Interpretive structural model for influential factors in electric vehicle charging station location

IF 9 1区工程技术 Q1 ENERGY & FUELS

Energy Pub Date : 2025-04-15 DOI:10.1016/j.energy.2025.136154

Irfan Ullah , Jianfeng Zheng , Muzaffar Iqbal , Muneer Ahmad , Arshad Jamal , Alessandro Severino

{"title":"Interpretive structural model for influential factors in electric vehicle charging station location","authors":"Irfan Ullah , Jianfeng Zheng , Muzaffar Iqbal , Muneer Ahmad , Arshad Jamal , Alessandro Severino","doi":"10.1016/j.energy.2025.136154","DOIUrl":null,"url":null,"abstract":"<div><div>Electric vehicles (EVs) are emerging as a pivotal solution for achieving sustainable and eco-friendly transportation. Integrating EVs into transport networks is crucial for fostering environmentally sustainable growth in smart cities, addressing carbon emissions, and reducing reliance on fossil fuels. With the increasing popularity of EVs, the demand for charging stations proliferates. However, selecting optimal locations for electric vehicle charging stations (EVCS) is a complex task that requires careful consideration of various factors. Existing studies have not adequately addressed these factors' intricate relationships and interdependencies. This study aims to fill this gap by employing interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) to analyze the influence factors and interactions between them. Based on a comprehensive literature review and expert input, we identify 18 key factors that influence EVCS. The result shows that proximity to EV drivers, strategic placement, integration with daily routines, timely availability, and EV ownership rates are the most influential and objective considerations. The established integrated structured model provides a valuable tool for understanding and optimizing the complex relationships among the identified factors, aiding in informed decision-making for EV charging station locations.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"325 ","pages":"Article 136154"},"PeriodicalIF":9.0000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360544225017967","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}

引用次数: 0

Abstract

Electric vehicles (EVs) are emerging as a pivotal solution for achieving sustainable and eco-friendly transportation. Integrating EVs into transport networks is crucial for fostering environmentally sustainable growth in smart cities, addressing carbon emissions, and reducing reliance on fossil fuels. With the increasing popularity of EVs, the demand for charging stations proliferates. However, selecting optimal locations for electric vehicle charging stations (EVCS) is a complex task that requires careful consideration of various factors. Existing studies have not adequately addressed these factors' intricate relationships and interdependencies. This study aims to fill this gap by employing interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) to analyze the influence factors and interactions between them. Based on a comprehensive literature review and expert input, we identify 18 key factors that influence EVCS. The result shows that proximity to EV drivers, strategic placement, integration with daily routines, timely availability, and EV ownership rates are the most influential and objective considerations. The established integrated structured model provides a valuable tool for understanding and optimizing the complex relationships among the identified factors, aiding in informed decision-making for EV charging station locations.

查看原文本刊更多论文

电动汽车充电站选址影响因素的解释结构模型

电动汽车（ev）正在成为实现可持续和环保交通的关键解决方案。将电动汽车融入交通网络对于促进智慧城市的环境可持续增长、解决碳排放问题以及减少对化石燃料的依赖至关重要。随着电动汽车的日益普及，对充电站的需求激增。然而，选择电动汽车充电站（EVCS）的最佳位置是一项复杂的任务，需要仔细考虑各种因素。现有的研究没有充分解决这些因素之间错综复杂的关系和相互依赖性。本研究旨在通过运用解释结构模型（ISM）和交叉影响矩阵乘法应用于分类（MICMAC）来分析二者之间的影响因素和相互作用来填补这一空白。在综合文献综述和专家意见的基础上，我们确定了18个影响EVCS的关键因素。结果表明，靠近电动汽车司机、战略布局、与日常生活的融合、及时性和电动汽车拥有率是最具影响力和客观的考虑因素。所建立的集成结构化模型为理解和优化识别因素之间的复杂关系提供了有价值的工具，有助于电动汽车充电站选址的明智决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Energy 工程技术-能源与燃料

CiteScore

15.30

自引率

14.40%

发文量

审稿时长

14.2 weeks

期刊介绍： Energy is a multidisciplinary, international journal that publishes research and analysis in the field of energy engineering. Our aim is to become a leading peer-reviewed platform and a trusted source of information for energy-related topics. The journal covers a range of areas including mechanical engineering, thermal sciences, and energy analysis. We are particularly interested in research on energy modelling, prediction, integrated energy systems, planning, and management. Additionally, we welcome papers on energy conservation, efficiency, biomass and bioenergy, renewable energy, electricity supply and demand, energy storage, buildings, and economic and policy issues. These topics should align with our broader multidisciplinary focus.