259 research outputs found
An optimization model for a battery swapping station in Hong Kong
In this paper, a battery swapping station (BSS) model is proposed as an economic and convenient way to provide energy for the batteries of the electric vehicles (EVs). This method would overcome some drawbacks to the use of electric vehicles like long charging time and insufficient running distance. On the economic concern of a battery swapping station, the station would optimize the availability of the batteries in stock, and at the same time determine the best strategy for recharging the batteries on hand. By optimizing the charging method of the batteries, an optimization model of BSS with the maximum number of batteries in stock has been developed for the bus terminal at the Hong Kong International Airport. The secondary objective would be to minimize a cost on the batteries due to the use of different charging schemes. The genetic algorithm (GA) has been used to implement the optimization model, and simulation results are shown.published_or_final_versio
Optimal scheduling of smart microgrids considering electric vehicle battery swapping stations
Smart microgrids belong to a set of networks that operate independently. These networks have technologies such as electric vehicle battery swapping stations that aim to economic welfare with own resources of smart microgrids. These resources should support other services, for example, the supply of energy at peak hours. This study addresses the formulation of a decision matrix based on operating conditions of electric vehicles and examines economically viable alternatives for a battery swapping station. The decision matrix is implemented to manage the swapping, charging, and discharging of electric vehicles. Furthermore, this study integrates a smart microgrid model to assess the operational strategies of the aggregator, which can act like a prosumer by managing both electric vehicle battery swapping stations and energy storage systems. The smart microgrid model proposed includes elements used for demand response and generators with renewable energies. This model investigates the effect of the wholesale, local and electric-vehicle markets. Additionally, the model includes uncertainty issues related to the planning for the infrastructure of the electric vehicle battery swapping station, variability of electricity prices, weather conditions, and load forecasting. This article also analyzes how both the user and the providers maximize their economic benefits with the hybrid optimization algorithm called variable neighborhood search - differential evolutionary particle swarm optimization. The strategy to organize the infrastructure of these charging stations reaches a reduction of 72% in the overall cost. This reduction percentage is obtained calculating the random solution with respect to the suboptimal solution
Charging Autonomous Electric Vehicle Fleet for Mobility-on-Demand Services: Plug in or Swap out?
This paper compares two prevalent charging strategies for electric vehicles,
plug-in charging and battery swapping, to investigate which charging strategy
is superior for electric autonomous mobility-on-demand (AMoD) systems. To this
end, we use a queueing-theoretic model to characterize the vehicle waiting time
at charging stations and battery swapping stations, respectively. The model is
integrated into an economic analysis of the electric AMoD system operated by a
transportation network company (TNC), where the incentives of passengers, the
charging/operating shift of TNC vehicles, the operational decisions of the
platform, and the planning decisions of the government are captured. Overall, a
bi-level optimization framework is proposed for charging infrastructure
planning of the electric AMoD system. Based on the proposed framework, we
compare the socio-economic performance of plug-in charging and battery
swapping, and investigate how this comparison depends on the evolving charging
technologies (such as charging speed, battery capacity, and infrastructure
cost). At the planning level, we find that when choosing plug-in charging,
increased charging speed leads to a transformation of infrastructure from
sparsely distributed large stations to densely distributed small stations,
while enlarged battery capacity transforms the infrastructure from densely
distributed small stations to sparsely distributed large stations. On the other
hand, when choosing battery swapping, both increased charging speed and
enlarged battery capacity will lead to a smaller number of battery swapping
stations. At the operational level, we find that improved charging speed leads
to increased TNC profit when choosing plug-in charging, whereas improved
charging speed may lead to smaller TNC profit under battery swapping. The above
insights are validated through realistic numerical studies
Renewable powered Battery Swapping Stations for sustainable urban mobility
Due to sustainability concerns raised by the transportation sector, still relying mostly on oil as main energy source, urban mobility is quickly shifting towards the adoption of electric vehicles (EVs), The EV charging process should heavily rely on Renewable Energy Sources (RES) and be smartly scheduled to promote sustainability and pollution reduction. In this context, renewable powered Battery Swapping Stations (BSS) represent a promising solution to enable sustainable and feasible e-mobility. Focusing on a BSS powered by photovoltaic panels, we investigate the issue of properly dimensioning its capacity (in terms of number of sockets) and the renewable energy supply to satisfy the battery swapping demand, trading off cost, Quality of Service and feasibility constraints. In addition, we analyse the potential benefits of smart scheduling strategies for battery recharging. Our results show that considerable cost saving of up to almost 40% can be achieved with a local RE supply to power the BSS. Furthermore, a proper tuning of the scheduling strategy configuration parameters is required to better trade off cost and Quality of Service, based on the desired performance targets
Scheduling of EV Battery Swapping, I: Centralized Solution
We formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best battery station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. To deal with the nonconvexity of power flow equations and the binary nature of station assignments, we propose a solution based on second-order cone programming (SOCP) relaxation of optimal power flow and generalized Benders decomposition. When the SOCP relaxation is exact, this approach computes a global optimum. We evaluate the performance of the proposed algorithm through simulations. The algorithm requires global information and is suitable for cases where the distribution grid, battery stations, and EVs are managed centrally by the same operator. In Part II of this paper, we develop distributed solutions for cases where they are operated by different organizations that do not share private information
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