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After the realization of very low exhaust gas emissions and corresponding OBD requirements to fulfill Euro VI and Tier 4 legislation, the focus in heavy-duty powertrain development is on the reduction of fuel consumption and thus CO₂ emissions again. Besides this, the total vehicle operation costs play another major role. A holistic view of the overall powertrain system including the combustion process, exhaust gas aftertreatment, energy recuperation and energy storage is necessary in order to obtain the best possible system for a given application. A management system coordinating the energy flow between the different subsystems while guaranteeing low exhaust emissions plays a major part in operating such complex architectures under optimal conditions.

The increasing complexity of automotive design includes elementary aspects such as lighting. In order to validate modern light systems, new approaches must be developed. Innovative solutions are provided with an indoor lighting facility which performs tests under ideal and repeatable environmental conditions, as well as a ground-breaking approach in evaluating the static and dynamic aspects of lighting. This combination enables the simulation of traffic scenarios and the stimulation of lighting assistance systems.

To meet the ever increasing requirements in the areas of performance, fuel economy and emission, more and more subsystems and control functions are being added to modern engines. This leads to a quick increase in the number of control parameters and consequently dramatic time and cost increase for engine calibration. To deal with this problem, the automotive industry has turned to model-based calibration for a solution. Model-based calibration is a method that uses modern Design of Experiments (DoE), statistical modeling and optimization techniques to efficiently produce high quality calibrations for engines. There are two major enablers for carrying out this method - fully automated engine control and measurement system, and advanced mathematical tools for DoE, modeling and optimization.