A multi-objective evolutionary approach for the electric vehicle charging stations problem

Electric vehicles (EVs) are becoming a popular mode of transportation. However, one of the main concerns for EV and CNG owners is the availability and accessibility of charging stations. In this project, we present the development and implementation of an EV and CNG charging station finder web application. The app uses the Google Maps API to display the location of nearby pay stations and provide details such as connection type, availability and price. The app also allows users to filter charging and CNG stations according to their preferences and report charging stations as false or unserviceable. User testing is done to evaluate the usability and performance of the application. The results show that the app is easy to use and provides accurate and up-to date information on payment facilities In this system, the User is able to manage All his EVs inside the app plus he has search/book as lot in advance in the charging station. The User can also search an EV station based on nearby, city or kilometers. The System also provides a Roadmap if you enter source & destination with the charging stations on the way according to the kilometers entered. Admin will manage all the stations and slots. The need for developing infrastructure such as charging stations is undeniable. This EV Charging Station web application has been developed to help EV drivers locate available charging stations near them. After locating a charging station, users can also book a slot at the station to charge their vehicle. EV owners can also use this system to plan their trips more efficiently. Users simply need to specify the source and destination. Based on these two parameters, this system prepares a roadmap with all available charging stations along the journey.

Energy-efficient Vienna rectifier for electric vehicle battery charging stations

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

Problem. The proliferation of electric vehicles has underscored the need for the development of energy-efficient charging stations and systems. These systems must deliver high efficiency, maintain a power factor close to unity, adhere to electromagnetic compatibility standards, emit low levels of higher harmonics, and enable fast charging capabilities. Current electric vehicle charging devices encounter issues such as significant power losses, high harmonic emissions to the power grid, and challenges in implementing fast charging modes. Consequently, enhancing the energy efficiency of charging stations – by reducing power losses, minimizing harmonic emissions, ensuring a high power factor, and supporting modes for regulated current and voltage sources for fast charging – is a pressing concern. Goal. The aim of this work is to perform a comparative analysis of different types of batteries for electric vehicles, exemplified by TESLA cars, presenting comparative characteristics of lithium-ion, lithium-iron-phosphate, and lithium-titanate batteries. Methodology. The study showcases an evaluation of battery units for electric vehicles and outlines a comparative assessment of lithium-ion, lithium-iron-phosphate, and lithium-titanate batteries. The research focuses on enhancing the energy efficiency of electric vehicle charging station systems through the use of active rectifiers, with modes for active power factor correction and optimal configuration. Results. An examination of the quality of converters for electric vehicle charging stations was conducted, proposing schemes for a three-phase active voltage source rectifier and an active current source rectifier. Power circuitry and space-vector switching states for power transistors are introduced. Analysis concludes that an active current rectifier may be a more promising topology. Originality. A model of a charging station based on an active current source rectifier was developed using Matlab/Simulink. Transient processes of the converter's operation and the charging of a 60 kWh battery pack were examined. It was found that the active rectifier facilitates regulation of the battery charging current, achieves a power factor close to unity, and maintains a low level of higher harmonic emissions with a harmonic distortion factor of 2.52%. Practical value. The advancement of electric vehicles necessitates the ongoing development and enhancement of the energy indicators for electric vehicle batteries and the converters of charging stations, especially those that enable fast charging. Active progress is being made in each of these areas.

Reasonable planning scheme of electric vehicle charging station directly affects the economic benefits of charging station operators, and also relates to the convenience and satisfaction of electric vehicle users. In order to balance the interests between charging station operators and electric vehicle users, and provide a reasonable basis for the number of charging piles, this paper studies the planning of electric vehicle charging stations. Firstly, the charging behavior of electric vehicles is analyzed, and the classical queuing theory is used to calculate the charging queuing index of electric vehicles to ensure the charging satisfaction of electric vehicle users. Secondly, the economic benefits of charging station operators and the time cost of users constitute the economic indicators of the system, and the planning model of electric vehicle charging station considering the efficiency of charging station utilization and user waiting time is proposed. Finally, the practicability and effectiveness of the proposed method are verified by 5-node charging station network.

With the development of electric vehicle industry, it is necessary to construct more electric vehicle charging stations to promote the popularization of electric vehicles. As photovoltaic generation owns flexible installing, convenient power supplying, and environmental protecting characteristics, it is suitable for providing power for electric vehicle charging stations and reducing a network loads. After analyzed electric vehicle charging demand, this paper proposed the designing concept of the electric system for the photovoltaic generation mix charging station, which was based on the battery charging and discharging characteristics as well as its usage. Then, the paper provided a selection of electric equipments for the charging station and an electric wiring diagram after designing the electric system. This study and design may help for promoting construction of electric vehicle charging stations, and development and popularization of electric vehicles.

Electric vehicles (EV) are an emerging mode of transportation, and big cities in the United States have witnessed an ever-growing demand for EV usage. The primary benefit of EVs is the high fuel efficiency by using only electricity, and hence lowers the dependency on fossil fuels and significantly reduces greenhouse gas emissions. Although the number of EVs has increased, the availability of EV charging stations for public use has been disproportionate to its demand. More recently, populations residing in the Southern California region have been faced with challenges such as range anxiety owing to the uneven spatial distribution of charging stations throughout the region. As the EV population continues to expand, identifying hotspots of EV charging and barriers to the equitable access of charging stations have gained much importance. Our study uses a geospatial data fusion approach with spatial statistics to combine EV charging station data, land use information, and American Community Survey (ACS) data at the census block group level in Orange County, California to discover optimal locations to broaden the EV charging network and identify potential equity issues surrounding charging station placements.

Optimal site selection and sizing of solar EV charge stations

Applications of electric vehicles need to build a large number of charging stations. In order to reduce the grid load, it is necessary to develop smart electric vehicle charging stations. The Electric vehicle charging stations communicate with the gird. The Electric vehicle charge at night to reduce the cost and the grid load, simultaneously fill the valley. When grid load increase, electric vehicles battery discharge to the grid to improve grid stability. As distributed storage units, electric vehicles are important components of smart grid. New technology of high power factor PWM rectifier-inverter is one of the key technologies for the smart electric vehicle charging system. In this paper, three-phase PWM rectifier used for smart charging and discharging system of electric vehicle is analyzed and designed. The paper includes the principle of PWM Rectifier-Inverter and direct current control strategy. Also, the SVPWM and system design of three-phase PWM rectifier are analyzed. A 10 kW prototype is developed. Simulation and experiment results show that the three-phase PWM rectifier can improve power factor. From the experimental results, PWM rectifier implements the grid current sinusoidal and achieves the unit power factor.

Global warming and some dangerous climate changes are becoming more prevalent as the demand for modern transportation systems grows for economic development and cultural comfort. To tackle this global warming issue due to transportation every country pushing for Electric Vehicles (EVs), As the number of electric vehicles on the road rises, Charging EVs with a fossil fuel-based infrastructure alone is not cost-effective or efficient. As a result, a charging station based on renewable energy has enormous potential and control for electric vehicle charging. In the current scenario, a solar-powered electric vehicle charging station and a Battery Energy Storage System (BESS) are required. Additional grid assistance is recommended to ensure that the charging station has uninterrupted power without putting additional burden on the grid, For effective power management in the charging station between solar, BESS, grid, and EVs an efficient charging station design with Adaptive Neuro-Fuzzy Inference System (ANFIS) voltage-controlled MPPT, PID controller, Grid with Neural Network technique is designed and evaluated in MATLAB/Simulink.

Evolutionary algorithms have been widely used to tackle multi-objective optimization problems. Despite many successful applications there is a lack of theoretical knowledge on how and why this type of algorithm works particularly well for multi-objective problems. This special issue presents current advances to the theoretical understanding of evolutionary algorithms for multi-objective optimization. It comprises four very high quality papers which have been selected via a rigorous reviewing process. The first paper “On the Effect of Populations in Evolutionary Multi-Objective Optimisation” by Oliver Giel and Per Kristian Lehre studies the basic question whether a population is provably useful to compute the whole Pareto front of a multi-objective optimization problem. They compare restart strategies of several single-objective approaches based on one individual to a natural multi-objective approach using a population. Their analyses point out the benefits of the multi-objective evolutionary algorithm in a rigorous way. Using illustrative example functions they provide not only proven results but also a good intuition about why population-based approaches can outperform individual-based approaches in multi-objective optimization. The paper “Exploring the Runtime of an Evolutionary Algorithm for the Multi-Objective Shortest Path Problem” by Christian Horoba examines the behavior of evolutionary algorithms for the classical NP-hard multi-objective shortest path problem. The author considers an evolutionary approach based on -dominance and proves that this algorithm constitutes a fully polynomial-time randomized approximation scheme (FPRAS) for the problem. The algorithm achieves this by mimicking a problemspecific algorithm for that problem. This provides general insight into why evolutionary algorithms can perform well for problems that allow for efficient problem-specific algorithms. Hypervolume-based algorithms have become very popular in evolutionary multiobjective optimization. In the paper, “An Efficient Algorithm for Computing Hypervolume Contributions,” Karl Bringmann and Tobias Friedrich present a faster algorithm for determining the individual with the smallest hypervolume contribution in a given population. Furthermore, they study the problem of removing λ > 1 individuals with the smallest hypervolume contribution from a given population and present an elegant and fast method to carry out this task.

The provision of electric vehicle (EV) charging at airports is a very new and evolving practice. EV charging technology and the costs associated with providing EV charging stations are still evolving and there are still many unknowns, conflicting information, and speculation about this new practice. Purchases of EVs are increasing throughout the United States, with the West Coast states accounting for a disproportionately high number. Research undertaken for this report has shown that at least 37 airports in the United States are providing EV charging stations, most commonly in short-term or long-term parking facilities, and usually with no fee for charging. The report presents a primer on EV charging and includes information to understand the policy approaches, infrastructure needs, and funding mechanisms that airports have used in EV hosting. This report (1) summarizes the state of the EV industry and motivations for airport sponsors to provide EV charging stations, (2) identifies current EV charging technologies, and (3) describes effective practices for installing EV charging stations at airports in terms of finances, planning, implementation, and operation of the charging stations. This report is intended to help airport management staff, specifically those environmental, parking, and facilities managers who may be interested in installed EV charging stations.

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

One of the most important problems in the widespread use of electric vehicles is the lack of charging infrastructure. Especially in tourist areas where historical buildings are located, the installation of a power grid for the installation of electric vehicle charging stations or generating electrical energy by installing renewable energy production systems such as large-sized PV (photovoltaic) and wind turbines poses a problem because it causes the deterioration of the historical texture. Considering the need for renewable energy sources in the transportation sector, our aim in this study is to model an electric vehicle charging station using PVPS (photovoltaic power system) and FC (fuel cell) power systems by using irradiation and temperature data from historical regions. This designed charging station model performs electric vehicle charging, meeting the energy demand of a house and hydrogen production by feeding the electrolyzer with the surplus energy from producing electrical energy with the PVPS during the daytime. At night, when there is no solar radiation, electric vehicle charging and residential energy demand are met with an FC power system. One of the most important advantages of this system is the use of hydrogen storage instead of a battery system for energy storage and the conversion of hydrogen into electrical energy with an FC. Unlike other studies, in our study, fossil energy sources such as diesel generators are not included for the stable operation of the system. The system in this study may need hydrogen refueling in unfavorable climatic conditions and the energy storage capacity is limited by the hydrogen fuel tank capacity.

This research examines the optimization of the deployment of electric vehicle (EV) charging stations by using machine vision technology, which involves analyzing real-time data and geographical information. Geospatial data analysis reveals prospective sites for charging stations by considering population density and accessibility to roads, hence identifying regions with increased demand for electric vehicle charging. The assessment of electric vehicle (EV) traffic patterns highlights the ever-changing charging requirements at various times and places, underscoring the need of flexible deployment techniques. Furthermore, evaluating the costs of implementing the deployment and the capabilities of charging, it becomes apparent that there are compromises to be made between the initial expenditures of installation, the amount of power generated, and the quantity of charging stations. These trade-offs are essential for optimizing resources. The usage study of charging stations using machine vision reveals variations in the number of available charging points at different stations and the need for adaptive resource distribution timestamps, techniques. The examination of percentage change reveals notable fluctuations in population density, installation costs, and the availability of charging points. This information is crucial for making well-informed decisions about the deployment of charging infrastructure. Combining machine vision insights with geographical and traffic analyses presents a promising method to create data-driven strategies for the placement of EV charging stations. This approach addresses the changing needs of electric mobility and provides guidance to stakeholders for efficient and flexible charging solutions.