Hogeschool van Amsterdam

Centre of Applied Research Technology

How do EV drivers adapt their charging behavior to battery size and charging capabilities?

a systematic datadriven analysis

Paper

Charging station infrastructure is designed to meet the demand of electric vehicle (EV) drivers. Prediction of the necessary supply of charging stations is often a data driven process in which charging patterns from current EV drivers are used as an exemplar. These patterns are than extrapolated to estimate future demand. This is a stationary approach to predicting the necessary supply. Battery and charging technology are however continuously changing; new EVs offer larger battery packs and higher fast charging speeds. It can be expected that drivers adapt their charging behavior to the technological capabilities of the car but on the other hand, opportunistic behavior and habits also play a role. So far, little insight has been provided in how EV drivers adapt their charging behavior to these technological factors. Such information is crucial for planning of charging infrastructure in the future. This study analyses how technological factors such as battery capacity and charging speed determine the charging behavior of EV drivers. Using a large database (6+ million charging sessions) on public charging infrastructure and a database with charging profiles of those drivers that have home charging available, it is systematically evaluated how the technological capabilities affect charging behavior. Conclusions are drawn for how this has an impact on infrastructure planning.

Due to a decline in battery costs and more stringent policy measures the sales of EVs is expected to rise soon. Policy makers are looking to optimize the way deployment of charging infrastructure to facilitate growth in charging demand form these new vehicles. In recent years, policy makers have used studies that rely on charging data of early adopters to examine how charging infrastructure matches charging demand. However, to assume that these charging patterns match the charging demand of future EV drivers is a static approach.

Due to continuous developments on battery and charging technology, EVs are built with larger ranges and faster charging speeds. Previously, 300km+ ranges were only available in the luxury segment; newly launched models (e.g. Tesla Model 3, Volkswagen ID.3 etc.) make larger driving ranges available for the masses. While, up to now 50kW was the most common fast charging standard, currently available vehicles tout with fast charging speeds from 150 up to 350kW.

Policy makers and businesses try to keep up with sufficient charging infrastructure for the increasing number of EVs on the road. They face uncertain strategic decisions. Uncertainty increases due to rapidly changing technology. This increases the risk of investments into potentially soon-to-be-obsolete technology. Additionally, it is not well understood how charging behavior of EV driver adapts to such technological developments. A systematic analysis about how these technological factors impact charging behavior has been missing.

This research is the first to systematically evaluate how vehicle capabilities (battery capacity and charging power) influence charging behavior. The paper does not assume a one-way relation between vehicle capacity and charging habits but also that availability influences charging choices. With two different datasets on charging behavior (in total more 4 million charging sessions), the analysis compares different vehicle types in their charging behavior such as frequency of charging, unique charging stations used and the ratio of home, workplace, public and fast charging. To investigate the reciprocal relationship the paper uses the second dataset to compare charging behavior of those that do or do not have access to home charging.

Reference Wolbertus, R., & van den Hoed, R. (2020). How do EV drivers adapt their charging behavior to battery size and charging capabilities? a systematic datadriven analysis. Paper presented at Electric Vehicle Symposium 33, Oregon, United States.
Published by  Centre for Applied Research Technology 14 June 2020