1,659 research outputs found
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
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, ..
Planning long-term maintenance for electric vehicle charging infrastructure using the Reliability Centered Maintenance (RCM) method
Electric vehicles (EVs) are mainly known for their advantages as emission free, energy efficient and noiseless transport, but electric mobility has never matured in the automotive market and it remains in the shadow of the internal combustion engine (ICE) vehicles. The EV penetration depends so much on the availability of the charging facilities. The availability and the performances of the charging infrastructure will have a major impact on the satisfaction of electric vehicle drivers and therefore on the future viability and successful of the technology. In this context, maintenance will play a key role to ensure appropriate levels of availability and reliability and also to keep the expensive infrastructure in good conditions for a long time: it will need to have a long and trouble free life, if it is to persuade the typical car user to change his behavior and choices.
This paper will provide a long-term maintenance plan, in which the preventive maintenance tasks will be defined based on the Reliability Centered Maintenance (RCM) approach, starting from the definition of the electric vehicle charging infrastructure and explaining how it works and by which components it is constituted
Recommended from our members
Factors Affecting Demand for Plug-in Charging Infrastructure: An Analysis of Plug-in Electric Vehicle Commuters
The public sector and the private sector, which includes automakers and charging network companies, are increasingly investing in building charging infrastructure to encourage the adoption and use of plug-in electric vehicles (PEVs) and to ensure that current facilities are not congested. However, building infrastructure is costly and, as with road congestion, when there is significant uptake of PEVs, we may not be able to “build out of congestion.” We modelled the choice of charging location that more than 3000 PEV drivers make when given the options of home, work, and public locations. Our study focused on understanding the importance of factors driving demand such as: the cost of charging, driver characteristics, access to charging infrastructure, and vehicle characteristics. We found that differences in the cost of charging play an important role in the demand for charging location. PEV drivers tend to substitute workplace charging for home charging when they pay a higher electricity rate at home, more so when the former is free. Additionally, socio-demographic factors like dwelling type and gender, as well as vehicle technology factors like electric range, influence the choice of charging location
- …