Localization of charging stations for electric vehicles using genetic algorithms

• Published in: Neurocomputing (Amsterdam) Vol. 452; pp. 416 - 423
• Main Authors: Jordán, Jaume, Palanca, Javier, del Val, Elena, Julian, Vicente, Botti, Vicent
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 10.09.2021
• Subjects: Charging station; Crossover; Electric vehicle; Genetic algorithm; Multi-agent system; Electric vehicle; Multi-agent system; Crossover; Genetic algorithm; Charging station
• ISSN: 0925-2312; 1872-8286; 1872-8286
• DOI: 10.1016/j.neucom.2019.11.122

The electric vehicle (EV) is gradually being introduced in cities. The impact of this introduction is less due, among other reasons, to the lack of charging infrastructure necessary to satisfy the demand. In today’s cities there is no adequate infrastructure and it is necessary to have action plans that allow an easy deployment of a network of EV charging points in current cities. These action plans should try to place the EV charging stations in the most appropriate places for optimizing their use. According to this, this paper presents an agent-oriented approach that analyses the different configurations of possible locations of charging stations for the electric vehicles in a specific city. The proposed multi-agent system takes into account data from a variety of sources such as social networks activity and mobility information in order to estimate the best configurations. The proposed approach employs a genetic algorithm (GA) that tries to optimize the possible configurations of the charging infrastructure. Additionally, a new crossover method for the GA is proposed considering this context.