Fast Charging Stations Research Articles

Optimal dynamic power allocation for electric vehicles in an extreme fast charging station

With the ever-increasing penetration of electric vehicles (EVs), extreme fast charging stations (XFCSs) are being widely deployed, wherein battery energy storages (BESs) are also installed for reducing the peak charging power. However, integrating the XFCS with a high-capacity power converter into the power distribution network (PDN) is difficult and uneconomical due to the restrictions regarding urban planning and high investment in PDN expansion. Considering the fluctuation in the EV charging demand and the limited capacity of the power converter, a collaborative policy for real-time EV charging power allocation and BES discharging power control is proposed based on Markov Decision Process (MDP), which is solved by the constraint deep deterministic policy gradient (CDDPG). The proposed model makes it possible to integrate the XFCS with reduced capacity power converter into the PDN with a minimal negative impact on the quality of service (QoS) of EV owners. Finally, the experimental evaluation with real-word data sets demonstrates that the proposed approach is more effective than benchmark methods in dynamically allocating charging power for XFCS.

Multi-layer control on DC fast charging stations equipped with distributed energy storages and connected to distribution network: Managing power and energy following events

In this paper, DC fast charging (DCFC) stations are integrated into the distribution network (DN). The designed DCFC stations are equipped with several charging devices (CDs) at different rated powers, which can charge electric vehicles (EVs) at various power levels through charging points (CPs). A central control system (CCS) is designed for each DCFC, which is applied for managing its local controllers. The CDs also use distributed energy storage (DES) alongside the DC chargers in order to increase the speed of the charging process and utilize the stored energy for improving the DN operation. The DN central controller scheme is as well designed to control the CCS of DCFCs and make positive effects on the upstream distribution grid. The CCS of DN, in addition to managing the CCS of DCFCs, is responsible for controlling the charge level of DCFCs according to four control strategies in each station including improving voltage fluctuations on the DN side, injecting reactive power from DCFCs to DN during a fault on the DN side, increasing DN resiliency by supplying critical loads in the time of upstream network outage, and supplying loads with time-varying active-reactive powers. The nonlinear simulations using MATLAB-SIMULINK demonstrate that the proposed strategies can effectively improve the performance of DN in addition to accurate control of the charging process in the EVs.

Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks

The high penetration of renewable energy resources’ (RESs) and electric vehicles’ (EVs) demands to power systems can stress the network reliability due to their stochastic natures. This can reduce the power quality in addition to increasing the network power losses and voltage deviations. This problem can be solved by allocating RESs and EV fast charging stations (FCSs) in suitable locations on the grid. So, this paper proposes a new approach using the red kite optimization algorithm (ROA) for integrating RESs and FCSs to the distribution network through identifying their best sizes and locations. The fitness functions considered in this work are: reducing the network loss and minimizing the voltage violation for 24 h. Moreover, a new version of the multi-objective red kite optimization algorithm (MOROA) is proposed to achieve both considered fitness functions. The study is performed on two standard distribution networks of IEEE-33 bus and IEEE-69 bus. The proposed ROA is compared to dung beetle optimizer (DBO), African vultures optimization algorithm (AVOA), bald eagle search (BES) algorithm, bonobo optimizer (BO), grey wolf optimizer (GWO), multi-objective multi-verse optimizer (MOMVO), multi-objective grey wolf optimizer (MOGWO), and multi-objective artificial hummingbird algorithm (MOAHA). For the IEEE-33 bus network, the proposed ROA succeeded in reducing the power loss and voltage deviation by 58.24% and 90.47%, respectively, while in the IEEE-69 bus it minimized the power loss and voltage deviation by 68.39% and 93.22%, respectively. The fetched results proved the competence and robustness of the proposed ROA in solving the problem of integrating RESs and FCSs to the electrical networks.

Parallel Power Sharing Control of Multi-Controllable Rectifiers in a High-Power DC Fast Charging Station

The increase in demand for the fast charging of electric vehicles (EVs) has promoted research and the application of high-power direct current (DC) fast charging stations, and research on the stability control of charging stations with a parallel structure of multi-controllable rectifier modules is of great significance. Taking into account the dynamic and steady-state performance, the parameters of the dual-loop controller based on virtual impedance control are designed to realize the power sharing of multi-controllable rectifier modules. The constant power load model of EV charging at a constant current is established, and the load capacity of the cascaded system of converters is studied by using the impedance analysis method. The experimental and simulation results verify the effectiveness and correctness of the power sharing control strategy and the cascaded system stability analysis.

Study on optimal configuration of EV charging stations based on second-order cone

The Electric Vehicle(EV) charging station frequently participates in the daily regulation and operation of the power distribution system, whether the installation position is reasonable and the configuration capacity is appropriate, it will directly affect the operation strategy and operating conditions of the distribution system, and then affect the economic and environmental benefits of the distributed power supply and EV load connected to the power grid, based on the analysis of fuel vehicles, EV travel characteristics and electrical geography coupled state, by fitting the charging data of EV with travel chain, more accurate EV charging load can be obtained. Secondly, the optimal allocation model of EV charging stations is established to optimize the grid loss, and the nonconvex problem is solved by combining the second-order cone relaxation technique with the distflow power flow model, the configuration scheme of EV charging station is obtained. Finally, taking an EV charging scene in a certain area as an example, the optimal configuration of the charging station is simulated by setting up two types of charging station: conventional charging station and conventional charging station and fast charging station, the effectiveness of the proposed configuration scheme is verified. The optimal configuration scheme of EV charging station described in this paper provides a theoretical basis for charging station planning and capacity expansion.

Quality of service measurement for electric vehicle fast charging stations: a new evaluation model under uncertainties

This study addresses the quality of service (QoS) evaluation problem for electric vehicle (EV) fast charging stations (FCSs). With the increasing market penetration of EVs, effective service quality evaluation under different charging scenarios is a pressing and open issue for planning FCSs to accommodate non-stationary customer charging demand. Unlike previous studies, we make the first attempt to define the connotation of QoS from the EV customers' standpoint based on an extended universal generating function (EUGF). First, we formulate the charging behaviour as a fuzzy queuing process, where the arrival rate and service rate are modelled as fuzzy numbers. Second, the QoS requirement level is taken into account to better reflect the real charging environment. The model is then extended to incorporate the charging station structure by introducing the concept of the composition operator. Finally, numerical experiments are conducted to examine the performance of the EUGF-based model. The results demonstrate that the proposed approach is able to obtain a realistic and precise QoS evaluation, and can serve as an effective indicator for FCS planning and operation problems.

Technical Review and Survey of Future Trends of Power Converters for Fast-Charging Stations of Electric Vehicles

The development and implementation of electric vehicles have significantly increased and are profoundly reshaping the automotive sector. However, long charging times, limited driving range, and difficulties as to suitable charger converter design are the main limitations of the adoption of EV technology. DC fast-chargers offer the best solution for mitigating the charging time problems of EVs. This paper provides an extensive review of the status of the technical development of fast-charging infrastructure architectures and standards, and a classification of fast-charging methods. Key power electronic converter topologies for fast-charging systems, with their advantages and comparisons, are also addressed.

Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations

The rapid development of electric vehicles (EVs) has increased the demand for public fast-charging stations (FCSs). Multiple self-interested charging service operators (CSOs) in an urban transportation network compete with each other through pricing signals to maximize their respective profits, where the information about customer demands and heterogeneous preferences work as important decision criteria. With the prevailing data transaction, the information superiority differences between CSOs may significantly affect the competitive pattern in the charging market. In this study, a Nash-Stackelberg-Nash game framework is established to investigate the strategic pricing of CSOs under different information distribution scenarios. The customer response pattern, namely, the routing and charging choices of EV drivers to pricing signals, is described via a computationally efficient extended stochastic user equilibrium model comprehensively considering heterogeneous customer preferences and decision stochasticity. Then, aiming at different data forms, both parameter-driven and sample-driven CSO pricing models are proposed. The pricing models are reformulated as a tractable single-level mixed-integer linear program, and the game equilibrium is solved alternately via Gauss-Seidel iteration. Numerical simulations are conducted to analyze the impact of single CSO information superiority and double CSO information distribution on CSO profitability and customer experience in both data forms. The influences of endogenous factors like CSO data amount and processing ability, as well as exogenous scenario parameters like customer preference distribution and perception error distribution, are discussed.

Hybrid Energy Storage System Taking Advantage of Electric Vehicle Batteries for Recovering Regenerative Braking Energy in Railway Station

Nowadays, nations are moving toward the electrification of the transportation section, and the widespread development of EV charging stations and their infrastructures supplied by the grid would strain the power grid and lead to overload issues in the network. To address this challenge, this paper presents a method for utilizing the braking energy of trains in railway stations to charge EVs located in strategic areas like park-and-ride regions close to railway stations improving energy efficiency and preventing grid overload. To validate the feasibility of the proposed system, a metro substation in Milan city is considered as a case study located in outskirts of the city and contains large number of parking space for vehicles. Three different scenarios are evaluated including DC fast charging station, AC low charging station and collaborative hybrid energy storage based AC charging station as EV charging station type. The results are studied for different EV population number, charging rate and the contractual power grid. Meanwhile, the possibility of proposed system in participating as V2G technology and taking advantage of the EV’s batteries to provide ancillary support to accelerating trains is investigated regarding peak shaving objective. The results indicated that the suggested interconnected system operates effectively when a significant quantity of EVs are parked at the station. However, the results revealed that the performance of the proposed system is notably influenced by other factors and a limited number of EVs during the early morning and late evening periods. Overall, this study confirms the feasibility of energy transfer between two types of transportation means in intermodal areas.

A Data-driven Agent-based Planning Strategy of Fast-Charging Stations for Electric Vehicles

Electric vehicles (EVs) are believed to play an important role in mitigating carbon emissions. To accommodate the increasing number of EVs, the co-planning of electrical distribution networks and the charging infrastructure, such as fast-charging stations (FCSs), becomes an emergent task. Both the charging demand of EVs and the stability of the electricity network should be satisfied. In this paper, we proposed a data-driven agent-based planning strategy for FCSs. Different from the conventional planning strategy, we utilized machine learning tools to consider EV behaviors at the microscopic level in a planning problem. First, a Partially Observable Markov Decision Process of EVs is established, and multi-agent deep reinforcement learning is utilized to learn the charging and driving decisions of EVs under different transportation network typologies and FCSs planning schemes. Second, a data-driven agent-based traffic assignment model (DA-TAM) is proposed to aggregate the atomic behaviors of EVs, which can present the sensitivity of the traffic flow and EV charging demand to the FCS planning schemes. Third, the DA-TAM is adapted to the proposed planning model to ensure the quality of service and prevent unbalanced traffic flow. Through the proposed method, microscopic behaviors of EVs can be reflected, and the impact of the planning scheme on the traffic condition can be revealed. The proposed methodologies are verified in the case studies. It can be concluded that compared with the baseline, the presented agent-based planning strategy can serve more EV charging demands (increased by 33%), cause less waiting time (decreased by 59%) in FCSs to enhance the quality of service, and encounter less severe traffic unbalance problems (increased by 17%).

Summation of supraharmonic currents (2–150 kHz) from EV fast charging stations

Summation of supraharmonic currents (2–150 kHz) from EV fast charging stations

Electric vehicle community charging hubs in multi-unit dwellings: Scheduling and techno-economic assessment

Democratizing access to charging infrastructure is a prerequisite for equitable electric vehicle (EV) adoption and use. Residential EV charging is the most prevalent and convenient option. However, multi-unit dwelling (MUD) residents have limited access to home charging, leading to higher operating costs and less flexibility. We introduce the concept of community charging hubs for shared charger use at MUDs. Our model minimizes the charging hub’s total waiting time. We measure the charging hub’s performance and evaluate the levelized cost of charging through a techno-economic assessment in Chicago, IL, New York City, NY, and Los Angeles, CA. We uncover trade-offs between the charging hub’s performance and its levelized cost of charging. Installing direct current fast charging stations costs more than adding level-2 stations but significantly reduces waiting times. The cost and performance metrics of small, medium, and large charging hubs and their average power profiles are presented for various hub configurations.

Sizing of stationary energy storage systems for electric vehicle charging plazas

Increasing numbers of electric vehicles (EV) and their fast charging stations might cause problems for electrical grids. These problems can be prevented by energy storage systems (ESS). Levelling the power demand of an EV charging plaza by an ESS decreases the required connection power of the plaza and smooths variations in the power it draws from the grid. In this article, a study of sizing of stationary ESSs for EV charging plazas is presented based on one year of data compiled from four direct current fast charging (DCFC) stations. Effects of the charging plaza size, grid connection power, and temporal resolution of input data on ESS requirements were studied. The ESS was controlled to reduce the connection power below certain limits which were altered from 5% to 100% with respect to the nominal power of the charging plaza. The charging plaza size ranged from 1 to 40 DCFC stations. The results show that the relative ESS power and energy requirements and the utilization rate of the ESS decrease, as the connection power and charging plaza size increase. The required connection power of an EV charging plaza can be decreased considerably by a relatively small ESS capacity. The effects of temporal resolution were studied, for the first time, by averaging the charging power time series of the charging plaza with averaging time intervals ranging from 1 s to 1 h. The temporal resolution of EV charging data had a notable effect on the results of ESS sizing: too sparse data distorts the results leading to an underestimation of ESS specifications and utilization.

Smart Self Monitoring Eco Friendly EV Charging Station

Due to their low expenses for operation and commitment to the environment, electric cars (EVs) are growing in popularity. However, the inadequate charging infrastructure limits their popularity. To keep their vehicles always ready for use, EV owners need access to convenient and dependable charging stations. Thus, promoting the adoption of EVs requires the installation of electric charging stations. A specialized infrastructure called an electric charging station is created to supply electricity to electric vehicles. It is made up of a unit for charging, one for distributing power, and one for communicating. The charging unit is in charge of feeding electricity from the grid to an electric vehicle's battery. The electric car receives power from the charging unit through the power distribution unit. To make sure that the charging process is secure, dependable, and effective, the communication unit is in charge of maintaining communication between the charging station and the electric car. There are two categories of electric charging stations: DC rapid charging stations and AC charging stations. While DC fast charging stations offer a faster charging rate of up to 350 kW, AC charging stations offer a slower rate of up to 22 kW. Although installing DC fast charging stations costs more money, they are essential for long- distance travel and in places where there is a high demand for quick charging.

Dynamic pricing for fast charging stations with deep reinforcement learning

With the rapid development of electric vehicles (EVs) and charging infrastructures, the unbalanced utilization rate of fast charging stations (FCSTs) and the long waiting time for charging have aroused considerable attention. The incurred low operation profit of FCSTs and low satisfaction of EVs impose difficulties on the further development of EV industry. Existing literature ignored the influence of real-time charging price changes on traffic flow variation and EV charging determination during the dynamic price regulating process. This paper focuses on solving these crucial issues in the dynamic pricing for FCSTs with deep reinforcement learning (DRL). Firstly, considering the spatial–temporal interactions of different roads, a traffic flow prediction model is proposed based on the LSTM combined with the GNN-FiLM. Then, the Origin-Destination (OD) estimation is used to estimate the charging requirements of EVs based on the predicted traffic flow, and a charging demand prediction method for FCSTs is developed by converting the EV satisfaction into economic costs with different dimensions. Then, the vehicle–road learning environment is built with the Markov decision process (MDP), and a dynamic pricing strategy based on the Deep Deterministic Policy Gradient (DDPG) learning is proposed to achieve the optimal charging prices of FCSTs with maximum operation profit. Moreover, during the learning process, the real-time charging price is renewed based on the predicted charging demand, and the future charging demand is further predicted under the renewed charging price until the optimal price is achieved. Finally, simulation results validate that the proposed dynamic pricing strategy effectively improves the profit of FCSTs, alleviates the road congestion, and improves the users’ satisfaction.

An Improved Energy Management Strategy for a DC Microgrid Including Electric Vehicle Fast Charging Stations

Growing world concern over greenhouse gas emission, the promises of efficient energy, the development of battery characteristics and falling of its prices have led to the rapid increasing adoption of EVs. The number of electric vehicles (EVs) on the road is expected to continue to increase during the next decades due to various factors such as the rapid progress in EV technology and decreasing battery prices The prolonged battery charging process, which is one of the main problems that affects the increased EV penetration, makes the fast-charging units more attractive and efficient option for the charging stations. In this study, a control strategy for a DC microgrid including electric vehicle fast charging station (EVFCS) and distributed generation units is presented to examine the impacts of EVFCS on the grid as well as their potential contributions to the system operation in the case of considering the vehicle-to-grid (V2G) technology. It is especially aimed to mitigate the voltage sag and swell problems by using the EV battery as a DC source of a distribution static compensator (D-STATCOM) device. Simulation studies in MATLAB Simulink/Sim Power systems show that considerable improvements can be achieved from the perspective of distribution system operation such as improved voltage quality and from the perspective of end users such as decreased charging durations.

Fast-charging station location problem: A two-phase approach with mathematical program with equilibrium constraints considering charging choice behaviour

Electric vehicles (EVs) produce zero emissions, which makes them an essential solution for reducing air pollution in urban areas. Fast-charging station (FCS) planning is crucial for ensuring a successful transition to EVs. Therefore, to address the FCS location problem (FCSLP) for EVs in urban areas, a two-phase approach consisting of data processing and model optimization is proposed. During data processing, the spatiotemporal charging demand distribution, existing FCSs, and potential locations for new FCSs are sequentially identified. The results are then used in the model optimization phase, where a spatiotemporal mathematical program with equilibrium constraints (MPEC) model is developed to minimize the total social cost (TSC). The MPEC model integrates the maximal coverage location model and charging equilibrium model, which is formulated as a variational inequality to capture the charging choice behaviour of the EVs within the FCS service radius. To solve the MPEC model, a modified genetic algorithm is developed, in which the charging equilibrium model is iteratively solved. Finally, a case study is conducted in a practical area in Shenzhen, China. The results indicate that a trade-off exists between the TSC and the system service level concerning the optimal FCS locations under a specific FCS service radius. Moreover, the FCS service radius significantly influences EV drivers’ charging choice behaviour and FCS planning.

A Futuristic Silicon-Carbide (SiC)-Based Electric-Vehicle Fast Charging/Discharging (FC/dC) Station

Medium-voltage (MV) grid-connected solid-state-transformer (SST) based fast-charging (FC) stations provide several merits in terms of improved efficiency, power density, current limiting capability, etc. In this paper, a MV grid-connected public multi-port FC/dC station is proposed which not only resembles a refuelling station’s functionality by simultaneously interfacing all three PEV categories (heavy or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</i> PEVs, medium or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> PEVs and light or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> PEVs), but also facilitates bidirectional power flow for V2G applications. The modulation, operational and control schemes of the front-end (FE) MVAC-LVDC single-stage conversion and back-end (BE) DC-DC conversion of the proposed architecture are explained in detail. Hardware-in-loop test results for full-scale 22 kV, 1 MVA architecture’s bidirectional operation verifies the proposed operation and control schemes. The architecture facilitates simultaneous FC/dC of 1 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</i> PEV within 49.5 min, 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> PEVs within 28 min and 4 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> PEVs within 16 min. Finally, a proportionally scaled down 1 kV, 13.2 kVA experimental verification validates the architecture’s performance during drastic net power flow change conditions and exhibits a peak efficiency of 96.4% with a power density of 3.2 kVA/L. A comprehensive benchmarking of the proposed architecture with commercially available FC products is also presented.

SAVIOR: A Sustainable Network of Vehicles with Near-Perpetual Mobility

Switching to Battery Electric Vehicles (BEV) can have a significant positive impact on our environment. However, the adoption of BEVs is vastly impeded by battery-related concerns, such as limited travel range, long charging time, high purchasing cost (battery-induced) and lack of charging stations. Additionally, it is very expensive to build a large infrastructure of fast charging stations that can cater to a full-scale BEV fleet. Alternative solutions, such as charging from the road and BEV-to-BEV stationary charge sharing, have been proposed to counteract range anxiety, but they are mostly ineffective and suffer from scalability issues. In this article, we present SAVIOR, an innovative Internet-of-Things (IoT) framework for replenishing BEV batteries on-the-go with the help of unmanned aerial vehicles (UAVs) and mobile charging stations (MoCS). This will allow rapid BEV battery replenishment, eliminating the need for BEVs to make prolonged and pre-planned halts for re-charging. We also observe that package delivery UAVs can utilize this framework to make long-distance trips with the help of mobile charging stations and BEVs. We quantitatively analyze the effectiveness of such a framework through a simulation platform that we have developed. There is a drastic improvement in the mobility of BEVs and UAVs. Through statistical analysis, we also observe that greenhouse gas emissions (even for BEVs and UAVs) can be significantly reduced by SAVIOR if the MoCS are powered by renewable energy sources (e.g., solar).

Modeling the temporal and economic feasibility of electric vehicles providing vehicle-to-grid services in the electricity market under different charging scenarios

Electric vehicles (EVs) could potentially act as the distributed energy storage devices to provide vehicle-to-grid (V2G) services to benefit the electric power system. Correspondingly, EV users can earn revenue based on the provision of grid services in a market environment. However, EVs would suffer the extra battery degradation incurred by the V2G operation. As such, it is essential to investigate the feasibility of EVs to conduct the V2G operation under different charging scenarios. Compared to existing studies focusing solely on the economic evaluation of V2G operation, this paper investigates the potential of EVs providing V2G services from two perspectives: time availability and economic viability. To this end, two critical indicators, viz., time indicator and economic indicator, are introduced in this paper to model the temporal and economic feasibility of EVs providing grid services to explore the applicable V2G scenarios. In addition, three charging scenarios at home, in workplace, and in fast charging stations (FCSs) are evaluated regarding the feasibility for V2G services. The simulation results reveal that, there is sufficient idle time for those EVs in the home-based and workplace-based charging scenarios and it is profitable to provide V2G services in the electricity market according to the cost-benefit analysis. However, for those EVs with FCS-based charging, it is unlikely for EV users to spend the extra time to conduct the V2G operation due to the fact that the V2G operation will prolong the grid-connection time of EVs.