Active Wake Aerodynamics

Aerodynamic drag, particularly in the wake behind SUVs, constitutes over one-third of total drag and significantly impacts driving resistance above 70 km/h. The energy expended in overcoming this is wasted; it cannot be recovered by means such as regenerative braking. Reductions in aerodynamic drag therefore translate directly to increasing the range of battery electric vehicles. Aerodynamic drag reductions can be achieved by using deployable aerodynamic devices such as spoilers, diffusers or strakes. Active aerodynamics would be an ambitious attempt to improve the effectiveness of deployable devices for rear wake control with Machine Learning (ML) algorithms. These would choose the deployment position or angle for optimum aerodynamic performance in response to sensing of vehicle driving conditions such as speed, pitch, and yaw angle.