Research on SFLA-Based Bidirectional Coordinated Control Strategy for EV Battery Swapping Station

As a good measure to tackle the challenges from energy shortages and environmental pollution,Electric Vehicles(EVs)have entered a period of rapid growth.Battery swapping station is a very important way of energy supply to EVs,and it is urgently needed to explore a coordinated control strategy to effectively smooth the load fluctuation in order to adopt the large-scale EVs.Considering bidirectional power flow between the station and power grid,this paper proposed a SFLA-based control strategy to smooth the load profile.Finally,compared simulations were performed according to the related data.Compared to particle swarm optimization(PSO)method,the presented SFLA-based strategy can effectively lower the peak-valley difference with the faster convergence rate and higher convergence precision.It is important for the swapping station that energy exchanging mode can supply energy for large-scale EVs with a smoother load profile than one-way charging mode.

Configuration and operation combined optimization for EV battery swapping station considering PV consumption bundling

Integration of electric vehicles(EVs),demand response and renewable energy will bring multiple opportunities for low carbon power system.A promising integration will be EV battery swapping station(BSS)bundled with PV(photovoltaic)power.Optimizing the configuration and operation of BSS is the key problem to maximize benefit of this integration.The main objective of this paper is to solve infrastructure configuration of BSS.The principle challenge of such an objective is to enhance the swapping ability and save corresponding investment and operation cost under uncertainties of PV generation and swapping demand.Consequently this paper mainly concentrates on combining operation optimization with optimal investment strategies for BSS considering multiscenarios PV power generation and swapping demand.A stochastic programming model is developed by using state flow method to express different states of batteries and its objective is to maximize the station’s net profit.The model is formulated as a mixed-integer linear program to guarantee the efficiency and stability of the optimization.Case studies validate the effectiveness of the proposed approach and demonstrate that ignoring the uncertainties of PV generation and swapping demand may lead to an inappropriate batteries,chargers and swapping robots configuration for BSS.

Operation Strategy of EV Battery Charging and Swapping Station

An operation strategy of the electric vehicle （EV） battery charging and swapping station is proposed in the paper. The strategy is established based on comprehensively consideration of the EV charging behaviors and the possible mutual actions between battery charging and swapping. Three energy management strategies can be used in the station： charging period shifting, energy exchange between EVs, and energy supporting from surplus swapping batteries. Then an optimization model which minimizes the total energy management costs of the station is built. The Monte Carlo simulation is applied to analyze the characteristics of the EV battery charging load, and a heuristic algorithm is used to solve the strategy providing the relevant information of EVs and the battery charging and swapping station. The operation strategy can efficiently reduce battery charging during the high electricity price periods and make more reasonable use of the resources. Simulations prove the feasibility and rationality of the strategy.

Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

Joint Optimal Scheduling for Electric Vehicle Battery Swapping-charging Systems Based on Wind Farms

Insufficiencies in charging facilities limit the broad application of electric vehicles(EVs).In addition,EV can hardly represent a green option if its electricity primarily depends on fossil energy.Considering these two problems,this paper studies a battery swapping-charging system based on wind farms(hereinafter referred to as W-BSCS).In a W-BSCS,the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station(CCS),which can centrally charge EV batteries and then distribute them to multiple battery swapping stations(BSSs).The operational framework of the W-BSCS is analyzed,and some preprocessing technologies are developed to reduce complexity in modeling.Then,a joint optimal scheduling model involving a wind power generation plan,battery swapping demand,battery charging and discharging,and a vehicle routing problem(VRP)is established.Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem.Numerical results verify the effectiveness of the joint optimal scheduling model,and they also show that the W-BSCS has great potential to promote EVs and wind power.

Research on dynamic matching model of electric vehicles and charging facilities in China:A case study of taxis in Beijing

Charging infrastructure supports the rapid development of China's new energy vehicle industry.It not only plays a decisive role in providing accessible and convenient services for electric vehicle(EV)users but also,in one of the seven new infrastructure areas,plays an important role in stabilizing growth and unleashing economic potential during the new coronavirus(COVID-19)pandemic,impacting China's economy.In this study,the system dynamics model was used to predict the development of the EV industry and the demand for charging infrastructure,while considering the influence of policy,increase in EV mileage,and consumer purchase intention index.Furthermore,using the matching of EVs and charging infrastructure in Beijing and policy oriented sensitivity analysis,a simulation of the construction of battery swap taxis and power stations under three policy scenarios was conducted.This research shows that with policies implemented to support charging infrastructure and swapping compatible taxis,Beijing can achieve its goal of replacing all EVs with fast-swap batteries and fast-charging functions within three years.