论文标题
深钢筋学习实验解决电动汽车充电分配问题的相扑框架
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem
论文作者
论文摘要
在现代城市，电动汽车（EV）的数量正在迅速增加，因为它们的排放率低和动态性能更好，从而导致对电动汽车充电的需求不断增加。但是，由于电动汽车充电设施数量有限，因此满足对耗时的电动汽车充电的巨大需求成为一个关键问题。在动态交通环境中派遣电动汽车并在代理之间进行协调相互作用是一个巨大的挑战。为了更好地为各种相关的深钢筋学习（DRL）EV调度算法提供进一步的研究，需要有效的仿真环境来确保成功。由于模拟器模拟城市移动性（SUMO）是使用最广泛的开源模拟器之一，因此它在创建满足相扑研究要求的环境方面具有重要意义。我们旨在提高电动汽车充电站使用的效率，并为EV用户节省进一步工作的时间。结果，我们根据本文中新加坡的宫贡区域设计了EV导航系统。在设计的测试台上部署了各种最先进的DRL算法，以验证框架的可行性，以电动汽车充电调度问题。除了电动汽车派遣问题外，环境还可以用于其他强化学习（RL）交通控制问题
In modern cities, the number of Electric vehicles (EV) is increasing rapidly for their low emission and better dynamic performance, leading to increasing demand for EV charging. However, due to the limited number of EV charging facilities, catering to the huge demand for time-consuming EV charging becomes a critical problem. It is quite a challenge to dispatch EVs in the dynamic traffic environment and coordinate interaction among agents. To better serve further research on various related Deep Reinforcment Learning (DRL) EV dispatching algorithms, an efficient simulation environment is necessary to ensure success. As simulator Simulation Urban Mobility (SUMO) is one of the most widely used open-source simulators, it has great significance in creating an environment that satisfies research requirements on SUMO. We aim to improve the efficiency of EV charging station usage and save time for EV users in further work. As a result, we design an EV navigation system on the basis of the traffic simulator SUMO using Jurong Area, Singapore in this paper. Various state-of-the-art DRL algorithms are deployed on the designed testbed to validate the feasibility of the framework in terms of EV charging dispatching problems. Besides EV dispatching problems, the environment can also serve for other reinforcement learning (RL) traffic control problems
