Active Wake Aerodynamics

空气动力学阻力，尤其是SUV后方的尾流阻力，占总阻力的三分之一以上，对70 km/h以上的行驶阻力有显著影响。在克服这一点时所消耗的能量被浪费了;它不能通过诸如再生制动之类的手段来回收。因此，空气动力阻力的减少直接转化为增加电池电动汽车的续航里程。空气动力阻力的减小可以通过使用可展开的空气动力装置来实现，例如扰流器、扩散器或边条。主动空气动力学将是一项雄心勃勃的尝试，旨在通过机器学习（ML）算法提高可部署设备的有效性。这些将响应于车辆驾驶条件（诸如速度、俯仰和偏航角）的感测来选择用于最佳空气动力学性能的展开位置或角度。

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.