Electric Vehicle Charging Infrastructure Research Articles

Design of a bidirectional EV charger using unit template-based current-controlled synchronous reference frame hysteresis

Abstract Existing distribution networks were not designed with large-scale electric vehicle (EV) charging infrastructure in mind. Integrating EV charging stations with the distribution grid might lead to power quality (PQ) issues at the point of common coupling (PCC). This work proposes a two-mode, unit template-based synchronous reference frame hysteresis current-controlled (SRF-HCC) three-phase Level 2 EV charger. Mode one focuses on charging the EV battery from the grid (G2V) and utilizes current and voltage control techniques to enhance battery life and performance. Whereas mode two enables the EV’s stored energy to be discharged to the grid (V2G) by the EV user, allowing the sale of power to support the transient effect of the grid voltage and frequency and enhancing the grid’s PQ. The HCC generates switching pulses for both the AC-DC and buck-boost converters. The SRF-based unit template-based control (SRF-UTC) method ensures system stability, voltage, and frequency regulation for power exchange with the grid by combining its efficiency with that of the HCC. The EV charger proposal comprises three primary components: a 3-phase bidirectional AC-DC converter, a bidirectional buck-boost converter, and a filter circuit. The proposed system was modeled using MATLAB/Simulink and evaluated in two case studies to assess its performance.

A Review of sustainable transportation solutions: Innovations, challenges, and future directions

The paper explores the need for sustainable transportation alternatives that minimize environmental repercussions, focusing on advancements in public transit systems, electric vehicles (EVs), alternative fuels, and innovative mobility concepts. It provides a comprehensive analysis of the strengths and limitations inherent in sustainable transportation solutions, highlighting the efficiency gains of well-designed public transit systems and the monumental leap represented by electric vehicles powered by rechargeable batteries. However, the journey towards sustainable transportation is not without challenges, such as infrastructure limitations, cost considerations, and the imperative for behavioral shifts. The paper dissects these challenges, shedding light on the intricacies that demand attention from researchers, policymakers, and stakeholders alike. Infrastructure development is a critical linchpin, demanding strategic investment and collaboration to overcome hurdles of building and maintaining extensive public transportation networks, EV charging infrastructure, and alternative fuel production facilities. Cost considerations are a formidable barrier, particularly with the initial costs of EVs and alternative fuel vehicles surpassing those of traditional counterparts. The paper advocates for government incentives and research funding to bridge the financial gap, making sustainable transportation options accessible to a broader demographic. Technological advancements, ranging from battery technology to autonomous vehicle capabilities, serve as the engine driving the evolution of sustainable transportation. The paper underscores the need for continuous research and development initiatives to refine these technologies, enhancing efficiency, affordability, and range. Behavioral shifts pose a unique challenge, necessitating effective public awareness campaigns and policy interventions. The paper explores the opportunities presented by education, advocacy, and policy innovation in instigating this cultural shift. The paper also emphasizes the importance of social equity and accessibility in the pursuit of sustainable transportation. It advocates for inclusive policies that ensure the benefits of sustainable transportation are accessible to all demographics. Community engagement and impact assessments are proposed as indispensable tools to identify and address gaps in accessibility.

Pro-environment consumer behaviour and electric vehicle adoptions: a comparative regional meta-analysis

ABSTRACT Numerous studies have explored the relationship between the environment and the adoption of electric vehicles (EVs); however, a consensus has not been reached among empirical findings. This study aims to conduct a comprehensive meta-regression analysis by synthesizing and scrutinizing data from existing literature to identify potential sources of heterogeneity among effect sizes in individual studies. To achieve this goal, we collected 429 estimates from 65 previously published studies spanning the period from 1998 to 2021. We investigate the connection between pro-environmental consumer behaviour and EV adoption at the aggregate level and compare this relationship across three distinct regions: North America, Western Europe, and emerging markets. Our research employs advanced empirical techniques, including Bayesian model averaging, frequentist model averaging, and weighted average least squares. The outcome of our analysis reveals a positive, albeit relatively weak synthesized effect of environmental factors on EV adoption. Notably, our meta-analysis findings indicate that the impact of environmental concerns on EV adoption varies significantly depending on the specific type of EV being considered. Consumers prefer versatile hybrid and plug-in hybrid electric vehicles due to fuel flexibility, especially in areas where battery electric vehicle charging infrastructure is scarce.

ELECTRIC VEHICLE CHARGING INFRASTRUCTURE: A COMPARATIVE REVIEW IN CANADA, USA, AND AFRICA

This research paper comprehensively analyzes electric vehicle (EV) charging infrastructure in Canada, the USA, and Africa. Examining technological landscapes, regulatory frameworks, funding mechanisms, and socio-environmental impacts, the study reveals key trends and challenges. The technical overview encompasses Level 1, Level 2, and DC fast charging, focusing on interoperability and advancements. Government grants, public-private partnerships, and international funding drive infrastructure funding, fostering job creation and economic growth. The analysis reveals diverse cultural and behavioral factors influencing EV adoption, emphasizing the need for tailored communication strategies. The future envisions ultra-fast charging, wireless technologies, and smart ecosystems, demanding collaborative solutions to grid capacity and standardization challenges. This research contributes valuable insights for policymakers, industry stakeholders, and researchers, guiding the sustainable development of EV charging infrastructure globally. 
 Keywords: Electric Vehicles, Charging Infrastructure, Sustainability, Socioeconomic Impact.

Analysis of Spatiotemporal Characteristics and Influencing Factors of Electric Vehicle Charging Based on Multisource Data

Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.

Charging Stations for Large-Scale Deployment of Electric Vehicles

The large-scale adoption of electric vehicles will require a charging infrastructure that meets the new needs that will arise. Currently, the charging infrastructure for electric vehicles is still in the early stages of development, not least because of the low number of electric vehicles in use. However, there are still many questions to be answered when it comes to standardization in terms of connectors, DC or AC charging, and power, as well as both operational and economic issues. Although this topic has been the subject of numerous studies over the last ten years, there are still gaps to be filled, particularly with regard to the mix of different recharging strategies (normal, accelerated, fast, induction-track, etc.), as well as the economic and operational aspects. Moreover, the relationship between users and private cars is changing rapidly, and charging behaviors are not yet well established.

Innovative Energy Approach for Design and Sizing of Electric Vehicle Charging Infrastructure

In Italy, the availability of service areas (SAs) equipped with charging stations (CSs) for electric vehicles (EVs) on highways is limited in comparison to the total number of service areas. The scope of this work is to create a prototype and show a different approach to assessing the number of inlets required on highways. The proposed method estimates the energy requirements for the future electric fleet on highways. It is based on an energy conversion that starts with the fuel sold in the highway network and ends with the number of charging inlets. A proposed benchmark method estimates energy requirements for the electric fleet using consolidated values and statistics about refueling attitudes, with factors for range correction and winter conditions. The results depend on assumptions about future car distribution, with varying numbers of required inlets. The analysis revealed that vehicle traffic is a critical factor in determining the number of required charging inlets, with significant variance between different SAs. This study highlights the necessity of incorporating factors like weather, car charging power, and the future EV range into these estimations. The findings are useful for planning EV charging infrastructure, especially along major traffic routes and in urban areas with high-range vehicles relying on High-Power DC (HPDC) charging. The model’s applicability to urban scenarios can be improved by considering the proportion of energy recharged at the destination. A key limitation is the lack of detailed origin–destination (OD) highway data, leading to some uncertainty in the calculated range ratio coefficient and underscoring the need for future research to refine this model.

Rural Photovoltaic Storage and Charging Integrated Charging Station Capacity Allocation Strategy based on Tariff Compensation Mechanism

Background: In order to help the carbon peaking and carbon neutrality goals, the current new energy vehicle to the countryside policy for the local use of renewable energy and demand-side carbon reduction provides a good opportunity but also requires rural townships and villages of electric vehicle charging infrastructure planning ahead. However, due to the current low rural electric vehicle ownership, the charging price compensation mechanism is not yet perfect, resulting in the planning period of electric vehicle growth and the willingness to respond to the tariff compensation policy is difficult to accurately assess. Methods: This paper proposes a rural photovoltaic storage and charging integrated charging station capacity allocation strategy based on the tariff compensation mechanism. Firstly, we construct a spatial-temporal dynamic distribution model of rural EV charging load coupled with distribution network-transportation network, and on this basis, we consider the rural EV charging time-sharing tariff and tariff compensation policy incentive, and amend the EV charging load transfer model; and then we construct an optimization planning model of charging station with the goal of minimizing the cost of construction, operation and maintenance, and maximizing the charging benefit of the integrated charging station in the rural area, and obtain the optimal synergistic planning scheme under the tariff compensation mechanism in the planning period. Results: The optimal collaborative planning scheme under the electricity price compensation mechanism is obtained, and the correctness and validity of the proposed optimal planning method of the rural optical storage charging station under the electricity price compensation mechanism is verified by the example, which is of positive significance in the promotion of the charging facilities to go to the countryside in an appropriate manner, and in the stimulation of the willingness of the rural consumers in the townships to purchase vehicles. Conclusion: (1) The spatial and temporal distribution and transfer model of rural EV charging loads with price incentives is constructed by taking into account the preferential charging price policies such as rural time-sharing tariff and tariff compensation mechanism. By comparing the operating revenues of optical storage-charging integrated charging stations with and without timesharing tariffs and tariff compensation policies, we verified the incentive effect of multiple types of price incentives for the over-planning of rural electric vehicle charging facilities. (2) The proposed optimal configuration method of rural photovoltaic, storage and charging integration charging station can realize the in-situ utilization of rural renewable energy, tap the price competitiveness of photovoltaic, storage and charging integration, and weaken the cost of electricity consumption. By comparing the optimized configuration scheme with and without joint planning, it is verified that the moderate configuration of in-situ photovoltaic and energy storage equipment on the basis of the planning of charging piles brings benefits far exceeding the investment cost, and has a great role in increasing the operational efficiency of rural charging facilities.

Technical and Economic Challenges and Future Prospects of a Smart Grid - A Case Study

In order to improve grid efficiency, dependability, and sustainability, smart grid technology is being developed and implemented at the vanguard of updating the world's electrical infrastructure. In addition to looking at the potential implications of smart grid technology, this case study analyzes the technical and financial difficulties encountered during the implementation of a smart grid. Technical issues with energy management and system stability arise when intermittent renewable energy sources are integrated into the smart grid. To make smooth transmission, sophisticated algorithms and grid management strategies are needed. Maintaining data security and privacy is crucial since smart grids mostly depend on digital technology and data exchange. It is a constant worry to defend the grid from hackers and illegal access. The installation of sensors, smart meters, and communication equipment might come with a significant initial cost when implementing a smart grid. One of the main economic challenges is financing these developments while maintaining reasonable power bills. It may be difficult to strike a balance between the interests of technology suppliers, customers, and utilities. Real-time monitoring and control are made possible by smart grids, which improve energy distribution and minimize energy waste. In addition to lowering greenhouse gas emissions, this improves overall energy efficiency. Smart grids can enable EV charging infrastructure as electric vehicle (EV) use increases, providing potential for grid optimization and new income streams for utilities. Reliability could be increased and downtime is decreased in a smart grid when defects can be promptly identified and isolated. The implementation of a smart grid is not without its technical and financial difficulties, but the future seems bright. Overcoming these obstacles may result in an electrical grid that is more sustainable, robust, and profitable for customers as well as utilities. Stakeholder cooperation, flexible regulations, and continuous technical development are needed to address these problems. By providing practical applications and real-world insights into the implementation of a smart grid, the case study acts as a link between theoretical concepts. Stakeholders obtain a comprehensive comprehension of the intricacies involved by analyzing technical and economic obstacles. To maximize the potential of smart grid technology, this knowledge is essential for fine-tuning strategies and creating focused solutions.

Computer Vision Applications in Intelligent Electric Vehicle Charging Infrastructure

The study examines the use of computer vision technologies into intelligent electric vehicle (EV) charging infrastructure. The objective is to increase station capabilities, maximize resource usage, and enhance user experiences. An examination of the data from charging stations indicates that there are differences in their capacities and capabilities. Certain stations can handle a greater number of cars at the same time because they have higher power outputs and numerous charging connections. The vehicle identification data illustrates the efficacy of computer vision in precisely recognizing various electric vehicle types, hence optimizing authentication procedures for efficient charging. An analysis of charging session data reveals variations in energy use and durations across sessions, underscoring the impact of charging practices on the utilization of charging stations. An examination of use reveals discrepancies in the number of sessions and energy usage among stations, highlighting the need for adaptive infrastructure. Percentage change study management solutions for demonstrates the fluctuating patterns of resource usage, emphasizing the need for flexible resource allocation techniques. The results emphasize the significant impact that computer vision may have on improving the efficiency and flexibility of electric vehicle charging infrastructure. The research highlights the significance of optimizing the allocation of resources, improving algorithms for various contexts, and applying adaptive solutions for optimal management of charging infrastructure. In essence, the study helps to further our knowledge of how computer vision contributes to the development of intelligent EV charging systems. It provides valuable insights into improving the efficiency of infrastructure and enriching user experiences in the field of electric mobility.

Optimizing Urban Electric Vehicle Charging Infrastructure Selection: An Approach Integrating GPS Data, Battery Levels, and Energy Availability

This research paper offers a comprehensive exploration of how to enhance the charging infrastructure for Electric Vehicles (EVs) in urban settings. The analysis assesses three hypothetical charging infrastructures in a city, each with generators connected to the national grid and strategically placed AC and DC chargers. The proposed method employs GPS data, EV battery levels, and energy availability across the charging infrastructures. Using an optimization algorithm implemented via the Pandapower Python library, the most appropriate charging infrastructure is identified for individual drivers. The algorithm considers factors such as GPS data and energy availability to suggest the optimal charging station for each EV’s battery level. This pioneering solution aims to streamline the charging process, enhance user experience, and encourage efficient use of urban charging infrastructure for electric vehicles.

Implementation of public charging infrastructure for electric vehicles in India - a strategic plan

Implementation of public charging infrastructure for electric vehicles in India - a strategic plan

Optimal Planning of Dynamic Wireless Charging Infrastructure for Electric Vehicles

Optimal Planning of Dynamic Wireless Charging Infrastructure for Electric Vehicles

Machine Learning for Anomaly Detection in Electric Transportation Networks

This study introduces a sophisticated anomaly detection system based on machine learning. The system is specifically developed to enhance the dependability and safeguard the security of electric transportation networks, with a particular emphasis on the charging infrastructure for electric vehicles (EVs). Utilizing extensive datasets, the research examines several facets of charging stations, charging records, identified abnormalities, and following maintenance measures. The examination of the charging station demonstrates the system’s versatility in accommodating many charging circumstances, as seen by the range of power ratings, consumption patterns, and energy provided. Further examination of charging records provides comprehensive understanding of individual charging sessions, enabling the detection of irregularities such as atypical energy surges and extended charging durations. The machine learning system, having been trained and verified using this data, has a commendable degree of precision in identifying anomalies, as shown by the congruence between anticipated abnormalities and real results. The maintenance and repair measures carried out in reaction to identified abnormalities highlight the practical ramifications of the system, with proactive tactics utilized to reduce downtime and enhance charging station operations. The performance measures, including accuracy, recall, and F1 score, unequivocally validate the resilience of the anomaly detection system, guaranteeing precise identification while mitigating the occurrence of false positives and negatives. The seamless incorporation of machine learning into electric transportation networks, as shown by the results, not only amplifies the dependability and safeguarding of EV charging infrastructure but also establishes the system as an invaluable instrument for practical implementations. The research, in addition to offering a thorough examination of the system’s performance, elucidates forthcoming avenues for scalability, real-time monitoring, and interpretability, thereby making a valuable contribution to the wider discussion on the revolutionary capabilities of machine learning in the ever-changing realm of electric transportation.

Integration Challenges and Solutions for Solar-Powered Electric Vehicle Charging Infrastructure: From Panel to Battery

The integration of solar power with electric vehicle (EV) charging infrastructure presents a promising avenue to foster sustainable transportation. This study delves into the multifaceted challenges encountered in the synthesis of solar-powered EV charging stations and proffers solutions that span the complete energy transfer chain from photovoltaic panels to EV batteries. Initial concerns address the intermittent nature of solar energy and its impact on the reliability of power delivery. Advanced energy management strategies are explored, incorporating predictive analytics and real-time adjustment mechanisms to enhance the consistency and efficiency of power flow. The second part of the investigation scrutinizes the power electronics interface, emphasizing the need for high-efficiency converters that can operate effectively over varied solar insolation levels. Novel circuit topologies are presented, alongside adaptive control algorithms designed to optimize the power conversion process. Collectively, the findings underscore the potential of solar-powered EV charging infrastructure to not only support the decarbonization of transportation but also to contribute to the stability and efficiency of the power grid. The intersection of power electronics, energy management, and grid integration forms the cornerstone of this endeavor, with each domain providing critical components to the holistic solution. It culminates in a set of recommendations for policy, design, and operation that can accelerate the adoption of this technology.

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

Optimal Operation of an Innovative Electric Vehicle Charging Hub directly fed by Renewables

The reduction of Green House Gases (GHGs) emissions has been the top priority of the European Union (EU) in recent times. The increase in use of Electric Vehicles (EVs) to combat climate change is viable only with the use of Renewable Energy Sources (RESs) to power them. Since the Paris Agreement in 2015, the integration of RESs into the charging infrastructure has increased in the EU. The increasing market share of EVs in various categories has called for immediate changes in the EV charging infrastructure and in the operation of charging hubs. This paper aims to present an Energy Management System (EMS) to efficiently manage an EV charging hub fed by RESs, reducing the daily costs of operation and GHG emissions. The mathematical model of the EMS is developed in Matlab as a Mixed Integer Linear Programming (MILP) model. The MILP-based EMS is applied to the real-world case of an innovative EV charging hub located in the Ligurian region of Italy and the results of the optimization are reported.

Optimizing Electric Vehicle Charging Infrastructures in the Smart Grid with a Multi-Objective Approach

This paper introduces a tool designed to optimize electric vehicle (EV) charging infrastructures within the smart grid framework. The tool utilizes a multi-objective approach and is programmed in Python. It enables dynamic management of energy distribution among different EV charging infrastructures, addressing scenarios where surplus photovoltaic (PV) power generation exceeds charging demands but faces challenges due to storage costs and electric energy transmission rates to alternative infrastructures. In instances of low PV production relative to charging demand, the algorithm strategically selects the optimal procurement strategy, either purchasing electric energy from neighboring infrastructures or utilizing surplus PV energy for direct charging. The tool empowers stakeholders to make informed decisions by facilitating comparisons between the cost of storing electric energy locally and the expense of procuring it from external sources, thereby enhancing the efficiency and cost-effectiveness of EV charging infrastructures in the smart grid ecosystem. Extensive simulations and case studies demonstrate the efficacy of the proposed approach, showcasing its potential to optimize energy distribution and promote sustainable practices within the EV charging domain.

PV-Assisted grid connected multi output electric vehicle charger with PV2V, G2V and PV2G functions.

The demand for renewable energy-based Electric Vehicle (EV) charging infrastructure is increasing in recent years. Solar PV based EV charging method is preferred as it has simple energy harvesting technique. The PV system is an uncertain power source, where the power generation is varied with respect to the availability of sunlight. So, that the charging station requires a backup power supply for the uninterrupted charging. For the integrated power sources, the charging station requires a simple and efficient conversion unit for the DC/AC/DC conversion. In this work, a modified Z-source inverter (MZSI) is developed for the multiport EV charger using PV and grid. The proposed MZSI is connected between the input and output sides to boost the voltage as per the demand at the battery side. In order to connect many battery units with the charger, the capacitors used in the MZSI are split as per the required number of charging ports. This developed converter topology operates the systems in four different modes like PV-Grid, PV-battery, grid-battery, and battery-grid. The performance of this proposed work has been validated in MATLAB/Simulink® and in the experimental setup. The experimental setup has been developed with two charging ports for obtaining 250W at each charger end which cumulatively produces 500W output across both chargers with an efficiency of 90.18%.

A Scheduling Algorithm for Optimizing Electric Vehicle Charging Infrastructure in Parking Lots

A Scheduling Algorithm for Optimizing Electric Vehicle Charging Infrastructure in Parking Lots