Journal articles: 'EV INFRASTRUCTURE'

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Khan, Hafiz Anwar Ullah, Sara Price, Charalampos Avraam, and Yury Dvorkin. "Inequitable access to EV charging infrastructure." Electricity Journal 35, no. 3 (April 2022): 107096. http://dx.doi.org/10.1016/j.tej.2022.107096.

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IKEYA, Tomohiko. "Activities and Issues of EV Charge Infrastructure Preparation Towards EV Popularization." Journal of The Institute of Electrical Engineers of Japan 133, no. 1 (2013): 10–12. http://dx.doi.org/10.1541/ieejjournal.133.10.

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Li, Qiushuo, Yong Xiao, Shuaishuai Zhao, Xianwen Zhu, Zongyi Wang, Zisheng Liu, Ling Wang, Xiangwu Yan, and Yan Wang. "Performance Status Evaluation of an Electric Vehicle Charging Infrastructure Based on the Fuzzy Comprehensive Evaluation Method." World Electric Vehicle Journal 10, no. 2 (May 30, 2019): 35. http://dx.doi.org/10.3390/wevj10020035.

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Performance status evaluation is essential for the safe running of electric vehicle (EV) charging infrastructure. With the development of the EV industry, the EV charging infrastructure industry has advanced considerably. Safe and reliable operation of the charging infrastructure is important for the development of EVs. As such, we propose a comprehensive evaluation method to assess the performance condition of an EV charging infrastructure. First, based on the analysis of the existing EV charging principles, we established an evaluation index system for EV charging infrastructure. Second, the subjective weight, objective weight, and comprehensive weight of the index system were determined through analytic hierarchy processes (AHP) and the entropy weight method. Then, we used fuzzy comprehensive evaluation to appraise the performance of the charging infrastructure through expert investigation. Finally, based on the actual data from an EV charger, the performance conditions of the EV charging infrastructure were evaluated to demonstrate the feasibility of the method and the reliability of the index system.

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Kore, Hemant Harishchandra, and Saroj Koul. "Electric vehicle charging infrastructure: positioning in India." Management of Environmental Quality: An International Journal 33, no. 3 (February 9, 2022): 776–99. http://dx.doi.org/10.1108/meq-10-2021-0234.

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PurposeThe study identifies the challenges of developing the “electric vehicle (EV)” charging infrastructure in India, having an ambitious target of 30% EV adoption by 2030.Design/methodology/approachFirst, a systematic literature review determined EV adoption and challenges in the EV charging infrastructure development globally and specifically in India. Secondly, a focussed group study in which 10 domain experts were consulted to identify additional challenges in India's EV adoption involving EV charging infrastructure.FindingsAccordingly, 11 significant challenges of EV charging infrastructure development in India have been identified–seven through the comparative analysis of the literature review and four from the focussed group study. Secondary data provides insight into the situation around developed countries and in developing countries, specifically in India. Finally, the Government of India's measures and priorities to facilitate such a development are emphasised.Research limitations/implicationsThe study can help policymakers/researchers understand the gaps and align measures to address the challenges. A focussed group study may have its limitations due to the perception of the experts.Originality/valueThe systematic literature review of 43 articles using comparative analysis and subsequently a focussed group study of experts to verify and add challenges has made the study unique.

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Almutairi, Abdulaziz. "Impact Assessment of Diverse EV Charging Infrastructures on Overall Service Reliability." Sustainability 14, no. 20 (October 16, 2022): 13295. http://dx.doi.org/10.3390/su142013295.

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A higher penetration of EVs may pose several challenges to the power systems, including reliability issues. To analyze the impact of EVs on the reliability of power systems, a detailed EV charging infrastructure is considered in this study. All possible charging locations (home, workplace, public locations, and commercial fast chargers) and different charging levels (level 1, level 2, and DC fast charging) are considered, and seven charging infrastructures are determined first. Then, the reliability impact of each charging infrastructure is determined using the two widely used reliability indices, i.e., the loss of load expectation (LOLE) and the loss of energy expectation (LOEE). The impact of mixed charging infrastructure portfolios is also analyzed by considering two different cases, which included the equal share of all charging infrastructure and charging infrastructure share based on consumer preferences. The performance is analyzed on a well-known reliability test system (Roy Billinton Test System) and different penetration levels of EVs are considered in each case. Test results have shown that fast-charging stations have the worst reliability impact. In addition, it was also observed that mixed charging portfolios have lower reliability impacts despite having a fair share of fast-charging stations.

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Clampitt, Colleen, and Syed Adeel Ahmed. "ELECTRIC VEHICLE SALES AND INFRASTRUCTURE ANALYSIS." Performance Improvement Journal 61, no. 1 (January 1, 2022): 10–18. http://dx.doi.org/10.56811/pfi-21-0045.

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ELECTRIC VEHICLES (EV) are a popular choice for many people looking to reduce their carbon footprint, reduce their reliance on oil, avoid fueling stations, or who just want to own the next step in technology. There are questions, however, about how prepared the United States is for an EV owning populace. Before everyone can own an EV, there needs to be enough infrastructure to support the growing numbers of EVs. This paper will examine the current growth of EVs in Louisiana and use that information to determine if Louisiana has enough infrastructure in place to meet the growing demand.

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CALATAYUD MARTÍ, PILAR, JULIAN ROMERO CHAVARRO, MARIO MONTAGUD AGUAR, LUCIA ARCOS USERO, MARTA GARCÍA PELLICER, and ALFREDO QUIJANO LÓPEZ. "THREE-LEVEL METHODOLOGY FOR SECURE AND EFFICIENT GRID INTEGRATION OF ELECTRIC VEHICLE." DYNA 96, no. 3 (May 1, 2021): 264–69. http://dx.doi.org/10.6036/10013.

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The secure integration of electric vehicle (EV) plays a key role in the energy transition through a resilient and decarbonised economy. However, a massive EV penetration means a rise in electricity demand with negative consequences to the distribution systems (voltage drops, branches congestion, etc) if the charging infrastructure is not cybersecure and does not perform smart charging mechanisms. Furthermore, these new infrastructures and their operating procedures provide new chances to cyberattacks to be performed, aimed at either exploiting those grid vulnerabilities or acquiring some user’s private information. Therefore, to ease the secure integration of EV charging infrastructures in the future network, this paper presents a three-level actuation methodology for charging infrastructures, which includes active management of EV supply equipment (EVSE) to allow dynamic control of charges, installation of ancillary protection systems, planning of EVSE’s location within the distribution system and cybersecure management of the whole infrastructure. The presented methodology is based on a thorough analysis of the possible cyberattacks that may occur during the transactions of the charging process, as well as tests carried out on a real pilot, which demonstrate the possible impacts that an uncontrolled charging of the EV can have on the distribution network, thus identifying the vulnerabilities of the distribution network. Keywords: Smart Grid, electrification, electric vehicle, charging station, Charge point operator, cybersecurity, smart charging.

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Kong, Qing, Michael Fowler, Evgueniy Entchev, Hajo Ribberink, and Robert McCallum. "The Role of Charging Infrastructure in Electric Vehicle Implementation within Smart Grids." Energies 11, no. 12 (December 1, 2018): 3362. http://dx.doi.org/10.3390/en11123362.

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In the integration of electric vehicle (EV) fleets into the smart grid context, charging infrastructure serves as the interlinkage between EV fleets and the power grid and, as such, affects the impacts of EV operation on the smart grid. In this study, the impacts of charging infrastructure on the effectiveness of different EV operational modes were simulated using a multi-component modelling approach, which accounts for both stochastic EV fleet charging behaviors as well as optimal energy vector dispatch operation. Moreover, a campus microgrid case study was presented to demonstrate the various design factors and impacts of charging infrastructure implementation affecting EV fleet adoption and operation. Based on results from the study, it was shown that charging infrastructure should be adopted in excess of the minimum required to satisfy EV charging for driving needs. In addressing uncontrolled charging behaviors, additional charging infrastructure improves EV owner convenience and reduces queuing duration. Meanwhile, controlled charging strategies benefit from increased resilience against uncertain charging behavior and operate more optimally in systems subject to time-of-use (TOU) electricity pricing. Lastly, it was demonstrated that successful vehicle-to-grid (V2G) implementation requires charging infrastructure to emulate the availability and fast response characteristics of stationary energy storage systems, which translates to excess charging port availability, long EV plug-in durations, and bi-directional power flow capabilities well beyond the level 2 charging standard.

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Dharmakeerthi, C. H., and Mithulananthan Nadarajah. "Stability Cogitated Electric Vehicle Charging Infrastructure Planning." International Journal of Smart Grid and Sustainable Energy Technologies 1, no. 1 (December 17, 2019): 10–14. http://dx.doi.org/10.36040/ijsgset.v1i1.180.

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Electrification of the transportation is taking place at an accelerated rate. Even though, electric vehicles (EV) evidently bring numerous environmental and economic benefits, their impact on power systems should not be overlooked. It has been identified that EV load characteristics can significantly affect power system voltage stability and small signal stability. Hence, it is important to consider mitigating of stability impacts right from the planning stage of bulk EV charging stations. However, unavailability of suitable stability evaluating indexes that could fit into planning algorithms is a hindrance. This study proposes two computationally efficient indexes to compare stability status in different planning options. The developed indexes have been tested, verified and utilized in a multi objective planning algorithm to identify a comprehensive solution, which satisfies the grid operator, EV customer and the charging facility investor optimally.

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Helmus, Jurjen, Mike Lees, and Robert van den Hoed. "Understanding Complexity in Charging Infrastructure through the Lens of Social Supply–Demand Systems." World Electric Vehicle Journal 13, no. 3 (February 24, 2022): 44. http://dx.doi.org/10.3390/wevj13030044.

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Since the first release of modern electric vehicles, researchers and policy makers have shown interest in the deployment and utilization of charging infrastructure. Despite the sheer volume of literature, limited attention has been paid to the characteristics and variance of charging behavior of EV users. In this research, we answer the question: which scientific approaches can help us to understand the dynamics of charging behavior in charging infrastructures, in order to provide recommendations regarding a more effective deployment and utilization of these infrastructures. To do so, we propose a conceptual model for charging infrastructure as a social supply–demand system and apply complex system properties. Using this conceptual model, we estimate the rate complexity, using three developed ratios that relate to the (1) necessity of sharing resources, (2) probabilities of queuing, and (3) cascading impact of transactions on others. Based on a qualitative assessment of these ratios, we propose that public charging infrastructure can be characterized as a complex system. Based on our findings, we provide four recommendations to policy makers for taking efforts to reduce complexity during deployment and measure interactions between EV users using systemic metrics. We further point researchers and policy makers to agent-based simulation models that capture interactions between EV users and the use complex network analysis to reveal weak spots in charging networks or compare the charging infrastructure layouts of across cities worldwide.

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Lucas, Alexandre, Giuseppe Prettico, Marco Flammini, Evangelos Kotsakis, Gianluca Fulli, and Marcelo Masera. "Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis." Energies 11, no. 7 (July 18, 2018): 1869. http://dx.doi.org/10.3390/en11071869.

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Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO2eq/MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.

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Yang, Yong Biao, Xiao Hua Ding, and Jin Da Zhu. "Discuss Charging-Discharging and Sale Technology of the Electric Vehicle." Advanced Materials Research 347-353 (October 2011): 3908–14. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.3908.

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Development of EV is an important means which optimize chinese energy supply structure and break security strategy of energy.As the state increasing subsidy strength and constructing large-scale charging infrastructure for EV,EV is expected to become an important means of transport,and in the future EV is becoming to an important mobile storage unit in smart grid, playing the dual role of energy comsume and energy storage. Combination of domestic charging infrastructure and charge mode, discuss the charge and discharging and sale of electric technology for EV, which provide some reference for business model of the charging infrastructure,proposed to help the orderly charging and discharging for EV, realize win-win situation for power grid and the user.

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Anthopoulos, Leonidas, and Polytimi Kolovou. "A Multi-Criteria Decision Process for EV Charging Stations’ Deployment: Findings from Greece." Energies 14, no. 17 (September 1, 2021): 5441. http://dx.doi.org/10.3390/en14175441.

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Electro-mobility (EV) is an emerging transportation method, whose charging infrastructure development concerns a key-factor for its growth. EV charging infrastructure has not grown yet in Greece, regardless of the ambitious national targets that have been grounded for 2030 towards a climate-neutral mobility. This study introduces a multi-criteria decision-making (MCDM) framework for EV charging infrastructure deployment and operation, which respects both the economic and the technical aspects for public charging stations. The analytic hierarchy process (AHP) was followed for the MCDM framework’s definition, which used criteria that were in the corresponding literature and performed with interviews by experts from the EV growing market in Greece. The results show that the installation and operation of public EV charging stations, located in private spaces to ensure their protection against vandalism, within the urban areas is the preferred deployment approach. Moreover, this article tests a market model for the EV charging infrastructure ownership and operation. Findings show that the incentive for investment in EV charging infrastructure market in Greece, is driven by the direct investments of limited vendors, while it is not economically oriented, but it focuses on sustainability and environmental protection.

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Peter, Van, Turcksin Tom, Omar Noshin, and Van Joeri. "Developments and Challenges for EV Charging Infrastructure Standardization." World Electric Vehicle Journal 8, no. 2 (June 24, 2016): 557–63. http://dx.doi.org/10.3390/wevj8020557.

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Rahul Kumar Jha and Sumina Neupane. "Securing the Future of Mobility: Electric Vehicle Charging Infrastructure Protection." September 2023 5, no. 3 (September 2023): 291–309. http://dx.doi.org/10.36548/jitdw.2023.3.005.

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The rapid growth of electric vehicles (EVs) has spurred the need for a robust and secure charging infrastructure to ensure the future of mobility. This comprehensive study explores the critical topic of securing electric vehicle charging infrastructure, focusing on the protection of the vital component of the EV ecosystem. The study begins by providing an overview of the different types of EV charging infrastructure and the current state of deployment. It then examines the inherent challenges and vulnerabilities associated with EV charging infrastructure security, encompassing both physical threats, such as vandalism and theft, as well as cybersecurity threats, such as unauthorized access and data breaches. Existing security measures, including physical site design considerations and cybersecurity protocols, are reviewed, along with industry standards and regulations that provide guidance in this domain. The emerging technologies and strategies, such as blockchain, artificial intelligence, and secure communication protocols, that can enhance the protection of EV charging infrastructure are also explored in the study. Furthermore, it analyses relevant case studies illustrating real-world attacks on charging infrastructure, successful deployment stories, and the valuable lessons learned from these experiences. Finally, the paper outlines future directions and recommendations, including research needs, policy considerations, and stakeholder collaboration, aimed at establishing a secure and resilient EV charging ecosystem. By comprehensively addressing the security challenges surrounding EV charging infrastructure, the study aims to contribute to the advancement of effective measures and strategies to safeguard the future of mobility in an increasingly electrified world.

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Tang, Wu Wu, Yu Ming Wu, and Jian Qin. "Comparative Study on Electric Vehicle Charging Standards at Home and Abroad." Advanced Materials Research 608-609 (December 2012): 1553–59. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.1553.

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Charging infrastructure is the fundamental conditions of electric vehicles(EV)’s application and dissemination, and advanced charging standards can guide and regulate the harmonious development of EV and infrastructure. In this paper, plenty of and latest EV charging standards were collected at home and abroad, which were compared in different classifications, then the standards differences were analyzed in term of relative merits to provide reference for the future development of EV charging standards in China.

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Gao, Hui, and Ying Jun Wu. "The Research Status and Development Trend of Electric Vehicle Power Supply Technology." Advanced Materials Research 1008-1009 (August 2014): 381–84. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.381.

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Electric vehicle (EV) power supply infrastructure construction and the related technologies are the prerequisite and core of the EV industry development respectively. This paper introduces the technology research and application situation of charging, battery swapping and smart charging/battery swap service network operation management in China, and meanwhile introduces the EV energy supply infrastructure construction and some typical pilot projects in China. And the EV energy supply technologies development trend is discussed at last.

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ÇAKMAK, Recep, and Abdullah TURAN. "Electric Vehicle Charging Station Infrastructure on the Basis of Provinces in Turkey: Analysis through the Metrics." Karadeniz Fen Bilimleri Dergisi 12, no. 1 (June 15, 2022): 246–68. http://dx.doi.org/10.31466/kfbd.1029677.

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The fact that fossil fuels are exhaustible and harmful to the environment, the goals of constituting a sustainable and cleaner environment, and developments in battery technologies have led to a tendency towards electric vehicles instead of the vehicles which have traditional internal combustion engines working with fossil fuels. Especially in the last five years, electric vehicle (EV) sales have increased, and this increase is expected to continue increasing in the coming years. The increasing number of electric vehicles leads to the need for EV charging stations. To make strategic plans, to guide investors and to provide the appropriate infrastructure for the electric vehicle customers, the infrastructure of electric vehicle charging stations in each region needs to be analyzed in detail. In this study, the quantitative situation of the electric vehicle charging station infrastructure on the basis of provinces in Turkey as of the end of 2020 is discussed and two new metrics which use to evaluate EV charging station infrastructure are proposed. In this context, the EV charging station status is presented with tables and infographic maps through the metrics in the literature used in the evaluation of the EV charging station infrastructure and the metrics proposed in this study. 81 provinces of Turkey are compared and presented in terms of EV charging station infrastructure through the metrics. It is foreseen that the obtained and presented findings in this study will be beneficial to electric vehicle customers, electric vehicle charging station investors, the government and other public institutions that could be developed the strategy to increase the proliferation of electric vehicles. In addition, this publication might be used as a reference study to track the pace of improvements in the EV charging station infrastructure and to compare the improvements with 2020.

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Boucetta, Mahdi, Niamat Ullah Ibne Hossain, Raed Jaradat, Charles Keating, Siham Tazzit, and Morteza Nagahi. "The Architecture Design of Electrical Vehicle Infrastructure Using Viable System Model Approach." Systems 9, no. 1 (March 9, 2021): 19. http://dx.doi.org/10.3390/systems9010019.

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Exponential technological-based growth in industrialization and urbanization, and the ease of mobility that modern motorization offers have significantly transformed social structures and living standards. As a result, electric vehicles (EVs) have gained widespread popularity as a mode of sustainable transport. The increasing demand for of electric vehicles (EVs) has reduced the some of the environmental issues and urban space requirements for parking and road usage. The current body of EV literature is replete with different optimization and empirical approaches pertaining to the design and analysis of the EV ecosystem; however, probing the EV ecosystem from a management perspective has not been analyzed. To address this gap, this paper develops a systems-based framework to offer rigorous design and analysis of the EV ecosystem, with a focus on charging station location problems. The study framework includes: (1) examination of the EV charging station location problem through the lens of a systems perspective; (2) a systems view of EV ecosystem structure; and (3) development of a reference model for EV charging stations by adopting the viable system model. The paper concludes with the methodological implications and utility of the reference model to offer managerial insights for practitioners and stakeholders.

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Christensen, Carsten, and John Salmon. "EV Adoption Influence on Air Quality and Associated Infrastructure Costs." World Electric Vehicle Journal 12, no. 4 (October 21, 2021): 207. http://dx.doi.org/10.3390/wevj12040207.

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Exploring the system-level interactions within the modern urban transportation system, factors such as human health, vehicle exhaust pollution, air quality, emerging personal transportation technologies, and local weather events, are increasingly expedient considering the growth of human population centers projected in the 21st century. Pollutants often accumulate to unhealthy concentrations during winter inversion events such as those that commonly occur in Utah’s Salt Lake valley and other mountainous regions. This work examines the degree to which replacing conventionally powered vehicles with electric vehicles (EV) could reduce the near-road accumulation of criteria pollutants under various degrees of inversion depth and wind speed. Vehicle emissions data are combined with inversion and wind factors to determine changes in the Air Quality Index, and a first-order estimate of the cost required to build an EV charging infrastructure to support a given EV adoption scenario is also derived. Results are presented in the form of multiple Pareto frontiers and a simplified cost–benefit formula that inform potential public and private EV charging infrastructure investments to drive the EV adoption that would result in optimal air quality improvements during average weather and winter inversion events.

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Kakkar, Riya, Rajesh Gupta, Smita Agrawal, Sudeep Tanwar, Ravi Sharma, Ahmed Alkhayyat, Bogdan-Constantin Neagu, and Maria Simona Raboaca. "A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure." Applied Sciences 12, no. 23 (November 26, 2022): 12096. http://dx.doi.org/10.3390/app122312096.

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The deployment of charging infrastructure is one of the main challenges that need to be tackled due to the increasing demand for electric vehicles (EVs). Moreover, EVs associated with different charging standards can face compatibility issues while charging via public or private infrastructure. Many solutions were surveyed by researchers on EVs, but they were not focused on addressing the issue of charging infrastructure standardization. Motivated by this, we present a comprehensive survey on standardizing EV charging infrastructure. We also present a taxonomy on various aspects such as charging levels, charging modes, charging standards, charging technologies (based on the different charging types such as conductive charging and wireless charging), and types of vehicle (i.e., 2-wheeler (2W), 3-wheeler (3W), and 4-wheeler (4W)). Furthermore, we target the benefits associated with community EV charging operated by the community charging service operator. Furthermore, we propose an architecture for standardized EV community charging infrastructure to provide adaptability for EVs with different charging standards. Finally, the research challenges and opportunities of the proposed survey have been discussed for efficient EV charging.

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Chowdhury, Amor, Saša Klampfer, Klemen Sredenšek, Sebastijan Seme, Miralem Hadžiselimović, and Bojan Štumberger. "Method for Planning, Optimizing, and Regulating EV Charging Infrastructure." Energies 15, no. 13 (June 28, 2022): 4756. http://dx.doi.org/10.3390/en15134756.

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The paper presents and solves the problems of modeling and designing the required EV charging service capacity for systems with a slow dynamic component. This includes possible bursts within a peak hour interval. A simulation tool with a newly implemented capacity planning method has been developed and implemented for these needs. The method can be used for different system simulations and simultaneously for systems with high, medium, and low service dynamics. The proposed method is based on a normal distribution, a primary mechanism that describes events within a daily interval (24 h) or a peak hour interval (rush hour). The goal of the presented approach, including the proposed method, is to increase the level and quality of the EV charging service system. The near-optimal solution with the presented method can be found manually by changing the service capacity parameter concerning the criterion function. Manual settings limit the number of rejected events, the time spent in the queue, and other service system performance parameters. In addition to manual search for near-optimal solutions, the method also provides automatic search by using the automation procedure of simulation runs and increasing/decreasing the service capacity parameter by a specifically calculated amount.

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Griffiths, S. "Power up EVs with blockchain [EV Infrastructure - supply chain]." Engineering & Technology 17, no. 9 (October 1, 2022): 28–29. http://dx.doi.org/10.1049/et.2022.0907.

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Park, Musik, Zhiyuan Wang, Lanyu Li, and Xiaonan Wang. "Multi-objective building energy system optimization considering EV infrastructure." Applied Energy 332 (February 2023): 120504. http://dx.doi.org/10.1016/j.apenergy.2022.120504.

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Zhang, Huai Ge. "Research on Developing Countermeasures of the EV Industry in China." Applied Mechanics and Materials 291-294 (February 2013): 878–81. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.878.

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Through the present situation research to the development of EV in China, analyzed of China's EV with a foreign related technology gap, EV development related facilities and EV marketing problems, combined with the development of our country market is actual, discussed EV technology innovation, EV infrastructure the construction and management and to improve the EV industry policy countermeasures.

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Dwyer, Scott, Claudine Moutou, Kriti Nagrath, Joseph Wyndham, Lawrence McIntosh, and Dean Chapman. "An Australian Perspective on Local Government Investment in Electric Vehicle Charging Infrastructure." Sustainability 13, no. 12 (June 9, 2021): 6590. http://dx.doi.org/10.3390/su13126590.

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Electric vehicle (EV) adoption is growing worldwide with increasing market pull from consumers and market push from manufacturers of vehicles and charging equipment, as well as others in the supply chain. Governments have begun developing policies to support EV uptake and local governments, in particular, are examining what role they should play. In Australia, a large country with low population density, EV uptake has been slower in comparison to other similar economies. This paper discusses the status of EV charging infrastructure deployment in Australia with regards to local governments, by considering the extent to which they are relied upon for the deployment of such technology and what motivates them to act. It also covers the work undertaken by the authors with one local government in developing an EV charging infrastructure business model that will help the local community adopt and benefit from EVs. An applied use of the business canvas methodology adapted to suit local government interests is presented to assess the risks and benefits that different business models offer. The paper offers insights into the strategic and pragmatic responsibilities local governments balance in seeking to expand the EV charging infrastructure in their jurisdiction.

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Jaslin, S. K., M. A. U. S. Navaratne, and J. B. Ekanayake. "Agent-based modelling of electric vehicle behaviour in a university environment." Ceylon Journal of Science 52, no. 3 (September 1, 2023): 339–50. http://dx.doi.org/10.4038/cjs.v52i3.8089.

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As the number of electric vehicles (EVs) increases, the strategic planning of charging infrastructure becomes a crucial matter. Vehicle parking time takes the longest at home and work. The residential is responsible for 75% of EV charging time, while the workplace is for 14%. The combination of EVs with intermittent energy sources has attracted considerable attention in recent years. It has several advantages, including significantly greening the entire EV usage cycle and attaining financial viability by lowering the direct peak demand on the grid. This study has described the agent-based infrastructure of the EV charging station model on university premises. It lets us obtain the best possible energy supply from solar PV, external batteries, and grid agents. Three charging scenarios (uncontrolled, vehicle-to-grid (V2G), and grid-to-vehicle (G2V)) are constructed and simulated with varying percentages of EV resemblance. Slow charging is included in the G2V scenario to improve the PV benefits in the EV charging model. The simulation result shows that slow charging in the workplace infrastructure increases the PV benefits of EV charging while reducing grid dependency.

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Sen, KB, and Rajkumar G. "Solar Powered Charging Station for Electric Vehicle." Journal of Electrical Engineering and Automation 5, no. 2 (June 2023): 238–51. http://dx.doi.org/10.36548/jeea.2023.2.007.

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As the demand for sustainable transportation continues to grow, the integration of renewable energy sources into Electric Vehicle (EV) charging infrastructure has become a vital focus. The concept and benefits of a solar-powered EV charging station proposed in this research, combines solar Photovoltaic (PV) technology with efficient charging infrastructure. The solar-powered EV charging station serves as an innovative solution that harnesses clean and renewable energy from the sun to charge electric vehicles. By leveraging solar PV panels, this charging station reduces reliance on traditional grid electricity, decreases carbon emissions, and promotes sustainable mobility. Furthermore, this study discusses the potential challenges and considerations involved in implementing a solar-powered EV charging station, including site selection, system sizing, grid interconnection, and maintenance requirements. It emphasizes the need for collaboration between renewable energy experts, charging infrastructure providers, and relevant stakeholders to address these challenges and drive the adoption of solar-powered EV charging stations. In conclusion, the abstract presents the solar-powered EV charging station as a sustainable and forward-thinking solution for promoting cleaner transportation. By leveraging solar energy, this charging infrastructure contributes to the reduction of greenhouse gas emissions, fosters energy resilience, and paves the way for a greener future in the realm of electric mobility. It is observed that the proposed system is more efficient than the existing system in terms of fast operation.

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Yoon, Guwon, Seunghwan Kim, Haneul Shin, Keonhee Cho, Hyeonwoo Jang, Tacklim Lee, Myeong-in Choi, et al. "Carbon-Neutral ESG Method Based on PV Energy Generation Prediction Model in Buildings for EV Charging Platform." Buildings 13, no. 8 (August 18, 2023): 2098. http://dx.doi.org/10.3390/buildings13082098.

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Energy prediction models and platforms are being developed to achieve carbon-neutral ESG, transition buildings to renewable energy, and supply sustainable energy to EV charging infrastructure. Despite numerous studies on machine learning (ML)-based prediction models for photovoltaic (PV) energy, integrating models with carbon emission analysis and an electric vehicle (EV) charging platform remains challenging. To overcome this, we propose a building-specific long short-term memory (LSTM) prediction model for PV energy supply. This model simulates the integration of EV charging platforms and offer solutions for carbon reduction. Integrating a PV energy prediction model within buildings and EV charging platforms using ICT is crucial to achieve renewable energy transition and carbon neutrality. The ML model uses data from various perspectives to derive operational strategies for energy supply to the grid. Additionally, simulations explore the integration of PV-EV charging infrastructure, EV charging control based on energy, and mechanisms for sharing energy, promoting eco-friendly charging. By comparing carbon emissions from fossil-fuel-based sources with PV energy sources, we analyze the reduction in carbon emission effects, providing a comprehensive understanding of carbon reduction and energy transition through energy prediction. In the future, we aim to secure economic viability in the building energy infrastructure market and establish a carbon-neutral city by providing a stable energy supply to buildings and EV charging infrastructure. Through ongoing research on specialized models tailored to the unique characteristics of energy domains within buildings, we aim to contribute to the resolution of inter-regional energy supply challenges and the achievement of carbon reduction.

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Baik, Se, Young Jin, and Yong Yoon. "Determining Equipment Capacity of Electric Vehicle Charging Station Operator for Profit Maximization." Energies 11, no. 9 (September 1, 2018): 2301. http://dx.doi.org/10.3390/en11092301.

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Related to global efforts to reduce greenhouse gases, numerous electric vehicles (EVs) are expected to be integrated to the power grid. However, the introduction of EVs, particularly in Korea, is still marginal due to the lack of EV charging infrastructure, even though various supportive policies exist. To address this shortage of EV charging stations, the EV charging business needs to be profitable. As with any business, the profitability of the EV charging business is significantly affected by the initial capital investment related to EV chargers and auxiliary equipment such as power conditioning system (PCS), battery energy storage system (BESS), and on-site photovoltaic (PV) generation system. Thus, we propose a formulation to determine the number of EV chargers and the capacity of auxiliary equipment with the objective of a charging station operator (CSO) maximizing profit under regulatory, economic, and physical constraints. The effectiveness of the proposed method is verified with simulations considering various EV charging patterns. The study results will help improve the EV charging infrastructure by encouraging individuals and companies to participate in EV charging business.

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Chavhan, Suresh, Subhi R. M. Zeebaree, Ahmed Alkhayyat, and Sachin Kumar. "Design of Space Efficient Electric Vehicle Charging Infrastructure Integration Impact on Power Grid Network." Mathematics 10, no. 19 (September 22, 2022): 3450. http://dx.doi.org/10.3390/math10193450.

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With an ever-increasing number of electric vehicles (EVs) on the roads, there is a high demand for EV charging infrastructure. The present charging infrastructure in the market requires a lot of space and sometimes leads to traffic congestion, increasing the risk of accidents and obstruction of emergency vehicles. As the current infrastructure requires ample space, the cost of setting up this charging infrastructure becomes very high in metropolitan cities. In addition, there are a lot of adverse effects on the power grid due to the integration of EVs. This paper discusses a space-efficient charging infrastructure and multi-agent system-based power grid balance to overcome these issues. The proposed multi-level EV charging station can save a lot of space and reduce traffic congestion as more vehicles can be accommodated in the space. Depending on the size, capacity, and type of multi-level vehicle charging system, it can serve as a reliable charging solution at sites with medium and high daily footfall. We integrated the EV charging station with IEEE 33 bus test system and analyzed the grid and charging stations. The proposed scheme is exhaustively tested by simulation in a discrete-time event simulator in MATLAB and analyzed with varying EV arrival rates, time periods, etc.

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Taqvi, Syed Taha, Ali Almansoori, Azadeh Maroufmashat, and Ali Elkamel. "Utilizing Rooftop Renewable Energy Potential for Electric Vehicle Charging Infrastructure Using Multi-Energy Hub Approach." Energies 15, no. 24 (December 16, 2022): 9572. http://dx.doi.org/10.3390/en15249572.

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Electric vehicles (EV) have the potential to significantly reduce carbon emissions. Yet, the current electric vehicle charging infrastructure utilizes electricity generated from non-renewable sources. In this study, the rooftop area of structures is analyzed to assess electricity that can be generated through solar- and wind-based technologies. Consequently, planning an electric vehicle charging infrastructure that is powered through ‘clean’ energy sources is presented. We developed an optimal modeling framework for the consideration of Renewable Energy Technologies (RET) along with EV infrastructure. After examining the level of technology, a MATLAB image segmentation technique was used to assess the available rooftop area. In this study, two competitive objectives including the economic cost of the system and CO2 emissions are considered. Three scenarios are examined to assess the potential of RET to meet the EV demand along with the Abu Dhabi city one while considering the life-cycle emission of RET and EV systems. When meeting only EV demand through Renewable Energy Technologies (RET), about 187 ktonnes CO2 was reduced annually. On the other hand, the best economic option was still to utilize grid-connected electricity, yielding about 2.24 Mt CO2 annually. In the scenario of meeting both 10% EV demand and all Abu Dhabi city electricity demand using RE, wind-based technology is only able to meet around 3%. Analysis carried out by studying EV penetration demonstrated the preference of using level 2 AC home chargers compared to other ones. When the EV penetration exceeds 25%, preference was observed for level 2 (AC public 3ϕ) chargers.

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Yang, Shuqiang, Wenyi Fan, Yang Zhao, and Ziheng Zhao. "Research on load modelling of new infrastructure of power system-a case study of electric vehicle." E3S Web of Conferences 233 (2021): 01022. http://dx.doi.org/10.1051/e3sconf/202123301022.

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The continuous rapid development of new infrastructure load represented by electric vehicles (EVs) has brought new opportunities and challenges to the power system, as well as new propositions for traditional power system load modelling. It is of great practical significance to study the planning and operation of power systems considering EVs and other new infrastructure loads. Based on the analysis of the real historical data of EVs, this paper proposes an EV load modelling method based on the charging power scenario model. Based on the key variables of EV charging, the proposed model considers the joint distribution model of the uncertainty and correlation of the key variables of EV charging. Power scenarios are aggregated to obtain the EV load curve. Finally, the actual EV charging power data is used to verify the effectiveness of the proposed method.

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Statharas, Stergios, Yannis Moysoglou, Pelopidas Siskos, and Pantelis Capros. "Simulating the Evolution of Business Models for Electricity Recharging Infrastructure Development by 2030: A Case Study for Greece." Energies 14, no. 9 (April 21, 2021): 2345. http://dx.doi.org/10.3390/en14092345.

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It is widely accepted that the market uptake of electric vehicles is essential for the decarbonisation of transport. However, scaling up the roll out of electric vehicles (EV) is challenging considering the lack of charging infrastructure. The latter is, currently, developing in an uneven way across the EU countries. A charging infrastructure with wide coverage addresses range limitations but requires high investment with uncertain returns during the early years of deployment. The aim of this paper is to assess how different policy options affect EV penetration and the involvement of private sector in infrastructure deployment. We propose a mathematical programming model of the decision problem and the interaction between the actors of EV charging ecosystem and apply it to the case of Greece from the time period until 2030. Greece represents a typical example of a country with ambitious targets for EV penetration by 2030 (10% of the total stock) with limited effort made until now. The results indicate that it is challenging to engage private investors in the early years, even using subsidies; thus, publicly financed infrastructure deployment is important for the first years. In the mid-term, subsidization on the costs of charging points is necessary to positively influence the uptake of private investments. These are mainly attracted from 2025 onwards, after a critical mass of EVs and infrastructure has been deployed.

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Visaria, Anant Atul, Anders Fjendbo Jensen, Mikkel Thorhauge, and Stefan Eriksen Mabit. "User preferences for EV charging, pricing schemes, and charging infrastructure." Transportation Research Part A: Policy and Practice 165 (November 2022): 120–43. http://dx.doi.org/10.1016/j.tra.2022.08.013.

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Kim, Kyunghyun, and Yunmo Goo. "Optimizing Fast EV Charging Infrastructure Location with Traffic flow Data." Innovation studies 15, no. 4 (November 30, 2020): 61–91. http://dx.doi.org/10.46251/innos.2020.11.15.4.61.

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Jang, Chan-Guk, and Okyeon Yi. "Blockchain Network Configuration for Smart Contract in EV Charging Infrastructure." Journal of Korean Institute of Communications and Information Sciences 44, no. 8 (August 31, 2019): 1597–604. http://dx.doi.org/10.7840/kics.2019.44.8.1597.

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Lucas, Alexandre, Ricardo Barranco, and Nazir Refa. "EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions." Energies 12, no. 2 (January 16, 2019): 269. http://dx.doi.org/10.3390/en12020269.

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The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.

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Nagi, Farrukh, Aidil Azwin, Navaamsini Boopalan, Agileswari K. Ramasamy, Marayati Marsadek, and Syed Khaleel Ahmed. "Grid reactive voltage regulation and cost optimization for electric vehicle penetration in power network." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 741. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp741-754.

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Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.

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Taware, Yogesh, Nitesh Kale, Neha Jadhav, Uttam Chauhan, and Prof R. M. Shau. "EV Charging Station with Coin Based Payment System with Solar Power." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2899–903. http://dx.doi.org/10.22214/ijraset.2022.42980.

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Abstract: The transportation sector of the world is in the transformation stage, shifting from conventional fossil fuel-powered vehicles to zero or ultra-low tailpipe emission vehicles. To support this transformation, a proper charging station (CS) infrastructure in combination with information technology, smart distributed energy generating units, and favorable government policies are required. The motive of this Project is to address the key aspects to be taken care of while planning for the charging station infrastructure for electric vehicles. The Project also provides major indignation and developments in planning and technological aspects that are going on for the enhancement of the design and efficient management of charging station infrastructure. The Project addresses the present scenario of India related to electric vehicle charging station developments. The Project specially provides a critical review on the research and developments in the charging station infrastructure, the problems associated with it, and the efforts that are going on for its standardization to help the researchers address the problems Keywords: electric vehicle; charging station; EV charging Station; smart charging; charging infrastructure etc

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Aluisio, Benedetto, Maria Dicorato, Imma Ferrini, Giuseppe Forte, Roberto Sbrizzai, and Michele Trovato. "Optimal Sizing Procedure for Electric Vehicle Supply Infrastructure Based on DC Microgrid with Station Commitment." Energies 12, no. 10 (May 18, 2019): 1901. http://dx.doi.org/10.3390/en12101901.

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The diffusion of electric vehicles (EVs) can be sustained by the presence of integrated solutions offering parking and clean power supply. The recourse to DC systems allows better integration of EV bidirectional energy exchange, photovoltaic panels, and energy storage. In this paper, a methodology for optimal techno-economic sizing of a DC-microgrid for covering EV mobility needs is carried out. It is based on the definition of different scenarios of operation, according to typical EV usage outlooks and environmental conditions. In each scenario, optimal operation is carried out by means of a specific approach for EV commitment on different stations. The sizing procedure is able to handle the modular structure of microgrid devices. The proposed approach is applied to a case study of an envisaged EV service fleet for the Bari port authority.

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Singh, Praveen Prakash, Fushuan Wen, Ivo Palu, Sulabh Sachan, and Sanchari Deb. "Electric Vehicles Charging Infrastructure Demand and Deployment: Challenges and Solutions." Energies 16, no. 1 (December 20, 2022): 7. http://dx.doi.org/10.3390/en16010007.

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Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper.

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Yu, Jiali, Peng Yang, Kai Zhang, Faping Wang, and Lixin Miao. "Evaluating the Effect of Policies and the Development of Charging Infrastructure on Electric Vehicle Diffusion in China." Sustainability 10, no. 10 (September 23, 2018): 3394. http://dx.doi.org/10.3390/su10103394.

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China has been one of the most aggressive countries in electric vehicle (EV) promotion. However, private EV sales fail to achieve the government’s target. In particular, cutting purchase subsidies poses great uncertainty in relation to EV diffusion. In this research, a system dynamics model aims to investigate the influence of government policies, infrastructure development plans, the duration of policies, and the phase out strategy of policies. Parameters relating to consumers’ preferences are drawn from a questionnaire survey, which is conducted in Shenzhen, the pioneer city in China’s EV promotion. The result of a scenario analysis shows that purchase subsidies, purchase restrictions and driving restrictions are the most effective policies for EV promotion. Driving restrictions are more effective but less easy to enforce than purchase restrictions. The number and location of charging piles are much more important than large charging stations. Moreover, EV diffusion can be self-sufficient after current policies have been maintained for 11 years. We find that the gradual removal of subsidies will cause a four-year delay in EV sales entering rapid growth in Shenzhen. However, cutting subsidies in cities without purchase restrictions will cause the failure of EV promotion.

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Mani, S., R. Raguraj, R. Harikaran, S. Hariramselvakanth, and K. S. Gowthaman. "Development of Electric Vehicle Charging Infrastructure Based on Population." International Journal for Modern Trends in Science and Technology 6, no. 6 (May 30, 2020): 14–16. http://dx.doi.org/10.46501/ijmtst060604.

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This research investigates electric vehicle(EV) charging behavior and aims to find the best method for its prediction in order to optimize the EV charging station(CS). This paper discusses several commonly used machine learning algorithm or k-Nearest Neighbor(k-NN) to predict charging station based on population data records. According to the objective of the charging station planning, use the concept of group to do clustering evolution search. Hence the results of k-NN algorithm achieved through MATLAB software. Based on the population, the initial time location of the charging station will be randomly considered in Manapparai, Lalgudi, Vaiyampatti, Thiruverumbur in Trichy based on population.

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Pop, Horatiu, Alin Grama, Alexandra Fodor, and Corneliu Rusu. "Infrastructure Development for Electric Vehicle Charging Stations in Cluj-Napoca Municipality—A Case Study." Energies 16, no. 8 (April 19, 2023): 3552. http://dx.doi.org/10.3390/en16083552.

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This paper presents the status regarding electric vehicles (EVs) and public charging stations managed by Cluj-Napoca City Hall. The types of charging stations and the quantity of the used energy were stated. The behavior of EV owners in charging their vehicles was also studied. The presented work proposes an extension of the EV charging infrastructure to support current and future EV owners. Simulations were carried out in Simulink to create an overview of the existing infrastructure. The simulation results showed the amount of greenhouse gas (CO2 emissions) reduced by using EVs. An estimated number of kilometers traveled with EVs was calculated by tracking the loads in all public charging stations.

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Valdmanis, Gunars, and Gatis Bazbauers. "Relation between Electric Vehicles and Operation Performance of Power Grid." Environmental and Climate Technologies 25, no. 1 (January 1, 2021): 1142–51. http://dx.doi.org/10.2478/rtuect-2021-0086.

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Abstract Rapidly increasing number of electric vehicles (EV) is expected in the future. It is important to understand the consequences of this process for operation of power grids. The goal of this study was to determine an impact of increasing number of EV on the economic performance of electricity distribution system, including the impact on users of power distribution infrastructure. Factors, such as expected network load changes and required investment in the networks as well as possible changes in power distribution tariff were considered. Analysis of Latvia’s power distribution system shows that the installed capacity significantly exceeds the load. It means that connection of EV charging infrastructure to the system and resulting increase of the system’s load may not require additional investments and even could bring a positive economic effect due to better utilization of the infrastructure.

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Liu, Jiaqi, Chuanneng Zhang, Xing Tang, Jiaqi Yang, and Haobo Guo. "Layout and optimization of EV charging infrastructure based on service scope constraints: A case study of Tianjin." E3S Web of Conferences 372 (2023): 01017. http://dx.doi.org/10.1051/e3sconf/202337201017.

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The development of electric vehicles (EV) is constrained by factors such as the spatial distribution and planning volume of charging infrastructure. In order to determine the planning volume of charging piles in charging stations and the spatial layout of charging infrastructure service area within the service radius of Tianjin, this paper firstly determines the service area demand based on the service radius, combined with the demand prediction function and the EV ownership, and secondly forms the charging infrastructure service area with the central place theory large spatial layout, and finally determine the planning volume of charging infrastructure through queuing service system. The research results can provide a new theoretical basis for the layout planning of charging infrastructure, and finally put forward the relevant factors and development trends for the optimal layout of charging facilities.

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Poliakova, O. M., T. I. Lipska, and O. M. Kuchinska. "Public-private partnership as an element of the transport infrastructure development mechanism in Ukraine." Economic Bulletin of the National Mining University 70 (2020): 44–52. http://dx.doi.org/10.33271/ev/70.044.

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Chinda, Thanwadee. "Long-term trend of electric vehicle sales in Thailand." Engineering Management in Production and Services 14, no. 1 (March 1, 2022): 13–25. http://dx.doi.org/10.2478/emj-2022-0002.

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Abstract This research study aims at examining the long-term trend of EV sales in Thailand, utilising the system dynamics (SD) modelling approach. This approach is commonly used to model complex systems with causal relationships among key factors within the system. The developed SD model consists of five key factors affecting electric vehicle (EV) sales, namely, the environment, economy, charging infrastructure, government support, and battery maintenance. The simulation results show the increase in EV sales by ten times in the next 20 years with implementation plans related to the five key factors. The government support factor is the most important in enhancing EV sales in the short term. Several government support plans should be initiated to attract more EV consumers, such as subsidies and tax reductions. The environment and charging infrastructure factors are crucial to increasing EV sales in the long term. The enforcement of the CO2 tax and the provision of charging stations all around the country should be established to achieve a sustainable EV market in the long term. This research study contributes to the Thai government and automotive industry to better understand the complex relationships among key factors affecting EV sales. The related sectors may use the study results to plan for EV campaigns to promote the use of EVs and achieve a sustainable EV market.

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Gao, Shuang, Jianzhong Wu, and Bin Xu. "Controllability Evaluation of EV Charging Infrastructure Transformed from Gas Stations in Distribution Networks with Renewables." Energies 12, no. 8 (April 25, 2019): 1577. http://dx.doi.org/10.3390/en12081577.

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A considerable market share of electric vehicles (EVs) is expected in the near future, which leads to a transformation from gas stations to EV charging infrastructure for automobiles. EV charging stations will be integrated with the power grid to replace the fuel consumption at the gas stations for the same mobile needs. In order to evaluate the impact on distribution networks and the controllability of the charging load, the temporal and spatial distribution of the charging power is calculated by establishing mapping the relation between gas stations and charging facilities. Firstly, the arrival and parking period is quantified by applying queuing theory and defining membership function between EVs to parking lots. Secondly, the operational model of charging stations connected to the power distribution network is formulated, and the control variables and their boundaries are identified. Thirdly, an optimal control algorithm is proposed, which combines the configuration of charging stations and charging power regulation during the parking period of each individual EV. A two-stage hybrid optimization algorithm is developed to solve the reliability constrained optimal dispatch problem for EVs, with an EV aggregator installed at each charging station. Simulation results validate the proposed method in evaluating the controllability of EV charging infrastructure and the synergy effects between EV and renewable integration.

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Journal articles on the topic 'EV INFRASTRUCTURE'

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