979 research outputs found
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
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
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
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
Towards a Multimodal Charging Network: Joint Planning of Charging Stations and Battery Swapping Stations for Electrified Ride-Hailing Fleets
This paper considers a multimodal charging network in which charging stations
and battery swapping stations are built in tandem to support the electrified
ride-hailing fleet in a synergistic manner. Our central thesis is predicated on
the observation that charging stations are cost-effective, making them ideal
for scaling up electric vehicles in ride-hailing fleets in the beginning, while
battery swapping stations offer quick turnaround and can be deployed in tandem
with charging stations to improve fleet utilization and reduce operational
costs for the ride-hailing platform. To fulfill this vision, we consider a
ride-hailing platform that expands the multimodal charging network with a
multi-stage investment budget and operates a ride-hailing fleet to maximize its
profit. A multi-stage network expansion model is proposed to characterize the
coupled planning and operational decisions, which captures demand elasticity,
passenger waiting time, charging and swapping waiting times, as well as their
dependence on fleet status and charging infrastructure. The overall problem is
formulated as a nonconvex program. Instead of pursuing the globally optimal
solution, we establish a theoretical upper bound through relaxation,
reformulation, and decomposition so that the global optimality of the derived
solution to the nonconvex problem is verifiable. In the case study for
Manhattan, we find that the two facilities complement each other and play
different roles during the expansion of charging infrastructure: at the early
stage, the platform always prioritizes building charging stations to electrify
the fleet, after which it initiates the deployment of swapping stations to
enhance fleet utilization. Compared to the charging-only case, ..
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