Flow Distribution for Electric Vehicles Under Nodal-Centrality-Based Resource Allocation

In recent years, the popularity of electric vehicles (EVs) has been rapidly expanding, thanks to the government's supportive policies. However, managing EV's en-route re-charge activities under different operation scenarios is still a critical issue, when the EV's limited driving range and long re-charge time are concerned. In this paper, an EV flow distribution problem is formulated for the guidance of EV's re-charge activities. The problem manipulates EV flows directly with the consideration of EV's queuing and re-charge delay at charging stations, which makes it greatly different from the classic problems. To solve the problem effectively, a dedicated flow distribution algorithm (FDA) is devised. Furthermore, based on the centrality properties in the context of complex network science, the interdependence of EV flow distribution and charging resource allocation is investigated. Simulation results show that a proportional allocation of chargers to nodes with high weighted betweenness leads to the most efficient flow distribution. In addition, robustness is introduced to measure the flow distribution solution's endurance under EV drivers' ignorance of guidance. The comparison among centrality-based allocations and an optimization-based allocation reveals that efficiency and robustness are two conflicting properties in flow distribution, dependent on the allocation of charging resources.