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The foremost aim of the work presented in this paper is to improve fuel economy and decrease CO2 emissions by reducing the aerodynamic drag of passenger vehicles. In vehicle development, computer aided engineering (CAE) methods have become a development driver tool rather than a design assessment tool. Exploring and developing the capabilities of current CAE tools is therefore of great importance. An efficient method for vehicle shape optimization has been developed using recent years' advancements in neural networks and evolutionary optimization. The proposed method requires the definition of design variables as the only manual work. The optimization is performed on a solver approximation instead of the real solver, which considerably reduces computation time. A database is generated from simulations of sampled configurations within the pre-defined design space. The database is used to train an artificial neural network which acts as an approximation to the simulations.

Under a global impulse for less man-made emissions, the automotive manufacturers search for innovative methods to reduce the fuel consumption and hence the CO2-emissions. Aerodynamics has great potential to aid the emission reduction since aerodynamic drag is an important parameter in the overall driving resistance force. As vehicles are considered bluff bodies, the main drag source is pressure drag, caused by the difference between front and rear pressure. Therefore increasing the base pressure is a key parameter to reduce the aerodynamic drag. From previous research on small-scale and full-scale vehicles, rear-end extensions are known to have a positive effect on the base pressure, enhancing pressure recovery and reducing the wake area. This paper investigates the effect of several parameters of these extensions on the forces, on the surface pressures of an SUV in the Volvo Cars Aerodynamic Wind Tunnel and compares them with numerical results.