Electric Charging Stations Research Articles

Analysis of The Impact of Temperature and Discharge Current on The Efficiency of LiFePO4 Batteries in Solar Charging Stations

The demand for electrical energy is increasing, along with technological advancements and population growth. Many countries still rely on petroleum, coal, and aerosol gasoline, exacerbating global warming. Electric vehicles offer a promising solution to reduce greenhouse gas emissions and dependence on fossil fuels, although their primary challenge is the availability of charging infrastructure. Solar-powered electric motor charging stations can help reduce electricity demand and global warming. An efficient charging system is needed to analyse the impact of temperature and discharge current on the energy produced to achieve this. Several load tests and temperature measurements over 5 days were conducted to cluster temperatures occurring throughout a full day. The tests using data acquisition showed energy losses caused by the effect of temperature on the charging station's storage battery. Energy efficiency graphs for each test case indicated a varied decrease in energy efficiency, with higher efficiency at lower temperatures and smaller energy losses compared to other temperatures. The load amount also affects the magnitude of energy losses. At a 500W load, the average energy loss was 46Wh, while at a 1000W load, the average energy loss was 52Wh per hour of testing the storage battery discharge. In summary, temperature and load amount can affect energy efficiency and the resulting losses.

FORMATION OF A SYSTEM FOR ASSESSING THE EFFECTIVENESS OF INNOVATIVE DEVELOPMENT OF LOGISTICS ACTIVITIES OF MOTOR TRANSPORT ENTERPRISES ENGAGED IN FREIGHT TRANSPORTATION IN TERMS OF SUSTAINABLE DEVELOPMENT

The article discusses the importance of implementing innovations at road transport enterprises in the context of achieving the region's sustainable development goals. The author emphasizes that road transport enterprises are significant sources of harmful emissions and environmental pollution, so using innovative technologies, such as electric vehicles, hybrid cars, and alternative fuels, is an important tool for reducing the negative environmental impact. In particular, these technologies help to reduce greenhouse gas emissions and other harmful substances, which is in line with Sustainable Development Goals 7 (access to modern energy), 13 (combating climate change), and 15 (protecting terrestrial ecosystems). In addition, innovations in transport technologies, such as automated driving systems, help to improve road safety and reduce the number of road accidents, which is in line with Goal 3 (good health and well-being). The creation of new jobs and infrastructure development through the construction of electric charging stations and the introduction of intelligent transport systems (smart cities) are also linked to Sustainable Development Goals 9 (sustainable infrastructure) and 11 (sustainable urban development). The author analyses the performance indicators of innovations at motor transport enterprises, taking into account economic, social, and environmental aspects. Particular emphasis is placed on the adaptation of these indicators to the current realities of Ukraine. Using game theory, the article develops a model for the selection of optimal strategies for motor transport enterprises and the region. This model allows us to find the Nash equilibrium - a situation where both parties achieve the optimal result. Enterprises that implement innovations not only improve their own performance indicators, but also contribute to the achievement of sustainable development goals of the region. The article also presents two sets of strategies: for the development of logistics activities of a motor transport enterprise and the sustainable development of the region. Both approaches aim to comprehensively improve the region's environmental, economic, and social situation. The conclusions emphasize the importance of implementing innovation development assessment systems as a tool for making strategic decisions and improving the efficiency of logistics operations.

Future of Gasoline Stations

The gasoline fueling stations’ future during the automobile industry shift towards electric is questionable. With more than 196,000 gasoline fueling stations in the United States, many of them require an upgrade to electric charging stations. The regular fueling time from gasoline stations is significantly less than the charging time from an electric charging station. Preparedness for shifting from gasoline stations to electric vehicle (EV) charging stations requires detailed research on how consumer behavior changes given they will have a readily available charging port in their households. It would be essential to categorize the customers based on their preferences and thus plan the approach for meeting their needs. For large gasoline stations, the shift to EV charging stations becomes a viable choice as they tend to attract trucks and other travelers throughout the year. A model of electrical infrastructure was prepared for a large gasoline station by using the estimated number of customers visited. Improvement of amenities or devising a way to attract customers will be required to allow them to spend the waiting duration comfortably.

A Heuristic Algorithm for Deploying Electric Taxi Charging Stations to Enhance Service Quality

With the growing maturity of electric vehicles technology and the increase in environmental awareness, electric vehicles have emerged as a feasible way to reduce carbon emissions due to transportation. In response, numerous cities have adopted electric vehicles into taxi and bus fleets to increase their use. As the use of electric taxis increases, the strategic deployment of charging stations becomes crucial to ensuring taxi operations. This study aims to optimize the deployment of electric taxi charging stations, with a focus on improving service quality. A heuristic algorithm, Improved K-means iterated with Queuing Theory (IKQT), is proposed. To validate the algorithm, over 11,000 GPS tracking trajectory data from Shanghai Qiangsheng taxis in April 2018 were analyzed. The results of the study demonstrate that the IKQT algorithm can significantly increase the utilization rate of charging stations, enabling them to serve more electric taxis during peak hours and thereby improving overall service quality. Specifically, the total waiting time for all charging services was reduced by approximately 6%, while the total number of unserved taxis across all charging stations decreased by roughly 19%. These improvements underscore the novelty and practical value of the IKQT in the deployment of electric taxi charging stations.

Decision Support System for Determining the Location of Public Electric Charging Stations (SPLU) with Machine Learning

Electric vehicles are becoming increasingly popular as an environmentally friendly alternative, but the main challenge is the availability of adequate charging infrastructure. This research aims to develop a decision making system for determining the best public electric vehicle charging station (SPLU) locations utilizing machine learning. The purpose of this research is to find out the process of determining the location of Public Electric Charging Stations (SPLU) using a Machine Learning-based decision making system. This research uses quantitative methods with AHP techniques to determine the criteria weights and machine learning to recommend the best SPLU locations based on spatial data, surveys, and social media. The location recommendations are displayed in the form of an interactive digital map with visualizations of the suitability level to facilitate decision making.

Energy-efficient Vienna rectifier for electric vehicle battery charging stations

Global DC fast-charging infrastructure is being rapidly developed to accommodate the ever-increasing demand from the electric vehicle market. A major complication in the electric power system's process is the proliferation of ultra-fast charging (UFC) stations, which are known for their enormous power consumption and unpredictable, intermittent performance. By implementing grid-supportive features and ensuring an improved power consumption profile for the grid, installing regional energy storage can solve these challenges. This study presents a control approach for managing the grid-side AC/DC converters of rapid charging stations focused on future-oriented regulation. The paper primarily concentrates on various Vienna rectifier topologies. The technology, characteristics, benefits, and operational aspects of Vienna rectifier topologies are vital to improving the performance, efficiency, and grid integration of electric vehicle charging systems. Fast charging, grid stability, energy economy, and the smooth integration of electric vehicles into the electrical grid are all made possible by Vienna rectifiers. When used in battery energy storage systems (BESS) for electric vehicle charging infrastructure, Vienna rectifiers allow for effective discharge and charging of the batteries. The configurations and assessments of these converters are examined, assessed, and compared based on power output parameters, element count, power factor, THD, and efficiency. The Vienna rectifier has been identified as the best suitable DC fast-charging converter architecture for power levels exceeding 15 kW due to its exceptional efficiency, limited output voltage ripples, high power density, reduced current ripples, and reliable performance. Research on ways to improve electric vehicle charging infrastructure can be spurred by a better grasp of the Vienna rectifier's technological complexities and performance benefits, leading to greener and more efficient transportation options. As a result, the system's power density will eventually rise and elemental stress will decrease.

Optimal location of electric vehicle charging stations using proximity diagrams

The concern over the planet environmental crisis is propelling administrations to promote anti-pollution regulations progressively restricting the use of fossil fuels. In this context, the integration of electric vehicles is increasingly being explored to reduce traffic emissions. To facilitate the transition from conventional vehicles to electrical ones, establishment of a robust charging infrastructure is essential. This article presents a numerical proposal for identifying potential optimal locations of additional electrical charging stations building upon the current infrastructure. For this purpose, Voronoi diagrams, constructed using the existing charging stations as point generators of the structure, will be stated as the primary framework. The extracted proximity data, delimited by geographical, legislative or user behavior-related factors, facilitates the identification of locations where companies in the sector are inclined to invest. This procedure holds significant relevance for industry in the sector seeking strategic investment locations that minimize competition while ensuring a high level of convenience for long-distance electrical vehicles users. The significance of this modeling lies in its simplicity and applicability to any region worldwide, making it a versatile tool for similar studies.

Electrification of marinas in Stockholm: Optimizing charging infrastructure for electric boats

The adoption of electric boats faces challenges due to range anxiety. This study addresses this challenge by optimizing the allocation of slow and fast chargers for public electric boat charging stations. A mixed-integer linear programming model was developed to minimize charging infrastructure and electricity costs while meeting the demand of each participating marina in Stockholm, Sweden. The model identified three distinct categories of stations based on the optimal charger allocation: stations prioritizing fast chargers (e.g., Sickla station with 13 fast and 8 slow chargers for 208 boats), stations prioritizing slow chargers (e.g., Kungsholmen station with 2 slow and 3 fast chargers for 50 boats), and stations with an equal number of both charger types (Äppelviken station with 2 each for 25 boats). This study found out that all stations will achieve financial viability within seven years, indicating a promising return on investment. This study also proposes a novel way for optimizing charger allocation in electric boat charging infrastructure, promoting wider electric boat adoption.

Sizing requirements of the photovoltaic charging station for small electrical vehicles

Electric vehicles (EVs) are being introduced in Rwanda and becoming attractive for different reasons. For instance, these types of vehicles can help decrease air pollution and noise emissions. In addition, it presents an alternative to combustion engines, given the increased price of fuel resources in Rwanda and around the world. This paper presents a tool tailored to optimize the design of an electrical charging station serving small-sized electric vehicles, utilizing the algorithm to assist in sizing stand-alone mopped charging stations. The developed tool is based on the toolbox EventSim from MathWorks, which permits the combination of the simulation of discrete events (such as the arrival of customers at the station) with continuous states (such as the simulation of the charging process). The required PV power was estimated by utilizing solar resources, for the location, from renewables. Ninja. The number of customers arriving at the existing oil station is normalized to estimate the energy requirements of the mopped fleet. A Poisson distribution was proposed to model the battery discharge upon arrival, and different related parameters were evaluated through a sensitivity analysis to identify their effects on the performance of photovoltaic charging station. For the testing values, the station parameters were changed by ±25% to determine the impact of key design parameters on station performance, as well as other satisfaction measures such as average waiting time and average queue length. With a 25% increase in photovoltaic panels, the blackout period decreases by 2.12%, while a 25% decrease in photovoltaic panels causes an increase of 2.18% in the blackout period. Utilizing the energy management system (EMS), the waiting time was reduced by 8%.

Research and design an electric car charging station using solar energy integrating an IoT technology monitoring system

As technology advances, the cost of renewable energy equipment decreases, which has resulted in a massive increase in solar photovoltaic installations. In this study, we designed and calculated a solar power system for a charging station for 12 electric cars that integrates an energy protection system using IOT technology. The analysis results of the independent EV-PV charging station model and the grid-connected model with storage have shown the efficiency and profitability of each model. The solar energy system utilizes an IoT-based monitoring system to enhance the capacity and quality of electricity for electric vehicle charging stations. This is achieved through a sensor network that monitors factors affecting the efficiency of solar panels, such as dust, solar radiation intensity, weather conditions, and the tilt angle of the solar panel arrays. Smart Monitoring displays the daily usage of renewable energy, helps the users to analyze energy usage, will help us in its maintenance, fault detection, and will give us a record of all the data and electricity issues.

Off-Grid Electric Vehicle Charging Station with Integrated Local Server OCPP Protocol as a Management System

Abstract Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, Our main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. We analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. Our research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, we tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.

OptiCharge: A firefly algorithm-based approach for minimizing electric vehicle waiting time at charging stations

The use of electric vehicles has raised need for infrastructure for effective charging. Wait periods at charging stations are a problem for both owners and operators of electric vehicles. Owners of electric vehicles are irritated by lengthy wait periods, and charging facilities are underutilised. Wait times at electric car charging stations are decreased using “OptiCharge,” a Firefly Algorithm-based solution. Scheduling charging stations makes sense given the Firefly Algorithm’s ability to adjust to changing conditions and solve challenging problems. In the present paper, we incorporate dynamic scheduling and waiting time computation mathematically into OptiCharge. Extensive testing and comparative analysis with different optimisation techniques demonstrate that OptiCharge decreases waiting times and enhances charging station performance. The results show how OptiCharge may enhance EV charging and promote intelligent, sustainable transportation.

Aircraft Designed for Cargo Transportation and Investigation of its Benefits

In this study, an Octorotor UAV capable of carrying 10 desi/2 kg cargo was designed. It is an eight-rotor rotary wing aircraft and its body consists of eight arms connected to each other by two moving mechanisms. When requested in emergency situations, the fastest intervention can be provided by aircrafts. The auxiliary battery and generator system carried by the aircraft is to provide enough energy to reach the nearest electric charging station for the stranded vehicles. In cases such as disasters, the rapid delivery of health supplies can also be provided by this method. The aircraft designed for this purpose has a rotary wing and a feature that can change its body structure according to different load and weather conditions. The shape change improves its performance compared to conventional aircraft and increases its ability to fulfil its mission. For this purpose, the design of a shape-changing aircraft was realized.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

WEB BASED EV CHARGING STATION FINDER AND SLOT BOOKING

One of the primary challenges is the inadequate availability of EV charging stations, particularly in certain regions or areas with high population density. This scarcity makes it inconvenient for electric vehicle owners to find accessible charging points, leading to range anxiety and reluctance in adopting electric vehicles. This project introduces an innovative solution for optimizing on-road vehicle services and electric vehicle (EV) charging station slot booking systems. Through this integrated platform, users can seamlessly access a range of on-road services, including vehicle maintenance, repair, and emergency assistance, while also reserving slots for EV charging stations. Leveraging advanced technologies, such as real-time GPS tracking and automated scheduling algorithms, the platform facilitates efficient dispatching of service providers to users’ locations, ensuring timely assistance and minimizing downtime for vehicles. Key Words: Electric vehicle charging locator, EV charging point finder, Charging station locator app, Electric car charging station map, EV charger locator service, Electric vehicle charging spot finder, Charging station finder tool, EV charging station search engine.

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.

RANCANG BANGUN SISTEM MONITORING STASIUN PENGISIAN LISTRIK UMUM (SPLU) HYBRID BERBASIS IOT DI SMKN 2 KOTA BEKASI

This research is motivated by the development of electric vehicles in Indonesia which is regulated in Presidential Regulation Number 55 of 2019 which encourages the use of electric vehicles. Therefore, the development of electric vehicles began to increase every year. Along with the development of electric vehicles, it must also be followed by the development of charging stations. This research focuses more on developing a Hybrid public electric charging station (SPLU) monitoring system to create a Hybrid SPLU monitoring system that can be accessed easily anytime and anywhere in real-time and is IoT-based. For the Hybrid SPLU monitoring system to work well, the data results from sensor readings can be monitored in real-time and IoT-based. Then the data from this study has been compared (calibration) with existing measuring instruments with the average error of each sensor being no more than 5% and the Mean Absolute Percentage Error (MAPE) not more than 10%. From the existing provisions, if the MAPE value is <10%, the data can be said to be very accurate. So it can be concluded that this research can be said to be successful because it has fulfilled the initial objectives of this research.

Review of Electrical charging station and its infrastructure

Review of Electrical charging station and its infrastructure

Novel architectures for power management in AC ring main system connected to electric vehicle charging station

The Domestic Photovoltaic (DPV) installation along with Domestic Energy Storage System (DESS) can play effective role in AC Ring Main Residential Distribution Network (ACRMRDN) to address the impact of Electric Vehicle (EV) charging on residential distribution network. This paper proposed two different architectures with structural changes for effective energy management in AC ring main system connected to electric charging station. The main aim of this research is to design the electric vehicle charging infrastructure in support of DPV and DESS. The effective implementation of such charging infrastructure motivate domestic consumer to play a role of prosumer. It also encourages prosumer to install independent domestic energy storage system to speedily recover installation cost of DPV and earn regular income by selling out power to electric charging stations during peak load condition. It creates win-win situation for prosumers and EV charging station to earn profit. The suggested architectures help the power system to grow individually. Such decentralization of power system reduces the impact of EV load demand and improves the reliability and stability of power system. A proposed novel architecture helps in promoting use of electric vehicle and Renewable Energy Sources (RES). On the basis of number of EVs predictions and corresponding EV load data the profit of prosumers and electric charging stations are increased to 50% and impact of EV load on the AC grid is reduced to 100%. The analysis of equivalent model of proposed architecture is done through MATLAB software.

Electric bus arrival and charging station placement assessment using machine learning techniques

ABSTRACT The goal of this study is to create a dynamic model that can accurately forecast the estimated time of arrival of a bus at a specific bus stop using data obtained from the global positioning system (GPS) in Addis Ababa, Ethiopia. Both operators and customers use accurate and timely bus arrival information. In turn, it helps determine the correct placement of charging stations when green-energy vehicles are introduced in the urban mass transit system. A plethora of machine learning models have been used for prediction. Additionally, we tailored an ensemble method based on the average prediction. The performances of these machine learning methods were estimated and compared using conventional measures, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. The results showed that the performance of the Random Forest technique used in this study was impressive, with MAE of 0.137, MSE of 0.054, RMSE of 0.233, and R2 value of 0. 999. The findings demonstrate that the proposed method is reliable for predicting the arrival times of urban and rural buses. Next, in order to position the charging points for Electric Vehicles (EV) along specific bus routes, self-organising map (SOM) is employed to optimise EV charging point placement locations, and the result signify better performance than the kNN and DBSCAN clustering approaches.