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Joint Scheduling of Charging and Service Operation of Electric Taxi Based on Reinforcement Learning

Since the high daily power consumption, electric taxis require frequently recharging. Affected by the step tariff and shifting of duty, congestion often occurs during peak hours at charging stations, which seriously affects the normal operation of the traffic and electricity grid. This paper proposes a joint management architecture that integrates the service operation of e-taxis and charging networks. Aiming at minimizing drivers' charging overhead, a scheduling scheme that combines taxi service operation scheduling with charging planning is designed based on reinforcement learning method. The low battery e-taxi is arranged to pick up the passenger whose destination is close to an appropriate charging station. Simulation results show that the proposed scheme can effectively reduce drivers' charging overhead by shortening deadhead kilometers and waiting time at charging station.