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Using Genetic Algorithms to Optimize the Location of Electric Vehicle Charging Stations

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International Joint Conference SOCO’18-CISIS’18-ICEUTE’18 (SOCO’18-CISIS’18-ICEUTE’18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 771))

Abstract

The creation of a suitable charging infrastructure for electric vehicles (EV) is one of the main challenges to increase the adoption of this new vehicle technologies. In this article, we present a Multi-Agent System (MAS) that performs an analysis of a set of possible configurations for the location of EV charging stations in a city. To estimate the best configurations, the proposed MAS considers data from heterogeneous sources such as traffic, social networks, population, etc. Based on this information, the agents are able to analyze a large set of configurations using a genetic algorithm that optimizes the configurations taking into account a utility function.

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Notes

  1. 1.

    www.xmpp.org.

  2. 2.

    https://github.com/DEAP/deap.

  3. 3.

    http://gobiernoabierto.valencia.es/es/data/.

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Acknowledgments

This work was partially supported by MINECO/FEDER TIN2015-65515-C4-1-R and MOVINDECI project of the Spanish government.

Author information

Authors and Affiliations

  1. Universitat Politècnica de València, Camino de Vera s/n, Valencia, Spain

    Jaume Jordán, Javier Palanca, Elena del Val, Vicente Julian & Vicente Botti

Authors
  1. Jaume Jordán
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  2. Javier Palanca
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  3. Elena del Val
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  4. Vicente Julian
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  5. Vicente Botti
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Corresponding author

Correspondence to Jaume Jordán .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    Manuel Graña

  2. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Salamanca, Spain

    José Antonio Sáez

  6. Department of Industrial Engineering, University of A Coruña, La Coruña, La Coruña, Spain

    Héctor Quintián

  7. University of Salamanca, Salamanca, Salamanca, Spain

    Emilio Corchado

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Cite this paper

Jordán, J., Palanca, J., del Val, E., Julian, V., Botti, V. (2019). Using Genetic Algorithms to Optimize the Location of Electric Vehicle Charging Stations. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_2

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