At this moment, charging your electric vehicle is common good, however smart charging is still a novelty in the developing phase with many unknowns. A smart charging system monitors, manages and restricts the charging process to optimize energy consumption. The need for, and advantages of smart charging electric vehicles are clear cut from the perspective of the government, energy suppliers and sustainability goals. But what about the advantages and disadvantages for the people who drive electric cars? What opportunities are there to support the goals of the user to make smart charging desirable for them? By means of qualitative Co-design methods the underlying motives of early adaptors for joining a smart charging service were uncovered. This was done by first sensitizing the user about their current and past encounters with smart charging to make them more aware of their everyday experiences. This was followed by another generative method, journey mapping and in-depth interviews to uncover the core values that drove them to participate in a smart charging system. Finally, during two co-design sessions, the participants formed groups in which they were challenged to design the future of smart charging guided by their core values. The three main findings are as follows. Firstly, participants are looking for ways to make their sustainable behaviour visible and measurable for themselves. For example, the money they saved by using the smart charging system was often used as a scoreboard, more than it was about theactual money. Secondly, they were more willing to participate in smart charging and discharging (sending energy from their vehicle back to the grid) if it had a direct positive effect on someone close to them. For example, a retiree stated that he was more than willing to share the energy of his car with a neighbouring family in which both young parents work, making them unable to charge their vehicles at times when renewable energy is available in abundance. The third and last finding is interrelated with this, it is about setting the right example. The early adopters want to show people close to them that they are making an effort to do the right thing. This is known as the law of proximity and is well illustrated by a participant that bought a second-hand, first-generation Nissan Leaf with a range of just 80 km in the summer and even less in winter. It isn’t about buying the best or most convenient car but about showing the children that sometimes it takes effort to do the right thing. These results suggest that there are clear opportunities for suppliers of smart EV charging services to make it more desirable for users, with other incentives than the now commonly used method of saving money. The main takeaway is that early adopters have a desire for their sustainable behaviour to be more visible and tangible for themselves and their social environment. The results have been translated into preliminary design proposals in which the law of proximity is applied.
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Residential public charging points are shared by multiple electric vehicle drivers, often neighbours. Therefore, charging behaviour is embedded in a social context. Behaviours that affect, or are influenced by, other publiccharging point users have been sparsely studied and lack an overarching and comprehensive definition. Consequently, very few measures are applied in practice to influence charging behaviour. We aim to classify and define the social dimension of charging behaviour from a social-psychological perspective and, using a behaviour change framework, identify and analyse the measures to influence this behaviour. We interviewed 15 experts onresidential public charging infrastructure in the Netherlands. We identified 17 charging behaviours rooted in interpersonal interactions between individuals and interactions between individuals and technology. These behaviours can be categorised into prosocial and antisocial charging behaviours. Prosocial charging behaviour provides or enhances the opportunity for other users to charge their vehicle at the public charging point, for instance by charging only when necessary. Antisocial charging behaviour prevents or diminishes this opportunity, for instance by occupying the charging point after charging, intentionally or unintentionally. We thenidentified 23 measures to influence antisocial and prosocial charging behaviours. These measures can influence behaviour through human–technology interaction, such as providing charging etiquettes to new electric vehicle drivers or charging idle fees, and interpersonal interaction, such as social pressure from other charging point users or facilitating social interactions to exchange requests. Our approach advocates for more attention to the social dimension of charging behaviour.
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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.
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Fast charging is usually seen as a means to facilitate long distance driving for electric vehicles and roll-out therefore often happens with corridors in mind. Due to limited charging speeds, EV drivers usually tend to charge at home or work when the car is parked for a longer period to avoid unnecessarily time loss. However with increasing charging speeds and different modes (taxi, car sharing) also switching to electric vehicles, a different approach to fast charging is needed. This research investigates the different intentions of EV drivers at fast charging stations in inner-cities and along highways to see how usage at such stations differs to inform policy makers and charging point operators to accommodate an efficient roll-out strategy.
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With a growing number of electric vehicles (EVs) on the road and charging infrastructure investments lagging, occupation of installed charging stations is growing and available charging points for EV drivers are becoming scarce. Installing more charging infrastructure is problematic from both a public(tax payers money, parking availability) and private (business case) perspective. Increasing the utilization of available charging stations is one of the solutions to satisfy the growing charging need of EV drivers and managing other stakeholders interests. Currently, in the Netherlands only 15-25% of the time connected to a public charging station is actually used for charging. The longest 4% of all sessions account for over 20% of all time connected while barely using this time for actually charging. The behaviour in which EV users stay connected to a charging station longer than necessary to charge their car is called “charging station hogging”. Using a large dataset (1.3 million sessions) on publiccharging infrastructure usage, this paper analyses the inefficient use of charging stations along three axes: where the hogging takes place (spatial), by whom (the characteristics of the user) and during which time frames (day, week and year). Using the results potential solutions are evaluated and assessed including their potential and pitfalls.
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This paper analyses the effect of two new developments: electrification and ‘free floating’ car sharing and their impact on public space. Contrary to station based shared cars, free floating cars do not have dedicated parking or charging stations. They therefore park at public parking spots and utilize public charging stations. A proper network of public charging stations is therefore required in order to keep the free floating fleet up and running. As more municipalities are considering the introduction of an electric free floating car sharing system, the outline of such a public charging network becomes a critical piece of information. The objective of this paper is to create insights that can optimize charging infrastructure for free floating shared cars, by presenting three analyses. First, a business area analysis shows an insight into which business areas are of interest to such a system. Secondly, the parking and charging behaviour of the vehicles is further examined. The third option looks deeper into the locations and their success factors. Finally, the results of the analysis of the city of Amsterdam are used to model the city of The Hague and the impact that a free floating electric car sharing system might have on the city and which areas are the white spots that need to be filled in.
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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.
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When considering which is faster, a pedestrian or a car, we often overlook many aspects in our reasoning. For a car to be successful, thousands of kilometers of asphalt, sewers, filling and charging stations must be built and maintained, right across the (ecological) landscape and so on. We tend to focus on only one aspect, such as speed (cars are faster than walking or cycling), emissions (cars are polluting, particulate matter, CO2), energy, (material) costs, accident risk, convenience, etc. Rarely we can zoom out to see the whole picture, so think integrally. It is argued that to achieve a sustainable world, we should think more integrally (IDG2)!
MULTIFILE
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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Currently EVs constitute only 1% of all vehicles on the road. We are at the eve of the large scale introduction of EVs. Large scale introduction requires a significant growth in charging infrastructure. In an urban context, in which many rely on on-street charging facilities, policy makers deal with a large number of concerns. Policy makers are uncertain about which charging deployment strategy to follow. This paper presents results from simulating different strategies for charging infrastructure roll to facilitate a large scale introduction of EVs using agent based simulation. In contrast to other models, the model uses observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. The simulation incorporates different user types (Inhabitants, visitors, taxis and sharing) to model the complexity of charging in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging), the intensity of rollout (EV to Charging point ratio) and adoption rates. The simulation measures both the success rate and the additional miles cruising for a charging station. Results shows that scaling effects in charging infrastructure exist allowing for more efficient use of the infrastructure at a larger size.
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