Optimal EV Charge Scheduling Considering FCR Participation and Battery Degradation

Emerging vehicle-to-grid (V2G) technology gives more flexibility to electric vehicles (EVs) for participating in ancillary service markets. This paper presents an optimal charge scheduling model for EVs by considering V2G, frequency containment reserve (FCR), and battery degradation, to investigate the profitability of FCR participation for an individual EV. The model considers the EV owners’ preferences for desired energy at the departure times while participating in FCR. The total scheduling cost of the EV is minimized through a mixed integer linear programming (MILP) problem. The outputs of theMILP model are the EV’s charge/discharge pattern and the amount of power for each scheduling horizon. It is found that FCR participation is quite profitable for EV owners

Analysing the impacts of parking price policies with the introduction of connected and Automated Vehicles

It is known that parking prices can affect multiple characteristics such as traffic flow, delays, and congestion. Connected and autonomous vehicles (CAVs) do not need drivers and may return to the origin, if necessary, avoiding parking fees. However, if the destination area is not near the origin, it may not be economically viable to return. Hence, in the present study, four scenarios were tested to find the optimal parking strategy: (i) enter and park inside area (ii) enter, drop off and return to the origin (iii) enter, drop off and return to outside parking and (iv) enter and drive around. Different parking prices were used to determine the suitable option. The ‘Balanced’ scenario with multiple parking choices was found to be better compared to other scenarios, where the flow and travel distance were moderately (-19 and -26.3%) affected. Emissions were reduced significantly with CAVs

V2G-capable shared autonomous electric vehicles fleet: Economic viability and environmental co-benefits

The pursuit of energy efficiency, increasing consumption of non-renewable energy related to fossil fuels, and concerns about the impact of climate change are some of the primary motivators for the introduction of electric vehicles. Battery electric vehicles (BEV) may be used in potential commercial autonomous taxi fleets; in addition to saving energy and maintenance costs, the introduction of these electric vehicles will also provide fleet operators with possible vehicle-to-grid (V2G) service opportunities. This study investigates the life-cycle total cost, greenhouse gas emissions, and energy consumption of automated shared vehicle fleets consisted of internal combustion engine vehicles and electric vehicles with 100-mile short-range and 250-mile long-range capable of achieving the same level of service. The results show that the 250-mile long-range electric vehicle fleet with V2G service has significant advantages in cost, emissions, and energy consumption.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/167196/1/Liao_Zitong_Thesis.pd

Mode substitution induced by electric mobility hubs: results from Amsterdam

Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found users of different modes not only have a varied general preference for different shared modes, but also have different sensitivity for attributes such as travel time and cost. Compared to car users, public transport users are more likely to switch towards the eHUBS modes. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km)

Mode substitution induced by electric mobility hubs:Results from Amsterdam

Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found that users of different modes not only have varied general preferences for different shared modes but also have different sensitivity for attributes such as travel time and cost. Public transport users are more likely to switch to eHUBS modes than car users. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km).</p

Crowdsourced Quantification and Visualization of Urban Mobility Space Inequality

Most cities are car-centric, allocating a privileged amount of urban space to cars at the expense of sustainable mobility like cycling. Simultaneously, privately owned vehicles are vastly underused, wasting valuable opportunities for accommodating more people in a livable urban environment by occupying spacious parking areas. Since a data-driven quantification and visualization of such urban mobility space inequality is lacking, here we explore how crowdsourced data can help to advance its understanding. In particular, we describe how the open-source online platform What the Street!? uses massive user-generated data from OpenStreetMap for the interactive exploration of city-wide mobility spaces. Using polygon packing and graph algorithms, the platform rearranges all parking and mobility spaces of cars, rails, and bicycles of a city to be directly comparable, making mobility space inequality accessible to a broad public. This crowdsourced method confirms a prevalent imbalance between modal share and space allocation in 23 cities worldwide, typically discriminating bicycles. Analyzing the guesses of the platform’s visitors about mobility space distributions, we find that this discrimination is consistently underestimated in the public opinion. Finally, we discuss a visualized scenario in which extensive parking areas are regained through fleets of shared, autonomous vehicles. We outline how such accessible visualization platforms can facilitate urban planners and policy makers to reclaim road and parking space for pushing forward sustainable transport solutions

Mode substitution induced by electric mobility hubs: Results from Amsterdam

\ua9 2024 The Author(s)Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found that users of different modes not only have varied general preferences for different shared modes but also have different sensitivity for attributes such as travel time and cost. Public transport users are more likely to switch to eHUBS modes than car users. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km)