The exponential growth of electric vehicles (EVs) presents novel challenges in preserving battery health and in addressing the persistent problem of vehicle range anxiety. To address these concerns, wireless charging, particularly, Mobile Energy Disseminators (MEDs) have emerged as a promising solution. The MED is mounted behind a large vehicle and charges all participating EVs within a radius upstream of it. Unfortuantely, during such V2V charging, the MED and EVs inadvertently form platoons, thereby occupying multiple lanes and impairing overall corridor travel efficiency. In addition, constrained budgets for MED deployment necessitate the development of an effective dispatching strategy to determine optimal timing and locations for introducing the MEDs into traffic. This paper proposes a deep reinforcement learning (DRL) based methodology to develop a vehicle dispatching framework. In the first component of the framework, we develop a realistic reinforcement learning environment termed "ChargingEnv" which incorporates a reliable charging simulation system that accounts for common practical issues in wireless charging deployment, specifically, the charging panel misalignment. The second component, the Proximal-Policy Optimization (PPO) agent, is trained to control MED dispatching through continuous interactions with ChargingEnv. Numerical experiments were carried out to demonstrate the demonstrate the efficacy of the proposed MED deployment decision processor. The experiment results suggest that the proposed model can significantly enhance EV travel range while efficiently deploying a optimal number of MEDs. The proposed model is found to be not only practical in its applicability but also has promises of real-world effectiveness. The proposed model can help travelers to maximize EV range and help road agencies or private-sector vendors to manage the deployment of MEDs efficiently.

翻译：电动汽车的指数级增长在保持电池健康以及解决长期存在的车辆续航里程焦虑问题方面带来了新的挑战。为解决这些问题，无线充电，特别是移动能量分发器（MED），已成为一种有前景的方案。MED安装于大型车辆后方，可为其上游一定半径范围内的所有参与电动汽车充电。不幸的是，在此类车对车（V2V）充电过程中，MED与电动汽车会无意中形成队列，从而占用多个车道并降低整体道路通行效率。此外，有限的MED部署预算需要制定有效的调度策略，以确定将MED引入交通流的最佳时机和地点。本文提出了一种基于深度强化学习（DRL）的方法来开发车辆调度框架。在该框架的第一个组成部分中，我们构建了一个名为“ChargingEnv”的真实强化学习环境，该环境包含一个可靠的充电模拟系统，并考虑了无线充电部署中常见的实际问题，特别是充电面板未对准问题。第二个组成部分，即近端策略优化（PPO）智能体，通过与ChargingEnv的持续交互进行训练，以控制MED调度。我们进行了数值实验以证明所提出的MED部署决策处理器的有效性。实验结果表明，所提出的模型能够显著增加电动汽车的行驶里程，同时有效部署最优数量的MED。研究发现，该模型不仅在适用性上具有实用性，而且在现实世界中也有望取得效果。该模型可以帮助出行者最大化电动汽车续航里程，并帮助道路管理机构或私营供应商高效管理MED的部署。