A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations

One of the main current challenges of electric vehicles (EVs) is the creation of a reliable, accessible and comfortable charging infrastructure for citizens in order to enhance demand. In this paper, a multi-agent system (MAS) is proposed to facilitate the analysis of different placement configurati...

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Published inApplied sciences Vol. 8; no. 2; p. 313
Main Authors Jordán, Jaume, Palanca, Javier, del Val, Elena, Julian, Vicente, Botti, Vicente
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 23.02.2018
Subjects
Automobile rentals
charging stations
Configurations
Electric vehicles
genetic algorithm
Genetic algorithms
multi-agent systems
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Abstract One of the main current challenges of electric vehicles (EVs) is the creation of a reliable, accessible and comfortable charging infrastructure for citizens in order to enhance demand. In this paper, a multi-agent system (MAS) is proposed to facilitate the analysis of different placement configurations for EV charging stations. The proposed MAS integrates information from heterogeneous data sources as a starting point to characterize the areas where charging stations could potentially be placed. Through a genetic algorithm, the MAS is able to analyze a large number of possible configurations, taking into account a set of criteria to be optimized. Finally, the MAS returns a configuration with the areas of the city that are considered most appropriate for the establishment of charging stations according to the specified criteria.
AbstractList One of the main current challenges of electric vehicles (EVs) is the creation of a reliable, accessible and comfortable charging infrastructure for citizens in order to enhance demand. In this paper, a multi-agent system (MAS) is proposed to facilitate the analysis of different placement configurations for EV charging stations. The proposed MAS integrates information from heterogeneous data sources as a starting point to characterize the areas where charging stations could potentially be placed. Through a genetic algorithm, the MAS is able to analyze a large number of possible configurations, taking into account a set of criteria to be optimized. Finally, the MAS returns a configuration with the areas of the city that are considered most appropriate for the establishment of charging stations according to the specified criteria.
Author del Val, Elena
Julian, Vicente
Palanca, Javier
Jordán, Jaume
Botti, Vicente
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ContentType Journal Article
Copyright 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms Automobile rentals
charging stations
Configurations
Electric vehicles
genetic algorithm
Genetic algorithms
multi-agent systems
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Title A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations
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