On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.@en

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 2.6 million charging, sessions of 64,000 (i.e., 60% 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 behaviour. Using, multinomial logistic regression techniques, we identify key factors explaining heterogeneity in charging, duration behaviour across charging stations. We show how these explanatory variables can be used to, predict EV-charging behaviour in urban areas and we derive preliminary implications for policy-makers, and planners who aim to optimize types and size of charging infrastructure.@en

Policy makers are looking for effective ways to promote the adoption of electric vehicles (EVs). Among the options is the roll-out and management of charging infrastructure to meet the EV drivers’ refuelling needs. However, policies in this area do not only have a long-term effect on the adoption of EVs among prospective owners, they also have short-term impacts on the usage of public charging infrastructure among current EV owners and vice versa. Presently, studies focusing on both effects simultaneously are lacking, missing out on possible cross-pollination between these areas. This study uniquely combines stated and revealed preference data to estimate the effect of particular policy measures aimed at EV adoption, on the one hand, and charging behaviour, on the other. Using a large dataset (1.7 million charging sessions) related to charging behaviour using public charging infrastructure in the Netherlands we quantify the effects of (i) daytime-parking (to manage parking pressure) and (ii) free parking (to promote purchase of EVs) policies on charging behaviour. To estimate the effects of these particular policies on EV purchase intentions, a stated choice experiment was conducted among potential EV-buyers. Results show that cross-pollinations between EV charging and adaptation policies exist and should be taken into account when designing policies for EV adoption.@en

Fast charging is seen as a means to facilitate long-distance driving for electric vehicles (EVs). As a result, roll-out planning generally takes a corridor approach. However, with higher penetration of electric vehicles in urban areas, cities contemplate whether inner-city fast chargers can be an alternative for the growing amount of slow public chargers. For this purpose, more knowledge is required in motives and preferences of users and actual usage patterns of fast chargers. Similarly, with increasing charging speeds of fast chargers and different modes (taxi, car sharing) also switching to electric vehicles, the effect of charging speed should be evaluated as well as preferences amongst different user groups. This research investigates the different intentions and motivations of EV drivers at fast charging stations to see how charging behaviour at such stations differs using both data analysis from charging stations as a survey among EV drivers. Additionally, it estimates the willingness of EV drivers to use fast charging as a substitute for on-street home charging given higher charging speeds. The paper concludes that limited charging speeds imply that EV drivers prefer parking and charging over fast charging but this could change if battery developments allow higher charging speeds@en

Statement of contribution: 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. 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. Based on a estimation of a series of mixed logit- and latent class-based ordinal regression models, 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 to optimize types and numbers of charging infrastructure. @en

Statement of contribution: 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. 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. Based on a estimation of a series of mixed logit- and latent class-based ordinal regression models, 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 to optimize types and numbers of charging infrastructure. @en

Statement of contribution: 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. 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. Based on a estimation of a series of mixed logit- and latent class-based ordinal regression models, 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 to optimize types and numbers of charging infrastructure. @en