The transition from diesel-driven urban freight transport towards more electric urban freight transport turns out to be challenging in practice. A major concern for transport operators is how to find a reliable charging strategy for a larger electric vehicle fleet that provides flexibility based on different daily mission profiles within that fleet, while also minimizing costs. This contribution assesses the trade-off between a large battery pack and opportunity charging with regard to costs and operational constraints. Based on a case study with 39 electric freight vehicles that have been used by a parcel delivery company and a courier company in daily operations for over a year, various scenarios have been analyzed by means of a TCO analysis. Although a large battery allows for more flexibility in planning, opportunity charging can provide a feasible alternative, especially in the case of varying mission profiles. Additional personnel costs during opportunity charging can be avoided as much as possible by a well-integrated charging strategy, which can be realized by a reservation system that minimizes the risk of occupied charging stations and a dense network of charging stations.
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Charging an electric vehicle needs to be as simple as possible for the user. He needs to park his car, plug his vehicle and identify to start charging. There is no need to understand the technology and protocols needed to reach this simple task.For the students and researchers of the Amsterdam University of Applied Science (AUAS / HvA), there is a need to understand as deep as possible all the techniques involved in this technology.The purpose of this document is to give to the reader the information he needs to understand how an electric car can be charged and how he can use these knowledges to analyses and interpret data.
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Electric vehicles have penetrated the Dutch market, which increases the potential for decreased local emissions, the use and storage of sustainable energy, and the roll-out and use of electric car-sharing business models. This development also raises new potential issues such as increased electricity demand, a lack of social acceptance, and infrastructural challenges in the built environment. Relevant stakeholders, such as policymakers and service providers, need to align their values and prioritize these aspects. Our study investigates the prioritization of 11 Dutch decision-makers in the field of public electric vehicle charging. These decision-makers prioritized different indicators related to measurements (e.g., EV adoption rates or charge point profitability), organization (such as fast- or smart-charging), and developments (e.g., the development of mobility-service markets) using the best-worst method. The indicators within these categories were prioritized for three different scenario's in time. The results reveal that priorities will shift from EV adoption and roll-out of infrastructure to managing peak demand, using more sustainable charging techniques (such as V2G), and using sustainable energy towards 2030. Technological advancements and autonomous charging techniques will become more relevant in a later time period, around 2040. Environmental indicators (e.g., local emissions) were consistently valued low, whereas mobility indicators were valued differently across participants, indicating a lack of consensus. Smart charging was consistently valued higher than other charging techniques, independent of time period. The results also revealed that there are some distinct differences between the priorities of policymakers and service providers. Having a systematic overview of what aspects matter supports the policy discussion around EVs in the built environment.
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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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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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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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The understanding of charging behavior has been recognized as a crucial element in optimizing roll out of charging infrastructure. While current literature provides charging choices and categorizations of charging behavior, these seem oversimplified and limitedly based on charging data. In this research we provide a typology of charging behavior and electric vehicle user types based on 4.9 million charging transactions from January 2017 until March 2019 and 27,000 users on 7079 Charging Points the public level 2 charging infrastructure of 4 largest cities and metropolitan areas of the Netherlands. We overcome predefined stereotypical expectations of user behavior by using a bottom-up data driven two-step clustering approach that first clusters charging sessions and thereafter portfolios of charging sessions per user. From the first clustering (Gaussian Mixture) 13 distinct charging session types were found; 7 types of daytime charging sessions (4 short, 3 medium duration) and 6 types of overnight charging sessions. The second clustering (Partition Around Medoids) clustering result in 9 user types based on their distinct portfolio of charging session types. We found (i) 3 daytime office hours charging user types (ii) 3 overnight user types and (iii) 3 non-typical user types (mixed day and overnight chargers, visitors and car sharing). Three user types show significant peaks at larger battery sizes which affects the time between sessions. Results show that none of the user types display solely stereotypical behavior as the range of behaviors is more varied and more subtle. Analysis of population composition over time revealed that large battery users increase over time in the population. From this we expect that shifts charging portfolios will be observed in future, while the types of charging remain stable.
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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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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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