Stations For Electric Vehicles Research Articles

Design and Economic Analysis of a Solar-Powered Charging Station for Personal Electric Vehicles in Indonesia

Design and Economic Analysis of a Solar-Powered Charging Station for Personal Electric Vehicles in Indonesia

Design of a Charging Station for Electric Vehicles Based on a Photovoltaic-Biodiesel Hybrid Renewable Energy System Combined with Battery Storage

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

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.

Wireless Charging Station for Electric Vehicles

The project mainly focuses on the model of wireless charging station for electric vehicles. In this charging station the user no need to use physical cables for charging their electric vehicles. When the car arrives on the particular slot for charging the IR sensor detects the vehicle and sends signal to the Arduino Board, then the required voltage is transferred to the transmitter coil which is placed in the parking slot, with the help of magnetic induction principle the energy from transmitter coil is transmitted to the receiver coil which is placed in the bottom of the vehicle.

Novel Energy Management Options for Charging Stations of Electric Vehicles in Buildings without Increasing Peak Demand for Sustainable Cities

Electric vehicles are recognised as critical step in making transportation sector more environmentally friendly, especially when powered by renewable energy sources. A major hindrance to their widespread adoption is the scarcity of charging stations and the absence of grid access. This study explores and examines four distinct ways for enhancing the energy grid of buildings. The primary goal of these solutions is to generate more capacity without raising the electricity peak load of the building through the incorporation of energy storage technologies. The target location is Southern Ontario, Canada, where one of populated-intensive region in Canada. Strategy 3 stands out as the most promising alternative, with an impressive 35% increase in charging station capacity compared to other strategies. Using this method, it is feasible to store 353.4 GWh of energy during summer and 480.1 GWh during winter. In summer, the station can release 243.1 GWh, and in winter, 345.6 GWh of energy can be efficiently used to charge electric vehicles. Strategy 4, which includes hydrogen generation and fuel cell systems, offers a competitive alternative with a 28% increase in capacity. This method allows for charging station capabilities of 52 GW during summer and 13 GW during winter. The strategies emphasize the importance of adaptable capacity solutions to address variations in demand on a seasonal and daily basis, while considering both economic and environmental sustainability. As the adoption of electric vehicles continues to rise, the nuanced operational aspects of these strategies are pivotal for the development of sustainable and efficient transportation ecosystems, ensuring that electric vehicle charging infrastructure keeps pace with the growing demand.

When LoRa Meets Distributed Machine Learning to Optimize the Network Connectivity for Green and Intelligent Transportation System

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (Data Rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a Log-distance path loss model (Log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.

PERANCANGAN POWER SUPPLY UNTUK CHARGING STATION KENDARAAN LISTRIK RODA DUA DENGAN SISTEM PLTS ON GRID

Electric vehicles use electrical energy as a substitute for fossil energy. Like gasoline vehicles that require gasoline filling facilities, electric vehicles also require electric energy charging facilities or commonly referred to as charging stations. One of the utilizations of solar power plant can be used for charging electrical energy at the charging station. In this study, a Power Supply Design was carried out for Charging Station for Two-Wheeled Electric Vehicles with Solar Power Plant System on grid in Widya Sabha Building Parking Lot, Udayana University. With the calculation of the energy needed in the Power Supply, the solar power plant design and manufacturing costs can be made. The design of this charging station can accommodate 10 electric motorbikes with solar power plant that can provide energy for 2 electric motorbikes per day for 9 hours of work alternately. By using 11.450 Wp solar panels arranged in series and 1 inverter with a capacity of 4.6 kW, this design produces 8.229 MWh per year. The total initial investment in this design based on market prices in Indonesia is IDR 99.213.000. Based on the investment feasibility, it shows that the investment in this design is feasible to implement.

A reliable optimal electric Vehicle charging stations allocation

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation.

Modeling of the Acceptable Waiting Time for EV Charging in Japan

The limited number of charging stations for electric vehicles (EVs) necessitates periodic charging, resulting in extended queues at charging stations as drivers await their availability. This study contributes to the existing body of literature by providing estimates of consumer preferences for allowable waiting times at charging stations, as well as furthering the understanding of the roles of the explanatory variables influencing these preferences. The study also compares the average and maximum waiting times experienced by EV drivers, with the acceptable waiting time. Responses from the stated preference survey in Japan in 2021 were analyzed using a generalized ordered logit model. The results show that (a) the sex, age, household income, employment status, and vehicle usage frequency significantly influenced the preferences for allowable waiting times, and (b) the allowable waiting time preferences were significantly associated with the charging locations. Our estimation model indicated a positive association of convenience stores, large commercial facilities, and highway locations with short and medium allowable waiting times. The results provide useful insights into the policy implications of the charging infrastructure.

Optimal Design of Grid-Connected Hybrid Renewable Energy System Considering Electric Vehicle Station Using Improved Multi-Objective Optimization: Techno-Economic Perspectives

Electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) have been widely integrated into distribution systems. Electric vehicles (EVs) offer advantages for distribution systems, such as increasing reliability and efficiency, reducing pollutant emissions, and decreasing dependence on non-endogenous resources. In addition, vehicle-to-grid (V2G) technology has made EVs a potential form of portable energy storage, alleviating the random fluctuation of renewable energy power. This paper simulates the optimal design of a photovoltaic/wind/battery hybrid energy system as a power system combined with an electric vehicle charging station (EVCS) using V2G technology in a grid-connected system. The rule-based energy management strategy (RB-EMS) is used to control and observe the proposed system power flow. A multi-objective improved arithmetic optimization algorithm (MOIAOA) concept is proposed to analyze the optimal sizing of the proposed system components by calculating the optimal values of the three conflicting objectives: grid contribution factor (GCF), levelled cost of electricity (LCOE), and energy sold to the grid (ESOLD). This research uses a collection of meteorological data such as solar radiation, temperature, and wind speed captured every ten minutes for one year for a government building in Al-Najaf Governorate, Iraq. The results indicated that the optimal configuration of the proposed system using the MOIAOA method consists of eight photovoltaic modules, two wind turbines, and thirty-three storage batteries, while the fitness value is equal to 0.1522, the LCOE is equal to 2.66 × 10−2 USD/kWh, the GCF is equal to 7.34 × 10−5 kWh, and the ESOLD is equal to 0.8409 kWh. The integration of RESs with an EV-based grid-connected system is considered the best choice for real applications, owing to their remarkable performance and techno-economic development.

Hierarchical Control of Power Distribution in the Hybrid Energy Storage System of an Ultrafast Charging Station for Electric Vehicles

This paper presents a two-level hierarchical control method for the power distribution between the hybrid energy storage system (HESS) and the main dc bus of a microgrid for ultrafast charging of electric vehicles (EVs). The HESS is composed of a supercapacitor and a battery and is an essential part to fulfill the charging demand of EVs in a microgrid made up of a 220 VRMS ac bus, two dc buses of 600 V and 1500 V, respectively, and four charging points. A state machine defines the four operating modes of the HESS and establishes the conditions for the corresponding transitions among them, namely, charging the battery and the supercapacitor from the bus, injecting the current from the HESS into the 1500 V dc bus to ensure the power balance in the microgrid, regulating the bus voltage, and establishing the disconnection mode. The primary level of the control system regulates the current and voltage of the battery, supercapacitor, and dc bus, while the secondary level establishes the operating mode of the HESS and provides the appropriate references to the primary level. In the primary level, sliding mode control (SMC) is used in both the battery and supercapacitor in the inner loop of a cascade control that implements the standard constant current–constant voltage (CC-CV) charging protocol. In the same level, linear control is applied in the CV phase of the protocol and for bus voltage regulation or the current injection into the bus. PSIM simulations of the operating modes and their corresponding transitions verify the theoretical predictions.

Suitable law-based location selection of high-power electric vehicles charging stations on the TEN-T core network for sustainability: a case of Poland

With the upcoming implementation of the amendment to Regulation (EU) 2019/631 of the European Parliament and of the Council, from 2035 there will be a ban on the registration of new vehicles with internal combustion engines (ICE) in the Member States of the European Union (EU). Consequently, changes in the transportation sector, resulting from the increasing use of electric vehicles, appear to be inevitable. According to the adopted legal acts, the European Union Member States will be obliged to develop, among others, a charging infrastructure and access to public charging stations for electric vehicles. As a result, there will be a need to ensure a significant increase in the power and the number of charging stations and to determine their appropriate location. The article presents the challenges faced by charging station operators and difficulties related to the further development of electric vehicle charging infrastructure in Poland. The still poorly developed public charging infrastructure for electric vehicles, especially in service areas located along the main communication routes, remains the main obstacle to the development of electromobility. In the context of legal, financial, technological, and organizational challenges, the problem of the proper distribution of electric vehicle charging stations along the main communication routes is therefore of particular importance. The aim of the article is to present a new, proprietary method for determining the location of electric vehicle charging stations in Poland within the Trans-European Transport Network (TEN-T), which considers objective location factors: adherence to AFIR requirements, the specificity of the Polish power system and existing parking infrastructure. As a result of using the developed method, a list of 188 recommended locations for the construction of electric vehicle charging stations in Poland along the Trans-European Transport Network (TEN-T) was created. It has been shown in this way that the use of the presented method enables the suitable determination of the location of electric vehicle charging stations along transport routes, considering legal, financial, and technological requirements, which will significantly facilitate the operation of zero-emission transport.

Tropical Modeling of Battery Swapping and Charging Station

We propose and investigate a queueing model of a battery swapping and charging station (BSCS) for electric vehicles (EVs). A new approach to the analysis of the queueing model is developed, which combines the representation of the model as a stochastic dynamic system with the use of the methods and results of tropical algebra, which deals with the theory and applications of algebraic systems with idempotent operations. We describe the dynamics of the queueing model by a system of recurrence equations that involve random variables (RVs) to represent the interarrival time of incoming EVs. A performance measure for the model is defined as the mean operation cycle time of the station. Furthermore, the system of equations is represented in terms of the tropical algebra in vector form as an implicit linear state dynamic equation. The performance measure takes on the meaning of the mean growth rate of the state vector (the Lyapunov exponent) of the dynamic system. By applying a solution technique of vector equations in tropical algebra, the implicit equation is transformed into an explicit one with a state transition matrix with random entries. The evaluation of the Lyapunov exponent reduces to finding the limit of the expected value of norms of tropical matrix products. This limit is then obtained using results from the tropical spectral theory of deterministic and random matrices. With this approach, we derive a new exact formula for the mean cycle time of the BSCS, which is given in terms of the expected value of the RVs involved. We present the results of the Monte Carlo simulation of the BSCS’s operation, which show a good agreement with the exact solution. The application of the obtained solution to evaluate the performance of one BSCS and to find the optimal distribution of battery packs between stations in a network of BSCSs is discussed. The solution may be of interest in the case when the details of the underlying probability distributions are difficult to determine and, thus, serves to complement and supplement other modeling techniques with the need to fix a distribution.

Efficient and Reliable Fast Charging Station for Electric Vehicles: Integrating PV System and Optimized Control

In this study, an innovative electric vehicle (EV) charging station that integrates multiple energy sources for efficient EV charging is introduced. It combines photovoltaic (PV) panels, a battery storage system, and grid connectivity to ensure reliable and sustainable charging. The PV panels act as the primary source of clean and renewable energy, prioritizing PV energy for EV charging to reduce grid reliance and promote eco-friendly charging. A modified bidirectional Zeta-KY converter is utilized to maintain a constant output voltage, with a chaotic dragonfly optimized PI controller for precise regulation. During periods of insufficient PV power, the bidirectional converter steps down grid power for EV charging. The flow of grid power to the EV station’s battery is managed by a dynamic power source management algorithm for uninterrupted power availability. The efficacy of the design is demonstrated through comprehensive simulation and experimental outcomes. The suggested converter outperforms other counterparts, achieving peak efficiencies of 97% in boost mode and 96.8% in buck mode. A comparative analysis of controllers reveals that the Chaotic Dragonfly optimized PI controller excels with a low settling time of 0.10 sec, zero overshoot, rapid rise time, and a low steady-state error of 0.1%.

Hybrid Charging Station for Authentic Electric Vehicle

Electric vehicles powered by batteries are becoming increasingly popular around the world. Several causes are driving this trend, including the need to reduce air and noise pollution and reliance on fossil fuels. To have a better understanding of how batteries behave in various situations. It is vital to be aware of certain battery performance factors in certain circumstances. A battery management system includes a battery fuel gauge, an optimal charging algorithm, and circuitry for cell and thermal balance. It estimates essential states and parameters of the battery system, such as battery impedance, battery capacity, state of charge, state of health, power decline, and remaining useful life, using three non-invasive measures from the battery: voltage, current, and temperature. This paper reviews several papers published regarding EV charging types, methods, BMS, state of charge

Impact of fast charging station for electric vehicles with grid integration: Forensic‐based investigation and Archimedes optimization algorithm approach

AbstractThis manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic‐based investigation (FBI) and Archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods.

DC fast charging stations for electric vehicles: A review

AbstractThe expansion of the DC fast‐charging (DCFC) network is expected to accelerate the transition to sustainable transportation by offering drivers additional charging options for longer journeys. However, DCFC places significant stress on the grid, leading to costly system upgrades and high monthly operational expenses. Incorporating energy storage into DCFC stations can mitigate these challenges. This article conducts a comprehensive review of DCFC station design, optimal sizing, location optimization based on charging/driver behaviour, electric vehicle charging time, cost of charging, and the impact of DC power on fast‐charging stations. The review is closely aligned with current state‐of‐the‐art technologies and encompasses academic research contributions. A critical assessment of 146 research articles published from 2000 to 2023 identifies research gaps and explores avenues for future study based on the literature review.

A Frequency Control Strategy for EV Stations Based on MPC-VSG in Islanded Microgrids

The inertia level of microgrids decreases with the access of inverter-based distributed generations. Uncertainty of source loads in islanded microgrids can lead to frequency instability. Considering electric vehicles (EVs) as a typical regulable load, a charging station (CS) virtual synchronous generator (VSG) frequency control (FC) strategy that considers the user charging and grid FC demand is proposed. First, the control architecture of CS based on the VSG is designed. Second, considering the impact of VSG FC on EV user charging demand, the charging power response model considering the charging power FC cost is designed. Furthermore, multi-objective model predictive control (MPC) algorithm is designed to improve the VSG control strategy, and the FC strategy is obtained. Simulation results show that the proposed method can provide inertial support and frequency regulation capability by the CS with VSG control strategy while satisfying EV charging demand. In addition, the FC cost can be decreased.

The Impact of the Presence of a High Number of Electric Vehicles in Future Smart Cities – A Review

Attention to the increasing growth of electric vehicles (8.3% in light type and 39% in heavy type) in smart grid structures, it is essential to consider bottlenecks like proper energy management, charging infrastructure, ICT and cybersecurity problems, and economic and environmental challenges. Tariffs for the production of distributors and charging stations for electric vehicles in smart cities are definitely in a state of smart changes. The correct response to the consumption of electric vehicles will make energy distribution completely intelligent and structured with a new framework. In this paper, the structures of future smart cities will be discussed in the presence of a significant number of electric vehicles. The benefits that smart energy grids bring to future smart cities are explored and discussed in detail. A complete review of past research in the field of potential and capacities and challenges faced by future smart cities in the presence of electric vehicles has been demonstrated and the latest achievements and methods in this field have been reviewed. In the end, the bottlenecks and basic challenges that microgrids face in future smart cities are described.