The number of Electric Vehicles (EVs) is expected to increase exponentially in the coming years. The growing presence of charging points generates a multitude of interactions between EV users, particularly in metropolitan areas where a charging infrastructure is largely part of the public domain. There is a current knowledge gap as to how current decisions on charging infrastructure deployment affect both current and future infrastructure performance. In the thesis an attempt is made to bridge this knowledge gap by creating a deeper understanding of the relation between charging behavior, charging infrastructure deployment, and performance.The results demonstrate shown how both strategic and demand-drive deployment strategies have an effect on performance metrics. In a case study in the Netherlands it was found that during the initial deployment phase, strategic Charging Points (CPs) facilitate EV users better than demand driven deployment. As EV user adoption increased, demand-driven CPs show to outperform strategic CPs.This thesis further shows that there are 9 EV user types each with distinct difference distinct behavior in terms of charging frequency and mean energy uptake, both of which relate to aggregate CP performance and that user type composition, interactions between users and battery size play an important role in explaining performance of charging infrastructure.A validated data-driven agent-based model was developed to explore effects of interactions in the EV system and how they influence performance. The simulation results demonstrate that there is a non-linear relation between system utilization and inconvenience even at the base case scenario. Also, a significant rise of EV user population will lead to an occupancy of non-habitual charging at the expense of habitual EV users, which leads to an expected decline of occupancy for habitual EV users.Additional simulations studies support the hypothesis that several Complex Systems properties are currently present and affecting the relation between performance and occupation.
The COVID-19 lockdowns showed that working from home and conducting meetings online can change mobility patterns and needs substantially. This global pandemic may have also substantially changed mobility patterns on the long-term and therefore, also the need of electric vehicle charging infrastructure. Charging need dropped significantly but also changed the distribution of the load on the electricity grid throughout the day. This paper analyses changes in electric charging for different user groups during different phases of the pandemic to assess the long-term effects on EV charging needs.
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.
In the coming decades, a substantial number of electric vehicle (EV) chargers need to be installed. The Dutch Climate Accord, accordingly, urges for preparation of regional-scale spatial programs with focus on transport infrastructure for three major metropolitan regions among them Amsterdam Metropolitan Area (AMA). Spatial allocation of EV chargers could be approached at two different spatial scales. At the metropolitan scale, given the inter-regional flow of cars, the EV chargers of one neighbourhood could serve visitors from other neighbourhoods during days. At the neighbourhood scale, EV chargers need to be allocated as close as possible to electricity substations, and within a walkable distance from the final destination of EV drivers during days and nights, i.e. amenities, jobs, and dwellings. This study aims to bridge the gap in the previous studies, that is dealing with only of the two scales, by conducting a two-phase study on EV infrastructure. At the first phase of the study, the necessary number of new EV chargers in 353 4-digit postcodes of AMA will be calculated. On the basis of the findings of the Phase 1, as a case study, EV chargers will be allocated at the candidate street parking locations in the Amsterdam West borough. The methods of the study are Mixed-integer nonlinear programming, accessibility and street pattern analysis. The study will be conducted on the basis of data of regional scale travel behaviour survey and the location of dwellings, existing chargers, jobs, amenities, and electricity substations.
To what extent does receiving information about either popular attractions or less-visited at-tractions, presented as “highlights” of the city, influence the movement of tourists to popular or less-visited attractions, and how does this differ by information channel through which the information is presented? To what extent does receiving information about either popular attractions or less-visited at-tractions, presented as “highlights” of the city, influence tourists’ experience, including their evaluations of the destination, their visit as a whole, and the specific information channel they received, and how does this differ by information channel through which the information is presented? What implementation models and financing mechanisms are available for DMO’s to spread tourists using the information channels tested, contingent on their effectiveness as measured by the previous experiment?Societal issueDestination Management Organisations (DMOs) are looking for interventions that effectively discourage tourists from visiting crowded hotspots and entice them to visit less crowded locations. Interventions like changing infrastructure, charging entrance fees and re-serving site access are either too expensive, too invasive or politically controversial. It is much easier to intervene on tourists' behaviour by informing them about alternatives.Collaborative partnersNHL Stenden, Travel with Zoey, Amsterdam and Partners, Wonderful Copenhagen, Mobidot.
