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Optimal Approach to Provide Electric Vehicles with Charging Service by Using Mobile Charging Stations in Heterogeneous Networks

机译:异构网络中利用移动充电站为电动汽车提供充电服务的最优方法

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Mobile charging stations (MCSs) can provide electric vehicles (EVs) with better charging services than the fixed charging stations, as the flexible and efficient charging sites can be available. However, how to schedule the tasks from the EVs and optimally place the MCSs becomes a new challenge. Therefore, in this paper we present a novel approach to help EVs' charging with MCSs through heterogeneous networks. Firstly, a novel heterogeneous network model is presented to improve the communication between EVs and MCSs by using macro cells and small cells. Next, a novel model is developed to make optimal decisions for MCSs to schedule the tasks from EVs. Then, a chaotic evolution particle swarm optimization (CEPSO) algorithm is presented to determine the optimal placement of MCSs based on the charging demand and the maintenance cost. Finally, the simulation experiments prove that the proposed approach can outperform the conventional methods.
机译:移动充电站(MCS)可以为电动汽车(EV)提供比固定充电站更好的充电服务,因为可以使用灵活高效的充电站。但是,如何从电动汽车安排任务并优化放置MCS成为一个新的挑战。因此,在本文中,我们提出了一种新颖的方法来帮助电动汽车通过异构网络对MCS进行充电。首先,提出了一种新的异构网络模型,以通过使用宏小区和小型小区来改善电动汽车和多用途汽车之间的通信。接下来,开发了一种新颖的模型来为MCS做出最佳决策,以调度电动汽车的任务。然后,提出了一种基于充电需求和维护成本的混沌进化粒子群算法(CEPSO)来确定MCS的最优配置。最后,仿真实验证明了该方法的性能优于传统方法。

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