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Prediction and Distribution of Ev Charging Stations

机译:EV充电站的预测和分布

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In this paper, we established a multi-objective model of quantitative prediction and resources allocation of electric-vehicle charging station, based on the American situation. We came up with the priorities for urban and rural development of charging stations and got the density map of charging station in America, which can help the maturity of the electric-vehicle industry. First, we built a model of quantitative prediction of charging stations based on the daily charging demand of Ev and the aggregate demand of charging stations. We used it to calculate the number of public charging stations when all the passenger vehicles turn to electric vehicles. We acquired the current distribution of charging stations according to the urban-rural demarcation standards from relevant scholars, then we get the proportion of urban and rural distribution based on the assumption. Second, we chose the charging demand, charging mode, charging station operating mode, charging station construction goal, charging station service radius, power grid planning, road network planning, land use planning, technology development and policy as influencing factors according to the central theory of three principles. Therefore, this model suits for countries with different population density, geography and wealth distribution. Ultimately, We chose the charging demand and the charging station service radius as the main factor, and we concluded that we should prioritize developing the cities.
机译:在本文中,基于美国情况,我们建立了电动车充电站的定量预测和资源分配的多目标模型。我们提出了对收费站的城乡发展的优先事项,并获得了美国充电站的密度图,可以帮助电动车行业的成熟度。首先,根据EV的日常充电需求和充电站的总需求,建立了充电站的定量预测模型。我们使用它来计算所有乘用车转向电动车辆时的公共收费站的数量。我们根据相关学者的城乡划分标准获得了当前的充电站分配,然后我们基于假设获得城乡分布的比例。其次,我们选择了充电需求,充电模式,充电站运行模式,充电站施工目标,充电站服务半径,电网规划,道路规划,土地利用规划,技术开发和政策作为根据中央理论的影响因素三个原则。因此,这款模型适用于不同人口密度,地理和财富分配不同的国家。最终,我们选择了充电需求和充电站服务半径作为主要因素,我们得出结论,我们应该优先考虑发展城市。

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