Abstract:

The automotive sector’s transition to Battery Electric Vehicles (BEVs) requires the extensive deployment of additional charging infrastructure. To determine optimal new locations, planners consider and rate many factors influencing the charging demand at candidate sites. Researchers have proposed various 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, this article aims to extract the most relevant factors from the literature and evaluate their ability to characterize charging point usage. First, the authors review the literature base to collect, analyze, and cluster existing influencing factors and to analyze how they affect charging demand. Second, the authors conduct a case study using real-life charging station data from Hamburg, Germany. The authors identify four clusters within Hamburg’s public charging infrastructure based on the extracted influencing factors. 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.