Hogeschool van Amsterdam

Intelligent Data-driven Optimisation of Charging Infrastructure

Fully charged

an empirical study into the factors that influence connection times at EV-charging stations

Article

This study is the first to systematically and quantitatively explore the factors that determine the length of charging sessions at public charging stations for electric vehicles in urban areas, with particular emphasis placed on the combined parking- and charging-related determinants of connection times. We use a unique and large data set – containing information concerning 3.7 million charging sessions of 84,000 (i.e., 70% of) Dutch EV-users – in which both private users and taxi and car sharing vehicles are included; thus representing a large variation in charging duration behavior. Using multinomial logistic regression techniques, we identify key factors explaining heterogeneity in charging duration behavior across charging stations. We show how these explanatory variables can be used to predict EV-charging behavior in urban areas and we derive preliminary implications for policy-makers and planners who aim to optimize types and size of charging infrastructure.

Reference Wolbertus, R., Kroesen, M., van den Hoed, R., & Chorus, C. (2018). Fully charged: an empirical study into the factors that influence connection times at EV-charging stations. Energy Policy, 123(December), 1-7. https://doi.org/10.1016/j.enpol.2018.08.030
Published by  Centre for Applied Research Technology 1 December 2018