Spatiotemporal model for estimating electric vehicles adopters

被引:27
作者
Rodrigues, Joao L. [1 ]
Bolognesi, Hugo M. [2 ]
Melo, Joel D. [1 ]
Heymann, Fabian [3 ,4 ]
Soares, F. J. [5 ]
机构
[1] Fed Univ ABC UFABC, Engn Modeling & Appl Social Sci Ctr, Santo Andre, SP, Brazil
[2] State Univ Campinas UNICAMP, FEM, Campinas, SP, Brazil
[3] Inst Syst & Comp Engn Technol & Sci INESC TEC, Porto, Portugal
[4] Univ Porto, Porto, Portugal
[5] INESC TEC, Porto, Portugal
基金
巴西圣保罗研究基金会;
关键词
Sustainable city planning; Geographical information systems; Spatial regression; Electric vehicle adopters; URBAN AREAS; ADOPTION; PENETRATION; DIFFUSION; IMPACT; TECHNOLOGY; MANAGEMENT;
D O I
10.1016/j.energy.2019.06.117
中图分类号
O414.1 [热力学];
学科分类号
摘要
The use of fossil fuel vehicles is one of the factors responsible for the degradation of air quality in urban areas. In order to reduce levels of air pollution in metropolitan areas, several countries have encouraged the use of electric vehicles in the cities. However, due to the high investment costs in this class of vehicles, it is expected that the spatial distribution of electric vehicles' adopters will be heterogeneous. The additional charging power required by electric vehicles' batteries can change operation and expansion planning of power distribution utilities. In addition, urban planning agencies should analyze the most suitable locations for the construction of electric vehicle recharging stations. Thus, in order to provide information for the planning of electric mobility services in the city, this paper presents a spatiotemporal model for estimating the rate of electric vehicles' adopters per subareas. Results are spatial databases that can be viewed in geographic information systems to observe regions with greater expectancy of residential electric vehicle adopters. These outcomes can help utilities to develop new services that ground on the rising availability of electric mobility in urban zones. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:788 / 802
页数:15
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