曼哈顿泊松线Cox过程中最优充电站搜索

Canqing Lai, Chen Zhu
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引用次数: 0

摘要

随着电动汽车的日益普及,无序充电引发的里程焦虑和充电等待是制约电动汽车发展的主要制约因素。本文采用随机几何建模方法对车辆道路和位置分布网络进行分析,并结合剩余里程估计模型对电动汽车在任意位置的剩余电量和充电概率进行估计。先到先得和同时刻低电量先充的充电调度策略。结果表明,仿真的MPLCP模型能够较好地估计车辆的荷电状态(SOC),使驾驶员能够进行合理的路径规划,实现最优充电选择。所采用的充电策略可以减少用户在充电站等待充电的时间,避免充电站拥堵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Charging Station Search in Manhattan Poisson Line Cox Process
With the increasing popularity of electric vehicles (EVs), mileage anxiety and charging waiting caused by disorderly charging are the main constraints restricting the development of EVs. In this paper, we use stochastic geometric modeling and analysis of the vehicle road and location distribution network, combined with the residual mileage estimation model to estimate the remaining charge and charging probability of EVs at any location. The charging scheduling strategies of the first-come-first-served and same-moment low state of charge first-charged. The results show that the simulated MPLCP model can estimate the state of charge (SOC), drivers can make appropriate path planning, and achieve optimal charging options. The adopted charging strategy can reduce users' waiting time for charging at charging stations (CSs) and avoid congestion in CSs.
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