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In modern cities, the number of Electric vehicles (EV) is increasing rapidlyfor their low emission and better dynamic performance, leading to increasingdemand for EV charging. However, due to the limited number of EV chargingfacilities, catering to the huge demand for time-consuming EV charging becomesa critical problem. It is quite a challenge to dispatch EVs in the dynamictraffic environment and coordinate interaction among agents. To better servefurther research on various related Deep Reinforcment Learning (DRL) EVdispatching algorithms, an efficient simulation environment is necessary toensure success. As simulator Simulation Urban Mobility (SUMO) is one of themost widely used open-source simulators, it has great significance in creatingan environment that satisfies research requirements on SUMO. We aim to improvethe efficiency of EV charging station usage and save time for EV users infurther work. As a result, we design an EV navigation system on the basis ofthe traffic simulator SUMO using Jurong Area, Singapore in this paper. Variousstate-of-the-art DRL algorithms are deployed on the designed testbed tovalidate the feasibility of the framework in terms of EV charging dispatchingproblems. Besides EV dispatching problems, the environment can also serve forother reinforcement learning (RL) traffic control problems

A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem