In this study we developed models in order to predict the need for public charging points. These models give municipalities an insight into various environmental and consumer related factors that determine the need for public charging points for electric vehicles in the neighbourhood. These factors include, amongst others, the average gross monthly income of households in a certain neighbourhood and the overall number of cars in a certain neighbourhood. On the basis of the models it turns out, among other factors, that neighbourhoods with households with a relatively high average gross monthly income, and a relatively high number of cars, need a relatively large number of public charging points for electric vehicles.
DOCUMENT
As society has to adapt to changing energy sources and consumption, it is driving away from fossil energy. One particular area of interest is electrical driving and the increasing demand for (public) charging facilities. For municipalities, it is essential to adapt to this changing demand and provide more public charging facilities.In order to accommodate on roll-out strategies in metropolitan areas a data driven simulation model, SEVA1, has been developed The SEVA base model used in this paper is an Agent-Based model that incorporate past sessions to predict future charging behaviour. Most EV users are habitual users and tend to use a small subset of the available charge facilities, by that obtaining a pattern is within the range possibilities. Yet, for non-habitual users, for example, car sharing users, obtaining a pattern is much harder as the cars use a significantly higher amount of charge points.The focus of this research is to explore different model implementations to assess the potential of predicting free-floating cars from the non-habitual user population. Most important result is that we now can simulate effects of deployement of car sharing users in the system, and with that the effect on convenience for habitual users. Results show that the interaction between habitual and non habitual EV users affect the unsuccessful connection attempts based increased based on the size of the car-sharing fleet up to approximately 10 percent. From these results implications for policy makers could be drawn.
MULTIFILE
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.
DOCUMENT
Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam The Amsterdam University of Applied Sciences in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht and the metropolitan region of Amsterdam developed both the back- and front-end of a decision support tool. This paper describes the design of the decision support tool and its DataWareHouse architecture. The back-end is based on a monthly update of charging data with Charge point Detail Records and Meter Values enriched with location specific data. The design of the front-end is based on Key Performance Indicators used in the decision process for charging infrastructure roll-out. Implementing this design and DataWareHouse architecture allows all kinds of EV related companies and cities to start monitoring their charging infrastructure. It provides an overview of how the most important KPIs are being monitored and represented in the decision support tool based on regular interviews and decision processes followed by four major cities and a metropolitan region in the Netherlands.
DOCUMENT
Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll-out of charging infrastructure, both public and semi-public, all over the world.
DOCUMENT
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.
MULTIFILE
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.
DOCUMENT
As the Dutch electric vehicle (EV) fleet continues to expand, so will the amount of charging sessions increase. This expanding demand for energy will add on to the already existing strain on the grid, primarily during peak hours on workdays in the early morning and evening. This growing energy demand requires new methods to handle the charging of EVs, to distribute the available energy in the most effective way. Therefore, a large number of ‘smart charging’ initiatives have recently been developed, whereby the charging session of the EV is based on the conditions of the energy grid. However, the term smart charging is used for a variety of smart charging initiatives, often involving different optimization strategies and charging processes. For most practitioners, as well as academics, it is hard to distinguish the large range of smart charging initiatives initiated in recent years, how they differentiate from each other and how they contribute to a smarter charging infrastructure. This paper has the objective to provide an overview of smart charging initiatives in the Netherlands and develop a categorization of smart charging initiatives regarding objectives, proposed measures and intended contributions. We will do so by looking at initiatives that focus on smart charging at a household level, investigating the smart charging possibilities for EV owners who either make use of a private or (semi-)public charging point. The different smart charging initiatives will be analyzed and explicated in combination with a literature study, focusing on the different optimization strategies and requirements to smart charge an electric vehicle.
DOCUMENT
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.
DOCUMENT
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.
DOCUMENT
