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.

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Charging infrastructure development is vital for the adoption of electric vehicles (EVs). Yet, on the surface, there seems to be significant disagreement about when, how and which kind of charging infrastructure should be developed and most importantly, for what reasons. These reasons are concealed in the stakeholders’ perspective on the future. Differences in stakeholders’ perspectives regarding expectations on the future EV charging infrastructure may be expected, but should they prove irreconcilable they may stall the roll-out. However, to date, it remains unknown what these stakeholders’ perspectives are, how they are aligned across stakeholders, which topics are heavily debated and which are agreed upon. This study uses Q-methodology to identify different perspectives on the future of roll-out of EV charging infrastructure. The analysis shows that stakeholders mainly differ in the extent fast charging should play an important role, the degree smart charging should be the standard in charging and how much government should intervene with infrastructure roll-out. There is a consensus on the importance of interoperability of charging stations. The four different perspectives were supported across different stakeholders, which supports the idea that perspectives are not strongly linked to the stakeholders’ interests.

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A lack of charging infrastructure is seen as the biggest barrier to electric vehicle adoption. This thesis analyses the use of public charging infrastructure and its relation to electric vehicle adoption in urban areas. With the use a large dataset on charging station use, charging behaviour is characterised and the influence of national and local policies is estimated. Analysis shows that purchase incentives, local roll-out strategies and parking policies have a substantial influence on charging behaviour and significant cross-pollination between charging and purchase policies exist. The thesis has a special attention for connection times at charging stations, which in contrast to previous work, are here proven to be an interplay between parking and charging behaviour. Policies, such as financial stimulus, to reduce excessive long connection times at charging stations should therefore also be reviewed in this light. Analysis of this behaviour is used to model the different roll-out strategies for urban areas on the longer term. Different roll-out strategies are tested to illustrate how local policy maker decisions can influence the charging security and convenience for electric vehicle drivers. Charging infrastructure all across the city can provide most security, but centralised infrastructure, such as charging hubs, more convenience. Such policies should be supported by stakeholders in the field. Results from this study with these stakeholders show that their perspectives on how to shape charging infrastructure is not necessarily only aligned with their interests. Perspectives on the future differ, but are shared across and within multiple stakeholders. @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

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.

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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.

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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

Recent studies show that charging stations are operated in an inefficient way. Due to the fact that electric vehicle (EV) drivers charge while they park, they tend to keep the charging station occupied while not charging. This prevents others from having access. This study is the first to investigate the effect of a pricing strategy to increase the efficient use of electric vehicle charging stations. We used a stated preference survey among EV drivers to investigate the effect of a time-based fee to reduce idle time at a charging station. We tested the effect of such a fee under different scenarios and we modelled the heterogeneity among respondents using a latent class discrete choice model. We find that a fee can be very effective in increasing the efficiency at a charging station but the response to the fee varies among EV drivers depending on their current behaviour and the level of parking pressure they experience near their home. From these findings we draw implications for policy makers and charging point operators who aim to optimize the use of electric vehicle charging stations.

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