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.

Designing an Effective Method for Automatic Electric Vehicle Charging Stations in a Static Environment

This article outlines an Effective Method for Automatic Electric Vehicle Charging Stations in a Static Environment. It consists of investigated wireless transformer structures with various ferrite forms. WPT technology has rapidly advanced in the last few years. At kilowatt power levels, the transmission distance grows from a few millimeters to several hundred millimeters with a grid to load efficiency greater than 90%. The improvements have made the WPT more appealing for electric vehicle (EV) charging applications in both static and dynamic charging scenarios. Static and dynamic WEVCS, two of the main applications, are described, and current developments with features from research facilities, academic institutions, and businesses are noted. Additionally, forthcoming concepts based WEVCS are analyzed and examined, including “dynamic” wireless charging systems (WCS). A dynamic wireless power transfer (DWPT) system, which can supply electricity to moving EVs, is one of the feasible alternatives. The moving secondary coil is part of the dynamic WPT system, which also comprises of many fixed groundside (primary) coils. An equivalent circuit between the stationary system and the dynamic WPT system that results from the stationary system is demonstrated by theoretical investigations. The dynamic WPT system’s solenoid coils outperform circular coils in terms of flux distribution and misalignment. The WPT-related EV wireless charging technologies were examined in this study. WPT can assist EVs in overcoming their restrictions on cost, range, and charging time.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

Deep Learning Based Automatic Charging Identification and Positioning Method for Electric Vehicle

Electric vehicle charging identification and positioning is critically important to achieving automatic charging.In terms of the problem of automatic charging for electric vehicles,a dual recognition and positioning method based on deep learning is proposed.The method is divided into two parts:global recognition and localization and local recognition and localization.In the specific implementation process,the collected pictures of electric vehicle charging attitude are classified and labeled.It is trained with the improved YOLOv4 networkmodel and the corresponding detectionmodel is obtained.The contour of the electric vehicle is extracted by the BiSeNet semantic segmentation algorithm.The minimum external rectangle is used for positioning of the electric vehicle.Based on the location relationship between the charging port and the electric vehicle,the rough location information of the charging port is obtained.The automatic charging equipment moves to the vicinity of the charging port,and the camera near the charging gun collects pictures of the charging port.The model is detected by the Hough circle,the KM algorithmis used for featurematching,and the homography matrix is used to solve the attitude.The results show that the dual identification and location method based on the improved YOLOv4 algorithm proposed in this paper can accurately locate the charging port.The accuracy of the charging connection can reach 80%.It provides an effective way to solve the problems of automatic charging identification and positioning of electric vehicles and has strong engineering practical value.

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.

Electric Vehicle Charging Capacity of Distribution Network Considering Conventional Load Composition

At present,the large-scale access to electric vehicles(EVs)is exerting considerable pressure on the distribution network.Hence,it is particularly important to analyze the capacity of the distribution network to accommodate EVs.To this end,we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load.First,the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed.Second,the charging behavior of an EVis simulated by combining the Monte Carlo method and the trip chain theory.After obtaining the temporal and spatial distribution of the EV charging load,themethod of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network.By adjusting the EV ownership,the EV capacity in the distribution network is analyzed and solved on the basis of the following indices:node voltage,branch current,and transformer capacity.Finally,by considering the 10-kV distribution network in some areas of an actual city as an example,we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.

Flexible predictive power-split control for battery-supercapacitor systems of electric vehicles using IVHS

The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.

A Novel Ultra Short-Term Load Forecasting Method for Regional Electric Vehicle Charging Load Using Charging Pile Usage Degree

Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduling plan of regional charging load,which can be derived to realize the optimal vehicle to grid benefit.In this paper,a regional-level EV ultra STLF method is proposed and discussed.The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles,and then constructed by our collected EV charging transactiondata in thefield.Secondly,these usagedegrees are combinedwithhistorical charging loadvalues toform the inputmatrix for the deep learning based load predictionmodel.Finally,long short-termmemory(LSTM)neural network is used to construct EV charging load forecastingmodel,which is trained by the formed inputmatrix.The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditionalmethods.In addition,load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us,and these fluctuation factors are used to assess the prediction accuracy of the EV charging load,together with the mean absolute percentage error(MAPE).

Electric Vehicles Lithium-Polymer Ion Battery Dynamic Behaviour Charging Identification and Modelling Scheme

Lithium-ion batteries are considered the substantial electrical storage element for electric vehicles(EVs). The battery model is the basis of battery monitoring, efficient charging, and safety management. Non-linearmodelling is the key to representing the battery and its dynamic internal parameters and performance. This paperproposes a smart scheme to model the lithium-polymer ion battery while monitoring its present charging currentand terminal voltage at various ambient conditions (temperature and relative humidity). Firstly, the suggestedframework investigated the impact of temperature and relative humidity on the charging process using the constantcurrent-constant voltage (CC-CV) charging protocol. This will be followed by monitoring the battery at thesurrounding operating temperature and relative humidity. Hence, efficient non-linear modelling of the EV batterydynamic behaviour using the Hammerstein-Wiener (H-W) model is implemented. The H-W model is considered ablack box model that can represent the battery without any mathematical equivalent circuit model which reducesthe computation complexity. Finally, the model beholds the boundaries of the charging process, not affecting onthe lifetime of the battery. Several dynamic models are applied and tested experimentally to ensure theeffectiveness of the proposed scheme under various ambient conditions where the temperature is fixed at40°C and the relative humidity (RH) at 35%, 52%, and 70%. The best fit using the H-W model reached 91.83% todescribe the dynamic behaviour of the battery with a maximum percentage of error 0.1 V which is in goodagreement with the literature survey. Besides, the model has been scaled up to represent a real EV and expressedthe significance of the proposed H-W model.

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.

A Low-Cost, Smart AC Charging System for Electric Vehicle

In industry development strategy of electric vehicles, apart from concerns on the development of electric vehicles, we also need to consider the issue of charging facilities construction. Firstly, through analysis, this paper discusses the importance of AC charging points for electric vehicle development. By studying existing AC charging points on the market, it presents a low-cost smart AC charging system to reduce the cost investigated by power companies and operational bodies when laying of a large number of AC charging points. Compared with the conventional one, the proposed system has prominent features of low cost, small footprint and low investment.

Vehicle-to-grid power system services with electric and plug - in vehicles based on flexibility in unidirectional charging

With proper power scheduling and dynamic pricing,a unidirectional charger can provide benefits and regulation services to the electricity grid,at a level approaching that of bidirectional charging.Power scheduling and schedule flexibility of electric and plug-in hybrid vehicles are addressed.The use of electric vehicles(EVs)as flexibility resources and associated unidirectional vehicle-to-grid benefits are investigated.Power can be scheduled with the EV charger in control of charging or via control by a utility or an aggregator.Charging cost functions suitable for charger-and utility-controlled power scheduling are presented.Ancillary service levels possible with unidirectional vehicle-to-grid are quantified using sample charging scenarios from published data.Impacts of various power schedules and vehicle participation as a flexibility resource on electricity locational prices are evaluated.These include benefits to both owners and load-serving entities.Frequency regulation is considered in the context of unidirectional charging.

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.

Equalization Charging and Protection System for Electric Vehicle

A system is developed to improve the series battery packs uniformities and charging protection and the implementation of battery equalization charging and protection system is also introduced. The functions of equalization charging and overcharging protection are analyzed and the control model of series battery packs equalization charging is setup. The diverting-current and feedback bus voltage are measured during the series Li-ion battery packs equalization charging experiment. The field operation on Electric luxury transit bus BFC6100EV shows that the system betters the battery series charging uniformities and overcharging protection, improves the battery performance and extends the battery life.

The General Configuration of CEV1 Electric Vehicle's Electrical System and the Design of Its Control Sequence

The general configuration of CEV1 electric vehicle’s electrical system and the design scheme of its control sequence are presented, which are modularized by using VMU as master control unit, PMU as power management unit, BMU as battery management unit. It is a rather advanced and practical general design scheme of electric vehicle, because the division of its module function is definite, which is advantage for research, manufacture and maintenance.

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.

考虑EV充放电意愿的园区综合能源系统双层优化调度

随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性。