Deriving electric vehicle charge profiles from driving statistics

Abstract

The impacts of EV charging on electricity grids is becoming an increasingly important subject of study, but detailed knowledge about the future charging profiles of EVs appears to be missing. In this study we construct EV charge profiles based upon a large dataset of driving patterns. We consider both controlled and uncontrolled charging scenarios, where the main rationale of the controlled charging scenario is to shift the EV electricity demand away from the standard household peak. We show that applying charge control results in only slightly higher peaks compared to the situation without EVs, whereas in the uncontrolled case, the peaks will be significantly higher. Moreover, it is shown that the aggregated charge profiles give a good approximation for the demand of approximately 50 EVs or more. The EV charge profiles can be used as a tool for future network planning and EV impact studies.