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Intelligente Data-gedreven Optimalisatie Laadinfrastructuur

Charging station hogging

a data-driven analysis

Rapport
Logo EVS30 Stuttgart, Germany

With a growing number of electric vehicles (EVs) on the road and charging infrastructure investments lagging, occupation of installed charging stations is growing and available charging points for EV drivers are becoming scarce. Installing more charging infrastructure is problematic from both a public (tax payers money, parking availability) and private (business case) perspective. Increasing the utilization of available charging stations is one of the solutions to satisfy the growing charging need of EV drivers and managing other stakeholders interests. Currently, in the Netherlands only 15-25% of the time connected to a public charging station is actually used for charging. The longest 4% of all sessions account for over 20% of all time connected while barely using this time for actually charging. The behaviour in which EV users stay connected to a charging station longer than necessary to charge their car is called “charging station hogging”. Using a large dataset (1.3 million sessions) on public charging infrastructure usage, this paper analyses the inefficient use of charging stations along three axes: where the hogging takes place (spatial), by whom (the characteristics of the user) and during which time frames (day, week and year). Using the results potential solutions are evaluated and assessed including their potential and pitfalls.

Referentie Wolbertus, R., & van den Hoed, R. (2017). Charging station hogging: a data-driven analysis. Paper presented at The 30th International Electric Vehicle Symposium & Exhibition, Stuttgart, Germany.
Gepubliceerd door  IDO-Laad 9 oktober 2017