使用基于递归子空间的预测控制方法对变化上游流动条件下道路车辆阻力进行主动控制
Active control of road vehicle's drag for varying upstream flow conditions using a Recursive Subspace based Predictive Control methodology
AI总结：
针对真实行驶中变化来流导致的车辆气动阻力问题，提出基于襟翼的RSPC实时控制系统，并通过风洞、压力测量与PIV验证其静动态扰动下可实现5%底部压力恢复。
摘要：
减少能源消耗日益受到关注，尤其是在续航里程有限的电动汽车中，这推动了创新解决方案的出现。在此背景下，我们提出一种基于襟翼的实时控制系统，旨在改善真实驾驶条件下的气动阻力。采用基于递归子空间的预测控制（RSPC）方法，我们在配备襟翼的代表性缩比模型车辆上进行了风洞试验。研究利用压力测量和粒子图像测速（PIV）开展综合评估，以评价控制效率。静态和动态扰动试验均已进行，结果表明该系统在两种情形下均有效。闭环受控系统展现出显著收益，实现了5%的底部压力恢复。
英文摘要：
The growing focus on reducing energy consumption, particularly in electric vehicles with limited autonomy, has prompted innovative solutions. In this context, we propose a real-time flap-based control system aimed at improving aerodynamic drag in real driving conditions. Employing a Recursive Subspace based Predictive Control (RSPC) approach, we conducted wind tunnel tests on a representative model vehicle at reduced scale equipped with flaps. Comprehensive assessments using pressure measurements and Particle Image Velocimetry (PIV) were undertaken to evaluate the control efficiency. Static and dynamic perturbation tests were conducted, revealing the system's effectiveness in both scenarios. The closed-loop controlled system demonstrated a substantial gain, achieving a 5\% base pressure recovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
