Jordán, J.; Palanca Cámara, J.; Del Val Noguera, E.; Julian Inglada, VJ.; Botti, V. (2021). Localization of charging stations for electric vehicles using genetic algorithms. Neurocomputing. 452:416-423. https://doi.org/10.1016/j.neucom.2019.11.122
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/176241
Title:
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Localization of charging stations for electric vehicles using genetic algorithms
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Author:
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Jordán, Jaume
Palanca Cámara, Javier
Del Val Noguera, Elena
Julian Inglada, Vicente Javier
Botti, V.
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UPV Unit:
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Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
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Issued date:
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Abstract:
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[EN] 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 ...[+]
[EN] 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.
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Subjects:
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Genetic algorithm
,
Crossover
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Multi-agent system
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Charging station
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Electric vehicle
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Copyrigths:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Source:
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Neurocomputing. (issn:
0925-2312
)
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DOI:
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10.1016/j.neucom.2019.11.122
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Publisher:
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Elsevier
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Publisher version:
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https://doi.org/10.1016/j.neucom.2019.11.122
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Project ID:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095390-B-C31/ES/HACIA UNA MOVILIDAD INTELIGENTE Y SOSTENIBLE SOPORTADA POR SISTEMAS MULTI-AGENTES Y EDGE COMPUTING/
info:eu-repo/grantAgreement/UPV//PAID-06-18/
info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//APOSTD%2F2018%2F010//CONTRATACION DE INVESTIGADOR POSTDOCTORAL GVA-JORDAN PRUNERA. PROYECTO: TECNOLOGIAS INTELIGENTES PARA OPTIMIZACION DE FLOTAS URBANAS DE VEHICULOS ELECTRICOS. /
info:eu-repo/grantAgreement/UPV-VIN//SP20180184//Técnicas inteligentes para optimización de la localización de estaciones de recarga de vehículos
eléctricos y mejora de la movilidad en ciudades/
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Thanks:
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This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and MODINVECI project of the Spanish government. Vicent Botti and Jaume Jordan are funded by UPV PAID-06-18 project. Jaume Jordan is funded by grant ...[+]
This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and MODINVECI project of the Spanish government. Vicent Botti and Jaume Jordan are funded by UPV PAID-06-18 project. Jaume Jordan is funded by grant APOSTD/2018/010 of GVA-FSE
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Type:
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Artículo
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