Integrating Transit Route Network Design and Fast Charging Station Planning for Battery Electric Buses

被引:16
|
作者
Zhang, Wenwei [1 ]
Zhao, Hui [1 ]
Song, Ziqi [2 ]
机构
[1] Beijing Jiaotong Univ, Sch Traff & Transportat, MOT Key Lab Transport Ind Big Data Applicat Techn, Beijing 100044, Peoples R China
[2] Utah State Univ, Dept Civil & Environm Engn, Logan, UT 84322 USA
基金
中国国家自然科学基金;
关键词
Charging stations; Batteries; Genetic algorithms; Planning; Genetics; Search problems; Programming; Battery electric bus; transit route network design; frequency setting; fast charging;
D O I
10.1109/ACCESS.2021.3068771
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
There are currently rising applications of battery electric buses in public transportation system, given their energy efficiency and societal benefits. However, limited range and time-consuming charging processes significantly reduce the operational efficiency of battery electric buses system. Thanks to fast charging technology, battery electric buses can run continuously through rational planning of fast charging system. Therefore, to achieve an effective and economic electric transit route network, electric transit route network and fast charging stations should be simultaneously and systematically deployed. This study develops a bi-level programming framework to formulate the electric transit route network design problem. Where, the upper-level model is to determine the route structure and charging station location to optimize the total costs of both user and operator, the lower-level passenger assignment model is to calculate user cost for upper-level model. Then, the proposed model is solved by a modified genetic algorithm (GA), which can search all possible route structures through specifically designed genetic operators. A numerical example from Swiss road network is employed to demonstrate the applicability of bi-level model and performance of modified GA.
引用
收藏
页码:51604 / 51617
页数:14
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