm. Looking at the measured currents the frequency dependency can be clearly seen. About three times higher current load in some strands is identified compared to ideal wire distribution. So overheating and possible cases of failure are possible. The measured data is used to validate a common analytical as well as a transient FEA model. While the FEA matches the measurement almost perfectly the analytical approach delivers some deviations, especially in bad case conditions. At the end an outlook regarding the temperature dependent loss scaling is given. Addicted to the ac loss amount different scaling behaviors occur.

Connected and automated driving functions are key components for future vehicles. Due to implementation issues and missing infrastructure, the impact of connected and automated vehicles on the traffic flow can only be evaluated in accurate simulations. Simulation of Urban Mobility (SUMO) provides necessary and appropriate models and tools. SUMO contains many car-following models that replicate automated driving, but cannot realistically imitate human driving behavior. When simulating queued vehicles driving off, existing car-following models are neither able to correctly emulate the acceleration behavior of human drivers nor the resulting vehicle gaps. Thus, we propose a time-discrete 2D Human Driver Model to replicate realistic trajectories. We start by combining previously published extensions of the Intelligent Driver Model (IDM) to one generalized model. Discontinuities due to introduced reaction times, estimation errors and lane changes are conquered with new approaches and equations. Above all, the start-up procedure receives more attention than in existing papers. We also provide a first evaluation of the advanced car-following model using 30 minutes of an aerial measurement. This dataset contains three hours of drone recordings from two signalized intersections in Stuttgart, Germany. The method designed for extracting the vehicle trajectories from the raw video data is outlined. Furthermore, we evaluate the accuracy of the trajectories obtained by the aerial measurement using a specially equipped vehicle.

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