Skip to main content

Evolving Controllers for Electric Vehicle Charging

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

We describe an algorithm to design controllers for the charging of electric vehicles. The controller is represented as a neural network, whose weights are set by an evolutionary algorithm in order to minimize the changes in the overall electrical consumption. The presented algorithm provides de-centralized controllers that also respect the privacy of the owner of electric vehicles, i.e. the controller does not share the information about charging with any third party. The presented controllers also require only a very small amount of memory and computational resources and are thus suitable for implementation in embedded systems.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Gan, L., Topcu, U., Low, S.H.: Optimal decentralized protocol for electric vehicle charging. IEEE Trans. Power Syst. 28(2), 940–951 (2013). https://doi.org/10.1109/CDC.2011.6161220

    Article  Google Scholar 

  2. Ma, Z., Callaway, D.S., Hiskens, I.A.: Decentralized charging control of large populations of plug-in electric vehicles. IEEE Trans. Control Syst. Technol. 21(1), 67–78 (2013). https://doi.org/10.1109/TCST.2011.2174059

    Article  Google Scholar 

  3. Clement, K., Haesen, E., Driesen, J.: Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids. In: 2009 IEEE/PES Power Systems Conference and Exposition, pp. 1–7 (2009). https://doi.org/10.1109/PSCE.2009.4839973

  4. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001). https://doi.org/10.1162/106365601750190398

    Article  Google Scholar 

  5. U.S. Department of Transportation, Federal Highway Administration: 2009 National Household Travel Survey (2009). http://nhts.ornl.gov

Download references

Acknowledgments

This work was supported by Czech Science Foundation project no. 17-10090Y.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Martin Pilát .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pilát, M. (2018). Evolving Controllers for Electric Vehicle Charging. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77538-8_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics