Multi-criteria, co-evolutionary charging behavior: An agent-based simulation of urban electromobility

Lennart Adenaw, Markus Lienkamp

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


In order to electrify the transport sector, scores of charging stations are needed to incen-tivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

Original languageEnglish
Article number18
Pages (from-to)1-26
Number of pages26
JournalWorld Electric Vehicle Journal
Issue number1
StatePublished - Jan 2021


  • Agent-based simulation
  • Battery electric vehicles (BEV)
  • Behavior learning
  • Charging behavior
  • Charging infrastructure
  • Co-evolutionary algorithm
  • Electromobility
  • MATSim


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