EV Charging performance: A dashboard to support

Demand driven expansion of charging infrastructure. Detection of charging infrastructure bottlenecks. Strategic expansion of charging infrastructure

Laadgedrag van PlugIn Hybride Elektrische Voertuigen op publieke laadpunten: RAAK-Pro project "Intelligent Data-driven Optimalisation of EV charge infrastructure"

Er is regelmatig discussie in Nederland rond het laadgedrag van Plug in Hybride Elektrische Voertuigen (PHEV’s) in vergelijking met full electric voertuigen (FEV’s). Veelal gaat het hierbij om de vraag in hoeverre fors-gesubsidieerde PHEV’s veel elektrisch laden en daadwerkelijk veel elektrische kilometers maken. Een veelgehoorde aanklacht is dat PHEV’s relatief weinig zouden laden, veelal op de verbrandingsmotor rijden en als zodanig onterecht in aanmerking komen voor subsidie. De Hogeschool van Amsterdam (HvA) doet onderzoek voor de vier grote gemeenten (G4: Amsterdam, Den Haag, Rotterdam, Utrecht) en de Metropool Regio Amsterdam (MRA) waarbij laadgedrag op het publieke laadnetwerk wordt geëvalueerd. Sinds 2012 zijn hierbij meer dan 2 miljoen laadsessies geregistreerd op meer dan 6000 laadpunten op het publieke laadnetwerk van de G4 en MRA

Laadgedrag van PlugIn Hybride Elektrische Voertuigen op publieke laadpunten in de G4 en MRA-E

Er is regelmatig discussie in Nederland rond het laadgedrag van Plug in Hybride Elektrische Voertuigen (PHEV’s) in vergelijking met full electric voertuigen (FEV’s). Veelal gaat het hierbij om de vraag in hoeverre fors-gesubsidieerde PHEV’s veel elektrisch laden en daadwerkelijk veel elektrische kilometers maken. Een veelgehoorde aanklacht is dat PHEV’s relatief weinig zouden laden, veelal op de verbrandingsmotor rijden en als zodanig onterecht in aanmerking komen voor subsidie. De Hogeschool van Amsterdam (HvA) doet onderzoek voor de vier grote gemeenten (G4: Amsterdam, Den Haag, Rotterdam, Utrecht) en de Metropool Regio Amsterdam (MRA) waarbij laadgedrag op het publieke laadnetwerk wordt geëvalueerd. Sinds 2012 zijn hierbij meer dan 2 miljoen laadsessies geregistreerd op meer dan 6000 laadpunten op het publieke laadnetwerk van de G4 en MRA. De G4 en MRA worden regelmatig geconfronteerd met kritische geluiden over het laadgedrag van PHEV’s, waarbij de stimulering van elektrisch vervoer inclusief de ontwikkeling van laadinfrastructuur kritisch wor-den bekeken. De G4 en MRA hebben de HvA de vraag gesteld in hoeverre op basis van het gebruik van hun publieke laadinfrastructuur iets gezegd kan worden over het laadgedrag van PHEV’s in vergelijking met Full Electric voertuigen (FEV’s). Hiertoe is een analyse uitgevoerd om de volgende vragen te beantwoorden: 1. Wat is de bijdrage van PHEV’s aan het totaal aantal schone kilometers gefaciliteerd door publieke laadinfrastructuur in de G4 en MRA? (in verhouding tot FEV’s). 2. Is een trend waarneembaar in (i) frequentie van laden, en (ii) kilowattuur per sessie voor PHEV en FEV rijders? Deze rapportage maakt gebruik van de beschikbare laaddata van de publieke laadinfrastructuur in de vier grote steden en de MRA om uitspraken te doen over trends in laadgedrag van PHEV’s en de bijdrage aan schone kilometers. De belangrijkste eerste stap is hierbij om onderscheid te maken tussen PHEV’s en FEV’s. Immers in de hui-dige dataset (waar de HVA over beschikt) zijn RFID’s (i.e. unieke codes voor gebruikers op basis van een laadpasnummer) niet gekoppeld aan het type voertuigen (PHEV of FEV). Hoofdstuk 2 zet uiteen hoe op ba-sis van enkele variabelen de beschikbare RFID’s zijn in te delen als PHEV’s, FEV’s dan wel als Unknown (niet in te delen op basis van de data)

Understanding Complexity in Charging Infrastructure through the Lens of Social Supply–Demand Systems

Since the first release of modern electric vehicles, researchers and policy makers have shown interest in the deployment and utilization of charging infrastructure. Despite the sheer volume of literature, limited attention has been paid to the characteristics and variance of charging behavior of EV users. In this research, we answer the question: which scientific approaches can help us to understand the dynamics of charging behavior in charging infrastructures, in order to provide recommendations regarding a more effective deployment and utilization of these infrastructures. To do so, we propose a conceptual model for charging infrastructure as a social supply–demand system and apply complex system properties. Using this conceptual model, we estimate the rate complexity, using three developed ratios that relate to the (1) necessity of sharing resources, (2) probabilities of queuing, and (3) cascading impact of transactions on others. Based on a qualitative assessment of these ratios, we propose that public charging infrastructure can be characterized as a complex system. Based on our findings, we provide four recommendations to policy makers for taking efforts to reduce complexity during deployment and measure interactions between EV users using systemic metrics. We further point researchers and policy makers to agent-based simulation models that capture interactions between EV users and the use complex network analysis to reveal weak spots in charging networks or compare the charging infrastructure layouts of across cities worldwide

Understanding Complexity in Charging Infrastructure through the Lens of Social Supply–Demand Systems

Since the first release of modern electric vehicles, researchers and policy makers have shown interest in the deployment and utilization of charging infrastructure. Despite the sheer volume of literature, limited attention has been paid to the characteristics and variance of charging behavior of EV users. In this research, we answer the question: which scientific approaches can help us to understand the dynamics of charging behavior in charging infrastructures, in order to provide recommendations regarding a more effective deployment and utilization of these infrastructures. To do so, we propose a conceptual model for charging infrastructure as a social supply–demand system and apply complex system properties. Using this conceptual model, we estimate the rate complexity, using three developed ratios that relate to the (1) necessity of sharing resources, (2) probabilities of queuing, and (3) cascading impact of transactions on others. Based on a qualitative assessment of these ratios, we propose that public charging infrastructure can be characterized as a complex system. Based on our findings, we provide four recommendations to policy makers for taking efforts to reduce complexity during deployment and measure interactions between EV users using systemic metrics. We further point researchers and policy makers to agent-based simulation models that capture interactions between EV users and the use complex network analysis to reveal weak spots in charging networks or compare the charging infrastructure layouts of across cities worldwide

Estimating the charging profile of individual charge sessions of electric vehicles in the Netherlands

The mass adoption of Electric Vehicles (EVs) might raise pressure on the power system, especially during peak hours. Therefore, there is a need for delayed charging. However, to optimize the charging system, the progression of charging from an empty battery until a full battery of the EVs based on realworld data needs to be analyzed. Many researchers currently view this charging profile as a static load and ignore the actual charging behavior during the charging session. This study investigates how different factors influence the charging profile of individual EVs based on real-world data of charging sessions in the Netherlands, enabling optimization analysis of EV smart charging schemes

Simulation of free-floating vehicle charging behaviour at public charging points

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

A validated agent-based model for stress testing charging infrastructure utilization

Deployment and management of environmental infrastructures, such as charging infrastructure for Electric Vehicles (EV), is a challenging task. For policy makers, it is particularly difficult to estimate the capacity of current deployed public charging infrastructure for a given EV user population. While data analysis of charging data has shown added value for monitoring EV systems, it is not valid to linearly extrapolate charging infrastructure performance when increasing population size. We developed a data-driven agent-based model that can explore future scenarios to identify non-trivial dynamics that may be caused by EV user interaction, such as competition or collaboration, and that may affect performance metrics. We validated the model by comparing EV user activity patterns in time and space. We performed stress tests on the 4 largest cities the Netherlands to explore the capacity of the existing charging network. Our results demonstrate that (i) a non-linear relation exists between system utilization and inconvenience even at the base case; (ii) from 2.5x current population, the occupancy of non-habitual charging increases at the expense of habitual users, leading to an expected decline of occupancy for habitual users; and (iii) from a ratio of 0.6 non-habitual users to habitual users competition effects intensify. For the infrastructure to which the stress test is applied, a ratio of approximately 0.6 may indicate a maximum allowed ratio that balances performance with inconvenience. For policy makers, this implies that when they see diminishing marginal performance of KPIs in their monitoring reports, they should be aware of potential exponential increase of inconvenience for EV users

A data driven typology of electric vehicle user types and charging sessions

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