  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
