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Efforts towards ever more energy efficient passenger cars have become one of the largest challenges of the automotive industry. This involves numerous different fields of engineering, and every finished model is always a compromise between different requirements. Passenger car aerodynamics is no exception; the shape of the exterior is often dictated by styling, engine bay region by packaging issues etcetera. Wheel design is also a compromise between different requirements such as aerodynamic drag and brake cooling, but as the wheels and wheel housings are responsible for up to a quarter of the overall aerodynamic drag on a modern passenger car, it is not surprising that efforts are put towards improving the wheel aerodynamics.

Plug-in hybrid electric vehicles (PHEVs) have rechargeable energy storage which can be used to run the vehicle on shorter range on electricity from the grid. In the absence of a priori information about the trip, a straightforward strategy is to first deplete the battery down to a minimum level and then keep the state of charge (SoC) around this level. However, largely due to the battery losses, the overall fuel economy can be improved if the battery is discharged gradually. This requires some a priori knowledge about the trip. This paper investigates the tradeoff between improved fuel economy and the need for a priori information. This investigation is done using a variant of telemetry equivalent consumption minimization strategy (T-ECMS) which is modified to be used for a PHEV. To implement this strategy, several parameters need to be tuned based on an assumption of the future trip.

In this case study, a human factors analysis was carried out in the preliminary design phase of the Cupola, a European Space Agency (ESA) module for manned space flights for the International Space Station (ISS). The manikin software Jack® was used early in the design process before any flight hardware production. All Cupola astronaut tasks were evaluated in a virtual environment of the Cupola. Methodological aspects concerning the analysis are described, e.g. file processing, use of coordinate systems and the use of a prior task analysis. Results show that the thorough manikin analysis supported with the hierarchical task analysis results, was an important help in the design process.