Active Wake Aerodynamics

空气动力阻力，特别是SUV后面的尾流阻力，占总阻力的三分之一以上，对超过70公里/小时的驾驶阻力有很大影响。为克服这一点而花费的能量被浪费了；它无法通过再生制动等手段恢复。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。气动减阻可以通过使用可展开的气动装置来实现，如扰流板、扩散器或隔板。主动空气动力学将是一项雄心勃勃的尝试，旨在利用机器学习(ML)算法提高可部署设备的后部尾流控制效率。它们将根据对车辆行驶条件(如速度、俯仰和偏航角度)的感测，选择最佳气动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.