Location of Electric Vehicle Charging Station Based on Spatial Clustering and Multi-hierarchical Fuzzy Evaluation

For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of charging station;the other is evaluating the location of charging station.To determine the charging station location,an spatial clustering algorithm is proposed and programmed.The example simulation shows the effectiveness of the spatial clustering algorithm.To evaluate the charging station location,a multi-hierarchical fuzzy method is proposed.Based on the location factors of electric vehicle charging station,the hierarchical evaluation structure of electric vehicle charging station location is constructed,including three levels,4first-class factors and 14second-class factors.The fuzzy multi-hierarchical evaluation model and algorithm are built.The analysis results show that the multi-hierarchical fuzzy method can reasonably complete the electric vehicle charging station location evaluation.

Study on site selection planning of urban electric vehicle charging station

The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.

Strategic Placement of Charging Stations for Enhanced Electric Vehicle Adoption in San Diego, California

California mandated that 100% of vehicles sold must be electric by 2035. As electric vehicles (EVs) reach a higher penetration of the car sector, cities will need to provide publicly accessible charging stations to meet the charging demand of people who do not have access to a private charging spot like a personal garage. We have chosen to limit our scope to San Diego County due to its non-trivial size, well-defined shape, and dependence on personal vehicles;this project models 100% of current vehicles as electric, roughly 2.5 million. By planning for the future, our model becomes more useful as well as more equitable. We anticipate that our model will find locations that can service multiple population centers, while also maximizing distance to other stations. Sensitivity analysis and testing of our algorithms are conducted for Coronado Island, an island with 24,697 residents. Our formulation is then scaled to set the parameters for the whole county.

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

Optimal Siting of Electric Vehicle Charging Stations Based on Voronoi Diagram and FAHP Method

The electric vehicle charging station should be allocated based on traffic density, geographical distribution and other factors, and Voronoi diagram is adopted to set the service area of charging station. In combination with the actual situation of site selection of electric vehicle charging station, the comprehensive benefits index system is established. There are numerous factors influencing the site selection, among which there are uncertainty and fuzziness. The comprehensive evaluation method based on the fuzzy analysis and Analytical Hierarchy Process (AHP) is used to evaluate the comprehensive benefits in the site selection of electric vehicle charging stations, with the consultation of experts. This paper contributes to the best selection of comprehensive benefits and provides the reference for the decision-making of building the electric vehicle charging station. Actual examples show that the method proposed is effective.

Modeling and Experimental Verification of Electric Vehicles Off-Grid Photovoltaic Powered Charging Station

With the increasing development of EVs, the energy demand from theconventional utility grid increases in proportion. On the other hand, photovoltaic(PV) energy sources can overcome several problems when charging EVs from theutility grid especially in remote areas. This paper presents an effective photovoltaic stand-alone charging station for EV applications. The proposed charging station incorporates PV array, a lithium-ion battery representing the EV battery, and alead-acid battery representing the energy storage system (ESS). A bidirectionalDC-DC converter is employed for charging/discharging the ESS and a unidirectional DC-DC converter is utilized for charging the EV battery. The proposed controllers achieve maximum power extraction from the PV and regulate the DC-linkvoltage. It also controls the voltage and current levels of both the ESS and the EVduring the charging/discharging process. The study has been applied to two caseswith different power levels. Analysis, simulation, and implementation of the proposed system are presented. A 120 W laboratory prototype is carried out to verifythe system performance, experimentally. Design guides for higher power levelsare proposed to help in choosing the proper parameters of the converters. Boththe simulation and experimental results are matched and verify the highperformance of the proposed system.

Break-Even Analysis on the Charging and Battery-Swapped Station of Electric Vehicles

The construction of electric vehicle charging station plays an important role in the development of electric vehicles and the promotion of the renewable resource. In the paper, a model to analyze the economic benefit of the charging station is presented, which is based on the break-even theory. Then the threshold price is calculated based on the model according to the construction plans of charging facilities in one district. Finally, the strategy for the development of charging faculties is proposed to improve the health growth of electric automotive industry.

Design scheme for fast charging station for electric vehicles with distributed photovoltaic power generation

The demand for fast charging is increasing owing to the rapid expansion of the market for electric vehicles. In addition, the power generation technology for distributed photovoltaic has matured. This paper presents a design scheme for a fast charging station for electric vehicles equipped with distributed photovoltaic power generation system taking the area with certain conditions in Beijing as an example construction site. The technical indexes and equipment lectotype covering the general framework and subsystems of the charging station are determined by analyzing the charging service demand of fast charging stations. In this study, the layout of the station is developed and the operation benefits of the station is analyzed. The design scheme realizes the design objective of "rationalization, modularization and intelligentization" of the fast charging station and can be used as reference for the construction of a fast charging network in urban area.

Optimal Simultaneous Allocation of Electric Vehicle Charging Stations and Capacitors in Radial Distribution Network Considering Reliability

The popularity of electric vehicles(EVs)has sparked a greater awareness of carbon emissions and climate impact.Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations(EVCSs),impacting radial distribution networks(RDNs).To reduce the impact of voltage drop,the increased power loss(PL),lower system interruption costs,and proper allocation and positioning of the EVCSs and capacitors are necessary.This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value(NPV),considering the reduction in energy losses and interruption costs.As a part of the analysis considering reliability,several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV.To locate the best nodes for EVCSs and capacitors,the hybrid of grey wolf optimization(GWO)and particle swarm optimization(PSO)(HGWO_PSO)and the hybrid of PSO and Cuckoo search(CS)(HPSO_CS)algorithms are proposed,forming a combination of GWO,PSO,and CS optimizations.The impact of EVCSs on NPV is also investigated in this paper.The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.

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.

Analysis of Electric and Hybrid Vehicle Usage in Proximity to Charging Infrastructure in Indiana

This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate the adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.

The Bi-directional Three-Phase PWM Rectifier Inverter for Electric Vehicle Charging Station

Applications of electric vehicles need to build a large number of charging stations. The electric vehicle charging stations communicate with the grid. In V2G （vehicle to grid） mode, electric vehicles can be used as energy storage units and transfer power to the grid. The electric vehicles charge at night to reduce the cost and the grid load, simultaneously to fill the valley. When grid load increases, electric vehicles＇ batteries discharge to the grid to improve the stability of the grid. As distributed storage units, electric vehicles are important components of the smart grid. In this paper, the three-phase PWM （pulse width modulation） rectifier used for smart charging and discharging system of electric vehicles are analyzed and designed. This paper includes the principle of PWM rectifier-inverter and direct current control strategy. Also, the SVPWM （space vector pulse width modulation） and system design of three-phase PWM rectifiers are analyzed. A 10 kW prototype is developed. Simulation and experiment results show that the three-phase PWM rectifiers reach the unit power factor. From the experimental results, PWM rectifier implements the sinusoidal grid current and achieves the unit power factor.

Energy-storage configuration for EV fast charging stations considering characteristics of charging load and wind-power fluctuation

Fast charging stations play an important role in the use of electric vehicles(EV)and significantly affect the distribution network owing to the fluctuation of their power.For exploiting the rapid adjustment feature of the energy-storage system(ESS),a configuration method of the ESS for EV fast charging stations is proposed in this paper,which considers the fluctuation of the wind power as well as the characteristics of the charging load.The configuration of the ESS can not only mitigate the effects of fast charging stations on the connected distribution network but also improve its economic efficiency.First,the scenario method is adopted to model the wind power in the distribution network,and according to the characteristics of the EV and the driving probability,the charging demand of each station is calculated.Then,considering factors such as the investment cost,maintenance cost,discharging benefit,and wind curtailment cost,the ESS configuration model of the distribution network is set up,which takes the optimal total costs of the ESS for EV fast charging stations within its lifecycle as an objective.Finally,General Algebraic Modelling System(GAMS)is used to linearize and solve the proposed model.A simulation on an improved IEEE-69 bus system verifies the feasibility and economic efficiency of the proposed model.

EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

The increasingly large number of electric vehicles(EVs)has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks.To address this issue,an EV charging station load predictionmethod is proposed in coupled urban transportation and distribution networks.Firstly,a finer dynamic urban transportation network model is formulated considering both nodal and path resistance.Then,a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature.Thirdly,the Monte Carlo method is applied to predict the distribution of EVcharging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model.Moreover,a dynamic charging pricing scheme for EVs is devised based on the EV charging station load requirements and the maximum thresholds to ensure the security operation of distribution networks.Finally,the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model.The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from 32.87%to 37.21%compared to the BPR model.Additionally,the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from27.2 to 31.49MWh by considering the influence of ambient temperature and speed on power energy consumption.

A Prediction Method of Charging Station Planning Based on BP Neural Network

The construction of charging service facilities is a very important factor in the popularization of electric vehicles. Therefore, the planning problems of electric vehicle charging station are urgent to be solved. Considering the standard of natural environment, society, traffic, power grid and economy, an evaluation system is created for electric vehicle charging station project through 15 sub-standards. Planning model of charging station is constructed based on BP neural network adopted in the analysis. It is used for location and capacity prediction of charging station planning. By analyzing the model with data samples, a stable network structure is established and the feasibility of the model is verified in the charging station planning.

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.

Development of Electric Vehicle Charging Corridor for South Carolina

We apply a flow-based location model,called Multipath Refueling Location Model(MPRLM),to develop an electric vehicle(BV)public charging infrastructure network for enabling long-haul inter-city EY trips.The model considers multiple deviation paths between every origin-destination(O-D)pairs and relaxes the commonly adopted assumption that travelers only take a shortest path between O-D pairs.This model is a mixed-integer linear program,which is intrinsically difficult to solve.With greedy-adding based heuristics,the MPRLM is applied to optimally deploy EV fast charging stations along major highway corridors in South Carolina.Compared to engineering methods,the optimization model reduces the capital cost of establishing a fast charging network by two thirds.We also explore the interplay between the spatial distributions of cities,vehicle range,and routing deviation tolerance as well as their impacts on the locational strategies.

Load Cluster Characteristic and Modeling of EV Charge Station in Residential District

电动汽车的大规模发展将会对配电网带来较大影响,开展电动汽车充电的负荷特性研究是保证电网安全运行的前提,是引导电动汽车进行有序充电的基础。在简化锂电池恒流–恒压2阶段充电模型的基础上,得到单个电动汽车的充电模型,以此为基本元素,从充电汽车电池的初始荷电状态（initial state-of-charge,SOC0）和车辆到达充电站时间的随机分布为出发点,提出2阶段泊松分布的电动汽车充电站集聚模型进行充电站集聚特性的模拟,并提出基于充电站的日充电负荷曲线的电动汽车充电站负荷集聚模型的建模方法,最后通过仿真算例验证了其有效性。

Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation

The transportation sector is characterized by high emissions of greenhouse gases(GHG)into the atmosphere.Consequently,electric vehicles(EVs)have been proposed as a revolutionary solution to mitigate GHG emissions and the dependence on petroleum products,which are fast depleting.EVs are proliferating in many countries worldwide and the fast adoption of this technology is significantly dependent on the expansion of charging stations.This study proposes the use of the hybrid genetic algorithm and particle swarm optimization(GA-PSO)for the optimal allocation of plug-in EV charging stations(PEVCS)into the distribution network with distributed generation(DG)in high volumes and at selected buses.Photovoltaic(PV)systems with a power factor of 0.95 are used as DGs.The PVs are penetrated into the distribution network at 60%and six penetration cases are considered for the optimal placement of the PEVCSs.The optimization problem is formulated as a multi-objective problem minimizing the active and reactive power losses as well as the voltage deviation index.The IEEE 33 and 69 bus distribution networks are used as test networks.The simulation was performed using MATLAB and the results obtained validate the effectiveness of the hybrid GA-PSO.For example,the integration of PEVCSs results in the minimum bus voltage still within accepted margins.For the IEEE 69 bus network,the resulting minimum voltage is 0.973 p.u in case 1,0.982 p.u in case 2,0.96 p.u in case 3,0.961 p.u in case 4,0.954 p.u in case 5,and 0.965 p.u in case 6.EVs are a sustainable means of significantly mitigating emissions from the transportation sector and their utilization is essential as the worldwide concern of climate change and a carbon-free society intensifies.