Designing locations and capacities for charging stations to support intercity travel of electric vehicles: An expanded network approach

Designing locations and capacities for charging stations to support intercity travel of electric vehicles: An expanded network approach
复制标题

设计充电站的位置和容量以支持电动汽车的城际旅行:扩展网络方法

DOI:
10.1016/j.trc.2019.03.013
复制
发表时间:
2019-05
期刊:
Transportation Research Part C: Emerging Technologies
影响因子:
--
通讯作者:
Zuo-Jun Max Shen
Zuo-Jun Max Shen
中科院分区:
其他
文献类型:
--
作者:
Chengzhang Wang;Fang He;Xi Lin;Zuo-Jun Max Shen

文献摘要

参考文献

被引文献

相似文献

本研究致力于设计充电站的位置和容量,以支持电动汽车(EV)的长途旅行。我们首先建立了一个扩展的网络结构来模拟电动汽车司机的有效充电策略集,然后通过将一个近似的排队时间函数的充电设施的变分不等式(VI)制定捕捉均衡的路线选择和电动汽车的充电行为。接下来,我们制定了在固定预算约束下设计充电设施的位置和容量的问题,并使用定制的邻域搜索策略来解决优化问题。系统成本的下限也开发,以评估使用我们提出的启发式获得的解决方案的质量。以长江三角洲地区的一个玩具网络和一个高速公路网络为例,验证了该方法的有效性,并观察到该策略可以在小于5%的最优性差距内解决大规模问题.
This study is devoted to designing locations and capacities of charging stations for supporting long-distance travel by electric vehicles (EVs). We first establish an expanded network structure to model the set of valid charging strategies for EV drivers, and then a variational inequality (VI) is formulated to capture the equilibrated route-choice and charging behaviors of EVs by incorporating an approximated queuing time function for a capacitated charging facility. Next, we formulate the problem of designing the locations and capacities of charging facilities under a fixed budget constraint and solve the optimization problem with a customized neighborhood search strategy. A lower bound for the system cost is also developed to evaluate the qualities of solutions acquired using our proposed heuristic. Numerical examples with a toy network and a highway network extracted from the Yangtze River Delta are used to show the effectiveness of the proposed methodology, and we observe that our strategy can solve a large-scale problem within an optimality gap of less than 5%.
DOI: 10.3141/2647-12
发表时间: 2017-11
影响因子: 1.7
作者:
Zhaocai Liu;Ziqi Song;Yi He
通讯作者: Zhaocai Liu;Ziqi Song;Yi He
DOI: 10.1016/j.tra.2017.01.005
发表时间: 2017-03
影响因子: 6.4
作者:
Meng Li;Yinghao Jia;Shen Zuojun;Fang He
通讯作者: Meng Li;Yinghao Jia;Shen Zuojun;Fang He
DOI: 10.1007/978-3-030-15843-9
发表时间: 2018
期刊: Lecture Notes in Computer Science
影响因子: --
作者:
Angelo Sifaleras;S. Salhi;J. Brimberg
通讯作者: Angelo Sifaleras;S. Salhi;J. Brimberg
DOI: 10.1177/0018720814546372
发表时间: 2015-02
期刊: Human Factors: The Journal of Human Factors and Ergonomics Society
影响因子: --
作者:
Nadine Rauh;T. Franke;J. Krems
通讯作者: Nadine Rauh;T. Franke;J. Krems
DOI: 10.1016/j.trb.2012.09.007
发表时间: 2013
影响因子: 6.8
作者:
Fang He;Di Wu;Yafeng Yin;Yongpei Guan
通讯作者: Fang He;Di Wu;Yafeng Yin;Yongpei Guan