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The optimum design of an electrical distribution system (EDS) is based on the profound understanding and measurement of its thermal behavior, because this determines wire diameter and insulation material, has a major impact on the fusing strategy, and enables minimizing technical risk. Current methods of calculation require an extensive database, whereas the temperature measurements at selected points with normal sensors allow neither the precise rating of the actual insulation temperature within a wire bundle, nor the determination of the thermal impact of load currents. The presented approach is based on both a new measurement method and on a related evaluation algorithm. A common automotive wire is applied as a sensing device using its resistance temperature coefficient as the measurement principle.

Both the integration of safety-critical electrical systems and the increasing power requirements in vehicles present a challenge for electrical distribution systems in terms of reliability, packaging, weight, and cost. In this regard, the wire protection device is a key element, as it determines the reliability of the short circuit detection, the immunity against false tripping, and the wire diameters. Currently, in most cases, thermal fuses are used, due to their low cost and robust design. However, the description of their tripping behavior based only on steady-state currents is insufficient for the increasingly complex current profiles in vehicles. Thus, to achieve an optimum dimensioning of a fuse-wire combination, a profound understanding of the thermal behavior of both components under dynamic load conditions is mandatory. However, the FEM tools used for the thermal design of fuses are relatively slow, require huge calculation resources, and must be well-parameterized.

The efficient operation of powertrain test benches in research and development is strongly influenced by the state of “health” of the functional test object. Hence, the use of Early Damage Detection Systems (EDDS) with Unit Under Test (UUT) monitoring is becoming increasingly popular. An EDDS should primarily avoid total loss of the test object and ensure that damaged parts are not completely destroyed, and can still be inspected. Therefore, any abnormality from the standard test object behavior, such as an exceeding of predefined limits, must be recognized at an early testing time, and must lead to a shutdown of the test bench operation. With sensors mounted on the test object, it is possible to isolate the damage cause in the event of its detection. Advanced EDDS configurations also optimize the predefined limits by learning new shutdown values according to the test object behavior within a very short time.

The demand for lower CO2 emissions requires not just the optimization of every single component but the complete system. For a transmission system, it is important to optimize the transmission hardware as we well as the interaction of powertrain components. For automatic transmission with wide ratio spreads, the main losses are caused by the actuation system, which can be reduced with use of ondemand actuation systems. In this paper, a new on-demand electromechanical actuation system with validation results on a clutch test bench is presented. The electro-mechanical actuator shows an increase in the efficiency of 4.1 % compared to the conventional hydraulic actuation in a simulated NEDC (New European Driving Cycle) cycle. This increase is based on the powerless end positions of the actuator (engaged and disengaged clutch). The thermal tension and wear are compensated with a disk spring. This allows a stable control over service life.

The paper presents a simulation environment for the test of driver assistance systems. It covers software-in-the-loop and hardware-in-the-loop test capabilities. In the hardware-in-the-loop (HiL) configuration, real components such as electronic control units (ECUs) and actuators are embedded in the system. First, requirements for a virtual environment are defined. They build the basis for the entire simulation. Special emphasis is given to the interaction between the simulated vehicle under test and its traffic environment. A virtual environment was developed in which the simulated vehicle can drive on a road together with the surrounding traffic. The simulation environment is composed mainly of a traffic scenario generator and a simulation of sensor behavior allowing the recognition of the vehicle's surroundings. Appropriate critical traffic scenarios are generated depending on the tested driver assistance system.

In the last two decades the abilities of neural networks as universal approximation tools of non linear functional relationships as well as identification tools for nonlinear dynamic systems have been recognized and used successfully in many applications areas like modelling, control and diagnosis of technical systems. At the same time an increasing interest in optimal design methods is observed. Design of experiment is used to cope with the growing amount of measurements needed for the calibration of engines due to the rising number of control variables to be considered and the need for more accuracy in the description of engine behaviour to derive the best control strategies. In this paper a strategy for the integration of the concept of D-optimality in the learning process of neural networks is proposed. This leads to an optimal selection of data to be presented to the training procedure of the neural network aiming to a generation of robust neural models using fewer training data.