Vehicle Fast Charging Station Research Articles

Hybrid Si/SiC Switch Modulation With Minimum SiC MOSFET Conduction in Grid Connected Voltage Source Converters

In this article, a hybrid Si/Si carbide (SiC) switch (HyS) modulation with minimum SiC MOSFET conduction (mcHyS) is experimentally characterized, so as to derive its conduction and switching performance. These are later used to derive a silicon (Si) area analytical model for the HyS configuration. The chip area model is used to benchmark the mcHyS modulation concepts against single-technology switches and typical HyS modulation when considering the implementation of a 100-kW two-level voltage-source converter (VSC) deployed for three industrial applications: photovoltaic inverter, electric vehicle fast-charging station, and battery storage systems for grid ancillary service. The two additional switching events of the SiC MOSFET, which differentiate the mcHyS modulation from the typical HyS one, are proven to happen in soft switching; therefore, the mcHyS switching performances are not penalized. Furthermore, the analysis presented shows how the studied mcHyS modulation performs against the single semiconductor technology and the typical HyS solution in terms of cost and power conversion efficiency. More specifically, it is shown that the HyS solutions are particularly competitive versus the full Si-based VSCs when the application at hand often operates at low partial loads. Finally, a 10-kW two-level VSC assembled with mcHyS is tested, so as to compare its efficiency versus single-technology switches.

Multi‐objective quantum atom search optimization algorithm for electric vehicle charging station planning

This paper presents an effective planning methodology for electric vehicle (EV) fast-charging stations (CS) using a multi-objective binary version of the atom search optimization (ASO) algorithm. The proposed method uses quantum operations to binarize the algorithm and achieve a higher convergence rate than the existing binary ASO algorithm. Additionally, a modified atom selection function is used to improve the searching capability of the ASO algorithm. Furthermore, the nondominated sorting procedure and pareto concepts are infused to solve the CS location problem (CSLP) considering the EV travel time, CS costs, and grid power loss as independent multi-objectives. The efficacy of the proposed multi-objective quantum ASO (MO-QASO) algorithm is evaluated using performance metrics namely, inverted generational distance (IGD), spacing (SP), and maximum spread (MS). The MO-QASO simulation results are compared with the results of other heuristic algorithms. MO-QASO achieves the best IGD (0.0021), SP (0.0002), and MS (0.9982) values, verifying the convergence and diversity of the algorithm. Importantly, the best CS planning solution obtained from MO-QASO is similar to the true solution obtained from the exhaustive search method. The MO-QASO efficiency is further validated by solving a CSLP from literature. Thus, the MO-QASO algorithm is a promising optimization tool for solving CSLP.

Sliding mode-based control of an electric vehicle fast charging station in a DC microgrid

The fast-charging units have become a more efficient and attractive option recently for reducing the challenges due to the long charging time of electric vehicles (EVs). To evaluate the impacts of the EV fast charging stations (EVFCS) on the power grid and also to assess their contributions to the system operation through the vehicle-to-grid (V2G) technology, two control methods, namely, sliding mode control (SMC) and fuzzy logic control (FLC), are developed in this study for a DC microgrid including EVFCS and distributed generation sources. In these methods, the EV battery is used as a DC source of a distribution static compensator (D-STATCOM) with the objective of mitigating the voltage sag in the microgrid. Various simulations are conducted in MATLAB Simulink/SimPowerSystems environment in order to examine the effectiveness of the proposed control approaches in terms of ensuring the stability and improving the dynamic performance of the EVFCS. The results show that considerable improvements can be achieved, especially in the case of using the SMC method.

Agent based online learning approach for power flow control of electric vehicle fast charging station integrated with smart microgrid

AbstractIn stochastic power systems, electric vehicle (EV) fast charging stations (FCS) are rapidly being installed, while adversely impacts the distribution network. Due to this, the improper offline charging control policies for EVs may increase the voltage fluctuation and instability. To analyse these aspects, this paper investigates the problems associated with offline (dis)charging control for effective utilization of battery storage and grid power through different modes of operations. Further, the need to develop real‐time charging control is identified to mitigate the adverse impacts of FCS on the distribution network. Hence, an online controller using reinforcement learning (RL) is designed to distinguish the uncertainties in real‐time and to schedule the (dis)charging of an EV against the uncertainties based on travelling pattern. The RL based online controller uses deep neural network (DNN), where the agents are programmed to control the bi‐directional power flow (V2G/G2V). The effectiveness of the RL rewards controller is fulfilled by the different charging states of the battery. The performance of online (dis)charging controllers that utilize DNN to act at its optimal power flow set of points for all sessions are examined in the details. Finally, the effectiveness of online RL controller and hardware results have been realized using real time hardware‐in‐the loop simulator.

Placement of electric vehicle fast charging stations in distribution network considering power loss, land cost, and electric vehicle population

ABSTRACT Recently, electric vehicles (EVs) gained tremendous attention from government agencies and the automotive industry due to lower CO2 emissions, low maintenance, and operating costs. However, due to increasing EV penetration, the EV’s load affects the distribution network parameters like power loss, voltage profile, and harmonic distortion. Therefore, the proper placement of EV fast-charging stations (FCSs) is required for the reliability of the distribution network. Further, this paper proposes two-stage processes for the placement of FCSs. In the first stage, the charging station owner decision index (CSODI) has been introduced considering the land cost index (LCI) and electric vehicle flow index (EVFI). The CSODI has been formulated to minimize the land cost and maximize the EVs flow for FCSs placement. In the next stage, an optimization problem is formulated for minimizing the total active power loss by considering the distribution system operator (DSO) constraints. In addition, the minimization problem has been solved using the hybrid gray wolf optimization-particle swarm optimization (GWOPSO) algorithm. Therefore, the best possible locations were obtained by the GWOPSO with 198.93 kW power loss. Furthermore, the average 2.02% power loss for the GWOPSO technique is lower when compared to the PSO technique.

Simultaneous allocation ofEVcharging stations and renewable energy sources: An EliteRERNN‐m2MPAapproach

This article proposes a hybrid optimization technique for optimal location and sizing of electric vehicle fast charging stations (EVFCSs) and renewable energy sources (RESs). The proposed hybrid optimization technique is the consolidation of recalling-enhanced recurrent neural network (RERNN) and Marine Predators Algorithm (MPA), hence it is called RERNN-m2MPA technique. Here, an enhanced MPA (m2MPA) is proposed. The major objective of this article is to energy loss reduction, voltage deviation of the power system network and minimization of the land cost with maximum weightage to serve maximum EV with minimum installation cost. The lessening of voltage deviation is considered as objective function to maintain a stable voltage profile in every nodes. The land cost is diminished with weightage maximization to serve maximal electric vehicle with minimal installation cost of electric vehicle charging station (EVCS). In this proposal, optimal location with capacity of fast charging stations and RESs are determined concurrently, while determined the deviation paths and uncertainties of RESs. To this intention, RERNN is proposed to cover the charging demand for EVs on the transportation network. Moreover, an m2MPA is proposed to optimal allocation of RESs and fast charging stations with respect to the distribution and transportation networks. Likewise, the proposed technique minimizes the adverse effects of fast charging demand on the grid, particularly during peak periods reducing the need for larger capabilities of energy storage units to absorb fast charging fluctuations and reduces the need for separate FCS installation. By then, the proposed technique is carried out in MATLAB/Simulink platform, its performance is compared with various existing techniques.

Emission Constrained Planning of Electric Vehicle Fast-Charging Stations

Emission Constrained Planning of Electric Vehicle Fast-Charging Stations

Optimal Battery Energy Storage Dispatch in Energy and Frequency Regulation Markets While Peak Shaving an EV Fast Charging Station

Battery Energy Storage Systems typically procure their primary revenues from regulated energy and ancillary services markets; nonetheless, they have great potential in supporting distribution network operators and their users. This paper evaluates the potential business case of battery storage systems integrating market application and services to a photovoltaic assisted electric vehicle fast-charging station. A mathematical deterministic optimization problem is formulated using mixed-integer linear programming to combine battery storage system operation in the day-ahead and frequency regulation market and the remunerated services offered to the charging station. The technical and economic feasibility of the solution and the applicability of the proposed framework is verified through a case study reflecting an existing photovoltaic assisted charging station in the Netherlands and considering the Dutch energy market framework and prices. The study shows that such battery storage system implementation is economically and technically advantageous for the players involved. The battery storage system can stack additional revenues on top of the market revenues. The charging station benefits from a reduced peak power and a 30% tariff reduction, and the system operator would indirectly benefit from the shaved charging station profile. Furthermore, the analysis shows that providing services to the charging station from the battery storage system does not significantly impact its market-related revenues.

Swarm intelligence‐based energy management of electric vehicle charging station integrated with renewable energy sources

This research paper proposes a detailed design problem of electrical vehicle (EV) fast-charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid. The performance indices of the design problem, such as the number of chargers and their rating, installed RES power, energy and power of the storage units, and dynamic power contracted by the grid to the EV charging station, is estimated through the proposed algorithm. The detailed modelling of the charging process considers the EV behavioural characteristics like arrival time, state of charge, departure time, and battery capacity and is simulated through sequential Monte-Carlo method. The hybrid Crow Search Algorithm (hCSA), along with the particle swarm optimization (hCSA-PSO), is adopted for the first time to optimize the charging station's installation and operational cost. The effectiveness of the proposed method is compared with a hybrid genetic pattern search algorithm, CSA, and PSO. Several case studies are considered, and it is observed that hCSA-PSO provides the best-optimized results in profit maximization. The cost-benefit analysis is also performed to estimate the financial feasibility of both RES and BESS.

Self-controlling resource management model for electric vehicle fast charging stations with priority service

Along with the increasing number of electric vehicles (EVs) on the roads, the demand for public fast-charging stations is growing. Long charging times for EVs may lead to congestion in charging stations, queues, and increased waiting times. Different vehicle owners have different sensitivities to waiting time and price. User prioritization is an effective solution for satisfying users with different requests. In this study, we develop a self-controlling resource management model for EV fast-charging stations that provide prioritized service. The model aims to control the delay times of express and normal vehicle classes such that the ratio of their average delay times tracks a target relative delay rate in real time. Each station can determine and change its target relative delay rate according to its policy. The model manages the allocation of resources to user classes in real time through a control mechanism to track the target. The control mechanism uses a simulation model to predict the outcomes of its actions. Numerical studies demonstrate that the model successfully achieves the relative delay target in both steady state and real time under different conditions. The model is applicable to most systems with a dynamically varying workload and a priority-based service goal.

Queue Based Dynamic Charging Resource Allocation and Coordination for Heterogeneous Traffic in an Electrical Vehicle Charging Station

ABSTRACT Electric vehicle (EV) fast-charging stations (CSs) with innovative operation management strategies can help to meet the growing EV charging needs. Despite the short charging time associated with the fast charging of EVs, massive deployment of plugged-in EVs for fast charging triggers network surges in the distributed network. Nevertheless, these challenges can be effectively addressed with appropriate EV admission control, charging resource allocation, and coordinated charging strategies. This work proposes an innovative EV-CS operation mechanism that maximizes the CS profit by allowing fast charging EVs to access the CS opportunistically without stressing the distributed network. Furthermore, a queue is employed in the dynamic resource allocation process to favor more fast charging requests through buffering non-critical slow charging EVs, as fast charging users would otherwise be blocked or forcibly terminated. Performance of the proposed priority-based charging coordination is analyzed in terms of the optimum utilization of demand limit, charging completion rate, and charging station utilization. Therefore, it keeps the average charging station utilization above 90% at higher arrival rates of EVs while the distribution transformer is being loaded only up to 50% of its rated capacity. Moreover, the blocking and forced termination probabilities of fast charging users are used to evaluate the service quality of EV charging. The presented dynamic resource allocation and charging coordination strategies for heterogeneous EV traffic maximize the CS profit while assuring quality service to EV users without stressing the distributed network.

Establishing a statewide electric vehicle charging station network in Maryland: A corridor-based station location problem

In this paper, we apply an optimization-based approach to locate electric vehicle (EV) fast charging stations that prioritize the deployment of stations considering the designated EV corridors. A designated corridor is a segment of a highway selected for EV charging infrastructure deployment to increase the adoption of EVs. We formulate the problem such that candidate charging stations on corridors are prioritized over the other candidate station locations outside the corridor. We also introduce two new objective functions based on corridor-utilizing and corridor-weighted traffic flow concept and a new constraint that primarily focuses on the corridor building. These new objectives fit better for enabling EV corridors when the underlying objective is to locate chargers along these corridors with high density. We conduct numerical experiments using the Maryland highway network, major population centers, and select EV corridors as input. Experiment results suggest that the traditionally used objectives in the literature, maximizing traffic flow and maximizing vehicle-miles traveled (VMT), may not be well-suited for corridor building. However, a corridor-focused objective function causes the removal of many O-D pairs from consideration; hence, its impact on the overall EV flow refueled is significant as well. Experiment results also indicate that corridor prioritization is crucial for selecting a solution with more stations on corridors.

Double-String Battery System with Reconfigurable Cell Topology Operated as a Fast Charging Station for Electric Vehicles

This paper introduces a novel design of an electric vehicle (EV) fast charging station, consisting of a battery energy storage system (BESS) with reconfigurable cell topology. The BESS comprises two battery strings that decouple the power flow between EV and grid, to enable charging powers above the grid capacity. The reconfigurable design is achieved by equipping the battery cells with semiconductor switches and serves two main purposes. First, it aims at solving cell unbalance issues to increase safety, reliability, and lifetime of the battery. Second, it enables the BESS to actively control the EV charging process by changing its cell configuration in a real-time fashion, making a DC-DC converter redundant. The paper presents a modelling approach that captures the reconfigurable design including the controlling algorithm used for cell engagement. The simulation results show that the BESS is able to fulfil the EV request with sufficient accuracy for most of the fast charging process. However, the switching of cells leads to variations in the charging current that can potentially exceed the tolerance band defined in IEC61851-23. Therefore, complementary measures are suggested to achieve a suitable current control during all phases of the charging process. The estimated BESS efficiency during the EV fast charging process is 93.3%. The losses caused by the reconfigurable design amount to 1.2% of the provided energy. It is demonstrated that the proposed design has a competitive efficiency compared to a battery buffered fast charging station with DC-DC converter.

Partial Power Processing Based Converter for Electric Vehicle Fast Charging Stations

This paper focuses on the design of a charging unit for an electric vehicle fast charging station. With this purpose, in first place, different solutions that exist for fast charging stations are described through a brief introduction. Then, partial power processing architectures are introduced and proposed as attractive strategies to improve the performance of this type of applications. Furthermore, through a series of simulations, it is observed that partial power processing based converters obtain reduced processed power ratio and efficiency results compared to conventional full power converters. So, with the aim of verifying the conclusions obtained through the simulations, two downscaled prototypes are assembled and tested. Finally, it is concluded that, in case galvanic isolation is not required for the charging unit converter, partial power converters are smaller and more efficient alternatives than conventional full power converters.

Analysis of an Optimal Planning Model for Electric Vehicle Fast-Charging Stations in Al Ain City, United Arab Emirates

For the large-scale promotion of electric vehicles (EV), reliable fast-charging stations (FCS) demand high priority among EV users. However, unplanned locations of charging stations (CSs) and station capacity determination have adverse effects on the operation and the performance of power-distribution network. In this study, we developed an optimal FCS-planning model considering the aspects of EV users’ convenience, station economic benefits, the impact on distribution systems and the effect on environment. A queuing-theory-based CS sizing algorithm that benefits EV users as well as improves CS capacity utilization was proposed. The proposed planning model was verified through a case study using real road network data by employing multi-objective binary and non-dominated sorting genetic algorithm. In addition, to evaluate the efficiency of the proposed sizing algorithm, sensitivity analyses for different EV penetration levels and station utilization were conducted. The simulation results show that the proposed CS-allocation model is beneficial in terms of achieving the satisfaction of EV users, cost savings, better station utilization, and less impact on power grids and the environment. Finally, to validate the effectiveness of the proposed planning model, a comparative study with one of the previous work on CS planning is also performed. The results demonstrate that the proposed charging station sizing method is highly efficient in optimizing EV users’ satisfaction and for better station utilization.

Siting and sizing of electric vehicle fast‐charging station based on quasi‐dynamic traffic flow

In engineering practice, the Voronoi diagram is often used to plan electric vehicle (EV) fast-charging stations but it does not consider the spatio-temporal distribution of EV trip. Research on the spatio-temporal distribution of EVs mainly uses the shortest path method to reduce the amount of iterative calculations. The influence of the external environment and traffic congestion on the travel of EVs has not been considered, which results in the inaccuracy of the EV charging demand. To solve the above problems, this study proposes a method for predicting the spatio-temporal distribution of EVs based on quasi-dynamic traffic flow. This method takes into account the external environment and the impact of traffic congestions on EV trips and balances the problem between simulation accuracy and calculation efficiency. Based on this, the particle swarm optimisation algorithm is used to optimise the travel cost of the EVs and the cost of the construction and operation of the charging infrastructure. An optimal siting and sizing model for the fast-charging station based on quasi-dynamic traffic flow is established. Simulation results verify the effectiveness of the model.

A model-based continuous differentiable current charging approach for electric vehicles in direct current microgrids

Microgrid-based electric vehicle (EV) fast charging station is believed to be a promising solution to lessen the enormous charging burden of large-scale EVs on the main grid, while the optimal charging protocol in microgrids is still missing. Thus, this paper presents a model-based continuous differentiable charging (CDC) approach for EV fasting charging in microgrids. The priority of the proposed method on bus voltage regulation over the conventional multi-stage constant current (MCC) strategy is first validated. The parameters of the CDC profile are obtained through the model-based particle swarm optimization framework. An electrothermal-coupled equivalent circuit model is adopted as the performance model while a physical-based semi-empirical battery degradation model is built to measure the capacity loss and lithium plating rate. The objective function of the optimization is to reduce the charging time, capacity fading, and bus voltage disturbance, with the constraints of preventing lithium deposition and limiting the maximum voltage and temperature. The optimization results of the CDC and MCC methods confirm that CDC protocol can reduce the charging time by about 33.5% without scarifying battery health. The sensitivity analyses of initial state of charge, ambient temperature, and heat dissipation coefficient further suggest the universality of the proposed CDC strategy.

A VIKOR-based framework to optimize the location of fast-charging stations with proportional hesitant fuzzy information

In this study, a multi-criteria group decision making (MCGDM) framework is constructed for electric vehicle fast-charging station (EVFCS) selection using a proportional hesitant fuzzy set (PHFS) that can describe two aspects of information: the possible membership degrees in the hesitant fuzzy elements and associated proportion representing statistical information from different groups. A newly extended distance measure for PHFSs is introduced and an extended maximizing deviation method is constructed to obtain criteria weights objectively. Accordingly, an integrated PHFS-VIKOR (VlseKriterijum-ska Optimizacija I Kompromisno Resenje) method embedded with a new distance measure and extended maximizing deviation method is presented. With increasing concerns about range anxiety, it is essential to seek an optimal location for EVFCS considering efficient utilization of resources and long-term development of socio-economy under proportional hesitant fuzzy environment. Lastly, an illustration with sensitivity analysis and comparative analyses is provided to demonstrate the validity and robustness of our proposal.

Willingness to pay for fast charging station for electric vehicles with limited market penetration making

This study investigates the potential public demand for investing in electric vehicles infrastructure using a stated preference method. Availability of electric vehicle fast charging stations can improve consumer penetration and acceptance level of purchasing electric vehicles. The outlook of passenger transport is expected to shift to using electricity as the main fuel source which requires a significant amount of energy through the electricity grid and provision of appropriate public charging infrastructure to help support commuter usage. To quantify the preference of users towards an energy related policy, a discrete choice experiment using a virtual payment system was designed to increment an annual levy amount for specific purpose over a set of years. The results from a sample of 1180 households in New South Wales Australia, revealed that depending on the policy setting, 74.2% of the population would be willing to pay some amount of levy. Moreover, we found that on average NSW households are willing to pay $31.9 as annual levy to help raise the fund to develop and install fast charging station state-wide.

PEV Fast-Charging Station Sizing and Placement in Coupled Transportation-Distribution Networks Considering Power Line Conditioning Capability

The locations and sizes of plug-in electric vehicle fast-charging stations (PEVFCS) can affect traffic flow in the urban transportation network (TN) and operation indices in the electrical energy distribution network (DN). PEVFCSs are supplied by the DNs, generally using power electronic devices. Thus, PEVFCSs could be used as power line conditioners, especially as active filters (for mitigating harmonic pollutions) and reactive power compensators. Accordingly, this paper proposes a mixed-integer linear programming model taking into account the traffic impacts and power line conditioning capabilities of PEVFCSs to determine optimal locations and sizes of PEVFCSs. Various load profile patterns and the variation of charging demand during the planning horizon are included in this model to consider different operation states of DN and TN. The proposed model is implemented in GAMS and applied to two standard test systems. Numerical results are provided for the case studies and various scenarios. The results confirm the ability and efficiency of the proposed model and its superiority to the existing models.