Open Access

ARTICLE

A Multi-Objective Decision-Making Approach for the Optimal Location of Electric Vehicle Charging Facilities

Weiwei Liu¹, Yang Tang², Fei Yang², Yi Dou³, Jin Wang^4,*

1 Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China.
2 Zhejiang University Urban and Rural Planning& Design Institute, Hangzhou, 310023, China.
3 Center for Social and Environmental Systems Research, National Institute for Environmental Studies (NIES), Ibaraki, 305-8506, Japan.
4 School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha, 410004, China.

* Corresponding Author: Jin Wang. Email: .

Computers, Materials & Continua 2019, 60(2), 813-834. https://doi.org/10.32604/cmc.2019.06754

Abstract

Electric vehicles (EVs) are recognized as one of the most promising technologies worldwide to address the fossil fuel energy resource crisis and environmental pollution. As the initial work of EV charging station (EVCS) construction, site selection plays a vital role in its whole life cycle. In this paper, a multi-objective optimization model for the location layout of EVCSs is established when considering various factors such as user demand, investment cost, soil locations, the emergency charging mileage limit, the actual road condition and service network reliability. The model takes the minimum investment cost and the minimum user charging cost as the dual objectives. On the basis of satisfying the user’s charging demand and the capacity constraints of EVCSs, the redundant design of the charging pile and station is considered to ensure the reliability of the service network. In the allocation of user charging demand, in this paper, two factors of time and distance are considered, and the equal time load distance method is adopted to distribute traffic flow under the limitation of emergency charging mileage. When calculating the average travel speed of a road section, an accounting method based on the land price level is proposed considering the congestion. Then, the linear weighting method is applied to normalizing the multi-objective function, and the genetic algorithm is employed to solve the problem. Finally, a computational experiment is presented to demonstrate the applicability and effectiveness of the proposed approach. The results show that the proposed approach is a useful, practical, and effective way to find the optimal location of EVCSs.

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