Electric Vehicle Charging Infrastructure Research Articles

Modular, Vision-Based Control of Automated Charging Systems for Electric Vehicles

Contemporary vehicle fleets include a variety of both manually and automated operated vehicles. A significant shift involves the increasing use of electric powertrains. However, the current electric charging infrastructure predominantly relies on manual processes. There is an opportunity to automate the charging process for these cars, with the intent to enhance convenience, aid people with accessibility needs, to support MW charging where EV cables are heavy, and finally to enable autonomous driving. The charging standards are common but the mechanisms to access the charging ports are not i.e., lids and protectives. This could be a challenge in the automation process given the complexity of the manipulation task at hand. With the increasing variety of electric vehicle models, standardizing charging mechanisms becomes imperative to streamline the charging process and enable broader adoption. In summary, this paper presents a holistic approach to addressing the evolving needs of electric vehicle charging infrastructure, emphasizing the importance of automation in enabling efficient, accessible, and future-ready charging solutions.

Equitable charging infrastructure for electric vehicles: Access and experience

Equitable charging infrastructure for electric vehicles: Access and experience

Using public–private partnerships to deliver electric vehicle charging infrastructure

Using public–private partnerships to deliver electric vehicle charging infrastructure

Coin Insertion Based EV Car Charging System Using Raspberry PI PICO

The coin insertion-based Electric Vehicle (EV) charging system represents a novel approach to enhance accessibility and convenience in EV charging infrastructure. This system integrates a coin based payment mechanism with Raspberry Pi technology to provide users with an alternative and straightforward payment option. Utilizing a Raspberry Pi as the central control unit, the system interfaces with EV charging hardware, a coin acceptor, and sensors to facilitate the charging process. Users can effortlessly charge their EVs by inserting coins, addressing the needs of individuals who prefer cash transactions or lack access to digital payment methods. The system ensures safety compliance, calculates charging duration based on coins inserted, and offers a user friendly interface displaying charging status and payment information. This abstract presents an innovative solution to broaden the accessibility of EV charging services by incorporating a familiar and efficient payment method in tandem with modern technology

Enhanced EV Battery Monitoring Using IoT with Improved SEPIC - ZETA Converter and Modified Lion Optimization for Photovoltaic Systems

The rising popularity of electric vehicles (EVs) and the increasing emphasis on sustainable transportation have highlighted the importance of advanced monitoring systems. These systems play a crucial role in providing real-time information about battery parameters to ensure optimal performance and longevity. Moreover, the integration of renewable energy sources, specifically photovoltaic (PV) systems, with EV charging infrastructure presents an exciting prospect to enhance energy efficiency and minimize environmental harm. As a consequence the proposed work develops an Internet of Things (IoT) based PV fed EV charging system. The system incorporates an improved SEPIC-Zeta converter together with Modified Lion Optimization algorithm (LOA) assisted Proportional Integral (PI) controller for optimizing PV system for charging EV battery. IoT connectivity monitors parameter such as PV voltage, PV current, and State of Charge (SOC) of battery. Real-time monitoring of these parameters is achieved by leveraging sensors. The collected data is transmitted through IoT networks to a central monitoring system, enabling operators to track the performance of PV system and EV battery. This facilitates optimized energy generation, efficient battery charging and discharging, and overall system reliability. IoT-enabled monitoring system provides remote accessibility, data analytics, and the generation of alerts and notifications for timely actions. Experimental validation demonstrates that, the proposed concept enhances the monitoring capabilities, improves energy efficiency, and ensures reliable operation of EV charging infrastructure.

Driving towards Sustainability: Wireless Charging of Low-Speed Vehicles with PDM-Based Active Bridge Rectifiers

The surge in demand for eco-friendly transportation and electric vehicle (EV) charging infrastructure necessitates innovative solutions. This study proposed a novel approach to charging slow-moving vehicles, prioritizing efficiency and minimizing output pulsation. Central to the research is the development of a receiver-side power-regulated constant charging system, focusing on power regulation and maintaining consistent charging parameters. This system integrates a receiver-side pulse density-modulated active bridge rectifier, dynamically adjusting driving pulse density to regulate delivered power. Additionally, a receiver-side reconfigurable compensation network ensures constant current and voltage delivery to the charging device, eliminating the need for an additional D.C.-D.C. converter. A 3.3 kW charging structure employing a multi-leg inverter topology and energizing four ground-side transmitter pads exemplifies the proposed approach. The vertical air gap of charging pads is 150 mm, and the system achieves a maximal efficiency of 93.4%. This innovative strategy holds significant promise for advancing sustainable transportation infrastructure and meeting the evolving demands of the EV market.

EV-EcoSim: A Grid-Aware Co-Simulation Platform for the Design and Optimization of Electric Vehicle Charging Infrastructure

<i>EV-EcoSim</i>: A Grid-Aware Co-Simulation Platform for the Design and Optimization of Electric Vehicle Charging Infrastructure

Parking Zones with EV Chargers

Parking Zones with EV Chargers

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

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

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

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

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

Computer-aided mobility solutions: Machine learning innovations to secure smart urban transportation

This study introduces a groundbreaking hybrid deep learning Intrusion Detection System (IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the context of computer-aided mobility solutions for smart urban transportation. The proposed system ingeniously integrates XGBoost and Convolutional Neural Network (CNN) technologies, leveraging their combined strengths to fortify the resilience of EV charging infrastructure. In response to the demand for transparency in decision-making, our approach incorporates Explainable AI Techniques, specifically employing Shapley Additive exPlanations (SHAP) values. This not only enables the IDS to identify anomalous behavior but also provides clear insights into the features influencing the detection process. The transparency achieved is pivotal for fostering stakeholder trust, ensuring regulatory compliance, and facilitating well-informed decision-making within the dynamic landscape of smart urban transportation. A sophisticated mechanism is implemented to enable the model to autonomously adjust and learn from evolving charging patterns over time. This adaptive capability ensures the IDS remains effective in the face of changing charging behaviors, contributing to the resilience of the overall smart urban transportation system. This research not only propels the field of cybersecurity in smart urban transportation but also establishes a robust framework for the development of intelligent systems. These systems play a critical role in securing essential processes while providing interpretable insights for stakeholders in the realm of computer-aided mobility solutions.

The Great Canadian (Electric) Road Trip: Evaluating EV Use in National Park Tourism

As part of its 2050 net zero emission commitment, Canada has a mandate to transition to electric vehicles (EVs) to decarbonize transportation. While the EV charging infrastructure is expanding in urban areas, the infrastructure for inter-city and nature-based tourism has not been assessed. This study combined four metrics of feasibility and convenience into an EV readiness index to evaluate 94 road trip itineraries associated with Canada’s iconic national parks. Index scores ranged from not possible (0) to equal to the travel experience of internal combustion vehicle (20). With the summer range, the average one-day route score was 19.2, as 82% of one-day itineraries could be completed without requiring additional charging. Multi-day routes scored lower (14.0), with 24% being highly inconvenient or impossible due to gaps between charging stations. With a reduced winter EV range, average index scores for one- and multi-day trips declined as charging needs increased (18.4 and 13.2, respectively). Across the 94 travel routes, EVs were estimated to produce only 17% of the carbon dioxide emissions of internal combustion vehicles (this was much lower in some provinces). The findings also highlight key regional differences in tourism EV readiness and where infrastructure is needed to provide equitable access to Canada’s national parks for tourists seeking to decarbonize their holiday travel.

Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure.

Wireless Electric Vehicle Charging with Solar Panels

This paper presents an innovative design of a solar-powered electric vehicle (EV) charging station and addresses critical issues of fuel consumption and environmental pollution. With the global spread of electric cars, their environmental benefits and cost-effectiveness are increasingly recognized. The proposed electric car charging system offers a unique solution that enables wireless charging of vehicles without stopping and even while driving. The system runs entirely on solar power, so no external power source is needed. The report describes the design and engineering of a solar-powered electric car charger for built-in roads that provides a novel and efficient solution to two challenges. fuel consumption and emissions. The number of countries where electric cars are on the road is constantly growing. In addition to helping the environment, electric cars have proven useful in reducing transportation costs by replacing expensive fuels with cheaper electricity. The proposed electric vehicle charging infrastructure structure eliminates the need to stop for charging, as the electric vehicle can be charged on the go. The system runs on solar energy, which eliminates the need for an additional power source. This research will contribute to ongoing efforts to make electric car charging more efficient and environmentally friendly. Keywords: Electric Vehicle, Wireless Charging, Solar Panels, Arduino Uno, Transmitter and Receiving Coils, LCD display, wireless Power Transfer

Innovative Compact Twin Whirly Bird-based Wind Turbine: A Sustainable Solution for EV Charging Infrastructure

The global adoption of whirly bird vent roofs across various industries underscores their utility in harnessing wind energy for cooling purposes within warehouses and similar environments. The innovation proposed in the current study builds upon this established technology by integrating semi-perpetual motion principles into a compact twin whirly bird-inspired wind turbine design. This novel approach aims to enhance power generation efficiency, thereby offering a sustainable solution for energy production. The compact twin whirly bird turbine concept represents a significant advancement, particularly in the context of powering electric vehicle (EV) chargers. As the demand for EVs continues to surge, concerns persist regarding the availability of charging infrastructure. By integrating the proposed innovative wind turbine technology into the EV charging ecosystem, we address this critical issue while simultaneously promoting the adoption of renewable energy sources. The key to our proposal is the deployment of small-scale wind turbines in diverse public locations, thereby democratizing access to clean energy generation. Unlike conventional large-scale wind turbines, our whirly bird-inspired turbines boast versatility in installation, akin to streetlamps, facilitating widespread deployment across urban landscapes. While these turbines may yield comparatively modest power outputs relative to megawatt-scale counterparts, their strategic placement ensures a decentralized network capable of powering EV charging stations. In summary, our research presents a compelling solution that bridges the gap between EV charging infrastructure availability and the utilization of renewable energy sources. By leveraging the inherent advantages of whirly bird-inspired wind turbines, we offer a practical pathway towards sustainable energy provision in public spaces, thereby facilitating the widespread adoption of electric vehicles.

Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning

The transition to sustainable transportation is imperative in mitigating environmental impacts, with electric vehicles (EVs) at the forefront of this shift. Despite their environmental benefits, the global adoption of EVs is curtailed by challenges such as nascent battery technology, high costs, and insufficient charging infrastructure. This study addresses the optimizing electric vehicle charging station (EVCS) locations as a critical step toward enhancing EV adoption rates. Thus, establishing efficient charging stations is critical to meet the increasing demand. By integrating location modeling with demand forecasts and market penetration, we propose a comprehensive approach to determine optimal locations and capacities for EVCS. Firstly, review existing literature, highlighting the significance of facility location models in optimizing EV charging infrastructure and identifying gaps in addressing demand and market penetration. Our methodology uses a genetic algorithm to solve the p-median problem for location selection and Arena 14 simulation software to model station traffic and optimize charging unit types and quantities. The model prioritizes public areas, considering potential demand points and station locations to propose optimal charging areas. Results indicate that our model minimizes travel distances and waiting times, offering a scalable solution adaptable to future EV market growth. This study contributes to the field by presenting a sustainable and economical model for EVCS placement and capacity planning, underlining the importance of a robust charging network in the broader adoption of electric transportation. The findings suggest that proactive infrastructure development, guided by accurate demand predictions and optimized location strategies, can significantly enhance the feasibility and attractiveness of EVs, supporting global efforts towards a cleaner, more sustainable transportation system.

Ukraine’s post-war recovery program as a basis for the development of electric vehicles and electric infrastructure

The article is devoted to the determination of the development of domestic electric vehicle transport and electric vehicle infrastructure on the basis of such an economic and legal instrument as the post-war recovery program of Ukraine, taking into account EU approaches in the legal regulation of decarbonization of the transport sector. The European Commission adopted the Communiqué to the European Parliament, the European Council, the European Economic and Social Committee and the Committee of the Regions «Assistance and Reconstruction of Ukraine» on 18.05.2022, where the provision of funds for the post-war reconstruction of Ukraine is conditioned by our country’s compliance with current European policies and standards, one of which there is decarbonization. Therefore, Ukraine should carry out post-war reconstruction taking into account the experience and approaches outlined by the international acts of the EU. The National Council for the Recovery of Ukraine from the Consequences of the War, a consultative and advisory body under the President of Ukraine, proposed a draft Plan for the Recovery of Ukraine. Based on the analysis of the provisions of the project component of the Recovery Plan of Ukraine «Restoration and development of infrastructure». It was concluded that the project of the Recovery Plan of Ukraine and its component «Recovery and development of infrastructure» is an economic and legal instrument of a programmatic nature. It establishes a systematic approach to the more active use of electric vehicles and the development of electric charging stations. The plan defines short-term, medium-term and strategic tasks for the development of electric mobility infrastructure. The general program approaches defined in this act correspond to the directions established by the normative legal acts that were adopted to regulate the issues of environmentalization of the domestic transport sector before the war, as well as the general directions of the development of the charging infrastructure of electric vehicles, outlined by the international acts adopted in the EU. The disadvantage is that a complete transition to electric transport for the transportation of passengers on urban and suburban routes is foreseen in the medium term, which is an extremely limited period. In the Plan, it is expedient to use the objective indicators of the development of the system of electric filling stations, which are established in the domestic regulatory and technical acts. Also, the directions of the development of electric transport and the network of electric gas stations should be reflected in the programs of comprehensive restoration of the region, the territory of the territorial community.

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.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.