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Optimization of future charging infrastructure for commercial electric vehicles using a multi-objective genetic algorithm and real travel data

  • Li Zeng
  • Timo Krallmann
  • Andrea FiegeEmail author
  • Marek Stess
  • Timo Graen
  • Michael Nolting
Original Paper

Abstract

Electric mobility has gained much interest in the automotive industry and among commercial customers. A well-developed charging infrastructure is a fundamental requirement to meet the rising demand for electricity. The aim of this contribution is to demonstrate how optimization can be used for the extension of public charging infrastructure for electric vehicles (EVs). The suitability of conversion from combustion engines to EVs for commercial customers is evaluated for different scenarios. The impact of an expanded charging infrastructure is measured by a multi-objective genetic algorithm. The location and type of charging stations is optimized with respect to the number of failed trips, due to empty batteries, and the total cost of infrastructure. Assuming that the usage of commercial vehicles ist unaltered when switching to EVs, travel data from commercial vehicles with combustion engines may serve as a starting point for the optimization of the charging infrastructure. The resulting pareto front may support decision makers in placing optimal public charging stations.

Keywords

Electric mobility Electric vehicles Multi objective optimization Genetic algorithm 

Notes

Acknowledgements

The authors received funding from Volkswagen AG, the company behind the ConnectFleet services and manufacturer of the e-Crafter van.

Funding

This study was funded by Volkswagen AG.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human/animal right statement

This work does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Volkswagen AGHannoverGermany
  2. 2.FOM HochschuleHannoverGermany

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