Communication-less Management Strategy for Electric Vehicle Charging in Droop-controlled Islanded Microgrids

Adopting high penetration levels of electric vehicles(EVs)necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on pow-er distribution networks.Currently,most of the proposed Ev charging management techniques rely on the availability of high-bandwidth communication links.Such techniques are far from realization due to①the lack of utility-grade communica-tion systems in many cases such as secondary(low-voltage)pow-er distribution systems to which EVs are connected,rural ar-eas,remote communities,and islands,and②existing fears and concerns about the data privacy of EV users and cyber-physical security.For these cases,appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements.Accordingly,this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded mi-crogrids.The proposed strategy is autonomous,as it is based on the measurement of system frequency and local bus voltages.The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed genera-tors(DGs)are in short supply by allocating more system capaci-ty to the EVs with less charging in the past.Furthermore,a novel communication-less EV load shedding scheme is incorpo-rated into the management strategy to provide relief to the mi-crogrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages.Numerical simulations demonstrate the superiority of the pro-posed strategy over the state-of-the-art controllers in modulat-ing the EV charging demand to counteract microgrid instability.

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.

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.

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.

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.

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.

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

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.

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.

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.

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.

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).

Considerations for Planning Charging Stations on College Campuses:A Focus on Safety and Economy

As intelligent networked cars become increasingly integrated into people’s lives,the charging infrastructure of new energy vehicles is becoming a significant factor in the development of the new energy vehicle market.In light of the rapid growth of this market,the problem of charging stations is gradually becoming apparent.This paper puts forward a charging station planning idea.Firstly,a forecast of the charging demand must be made.Subsequently,the economic viability,safety,ease of use for faculty and staff,and the rapid development of new automotive technology must be taken into account.Finally,research and analysis of the actual data must be carried out following the requirements of the different college campuses.

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.

Research on Ratio of New Energy Vehicles to Charging Piles in China

With the widespread of new energy vehicles, charging piles have alsobeen continuously installed and constructed. In order to make the number of pilesmeet the needs of the development of new energy vehicles, this study aims toapply the method of system dynamics and combined with the grey prediction theory to determine the parameters as well as to simulate and analyze the ratio ofvehicles to chargers. Through scenario analysis, it is predicted that by 2030, thisratio will gradually decrease from 1.79 to 1. In order to achieve this ratio as 1:1, itis necessary to speed up the construction of public charging station or privatecharging station. Due to global warming, the attitudes of countries towards fuelvehicles have become increasingly tough. There is huge uncertainty in the growthrate of electric vehicles. Therefore, it is recommended that the construction ofcharging station be deployed in advance to avoid hindering the development ofelectric vehicles in the future.

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.

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.

Progress in Analysis of Key Technologies for Dynamic Wireless Charging of Electric Vehicles

At present, electric vehicles are very common means of transportation in our life. Contact charging is the main method of electric vehicles in China. With the continuous improvement of people’s awareness of environmental protection, wireless charging technology is also under constant development. Currently, there are more static wireless charging technologies, while dynamic charging mode is only a perfection and supplement to it, which is crucial to the promotion of electric vehicles and is able to make charging work faster and easier. China has been researching dynamic wireless charging technology, but it has been affected by many factors. Therefore, it is necessary for the relevant personnel to solve the existing obstacles according to the characteristics of dynamic wireless charging technology and apply dynamic wireless charging technology in an efficient manner.

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.