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The potential influence of cost-related factors on the adoption of electric vehicle: An integrated micro-simulation approach

机译:成本相关因素对电动汽车采用的潜在影响:一种集成的微观模拟方法

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摘要

Cost-related factors (e.g., subsides) play a vital role in the diffusion of Electric Vehicle (EV). However, it remains unclear how these factors would influence the diffusion and further the associated urban elements (e.g., infrastructures) at the micro scale. In response, this paper tried to quantify the influence of two types of cost-related factors on the adoption of Electric Vehicle (EV), namely upfront cost and usage-related cost, using purchase subsides and fuel prices as examples, respectively. An agent-based integrated micro-simulation model (SelfSim-EV) was used here to simulate how the EV market in Beijing might evolve from 2016 to 2020, within several "what-if" scenarios considering different Plug-in Hybrid Electric Vehicle (PHEV) subsides, petrol prices and electricity prices. The results suggested that 1) doubling the PHEV subsidy would make PHEV price competitive and thus increase the PHEV sale from around zero to 2500 in 2019. The PHEV sale price increases by around 3500 RMB (from around 261,000 to 264,500 RMB) due to the increase in the PHEV penetrate rate. This further gives rise to the changes in those urban elements connected with the EV market, including the urban environment, electricity and infrastructure systems, especially at the disaggregate level; 2) both electricity and petrol prices have little influence on the adoption of EVs at the macro level (i.e. the city level), but they do influence the spatial distributions of both CV and EV owners (based on the analyses of their residential locations) and further geographical distributions of vehicular emissions, EV-related facilities (e.g., charging posts) and electricity demand of EV at multiple resolutions, ranging from the facility level to district level. (C) 2019 Elsevier Ltd. All rights reserved.
机译:成本相关因素(例如补贴)在电动汽车(EV)的普及中起着至关重要的作用。然而,目前尚不清楚这些因素将如何在微观规模上影响扩散并进一步影响相关的城市要素(例如基础设施)。作为回应,本文试图以购买补贴和燃料价格为例,量化两种成本相关因素对电动汽车采用的影响,即前期成本和使用相关成本。在基于考虑不同插电式混合动力汽车(PHEV)的几种“假设”场景中,这里使用了基于代理的集成微仿真模型(SelfSim-EV)来模拟北京的EV市场从2016年到2020年的发展趋势。 )补贴,汽油价格和电价。结果表明:1)将乘用车的补贴提高一倍,将使乘用车的价格具有竞争力,从而将乘用车的销售量从零增加到2019年的2500。乘用车的价格增加约3500元(从约261,000到264,500元)。在PHEV渗透率方面。这进一步引起了与电动汽车市场有关的城市要素的变化,包括城市环境,电力和基础设施系统的变化,特别是在分类层面上; 2)电价和汽油价格对电动汽车在宏观层面(即城市层面)的采用几乎没有影响,但它们确实影响了电动汽车和电动汽车所有者的空间分布(基于对居民区的分析);车辆排放,电动汽车相关设施(例如充电桩)的进一步地理分布以及电动汽车对电力的需求具有多种分辨率,范围从设施级别到地区级别。 (C)2019 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Journal of Cleaner Production 》 |2020年第20期| 119479.1-119479.14| 共14页
  • 作者单位

    Univ Cambridge Dept Geog Downing Pl Cambridge CB2 3EN England|Univ East Anglia Tyndall Ctr Climate Change Res Sch Environm Sci Norwich NR1 7TJ Norfolk England|Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hung Hom Kowloon Hong Kong Peoples R China;

    Univ Cambridge Dept Land Econ 19 Silver St Cambridge CB3 9EP England;

    Beijing Jiaotong Univ Key Lab Transport Ind Big Data Applicat Technol C 3 Shangyuancun Beijing 100044 Peoples R China;

    Univ Greenwich Fac Business London SE10 9LS England;

    Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Electric vehicle; Subsidy; Petrol price; Electricity price; Agent-based model; Impact assessment;

    机译:电动汽车;补贴;汽油价格;电价;基于代理的模型;影响评估;

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