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
This paper analyses the effect of two new developments: electrification and ‘free floating’ car sharing and their impact on public space. Contrary to station based shared cars, free floating cars do not have dedicated parking or charging stations. They therefore park at public parking spots and utilize public charging stations. A proper network of public charging stations is therefore required in order to keep the free floating fleet up and running. As more municipalities are considering the introduction of an electric free floating car sharing system, the outline of such a public charging network becomes a critical piece of information. The objective of this paper is to create insights that can optimize charging infrastructure for free floating shared cars, by presenting three analyses. First, a business area analysis shows an insight into which business areas are of interest to such a system. Secondly, the parking and charging behaviour of the vehicles is further examined. The third option looks deeper into the locations and their success factors. Finally, the results of the analysis of the city of Amsterdam are used to model the city of The Hague and the impact that a free floating electric car sharing system might have on the city and which areas are the white spots that need to be filled in.
The consistent demand for improving products working in a real-time environment is increasing, given the rise in system complexity and urge to constantly optimize the system. One such problem faced by the component supplier is to ensure their product viability under various conditions. Suppliers are at times dependent on the client’s hardware to perform full system level testing and verify own product behaviour under real circumstances. This slows down the development cycle due to dependency on client’s hardware, complexity and safety risks involved with real hardware. Moreover, in the expanding market serving multiple clients with different requirements can be challenging. This is also one of the challenges faced by HyMove, who are the manufacturer of Hydrogen fuel cells module (https://www.hymove.nl/). To match this expectation, it starts with understanding the component behaviour. Hardware in the loop (HIL) is a technique used in development and testing of the real-time systems across various engineering domain. It is a virtual simulation testing method, where a virtual simulation environment, that mimics real-world scenarios, around the physical hardware component is created, allowing for a detailed evaluation of the system’s behaviour. These methods play a vital role in assessing the functionality, robustness and reliability of systems before their deployment. Testing in a controlled environment helps understand system’s behaviour, identify potential issues, reduce risk, refine controls and accelerate the development cycle. The goal is to incorporate the fuel cell system in HIL environment to understand it’s potential in various real-time scenarios for hybrid drivelines and suggest secondary power source sizing, to consolidate appropriate hybridization ratio, along with optimizing the driveline controls. As this is a concept with wider application, this proposal is seen as the starting point for more follow-up research. To this end, a student project is already carried out on steering column as HIL
Possibly, the aviation sector’s decarbonization challenge (see Dutch knowledge key in international climate study for tourism | CELTH) has profound implications for the ability of aviation-de-pendent outbound tour operators to attract capital and with that their ability to maintain or trans-form their current business portfolio (understood here as the current product offers and approximate carbon footprints, business models, and ownership structures present in this economic do-main). Knowledge about these (possible) investment risks and their business and policy implications is lacking. This project therefore addresses this knowledge gap by means of the following research questions.1. What is the current business portfolio of Dutch outbound tour operators?a. To what extend do Dutch outbound tour operators depend on aviation in terms of product offer and turnover?b. What is the relative carbon footprint share of aviation-based products compared to the total outbound product offer and turnover of Dutch outbound tour operators?2. What are investment risks of this business portfolio as indicated by investors?a. How do investors evaluate investment risks in relation to climate change mitigation and de-carbonisation?b. What are investment risks of the business portfolio of Dutch outbound tour operators?c. What are the reflections on and implications of these investment risks from the perspective of policymakers and tour operators?
