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.

中文翻译：

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基于节点中心的资源配置下电动汽车的流量分配

近年来，得益于政府的扶持政策，电动汽车（EV）的普及迅速扩大。然而，在电动汽车有限的行驶里程和较长的充电时间方面，管理电动汽车在不同操作场景下的途中充电活动仍然是一个关键问题。在本文中，电动汽车的流量分配问题被制定出来以指导电动汽车的充电活动。该问题直接考虑电动汽车在充电站的排队和充电延迟，直接操纵电动汽车的流量，这使得它与经典问题有很大不同。为了有效地解决该问题，设计了专用的流量分配算法（FDA）。此外，基于复杂网络科学背景下的中心性属性，研究了电动汽车流量分配和充电资源分配的相互依存关系。仿真结果表明，将充电器按比例分配给具有高加权介数的节点会导致最有效的流量分配。此外，还引入了鲁棒性来衡量在 EV 驾驶员无知指导下的流量分配解决方案的耐久性。基于中心性的分配和基于优化的分配之间的比较表明，效率和稳健性是流量分配中的两个相互冲突的属性，取决于充电资源的分配。引入鲁棒性来衡量在 EV 驾驶员无知指导下的流量分配解决方案的耐久性。基于中心性的分配和基于优化的分配之间的比较表明，效率和稳健性是流量分配中的两个相互冲突的属性，取决于充电资源的分配。引入鲁棒性来衡量在 EV 驾驶员无知指导下的流量分配解决方案的耐久性。基于中心性的分配和基于优化的分配之间的比较表明，效率和稳健性是流量分配中的两个相互冲突的属性，取决于充电资源的分配。