A Model for the Data-based Analysis and Design of Urban Public Charging Infrastructure

Lennart Adenaw, Markus Lienkamp

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

With electromobility on the rise, several re-searchers have acknowledged the charging station location problem as a major challenge for city planners and decision-makers. Especially in urban areas, where there is little room for privately owned charging infrastructure, public charging infrastructure is relevant to users of battery electric vehicles. This paper reviews current literature on the charging station location problem and proposes a four-step model concept for the placement and allocation of urban public charging sites based on the shortcomings of existing approaches. The proposed model combines the advantages of existing approaches and is exclusively built on socio-demographic and geospatial data that is readily available in all larger municipalities of the world. The model consists of a demand estimation based on socio-demographic and geospatial data, a candidate site selection based on a genetic algorithm, an agent-based infrastructure simulation to account for user behavior and system dynamics, and a heuristic repositioning approach to optimize charger allocation.

Original languageEnglish
Title of host publication2020 15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728156415
DOIs
StatePublished - 10 Sep 2020
Event15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020 - Monte-Carlo, Monaco
Duration: 10 Sep 202012 Sep 2020

Publication series

Name2020 15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020

Conference

Conference15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020
Country/TerritoryMonaco
CityMonte-Carlo
Period10/09/2012/09/20

Keywords

  • Agent-based Simulation
  • Charging Station Location Problem
  • Demand Estimation
  • Genetic Algorithm
  • Urban Public Charging Infrastructure

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