Demand Modeling of a dc Fast Charging Station

Joint optimization of electric vehicle (EV) fast charging and DC fast charging (DCFC) station problems is presented in this paper. An electro-thermal-aging model is used for modeling EV's battery cell. The model is then expanded from a single cell to a battery pack based on the structure of commercially available EV batteries in the market. Moreover, the limitations imposed by the DCFC station are properly considered in the problem's constraints, including restrictions due to the cost of electricity, especially demand charge, and port power rating of chargers. The station's profit is maximized, and its cost is minimized considering demand charge and energy charge tariffs. Finally, the problem is discretized and numerically evaluated in a detailed case study.

The recent trends and need for green and sustainable energy tendered automobile industries to switch toward electric vehicles. DC fast charging stations are required for long-distance driving and large vehicles such as buses, trucks. Integration of DC fast charging stations to the existing grid results in congestion and stability problems. These problems can be overcome by deploying small DC microgrid clusters within the microgrid. This paper investigates the effect of massive electric vehicle integration on the operating parameters of DC microgrids, as DC fast charging stations are a necessity. Bidirectional DC/DC buck-boost converters are used for interfacing EVCS into DC microgrid. In addition to the droop controller, the converter control also consists of PI-based voltage and current controllers. Performance and stability analyses are evaluated through mathematical modeling and simulation analysis for the microgrid test model. The DC microgrid provided competent results with large-scale electric vehicle integration. The test results validate that the DC microgrid is a viable solution for integrating electric vehicles considering stability.KeywordsDc microgridElectric vehicleCharging station

The aim of this article is to provide a comprehensive overview of electric vehicles (EVs), covering various related aspects, and addressing the emerging challenges and future prospects associated with them. Within this framework, an examination is conducted of the fundamental categories of electric vehicles (EVs) and the related charging methodologies. Given that EVs are anticipated to play a pivotal role in upcoming smart electrical grids (SEG), deliberations also extend to the intricacies of grid integration, along with explorations into advanced charging methodologies such as wireless power transfer and communication between station and center with wireless (WIFI, WIMAX, 5G). This article also examines the current state of EV battery (EVB) chargers in terms of converter configurations, operational modes, and power regulation strategies for electric vehicles. EVB chargers are categorized according to their power capacities and the direction of power flow. Based on power ratings, these chargers are classified into Level 1, Level 2, and Level 3. Certain emerging charging technologies that have the potential to significantly influence the future of energy storage in the realm of transportation electrification have been discussed. Regarding the future of EV station chargers focused on DC fast charging stations, this paper conducts an examination of the design and assessment of various AC/DC converter topologies in the current context, as well as future strategies for integrating them into DC fast-charging infrastructures. With this aim, the fundamental characteristics and prerequisites for vehicle-to-grid (V2G) communications, and prospects for future advancements and electrification scenarios are also introduced and examined. Compensation reactive power is presented also as V4G (vehicle-for-grid) and some technologies are used to compensate the grid. Ultimately, the Model Predictive Control (MPC) algorithm has been elaborated upon using various approaches to provide a deeper understanding of its application in fast chargers through the utilization of artificial intelligence techniques.

In recent years, concerns about the environment and the depletion of fossil fuels have encouraged the incorporation of electric vehicle technology in the automotive market. As the increasing usage and growth of electric vehicles (EVs) along with the advancement of power electronic converters significantly impact the power quality of the electrical grid. This paper addresses a new ongoing power quality issue known as supraharmonics, which occurs in the frequency range of 2 to 150 kHz due to electric vehicle chargers. Among various levels of EV chargers, DC fast chargers have become more popular in recent years for quick charging of batteries. The charging time of the EV mainly depends on the optimized selection of the DC-DC converter implemented in the charging circuit. The effective use of a DC-DC converter should increase battery performance and lifespan. Consequently, this work investigates the supraharmonic emissions produced by a DC fast charging station with four number of chargers connected to an electrical distribution system. Several DC/DC converter topologies in EV fast chargers were designed and developed using MATLAB/Simulink software. The simulation has been carried out to analyze the supraharmonic emission from the designed converter topologies in different operating conditions. The results of the simulations have been analyzed for comparative analysis of supraharmonic emission from different converter topologies. Finally, from the analysis, a suitable converter topology with reduced supraharmonic emission has been suggested.

Electric Vehicles (EVs) are representing a great opportunity for major car manufacturers to invest resources in new technologies in order to support sustainable transportation and the reduction of CO 2 emissions, in particular in the metropolitan areas. In recent years, the increasing penetration of EVs and their charging systems are going through a series of changes. This paper addresses the design of a new DC Fast Charging Station (DCFCS) for EVs coupled with a local Battery Energy Storage (BES) by using the IEC 15118, which provides a communication interface among different actors. DCFCS is equipped with a bidirectional AC/DC converter for feeding power back to the grid, two lithium batteries and a DC/DC converter. The proposed solution decreases the charging time of EVs and facilitates the integration of fast chargers in existing low voltage (LV) grids. The charging station can also be used as a multifunctional grid-utility for ancillary services such as primary frequency control, load levelling and congestion management.

Ultrawide voltage regulation is required in dc/dc converters interfacing battery energy storage systems (BESSs) and electric vehicle (EV) batteries in dc fast-charging stations with energy storage. Attaining high efficiency of this converter can be challenging due to the wide variation of input and output voltage yet is important due to the high power transfer. This article proposes a novel control strategy for the semi-dual active bridge (DAB) converter to achieve wide voltage gain while increasing the efficiency at operational points with high input voltage and low output voltage, which is a commonly occurring scenario when the BESS is fully charged, and the EV battery is at low charge. Furthermore, this article also provides an algorithm to determine the required phase shift in real time for any operating point, eliminating the need to devise the control trajectory offline. A 550-V, 10-kW experimental prototype is built and tested to validate the proposed control strategy. With a 25-A constant charging current, the prototype shows that the proposed control strategy can improve efficiency by up to 3.5% compared to the well-known dual phase shift control at operating points with high input voltage (450–550 V) and low output voltage (150–275 V), with a peak efficiency of 97.6%.

The rise in the number of electric vehicles used by the consumers is shaping the future for a cleaner and energy-efficient transport electrification. The commercial success of electric vehicles (EVs) relies heavily on the presence of high-efficiency charging stations. This article reviews the design and evaluation of different AC/DC converter topologies of the present status and future implementation plans for DC fast-charging infrastructures. The design and evaluation of these converters are presented, analysed, and compared in terms of output power, component count, power factor, and total harmonic distortion effectiveness and reliability. This paper also evaluates the architecture, merit, and demerits of AC/DC converter topologies for DC fast-charging stations. Based on this analysis, it has found that the Vienna rectifier is the best suitable converter topology for the high-power DC fast-charging infrastructure (> 20 kW), thanks to its low current ripples, low output voltage ripples, high efficiency, high power density, and high reliability. The paper focuses specifically on different topologies of Vienna rectifier topologies on Level-3 DC fast-charging stations which direct to less CO2 emissions in electric vehicle charging stations, thus contributing to sustainable development goals of climatic action.

Vienna rectifiers have gained popularity in recent years for AC to DC power conversion for many industrial applications such as welding power supplies, data centers, telecommunication power sources, aircraft systems, and electric vehicle charging stations. The advantages of this converter are low total harmonic distortion (THD), high power density, and high efficiency. Due to the inherent current control loop in the voltage-oriented control strategy proposed in this paper, good steady-state performance and fast transient response can be ensured. The proposed voltage-oriented control of the Vienna rectifier with a PI controller (VOC-VR) has been simulated using MATLAB/Simulink. The simulations indicate that the input current THD of the proposed VOC-VR system was below 3.27% for 650V and 90A output, which is less than 5% to satisfy the IEEE-519 standard. Experimental results from a scaled-down prototype showed that the THD remains below 5% for a wide range of input voltage, output voltage, and loading conditions (up to 2 kW). The results prove that the proposed rectifier system can be applied for high power applications such as DC fast-charging stations and welding power sources.

The growing number of battery technologies and increasing environmental concerns have accelerated the transition to electric vehicles. As internal combustion engine vehicles are replaced by EVs, the impact of EV charging loads on power system operations has become a significant concern. However, the growing adoption of electric vehicles (EVs) presents a challenge for power grids that cannot be neglected. The high charging loads, particularly from fast charging stations, can negatively impact key distribution network parameters such as voltage stability, reliability, power loss, and harmonics. Ensuring the reliability of distribution networks is paramount for maintaining customer satisfaction in the power system. The charging station is integrated into the grid via a common DC bus, allowing multiple EVs to connect. Each EV is controlled locally, with independent management provided for the power exchange between the AC grid and the DC bus. This study investigates the impact of EV charging on the reliability of the bus distribution system. The analysis is conducted on the IEEE 33 bus test system, which represents a standard radial distribution network, across five different cases of EV charging station placement. It is observed that the system can withstand the placement of DC fast charging stations at strong buses up to a certain level, but placing fast charging stations at weak buses disrupts the smooth operation of the power system. Furthermore, these test results confirm the effectiveness of the reliability index-based approach for the allocation of EV charging stations within a distribution system.

<div class="section abstract"><div class="htmlview paragraph">ECOtality North America, OnStar, and the Idaho National Laboratory have partnered to collect and analyze electronic data from Chevrolet Volts enrolled in The EV Project, which is a large-scale plug-in electric vehicle infrastructure demonstration being conducted in 21 metropolitan areas across the United States. This paper presents results of an early analysis of these data. The data set analyzed came from 923 privately owned vehicles, which logged over 4.7 million driving miles from October 2011 to October 2012. These data are used to identify the potential of electric vehicle (EV) mode driving, based on driver and charging behavior.</div><div class="htmlview paragraph">Driving and charging behavior is quantified with metrics such as daily vehicle miles traveled, number of charging events performed per day, and distance driven between consecutive charging events. Drivers averaged 40.7 miles per day, with a median of 31.6 miles per day. Vehicles were charged 1.46 times per vehicle day driven on average, with a median of 1 charging event per day driven. This results in an average of 27.9 miles between consecutive charging events and a median distance of 19.8 miles between charging events. Underlying distributions for these metrics also are examined to find a wide variation in driving and charging behavior across vehicles and vehicle days.</div><div class="htmlview paragraph">Overall, 81% of the vehicles averaged 40 miles or less between consecutive charging events. Assuming a fixed EV mode range of 35 miles, vehicles in this study had the potential to drive 73% of their miles in EV mode. These results show that Chevrolet Volt drivers participating in The EV Project found frequent opportunities to charge their vehicles, such that a high percentage of their driving was performed in EV mode. Also, drivers took advantage of their vehicle's extended range mode to meet their driving needs beyond the all-electric range of their vehicle.</div></div>

Multi-stall fast charging stations are often thought to require megawatt-range grid connections. The power consumption profile of such stations results in high cost penalties due to monthly power peaks and expensive linkage fees. A local energy storage system (ESS) can be used to address peak power demands. However, no appropriate sizing method is available to match specific constraints, such as the contracted power available from the grid and the projected recharging demand. A stochastic distribution of charging events was used in this paper to model power demand profiles at the station, with a one minute resolution. Based on 100 simulated months, we propose an optimum number of charging points, and we developed an algorithm to return the required local ESS capacity as a function of the available grid connection. The role of ESSs in the range of 100 kWh to 1 MWh was studied for all stations with up to 2000 charging events per week. The relevance of ESS implementation was assessed along three parameters: the number of charging points, the available grid connection, and the ESS capacity. This work opens new possibilities for multi-stall charging station deployment in locations with limited access to the medium voltage grid, and provides sizing guidelines for effective ESSs implementation. In addition, it helps build business cases for charging station operators in regions with high demand charges.

Growing concerns over climate changes have driven regulatory pressures to reduce urban pollution, emissions from CO2 and other particulate matters, and city noise, which have motivated intense activity in the search for alternative road transportation propulsion systems. In this context, plug-in electric vehicles (PEVs), either as purely electric vehicles or as plug-in hybrid vehicles, have become a significant fraction of the overall transportation fleet in many countries worldwide.This ever-increasing penetration level of PEVs is posing significant challenges to the existing power grids' infrastructures, as a significant load in terms of the required energy (e.g., during night charging of the vehicles connected for charging) or in terms of the required power (e.g., during fast charging events). The uncertainty of the charging events (in terms of time, space and required energy) further challenge the ability of the power grids to seamlessly handle such new electrical loads in addition to the already existing base load. At the same time, however, the ability of PEVs to operate in a vehicle-to-grid (V2G) mode may be also exploited to provide ancillary regulatory services to facilitate the general operation of the power grid. The optimal trade-off between these two contrasting aspects of the charging problem is still a subject of intense study in the power research community.Accordingly, this chapter reviews the most recent research and technological advances in the charging process of PEVs; it presents through simple simulations the importance of controlled charging, and the advantages of utilizing decentralized schemes for practical implementation; also, it identifies some particularly interesting new trends that can be observed at the intersection between the transportation and the power networks, and it outlines interesting future directions in the context of electric vehicles.

The Effect of Traffic Growth on Characteristic Bridge Load Effects

Power load is an important element in power system. While, the traditional load model is fixed. So, when the load components change, the load model will be inappropriate. Based on the Multiple Model Theory, this paper proposes the Adaptive Synthesis Dynamic Load Model (ASDLM). In load modeling, the load model set is established, and every model in the model set has a weight. The time-varying classical load model (TVCLM) is proposed as the basic structure of the load models in load model set. The parameters and weights of the power load model can be adjusted when there is a maneuvering in the actual load system. Then, the models in the model set are integrated to ASDLM. The load data fitting results of ASDLM verify the adaptability and practicability of the proposed model structure and modeling ideology.

Fast charging stations are critical infrastructures to enable high penetration of plug-in electric vehicles (PEVs) into future distribution networks. They need to be carefully planned to meet charging demand as well as ensure economic benefits. Accurate estimation of PEV charging demand is the prerequisite of such planning, but a nontrivial task. This paper addresses the sizing (number of chargers and waiting spaces) problem of fast charging stations and presents an optimal planning solution based on an explicit temporal-state of charge characterization of PEV fast charging demand. The characteristics of PEV charging demand are derived through a vehicle travel behavior analysis using available statistics. The PEV dynamics in charging stations is modelled with a Markov chain and queuing theory. As a result, the optimal number of chargers and waiting spaces in fast charging stations can be jointly determined to maximize expected operator profits, considering profit of charging service, penalty of waiting and rejection, as well as maintenance cost of idle facilities. The proposed solution is validated through a case study with mathematical justifications and simulation results.