基于高效油耗的优化多车编队控制行为评估

Mohamed Maged, Dalia M. Mahfouz, Omar M. Shehata, E. I. Morgan
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引用次数: 2

摘要

在过去的几十年里,气候变化、空气污染和道路安全被列为对全球交通产生不利影响的重要问题。为了解决这些问题,人们对智能交通系统(ITS)进行了研究。智能交通系统的一个重要应用是车辆列队行驶,它被认为可以增强道路的组织性并降低整体油耗。本研究采用协同优化算法,通过车辆速度的调整,将多辆车辆协调成排,使排内车辆距离最大化,使总燃油成本最小。该算法基于两两协调,即在每两个车辆或子排之间根据节省燃料的潜力进行协调决策,形成一个排。优化问题输出每辆车离线时期望的最优速度剖面。然后将这些速度曲线发送给巡航控制器,以控制每辆车的动态以达到理想的最佳速度。研究了包括动力总成动力学在内的非线性车辆动力学模型。采用分层速度控制方法,将最优模型预测控制(MPC)作为上层控制器,将线性比例积分导数(PID)作为下层控制方法,用于管理车辆的速度。在四辆分散车辆在平坦道路上具有相同目的地的场景下,对协调算法和控制器进行了测试。通过仿真场景对协调算法进行了验证,验证了控制器在速度跟踪、逼真控制效果和降低油耗方面的性能。结果表明,优化控制目标顺利实现。
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Behavioral Assessment of an Optimized Multi-Vehicle Platoon Formation Control for Efficient Fuel Consumption
Over the past few decades, climate change, air pollution and road safety have been classified as vital problems affecting the globe adversely in terms of transportation. To solve these problems, Intelligent Transportation Systems (ITS) are investigated. One of the important ITS applications is vehicle platooning, which is contemplated to enhance road organization and reduce the overall fuel consumption. In this study a cooperative optimal algorithm is adopted to coordinate several vehicles to form platoons that minimize the total fuel cost by maximizing distance vehicles are in platoon, through the adjustment of the vehicles’ speeds. The algorithm is based on pairwise coordination by which the coordination decision is made between each two vehicles or sub-platoons to form a platoon based on fuel-saving potential. The optimization problem outputs the desired optimal speed profiles for each vehicle offline. These speed profiles are then sent to a cruise controller to control each vehicle’s dynamics to reach the desired optimal speeds. A nonlinear vehicle dynamic model including the powertrain dynamics is investigated. A hierarchical speed control approach is used, having an optimal Model predictive Control (MPC) as the upper level controller and a linear Proportional-Integral-Derivative (PID) as the lower level control approach used to manage the vehicles’ velocities. The coordination algorithm and the controller are tested on a scenario of four scattered vehicles moving on a flat road, having same destination point. The simulation scenario is conducted to test the coordination algorithm and demonstrate the performance of the controller in terms of velocity tracking, realistic control effort and reduced fuel consumption. Results show that the optimization and control objectives are achieved successfully.
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