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Study on Influence Factors of Electric Vehicles Charging Station Location Based on ISM and FMICMAC

机译:基于ISM和FMICMAC的电动车充电站位置影响因素研究

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Along with the rapid growth in the number of electric vehicles, there is urgent need to construct electric vehicles charging stations (EVCSs) to satisfy the charging demand. However, during the process of carrying out quantitative and qualitative analysis on location decisions, it is necessary to make clear the relationships and role between various factors which make impacts on charging station location. Studies are inadequate in analyzing the influence factors with regard to this respect. This study aims to identify the influence factors, as well as the driving and dependence power of these factors and to analyze the interactions among them. This work proposes to use interpretive structural modeling (ISM) and Matriced'Impacts Croisés Multiplication Appliquée á un Classement (fuzzy cross-impact matrix multiplication applied to classification) (FMICMAC) based approach which is a novel effort in this sector. Moreover, rankings of the identified factors have also been obtained. Based on review of literature and brainstorming among experts in the EVCS field and academia, this paper puts forward 12 factors that impact EVCS location in five aspects. After ISM and FMICMAC analysis, it is concluded that area attribute and geographical environment are defined as key factors while construction cost and annual operation and maintenance cost are the objective factors. The developed integrated structured model will be beneficial in understanding the interrelationship and dependency among the identified factors.
机译:随着电动车辆数量的快速增长,迫切需要构建电动车辆充电站(EVCS)以满足充电需求。然而,在对位置决定进行定量和定性分析的过程中,有必要清楚地明确各种因素之间的关系和作用,这对充电站位置产生了影响。研究不足以分析这方面的影响因素。本研究旨在识别影响因素,以及这些因素的驾驶和依赖权,并分析它们之间的相互作用。这项工作提出使用解释性结构建模(ISM)和Matriced'SumpactsCroisés乘法贴片Á联合国的进程(适用于分类的模糊交叉频率乘法)(FMICMAC)的方法,这是该部门的新精力。此外,还获得了所识别因子的排名。根据EVCS领域和学术界专家的文学和头脑风暴审查,本文提出了12个因素影响EVCS位置的五个方面。在ISM和FMICMAC分析后,得出结论,区域属性和地理环境被定义为关键因素,而施工成本和年度运营和维护成本是客观因素。发达的综合结构化模型将有益于了解所确定的因素之间的相互关系和依赖性。

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