Autonomous electric vehicles (A-EVs), regarded as one of the innovations to accelerate transportation electrification, have sparked a flurry of interest in trajectory planning and charging scheduling. In this regard, this work employs mobile edge computing (MEC) to design a decentralized hierarchical algorithm for finding an optimal path to the nearby A-EV parking lots (PLs), selecting the best PL, and executing an optimal charging scheduling. The proposed model makes use of unmanned aerial vehicles (UAVs) to assist edge servers in trajectory planning by surveying road traffic flow in real time. Furthermore, the target PLs are selected using a user-driven multiobjective problem to minimize the cost and waiting time of A-EVs. To tackle the complexity of the optimization problem, a greedy-based algorithm has been developed. Finally, charging/discharging power is scheduled using a local optimizer based on the PLs’ real-time loads, which minimizes the deviation of the charging/discharging power from the average load. The obtained results show that the proposed model can handle charging/discharging requests of on-move A-EVs and bring fiscal and nonfiscal benefits for A-EVs and the power grid, respectively. Moreover, it observed that user satisfaction in terms of traveling time and traveling distance is increased by using the edge-UAV model.

中文翻译：

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自动驾驶电动汽车分层用户驱动轨迹规划和充电调度


自动电动汽车（A-EV）被视为加速交通电气化的创新之一，引发了人们对轨迹规划和充电调度的浓厚兴趣。在这方面，本工作采用移动边缘计算（MEC）设计一种去中心化分层算法，用于寻找到附近 A-EV 停车场（PL）的最佳路径，选择最佳 PL，并执行最佳充电调度。所提出的模型利用无人机（UAV）通过实时测量道路交通流量来协助边缘服务器进行轨迹规划。此外，使用用户驱动的多目标问题来选择目标 PL，以最大限度地减少 A-EV 的成本和等待时间。为了解决优化问题的复杂性，开发了一种基于贪婪的算法。最后，使用基于PL实时负载的本地优化器来调度充电/放电功率，这最小化了充电/放电功率与平均负载的偏差。所得结果表明，所提出的模型可以处理行驶中的电动汽车的充电/放电请求，并分别为电动汽车和电网带来财政和非财政效益。此外，据观察，通过使用边缘无人机模型，用户在出行时间和出行距离方面的满意度有所提高。