In order to electrify the transport sector, scores of charging stations are needed to incentivize 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.

No abstract

No abstract

The automotive sector’s transition to Battery Electric Vehicles (BEVs) requires extensive deployment of additional charging infrastructure. To determine optimal new locations, planners consider and rate a multitude of factors that influence the charging demand at candidate sites. Researchers have proposed a variety of placement criteria and methods to automate site selection. However, no common set of criteria has emerged. In addition, due to the lack of usage data, the applicability of existing criteria remains unclear. Therefore, the goals of this article are to extract the most relevant factors from literature and to evaluate their ability to characterize charging point usage. First, we review the literature base to collect, analyze, and cluster existing influencing factors and to analyze how they affect charging demand. Second, we conduct a case study using real-life charging station data from Hamburg, Germany. Based on the extracted influencing factors, we identify four clusters within Hamburg’s public charging infrastructure. While the mean performance indicators duration, daily transactions, and connection ratio hardly differ among these clusters, the temporal occupancy curves clearly show distinct charging behavior for each cluster. This work contributes to the state of the art by structuring the diverse landscape of charging station location placement criteria, by deriving a set of measurable influencing factors, and by analyzing their effect on a location’s charging demand, yielding an open source data set of charging point usage.

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15 %. In transportation research this is an