Time-satisfaction of data series based on computer original genetic algorithm gradually covers the location and algorithm of electric vehicle charging station

Issue title: Special Section: Intelligent & fuzzy theory in engineering and science

Guest editors: Teresa Guarda, Isabel Lopes and Álvaro Rocha

Article type: Research Article

Authors: Runzhao, Yang | Qianni, Cao^{; *}

Affiliations: School of Electrical Engineering, Wuhan University, Wuhan, Hubei, China

Correspondence: [*] Corresponding author. Cao Qianni, School of Electrical Engineering, Wuhan University, Wuhan, Hubei, 430072, China. E-mail: 2016302540210@whu.edu.cn.

Abstract: With the increasing number of electric vehicles, the location problem of charging stations has been paid more and more attention. It is more efficient and scientific to select electric vehicle charging stations through intelligent algorithms. Aiming at the location selection of electric vehicle charging station based on time satisfaction, a bi-level planning model is constructed for electric vehicle charging station location, and introduces genetic algorithm into the model to scientifically calculate the location of charging station. The candidate data string is extracted by genetic algorithm, and the text candidate string and the image candidate string are obtained. The candidate string is used as the document attribute to construct the electric vehicle charging station location plan, and then the ideal charging station address is solved. Finally, the method is applied. It is used in the planning analysis of the area near Chaowai Street in Chaoyang District, Beijing. The research results show that the six charging points calculated by the method can meet the demand of the charging vehicles of the residents in the planned area, which is in line with the actual situation of the planned area. This also shows that the double-layer planning model is used for site selection. The research in this paper shows that the genetic algorithm can be effectively used in the location problem, which can improve the efficiency of work and the accuracy of site selection. The relevant conclusions can provide a theoretical reference for the development of site selection.

Keywords: Computer, genetic algorithm, electric car, charging station location

DOI: 10.3233/JIFS-179181

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 5993-6001, 2019