Electric vehicle fleet size for carsharing services considering on-demand charging strategy and battery degradation

2021 ◽  
Vol 127 ◽  
pp. 103146
Author(s):  
Min Xu ◽  
Ting Wu ◽  
Zhijia Tan
Keyword(s):  
Electric Vehicle ◽  
Fleet Size ◽  
On Demand ◽  
Battery Degradation ◽  
Vehicle Fleet ◽  
Charging Strategy

scholarly journals Pre-Day Scheduling of Charging Processes in Mobility-on-Demand Systems Considering Electricity Price and Vehicle Utilization Forecasts

2020 ◽  
Author(s):  
Florian Dandl ◽  
Fabian Fehn ◽  
Klaus Bogenberger ◽  
Fritz Busch
Keyword(s):  
Short Interval ◽  
Sustainable Transportation ◽  
Macroscopic Model ◽  
Computational Time ◽  
Exchange Market ◽  
Electricity Prices ◽  
Fleet Size ◽  
On Demand ◽  
Long Time ◽  
Charging Strategy

<div>Electrifying mobility-on-demand (MoD) fleets is</div><div>an important step towards a more sustainable transportation</div><div>system. With increasing fleet size, MoD operators will be</div><div>able to participate in the energy exchange market and will</div><div>have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of, e.g., a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a macroscopic model to study the tradeoffs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet size and different numbers of</div><div>charging units, we compare the performance of several reactive and scheduling policies in a simulation framework based on a macroscopic model. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.</div>

scholarly journals Pre-Day Scheduling of Charging Processes in Mobility-on-Demand Systems Considering Electricity Price and Vehicle Utilization Forecasts

2020 ◽  
Author(s):  
Florian Dandl ◽  
Fabian Fehn ◽  
Klaus Bogenberger ◽  
Fritz Busch
Keyword(s):  
Short Interval ◽  
Sustainable Transportation ◽  
Macroscopic Model ◽  
Computational Time ◽  
Exchange Market ◽  
Electricity Prices ◽  
Fleet Size ◽  
On Demand ◽  
Long Time ◽  
Charging Strategy

<div>Electrifying mobility-on-demand (MoD) fleets is</div><div>an important step towards a more sustainable transportation</div><div>system. With increasing fleet size, MoD operators will be</div><div>able to participate in the energy exchange market and will</div><div>have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of, e.g., a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a macroscopic model to study the tradeoffs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet size and different numbers of</div><div>charging units, we compare the performance of several reactive and scheduling policies in a simulation framework based on a macroscopic model. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.</div>

Demand-responsive rebalancing zone generation for reinforcement learning-based on-demand mobility

2021 ◽  
Vol 34 (1) ◽  
pp. 73-88
Author(s):  
Alberto Castagna ◽  
Maxime Guériau ◽  
Giuseppe Vizzari ◽  
Ivana Dusparic
Keyword(s):  
New York ◽  
Reinforcement Learning ◽  
Expectation Maximization ◽  
Coverage Area ◽  
Fleet Size ◽  
On Demand ◽  
Design Time ◽  
Multi Agent ◽  
Vehicle Fleet ◽  
Demand Patterns

Enabling Ride-sharing (RS) in Mobility-on-demand (MoD) systems allows reduction in vehicle fleet size while preserving the level of service. This, however, requires an efficient vehicle to request assignment, and a vehicle rebalancing strategy, which counteracts the uneven geographical spread of demand and relocates unoccupied vehicles to the areas of higher demand. Existing research into rebalancing generally divides the coverage area into predefined geographical zones. Division is done statically, at design-time, impeding adaptivity to evolving demand patterns. To enable more accurate dynamic rebalancing, this paper proposes a Dynamic Demand-Responsive Rebalancer (D2R2) for RS systems. D2R2 uses Expectation-Maximization (EM) technique to recalculate zones at each decision step based on current demand. We integrate D2R2 with a Deep Reinforcement Learning multi-agent MoD system consisting of 200 vehicles serving 10,000 trips from New York taxi dataset. Results show a more fair workload division across the fleet when compared to static pre-defined equiprobable zones.

scholarly journals Recollection center location for end-of-life electric vehicle batteries using fleet size forecast: Scenario analysis for Germany

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 260-265
Author(s):  
Ahmet Yükseltürk ◽  
Aleksandra Wewer ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
End Of Life ◽  
Electric Vehicle ◽  
Scenario Analysis ◽  
Fleet Size ◽  
Center Location

scholarly journals Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4349
Author(s):  
Niklas Wulff ◽  
Fabia Miorelli ◽  
Hans Christian Gils ◽  
Patrick Jochem
Keyword(s):  
Open Source ◽  
Electric Vehicle ◽  
Full Range ◽  
Mobility Patterns ◽  
Electric Vehicle Charging ◽  
Survey Results ◽  
Vehicle Demand ◽  
Vehicle Fleet ◽  
Electric Loads

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

scholarly journals The remaining CO2 budget: a comparison of the CO2 emissions of diesel and BEV drivetrain technology

2021 ◽  
Author(s):  
Christian Böhmeke ◽  
Thomas Koch
Keyword(s):  
Electric Vehicle ◽  
Carbon Footprint ◽  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Battery Electric Vehicle ◽  
Useful Life ◽  
Vehicle Fleet ◽  
Real Time Simulations ◽  
Diesel Vehicle

AbstractThis paper describes the CO2 emissions of the additional electricity generation needed in Germany for battery electric vehicles. Different scenarios drawn up by the transmission system operators in past and for future years for expansion of the energy sources of electricity generation in Germany are considered. From these expansion scenarios, hourly resolved real-time simulations of the different years are created. Based on the calculations, it can be shown that even in 2035, the carbon footprint of a battery electric vehicle at a consumption of 22.5 kWh/100 km including losses and provision will be around 100 g CO2/km. Furthermore, it is shown why the often-mentioned German energy mix is not suitable for calculating the emissions of a battery electric vehicle fleet. Since the carbon footprint of a BEV improves significantly over the years due to the progressive expansion of renewable-energy sources, a comparison is drawn at the end of this work between a BEV (29.8 tons of CO2), a conventional diesel vehicle (34.4 tons of CO2), and a diesel vehicle with R33 fuel (25.8 tons of CO2) over the entire useful life.

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