Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning

Battery electric vehicles (BEV) offer an opportunity to decrease transportation and mobility emissions significantly. The availability of charging station networks and infrastructure is crucial for the proliferation of BEVs. While the expansion of the charging networks is still slow, optimal utilization of the existing infrastructure and dispatching of mobile charging stations can serve as a bypass while more charging stations are built. In this work, we propose a novel multi-agent reinforcement learning - AdapMCS - approach for optimizing the adaptive dispatching of mobile charging stations to maximize the number of served charging requests by a charging station operator while improving the customer experience. By combining graph neural networks with reinforcement learning our approach is able to adapt to dynamic spatio-temporal changes in the demand distribution, for example, during big events such as concerts or fairs. Furthermore, we conduct a thorough evaluation using a publicly available real-world dataset and simulation of dynamic demand distribution changes. The results show that our adaptive dispatching approach is able to deal with the demand shifts and achieve significant gains for both customers, in terms of reducing waiting and charging times, and operators, in terms of increasing their profit.