Localization of charging stations for electric vehicles using genetic algorithms

Jordán, J. ; Palanca, J. ; del Val, E. (Universidad de Zaragoza) ; Julian, V. ; Botti, V.

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.neucom.2019.11.122

Año: 2021

Publicado en: Neurocomputing 452 (2021), 416-423
ISSN: 0925-2312

Factor impacto JCR: 5.779 (2021)

Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 39 / 146 = 0.267 (2021) - Q2 - T1

Factor impacto CITESCORE: 10.3 - Neuroscience (Q1) - Computer Science (Q1)

Factor impacto SCIMAGO: 1.66 - Cognitive Neuroscience (Q1) - Artificial Intelligence (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/RTI2018-095390-B-C31
Financiación: info:eu-repo/grantAgreement/ES/MINECO/MODINVECI

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)