Electric Vehicle Charging Stations Research Articles

Related Work and Motivation for Electric Vehicle Solar/Wind Charging Stations: A Review

The shift towards sustainable transportation is an urgent worldwide issue, leading to the investigation of creative methods to decrease the environmental effects of traditional vehicles. Electric vehicles (EVs) are a promising alternative, but the issue lies in establishing efficient and environmentally friendly charging infrastructure. This review explores the existing research on the subject of photovoltaic-powered electric vehicle charging stations (EVCSs). Our analysis highlights the potential for economic growth and the creation of robust and decentralized energy systems by increasing the number of EVCSs. This review summarizes the current knowledge in this field and highlights the key factors driving efforts to expand the use of PV-powered EVCSs. The findings indicate that MATLAB was predominantly used for theoretical studies, with projects focusing on shading parking lots. The energy usage varied from 0.139 to 0.295 kWh/km, while the cost of energy ranged from USD 0.0032 to 0.5645 per kWh for an on-grid system. The payback period (PBP) values are suitable for this application. The average PBP was demonstrated to range from 1 to 15 years. The findings from this assessment can guide policymakers, researchers, and industry stakeholders in shaping future advancements toward a cleaner and more sustainable transportation system.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Implementation of a Microgrid System with a Four-Phase Inductor Coupled Interleaved Boost Converter for EV Charging Stations

Electric vehicle charging stations are essential to enable broad reception due to the rise in electric vehicles in the transportation industry because they will lessen range anxiety concerns about distance. The primary objective of this work is to design a microgrid that is effective and affordable for an electric vehicle charging station that combines a photovoltaic, wind, and utility grid energy system (optional) as a principal source of energy. The proposed study employs a four-phase inductor coupled interleaved boost converter which is compact and effective with high power output which results in charging a vehicle within 33 min. A perturb and observe MPPT approach based on DC converters is used along with the digital 2PI controller to increase the effectiveness and performance of distributed energy systems. To make the converter a hassle-free operation, an interleaving technique is applied to the developed converter which results in ripple reduction, which results in an increase in the output current and voltage gain, with high power density and efficiency. For better understanding, real-time data for 2W/3W/4W are acquired and tested for various conditions and the maximum state of charge for the battery is gained within one-third of the usual time. At present, the interleaved converter’s operation is theoretically examined, and the behavior of the converter and the charging conditions of several electric vehicle systems are compared and shown in the simulation analysis.

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Enhancing electromagnetic compatibility and energy efficiency of electric vehicle charging stations

Problem. The article proposes a single-link structure for an electric vehicle charging station utilizing an active four-square rectifier with power factor correction. A Matlab model of the proposed charging station is developed, taking into account parameters such as the power network, the switches of the active rectifier, its automatic control system, and an equivalent model of the battery compartment. Additionally, a mathematical model for calculating static and dynamic losses is created based on polynomial approximation of the energy dependencies of IGBT modules. The analysis investigates power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Goal. The aim of this study is to propose a single-link structure for an electric vehicle charging station using an active four-square rectifier with power factor correction. It includes an analysis of power quality parameters, components of energy losses, and efficiency of the charging station at different charge currents and PWM frequencies during a full battery charge interval. Methodology. To achieve the goal, several key steps are considered. These include theoretical substantiation of the scheme of the electric microgrid charging station for electric vehicles with one-stage energy conversion, analysis of the battery connection scheme in the Tesla Model S electric car, research and calculation of efficiency, modeling of the charging station, development of a Matlab model of a microgrid system for the charging station, SAC analysis of battery charge voltage and current of a three-phase AV with PWM, modeling of losses in IGBT modules by polynomial approximation of dependencies, distribution of losses in the charging station system, and analysis of energy efficiency parameters. Results. The study presents the energy efficiency parameters of an external DC EV charging station using an active rectifier. It reveals that maximum efficiency of the system is achieved at minimum charge current. However, decreasing the charge current prolongs the charge process and slightly affects power quality parameters. Originality. A mathematical model for calculating static and dynamic losses was developed based on polynomial approximation of the energy dependencies of IGBT modules. The analysis encompasses power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Practical value. This study contributes to the further development of electric vehicles by improving the energy indicators of electric vehicle batteries and converters of electric vehicle charging stations, enabling fast charging modes. Active development is observed in each of these directions.

Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach

This study presents a hybrid solution for the charging station location-capacity problem. The proposed approach simultaneously determines the location and capacity of charging stations (i.e., number of charging piles), and assigns piles to electric vehicles based on their level of charge. The problem is formulated as a bi-objective mixed-integer nonlinear programming model to minimize the total cost of establishing charging stations together with the average customers’ waiting time. The proposed solution combines queueing theory with mathematical modelling to estimate the average waiting time. A deep learning algorithm is then developed to enhance the precision of waiting time estimation. Another contribution is involving a deep neural network model in improving NSGA-II algorithm. Numerical experiments are conducted in Halifax, Canada to assess the performance of the proposed framework. The results demonstrate the strong predictive performance of the deep learning algorithm and highlight the limitations of traditional queueing models in estimating waiting times in charging stations (i.e., 99.8% improvement in computation time, as well as accuracy improvement of time estimations from 13% to 1.6% deviation). Several valuable insights are obtained to improve the operational performance of charging stations such as achieving a significant (i.e., 61.5%) drop in the average waiting time across the network by a modest (i.e., 29.2%) increase in the initial investments. Also, it reveals that the variability of service rate significantly impacts the average waiting time (i.e., a 50% increase in the variability of service rate causes a substantial 950.56% surge in the average waiting time). The findings underscore the need to control service rate fluctuations to reduce wait times and boost driver satisfaction. The improved NSGA-II algorithm shows 12.77% improvement in the Pareto front solutions. Finally, the proposed prioritization strategy based on the charging level of vehicles could reduce the average waiting time and cost compared to the first-come-first-served strategy.

Wireless Charging Station for Electric Vehicles

Wireless Charging Station for Electric Vehicles

Minimizing the Impact of Electric Vehicle Charging Station with Distributed Generation and Distribution Static Synchronous Compensator Using PSR Index and Spotted Hyena Optimizer Algorithm on the Radial Distribution System

This research investigates the impact of electric vehicle charging station on power loss, voltage stability, and reliability within radial distribution systems. With the escalating use of electric vehicles, understanding these effects is crucial for ensuring the stability and efficiency of radial distribution systems infrastructure. The study formulates the electric vehicle charging station allocation problem as a multi-objective function, integrating power loss, voltage stability, and reliability metrics expressed as the PSR index. Using the IEEE 69-bus system as a test system, the research explores the ramifications of electric vehicle charging station integration and proposes strategies to mitigate their effects. An innovative combination of distributed generation and distribution static compensator are employed to alleviate the impact of electric vehicle charging station on radial distribution systems. A recent optimization methodology called spotted hyena optimizer algorithm is introduced to determine optimal electric vehicle charging station locations, with results compared to other existing algorithms. The study assesses potential power losses induced by additional electric vehicle loads, explores strategies to optimize charging rates for minimizing resistive losses, and evaluates voltage stability and reliability implications. The important results include objective function values obtained using various algorithms: Spotted hyena optimizer algorithm (0.7683), cuckoo search algorithm (0.7709), bat algorithm (0.8035), and African vultures optimization algorithm (0.7856). The findings demonstrate that the proposed algorithm outperforms other algorithms in minimizing the objective function value, indicating its efficacy in optimizing electric vehicle charging station placement within radial distribution systems. In conclusion, this research contributes valuable insights into the challenges associated with integrating electric vehicle charging station into radial distribution systems and provides innovative solutions for optimizing electric vehicle charging station placement, thereby advancing the understanding and management of distribution systems in the context of electric vehicle adoption.

Revolutionizing load management: A novel technique to diminish the impact of electric vehicle charging stations on the electricity grid

The proliferation of Electric Vehicle Charging Stations (EVCSs) raises challenges for power grids, resulting in increased losses and variations in voltage. The study describes a novel technique for reducing the impact of EVCSs on the electricity grid. Distributed Generators (DGs) are strategically deployed at susceptible nodes. To identify nodes vulnerable to EV charging-induced overloading, the weak bus placement approach is used. DGs are placed at these nodes to increase capacity and maintain grid stability. Contingency analysis is used to simulate the grid's response to various disruptions in order to assess the strategy's efficacy. The study includes 13 EVCSs, each with a 25 MW capacity, located at predetermined load buses. This integration increases the demand from 2,850 MW to 3,175 MW, resulting in 55.65 MW of system losses. Three more DGs, each with a different capacity, contribute to the system losses being effectively reduced to 51.016 MW, which is higher than the base losses of 51.025 MW. This study's main objectives were to: (1) thoroughly evaluate the impact of EVCSs using the Newton-Raphson (NR) load flow; and (2) provide a systematic strategy for addressing the power losses and voltage fluctuations brought on by EVCSs. This approach involves integrating DGs strategically.

Fault prevention strategy for coupled networks of power grids and transportation with the participation of electric vehicles

The increasing number of electric vehicles (EVs) that are connected to the power grid has raised concerns about the security of both transportation and power grids. This paper presents a fault prevention strategy that involves the use of social networks to improve the performance of electric vehicle charging stations. Firstly, the goal of this strategy is to improve the electric vehicle charging station’s performance by developing an evolutionary game model that takes into account the various factors that affect the electric vehicle’s response and travel time. Secondly, a number of charging station sample data is generated and analyzed using a convolutional neural framework. The capacity of the stations is then calculated by taking into account the different incentive measures offered by the power grid.. Furthermore, an optimal power flow model considering fault risk constraints and charging and discharging capacity constraints is established in the power grid or power grid subsystem to adjust the generator output and charging station power to realize the safe dispatching of the power grid. The simulation example shows that this method can fully consider the influence of traffic networks in the participation of EVs in power grid fault prevention, which is more reasonable.

Green Power Ev Charging Station Design and Analysis for Electric Vehicles

The primary goal of this research is to design on electric vehicle charging station with less emission in Chennai due to an increase in electric vehicles. The wind and solar are common renewable energy sources which produces green power. These renewable sources can also be implemented with diesel generator and grid connection to run the Electric Vehicle (EV) charging station. This research also focuses on the cost of energy and the total cost of the system for different sources to operate EV charging station. The sources to operate an EV charging station in various period of time to charge the vehicle are analyzed. The sensitivity analysis like derating of solar also done to examine the status of different parameters in entire system with low cost. The design of low-cost system for Electric Vehicle charging station will be a useful implementation to Chennai city for charge various EV vehicles. The result shows that the price of energy and total cost of the system are 0.176 $/kWh and $ 363,094 respectively for entire system which considers to charge different EV bike and EV car through EV charging station. The simulation for this research work is carried out in HOMER Grid software.

The Use of Renewable Energy Sources in Road Construction and Public Transport: A Review

The development and advantages of renewable energy technologies mean that their areas of application are constantly expanding. The development of roads, transport systems, and electromobility also increases the demand for electricity. Roads occupy a certain area that could be used to install wind turbines or photovoltaic systems that could be used to power, among others, electric vehicle charging stations and road technical infrastructure facilities (travel service areas, tunnel lighting, road signs). There are many examples around the world where such solutions have been used. This critical review of existing solutions and the possibilities of their application in new places may contribute to further development and research in this area. Particular attention was paid to the possibility of using renewable energy systems in Poland, which can be successfully transferred to other countries with a similar climate.

Smart Green Electric Vehicle Charging Station

Smart Green Electric Vehicle Charging Station

Optimizing DOCRs coordination when synergizing energy management with V2G using innovative optimization algorithms

This manuscript proposes an optimal coordination approach for directional overcurrent relays (DOCRs) within electric vehicle charging stations (EVCS), considering energy management and Vehicle-to-Grid (V2G) integration. The study focuses on maximizing efficiency and grid interaction in charging stations by addressing the challenge of DOCRs coordination. Three innovative optimization algorithms, namely the Hunger Games Search (HGS), Hunter Prey Optimization (HPO), and Sparrow Search algorithm (SSA), are employed to determine the optimal coordination for the DOCRs and get the time dial setting (TDS) and pickup current (IP) targeting minimum operating time for all DOCRs, considering factors such as load balancing, fault detection, and power quality in three different scenarios. EVSC has two types of charges DC and AC with two modes: V2G mode when EV is discharged into the grid, and G2V mode when EV is charged from the grid. Fast chargers are used to proceed as it can work in two modes V2G and G2V. Through extensive simulations and performance evaluations, the results demonstrate the effectiveness of the proposed approach. The three scenarios are simulated by using ETAP and MATLAB software according to fault location and to achieve voltage regulation and sufficient power for loads to achieved continuity of service for up to 1960.7 kW loads that kept fed. The proposed approach achieved the target of protecting the EVCS in minimum operating time of relays using the three algorithms and SSA introduces the best result more than other algorithms for the relay operating time with reduction up to 21 %.

Piece-Wise Droop Controller for Enhanced Stability in DC-Microgrid-Based Electric Vehicle Fast Charging Station

The need for public fast electric vehicle charging station (FEVCS) infrastructure is growing to meet the zero-emission goals of the transportation sector. However, the large charging demand of the EV fleet may adversely impact the grid’s stability and reliability. To improve grid stability and reliability, the development of a DC microgrid (MG) leveraging renewable energy sources to supply the energy demands of FEVCSs is the sustainable solution. Balancing the intermittent EV charging demand and fluctuating renewable energy generation with the stable DC bus voltage of a DC MG is a challenging objective. To address this objective, a piece-wise droop control strategy is proposed in this work. The proposed scheme regulates DC bus voltage and power sharing with droop value updating in a region-based load current distribution. Voltage compensation in individual regions is carried out to further improve the degree of freedom. In this paper, the performance of the proposed strategy is evaluated with the consideration of real-time solar PV dynamics and EV load dynamics. Further, to showcase the effectiveness of the proposed strategy, a comparative analysis with a maximum power point tracking (MPPT) controller against various dynamic EV load scenarios is carried out, and the results are validated through a hardware-in-loop experimental setup. Despite the intermittent source and EV load dynamics, the proposed piece-wise droop control can maintain voltage regulation with less than 1% deviation.

IOT Based Wireless Electric Vehicle Charging Station System

The automotive industry is quickly transitioning from IC engine vehicles to electric vehicles as we enter a new era of automobiles. The growing demand for electric vehicles is also driving up the number of charging stations. In this idea, the automobile is charged wirelessly through inductive coupling using a wireless charging system. All we have to do is pull the automobile into the charging station. Wireless Power Transmission is the process of sending electrical energy from a source to a load over a distance without the need of cables or other conducting wires. This project can open up new possibilities of wireless charging that can use in our daily lives. Wireless power transfer (WPT) using magnetic resonance is the technology which could set human free from the annoying wires. This system can be used to charge the battery of Electric Vehicle Key Words: Wireless, Power, Transmission, Charging Station, Magnetic Resonance.

Location decision of electric vehicle charging station based on a novel grey correlation comprehensive evaluation multi-criteria decision method

Location decision of electric vehicle charging station based on a novel grey correlation comprehensive evaluation multi-criteria decision method

A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning

The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.

Identifying factors that influence electric vehicle charging station performance in expanding networks.

Charging infrastructure deployment has taken off in many cities with the rise of the number of electric vehicles on the road. Expansion of infrastructure is a matter of prioritisation of resources to optimise the infrastructure. This paper explores how to measure charging station performance, to address the challenges that policy makers face. These performance indicators are used in a regression model, based upon current utilisation of the network, to predict which charging stations perform best. The results show that a model based on available geographical data and performance metrics of the current network are best combined to predict infrastructure performance. The variability between public charging stations is however big, as frequent user characteristics do determine the performance to a large extent.

Energy Management Strategies of Grid Connected Renewable Source for EV Charging Station

The growing popularity of electric vehicles (EVs) has made the need for effective charging infrastructure necessary. Combining renewable energy sources with EV charging stations offers a viable way to lessen transportation's negative environmental effects and cut back on fossil fuel use. This study looks into different grid-connected renewable energy source energy management techniques for EV charging stations. This study aims to optimize energy utilization, lower operating costs, and improve the sustainability of EV charging infrastructure by utilizing renewable energy generation, such as solar and wind power, in conjunction with intelligent charging algorithms. The study examines the body of research, evaluates various energy-management strategies, and suggests creative solutions to the problems arising from incorporating renewable energy sources into EV charging stations. The report also addresses the possible advantages, practical issues, and financial effects of putting these strategies into practice. This research advances our understanding of energy management systems for grid-connected renewable sources in the context of EV charging infrastructure through thorough analysis and simulation studies.