尺寸
选址
汽车工程
充电站
荷电状态
光伏系统
电动汽车
计算机科学
工程类
电池(电)
电气工程
视觉艺术
功率(物理)
艺术
法学
物理
量子力学
政治学
作者
Mohammad Hasan Ghodusinejad,Younes Noorollahi,Rahim Zahedi
标识
DOI:10.1016/j.est.2022.105904
摘要
Driving a conventional gasoline vehicle is an important polluting factor that causes environmental degradation. In order to reduce dependence on gasoline and its related environmental effects, electric vehicles are emerging as an alternative to conventional gasoline vehicles. Electric vehicles are both economical and environmentally friendly vehicles that draw their energy from rechargeable batteries in the car, which have many benefits such as reduced carbon emissions or pollution, cost-effectiveness, and less noise. The most important drawback of these vehicles is the problems with recharging. One way to deal with this problem is to build charge stations for electric vehicles. A suitable charge station for electric vehicles should also be located in a very precise place to get the most out of electric vehicles. Therefore, in this paper, an MCDA approach based on GIS for optimal site selection of charge stations has been conducted. A simple Hierarchical Analysis Process (AHP) is used to select the optimal locations for electric vehicle charge stations. The proposed method is applied in Kish (an island in the south of Iran) as the study area. The genetic optimization algorithm is applied to solve the optimization problem and is simulated in Matlab software. Using integrated modeling and mathematical optimization in a GIS operating system, the results showed that the proposed model can select suitable locations for charge stations for electric vehicles that can meet the demand for charging electric vehicles within a certain access distance. Also, the method of locating and determining the simultaneous capacity of solar sources and charge stations of electric vehicles and managing the charging process of vehicles in the network has been presented and suitable places for constructing charge stations of electric vehicles have been proposed.
科研通智能强力驱动
Strongly Powered by AbleSci AI