RETRACTED: Blockchain-Enabled Charging Right Trading Among EV Charging Stations

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.

Toward social equity access and mobile charging stations for electric vehicles: A case study in Los Angeles

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.

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.

A well-developed electric vehicle (EV) charging infrastructure is a key prerequisite for addressing their growing charging needs and driving the rapid development of EVs. However, the fluctuation of renewable energy generation, the load change of distribution network and the uncertainty of electric vehicle charging demand make the node load of distribution network uncertain, which poses a challenge to the safe operation of the grid. Therefore, a two-stage robust optimization model considering the planning of charging station construction is proposed in this study. Firstly, a robust optimization framework for the location and capacity of EV charging stations in the distribution network is constructed, and the charging demand uncertainty is innovatively modeled as DDUs. Then parametric column and constraint generation (C&CG) algorithm is used to solve the two-stage optimization problem with DDUs. Finally, the validity of the model is verified based on IEEE 33-node system.

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.

The strong growth of Electrical Vehicles (EVs) necessities the fast EV charging infrastructure. The variation of charging demand from commercial fast charging stations may significantly impact power system stability. To fast assess this impact, this paper proposes an uncertainty quantification algorithm based on polynomial chaos expansion (PCE). A stochastic transportation model is first introduced to project the regional Level 3 EV charging demand and the predicted profiles are then utilized to construct the orthogonal polynomials for PCE. The correlation among commercial Level 3 EV charging stations is also considered. Simulation results show that the proposed PCE method is able to solve the computational complexity issues from the uncertainties of random variables and to save computing resources without compromising the result accuracy when compared with Monte Carlo simulation (MCS). The high efficacy and accuracy of the proposed approach make it potential for fast uncertainty quantification in power system dynamic analysis.

This paper develops an intelligent energy management system for optimal operation of grid connected solar powered electric vehicle (EV) charging station at workplace. The optimal operation is achieved by controlling the power flow between the photovoltaic (PV) system, energy storage unit, EV charging station (EVCS) and the grid. The proposed controller is developed considering the PV availability, grid loading and the EV charging load data. This information is modelled using Markov decision process (MDP) to develop a control strategy that eliminates the conventional problem of immediate recharging of energy storage unit after each EV charging by setting a target state of charge (SOC) level. This maximizes the use of PV power for EV charging and minimizes the impact on the grid. To test the operation of the proposed controller, a charging station powered by a 5 kW PV system with 35 kW energy storage unit connected to grid is developed through numerical simulations and experiment. The experiments were carried out for three different conditions under varying irradiance profile and load profile for multiple days. The results estimated the EV load and PV power and optimized the energy storage unit SOC between 0.3-1. Further, the energy management strategy minimized the impact of energy exchange between the grid and charging station by a factor of 2.

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.

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

The rapid development of electric vehicles (EVs) increases the power demand, which causes an extra burden on the public grid, increasing the load fluctuations and, therefore, hindering the high penetration of EVs. In this paper, a real-time rule-based algorithm for electric vehicle (EV) charging stations empowered by a direct current (DC) microgrid is proposed. Such a DC microgrid model consists of EVs, an electrochemical storage system, a public grid connection, and photovoltaic sources. The EV charging station model is based on data-driven modelling while its management model takes into account discrete events. This paper focuses on power management strategy of an EV charging station under power limitation and considers most of the drivers’ choices. The EV charging system topology is presented and common problems during an EV charging process are discussed, e.g., disconnection operation, standby mode, shedding, and restoration operation. Furthermore, the proposed power management deals with the uncertainties of EV drivers’ behavior considering arbitrary and random choices through the human–computer interface. The simulation results obtained under MATLAB/Simulink verify the feasibility of the proposed management strategy that presents a good performance in terms of precise control.

Load management, energy economics, and environmental protection nexus considering PV-based EV charging stations

The project aims at sustainable transportation by offering an environmentally friendly option for users who are looking for alternatives to the traditional fossil fuel powered vehicles and thus helping to reduce the dependence on fossil fuels which are a major source of carbon emissions and environmental pollution. Although electric vehicles (EVs) are gradually gaining popularity, there is a need for systems that can assist in identifying the charging stations, check for available slots, and receive information about the charging status in real time since current EV charging and rental services are often fragmented and complicated. The proposed solution is a mobile application that integrates an EV charging station locator and slot booking service through which an EV owner can easily find the charging stations on Google Maps, check for the availability of slots, book slots for specific time, and even get real time updates on the charging. The system has two main modules: the EV charging station module that uses a smart system to control the charging current according to the vehicle's battery capacity and need, and the slot booking module that encourages the use of sustainable transport by allowing users to make bookings for charging slots. The system is developed based on IoT devices, cloud computing and mobile application technologies and the goal is to minimize the use of fossil fuels, encourage the use of eco friendly modes of transportation and for a comfortable and hassle free long distance travel by offering the locations of EV charging stations and controlling the charging process easily.

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.