With the rise of the number of electric vehicles, the installment of public charging infrastructure is becoming more prominent. In urban areas in which EV users rely on on-street parking facilities, the demand for public charging stations is high. Cities take on the role of implementing public charging infrastructure and are looking for efficient roll-out strategies. Municipalities generally reserve the parking spots next to charging stations to ensure their availability. Underutilization of these charging stations leads to increased parking pressure, especially during peak hours. The city of The Hague has therefore implemented daytime reservation of parking spots next to charging stations. These parking spots are exclusively available between 10:00 and 19:00 for electric vehicles and are accessible for other vehicles beyond these times. This paper uses a large dataset with information on nearly 40.000 charging sessions to analyze the implementation of the abovementioned scheme. An unique natural experiment was created in which charging stations within areas of similar parking pressure did or did not have this scheme implemented. Results show that implemented daytime charging 10-19 can restrict EV owners in using the charging station at times when they need it. An extension of daytime charging to 10:00-22:00 proves to reduce the hurdle for EV drivers as only 3% of charging sessions take place beyond this time. The policy still has the potential to relieve parking pressure. The paper contributes to the knowledge of innovative measures to stimulate the optimized rollout and usage of charging infrastructure.
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Underutilised charging stations can be a bottleneck in the swift transition to electric mobility. This study is the first to research cooperative behaviour at public charging stations as a way to address improved usage of public charging stations. It does so by viewing public charging stations as a common-pool resource and explains cooperative behaviour from an evolutionary perspective. Current behaviour is analysed using a survey (313 useful responses) and an analysis of large dataset (2.1 million charging sessions) on the use of public charging infrastructure in Amsterdam, The Netherlands. In such a way it identifies the potential, drivers and possible obstacles that electric vehicle drivers experience when cooperating with other drivers to optimally make use of existing infrastructure. Results show that the intention to show direct reciprocal charging behaviour is high among the respondents, although this could be limited if the battery did not reach full or sufficient state-of-charge at the moment of the request. Intention to show direct reciprocal behaviour is mediated by kin and network effects.
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Since the first uptake of electric vehicles, policy makers are questioning how to rollout public charging infrastructure in an efficient manner, such that user convenience balances with costs of investment. In some metropolitan areas, the first phase of rollout has been passed, meaning an optimized deployment of future charging stations for electric vehicles (EVs) becomes important to improve the charging infrastructure and ensure customer satisfaction and sufficient service provision. Complex system literature shows that network vulnerability is an important metric, yet, charging infrastructure has not yet been a subject of these simulation models so far. This research, based on real-world data, provides a novel approach for improving the roll-out strategy of municipalities, by treating the charge infrastructure as a complex network of charging stations and defining vulnerability in respect to the availability of its surrounding charging stations within relevant walking distance.
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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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This paper aims to answer the question: “Which factors influence the success of public charging stations?”. For the empirical analyses we used data provided by the public charging stations of the city The Hague. In the second half of 2015 more than 91.795 charge sessions were logged of more than 6.693 unique charge cards.---Analyse van de verschillen in het gebruik van de zogeheten ‘demand-driven’ en strategische laadpunten. Onderzoek naar de uitrol en het gebruik van E-Laad oplaadpunten in Nederland (EN).
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This paper explores current and potential future use of fast charging stations for electric passenger vehicles. The aim of the paper is to analyse current charging patterns at fast charging stations and the role of fast charging among different charging options. These patterns are explored along the lines of the technical capabilities of the vehicles and it is found that with increasing battery capacity the need for fast charging decreases. However, for those vehicles with large charging capacities there are indications that fast charging is perceived as more convenient as these are used more often. Such results indicate a larger share for fast charging if charging capacities increase in the future. Results from a spatial analysis show that most fast charging is done at a considerable distance from home, suggesting mostly ‘on the road’ charging sessions. Some fast charging sessions are relatively close to home, especially for those without private home charging access. This shows some future potential for fast charging in cities with many on-street parking facilities.
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City authorities want to know how to match the charging infrastructures for electric vehicles with the demand. Using camera recognition algorithms from artificial intelligence we investigated the behavior of taxis at a charging stations and a taxi stand.
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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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The Netherlands is a frontrunner in the field of public charging infrastructure, having a high number of public charging stations per electric vehicle (EV) in the world. During the early years of adoption (2012-2015) a large percentage of the EV fleet were Plugin Hybrid Electric Vehicles (PHEV)due to the subsidy scheme at that time. With an increasing number of Full Electric Vehicles (FEVs) on the market and a current subsidy scheme for FEV only, a transition of the EV fleet from PHEV to FEV is expected. This is hypothesized to have effect on charging behavior of the complete fleet, reason to understand better how PHEVs and FEVs differ in charging behavior and how this impacts charging infrastructure usage. In this paper, the effects of the transition of PHEV to FEV is simulated by extending an existing Agent Based Model. Results show important effects of this transitionon charging infrastructure performance.
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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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