Journal articles on the topic 'Smart charging system'
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Kim, MyeongHyun, KiSung Park, SungJin Yu, JoonYoung Lee, YoungHo Park, Sang-Woo Lee, and BoHeung Chung. "A Secure Charging System for Electric Vehicles Based on Blockchain." Sensors 19, no. 13 (July 9, 2019): 3028. http://dx.doi.org/10.3390/s19133028.
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Smart grids incorporating internet-of-things are emerging solutions to provide a reliable, sustainable and efficient electricity supply, and electric vehicle drivers can access efficient charging services in the smart grid. However, traditional electric vehicle charging systems are vulnerable to distributed denial of service and privileged insider attacks when the central charging server is attacked. The blockchain-based charging systems have been proposed to resolve these problems. In 2018, Huang et al. proposed the electric vehicle charging system using lightning network and smart contract. However, their system has an inefficient charging mechanism and does not guarantee security of key. We propose a secure charging system for electric vehicles based on blockchain to resolve these security flaws. Our charging system ensures the security of key, secure mutual authentication, anonymity, and perfect forward secrecy, and also provides efficient charging. We demonstrate that our proposed system provides secure mutual authentication using Burrows–Abadi–Needham logic and prevents replay and man-in-the-middle attacks using automated validation of internet security protocols and applications simulation tool. Furthermore, we compare computation and communication costs with previous schemes. Therefore, the proposed charging system efficiently applies to practical charging systems for electric vehicles.
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Medved, Dusan, Lubomir Bena, Maksym Oliinyk, Jaroslav Dzmura, Damian Mazur, and David Martinko. "Assessing the Effects of Smart Parking Infrastructure on the Electrical Power System." Energies 16, no. 14 (July 13, 2023): 5343. http://dx.doi.org/10.3390/en16145343.
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The forthcoming surge in electric vehicle (EV) adoption demands the comprehensive advancement of associated charging infrastructure. In this study, an exploration of EV charging’s impact on the power distribution system is conducted via the simulation of a parking lot equipped with six distinct types of EVs, each showcasing unique charging curves, charging power, and battery capacities. A charging profile is synthesized and compared with laboratory-obtained data to ascertain the implications on the grid. To further understand the effects of smart parking on the power distribution system, a mathematical algorithm was created and applied to a segment of an urban electrical grid that includes 70 private residences. Basic electrical parameters were computed using the node voltage method. Four scenarios were simulated: (1) the existing distribution system, (2) the current system plus smart parking, (3) the current system plus 50% of houses equipped with 3.5 kW photovoltaic installations, and (4) the current system plus photovoltaics and smart parking. This paper examines the core distribution system parameters, namely voltage and current, across these four scenarios, and the simulation results are extensively detailed herein.
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Voss, Mike F., Steven P. Haveman, and Gerrit Maarten Bonnema. "In-Company Smart Charging: Development of a Simulation Model to Facilitate a Smart EV Charging System." Energies 14, no. 20 (October 15, 2021): 6723. http://dx.doi.org/10.3390/en14206723.
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Current electric vehicle (EV) charging systems have limited smart functionality, and most research focuses on load-balancing the national or regional grid. In this article, we focus on supporting the early design of a smart charging system that can effectively and efficiently charge a company’s EV fleet, maximizing the use of self-generated Photo-Voltaic energy. The support takes place in the form of the Vehicle Charging Simulation (VeCS) model. System performance is determined by operational costs, CO2 emissions and employee satisfaction. Two impactful smart charging functions concern adaptive charging speeds and charging point management. Simulation algorithms for these functions are developed. The VeCS model is developed to simulate implementation of a smart charging system incorporating both charging infrastructure and local Photo-Voltaics input, using a company’s travel and energy data, prior to having the EVs in place. The model takes into account travel behaviour, energy input and energy consumption on a daily basis. The model shows the number of charged vehicles, whether incomplete charges occur, and energy flow during the day. The model also facilitates simulation of an entire year to determine overall cost and emission benefits. It also estimates charging costs and CO2 emissions that can be compared to the non-EV situation. With the VeCS model, the impact of various system design and implementation choices can be explored before EVs are used. Two system designs are proposed for the case company; a short-term version with current technology and a future version with various smart functionalities. Overall, the model can contribute to substantiated advice for a company regarding implementation of charging infrastructure.
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Chang, Chin-Wei, Patrick Riehl, and Jenshan Lin. "Alignment-Free Wireless Charging of Smart Garments with Embroidered Coils." Sensors 21, no. 21 (November 5, 2021): 7372. http://dx.doi.org/10.3390/s21217372.
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Wireless power transfer (WPT) technologies have been adopted by many products. The capability of charging multiple devices and the design flexibility of charging coils make WPT a good solution for charging smart garments. The use of an embroidered receiver (RX) coil makes the smart garment more breathable and comfortable than using a flexible printed circuit board (FPCB). In order to charge smart garments as part of normal daily routines, two types of wireless-charging systems operating at 400 kHz have been designed. The one-to-one hanger system is desired to have a constant charging current despite misalignment so that users do not need to pay much attention when they hang the garment. For the one-to-multiple-drawer system, the power delivery ability must not change with multiple garments. Additionally, the system should be able to charge folded garments in most of the folding scenarios. This paper analyses the two WPT systems for charging smart garments and provides design approaches to meet the abovementioned goals. The wireless-charging hanger is able to charge a smart garment over a coupling variance kmaxkmin=2 with only 21% charging current variation. The wireless-charging drawer is able to charge a smart garment with at least 20 mA under most folding scenarios and three garments with stable power delivery ability.
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Huber, Julian, Elisabeth Schaule, Dominik Jung, and Christof Weinhardt. "Quo Vadis Smart Charging? A Literature Review and Expert Survey on Technical Potentials and User Acceptance of Smart Charging Systems." World Electric Vehicle Journal 10, no. 4 (November 28, 2019): 85. http://dx.doi.org/10.3390/wevj10040085.
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Uncontrolled charging of plug-in Battery Electric Vehicles (BEV) represents a challenge for the energy system. As a solution, recent studies propose smart charging to avoid grid congestion and to integrate renewable energy. While financial benefits for smart charging schemes are currently quite low, there are other objectives for smart charging. However, it is unclear for which objectives smart charging can be used most effectively and which arguments are most likely to convince end users of BEVs to use smart charging schemes. To fill this gap, we conducted a literature review of the premises and the objectives of smart charging and how they fit the end-user’s motivation to use such smart charging systems. To evaluate the results, we present findings of 16 domain experts who evaluated various statements on smart charging according to their technical correctness and their persuasiveness towards end users. The results show that experts consider those smart charging objectives as most persuasive towards end users which they consider technically correct. Moreover, cost savings and integration of renewable energies are rated highest on both scales. On the contrary, experts do not expect a positive impact of smart charging systems on battery life and rate it as not very convincing.
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Saheb, Ahmed M., and Bashar Sakeen Farhan. "Design and Measurement of a Modern Charging System Based on IoT." Instrumentation Mesure Métrologie 21, no. 1 (February 28, 2022): 27–34. http://dx.doi.org/10.18280/i2m.210105.
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This paper presents a modern charging system to improve the reliability of locating the closest and available free charging slot to charge low-power smart-devices. Also, the presented system is based on the cloud and the used network is based on the internet of things technology. The basic idea of the charging system is to provide a public charging place for all individuals who wish to charge their smart devices when it is close to running out. First and foremost, the charging system was designed and implemented to have multiple power sources in the event that one of the system's power sources failed. Secondly, the charging system provides a special smart mobile application that has also been designed and implemented, which allows the user to know the locations of the charging systems on a map and choose the nearest available system near the user. After selecting the nearest charging system, the user will be able to know whether or not there is an available charging port and the number of associated devices in each system. The study successfully built this system in practical life.
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Gong, Lingbing, Chunyan Xiao, Bin Cao, and Yuliang Zhou. "Adaptive Smart Control Method for Electric Vehicle Wireless Charging System." Energies 11, no. 10 (October 9, 2018): 2685. http://dx.doi.org/10.3390/en11102685.
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In order to shorten the wireless charging time of electric vehicles (EVs) and achieve stable charging, an adaptive smart control method for EV wireless charging is proposed in the paper. The method dynamically tracks the rechargeable battery state during the whole charging process, realizes multi-stage charging of constant current (CC) or constant voltage (CV) by switching two kinds of compensation networks of bilateral L3C and L3C-C, and regulates the charging voltage and current to make it as close as possible to the battery charging characteristic curve. This method can be implemented because the voltage source connected to the coupler and the compensation networks of bilateral L3C and L3C-C have the CC and CV source characteristics, respectively. On the basis of the established adaptive smart control system of EV wireless charging, the experiments of wireless data transmission and adaptive smart charging were conducted. The results showed that the designed control system had a response time of less than 200 ms and strong anti-interference ability and it shortened the charging time by about 16% compared with the time using traditional charging methods, thereby achieving a fast, stable, safe, and complete wireless charging process.
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Chimakurthi, Venkata Naga Satya Surendra. "An Optimal Cloud Based Electric Vehicle Charging System." Asia Pacific Journal of Energy and Environment 8, no. 2 (July 30, 2021): 39–48. http://dx.doi.org/10.18034/apjee.v8i2.604.
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With the evolution of the internet-of-things and the emergence of cloud computing, the charging dynamics of vehicles have changed. This work discusses cloud-based monitoring and management used in charging electric vehicles and their impact on the smart charging system. Charging management plays a key role in assessing the charging infrastructure because of the automakers and charging service providers. As the market evolves, this system looks at the present public and private sectors that provide charging stations and contrasts them with modern cloud-based charging in electric vehicles. The cloud module developed contains layers, with the top layer of the robust calculating ability, which is globally optimized using machine learning technology. The bottom layer counters the real-time issues with the controller. The system also analyzes the current demands in the market and forms strategies to maximize profits through smart charging systems.
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Mokhtar, Mohamed, Mostafa F. Shaaban, Hatem Zeineldin, and Ehab F. El-Saadany. "A Customer-Centered Smart Charging Strategy Considering Virtual Charging System." IEEE Access 9 (2021): 117993–8004. http://dx.doi.org/10.1109/access.2021.3107348.
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D. Suja, Darling. "Optimizing battery charging efficiency and longevity through smart charging techniques in microcontroller-based systems." i-manager’s Journal on Electronics Engineering 13, no. 1 (2022): 13. http://dx.doi.org/10.26634/jele.13.1.19347.
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This paper explores the topic of optimizing battery charging efficiency and longevity through smart charging techniques in microcontroller-based systems. With the increasing demand for portable and wireless devices, efficient battery charging and longer battery life have become essential. The use of microcontroller-based systems allows for more precise and effective control of battery charging, resulting in improved efficiency and longevity. The current research is on intelligent battery charging systems and battery management systems and discusses various smart charging techniques that can be employed in microcontroller-based systems, including fuzzy logic control, model predictive control, and hybrid algorithms. The paper also highlights the importance of battery management and the need for a comprehensive battery management system to optimize charging efficiency and battery longevity. Finally, the paper presents several case studies and experimental results to demonstrate the effectiveness of these smart charging techniques in improving battery charging efficiency and longevity in microcontroller-based systems.
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Verbrugge, Boud, Haaris Rasool, Mohammed Mahedi Hasan, Sajib Chakraborty, Thomas Geury, Mohamed El Baghdadi, and Omar Hegazy. "Reliability Assessment of SiC-Based Depot Charging Infrastructure with Smart and Bidirectional (V2X) Charging Strategies for Electric Buses." Energies 16, no. 1 (December 23, 2022): 153. http://dx.doi.org/10.3390/en16010153.
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Nowadays, the implementation of smart charging concepts and management strategies with vehicle-to-everything (V2X) functionalities, is required to address the increasing number of battery electric buses (BEBs) in cities. However, the introduction of these new functionalities to the charging systems might affect the lifetime of the charging infrastructure. This has not been investigated yet, although it is an important aspect for the BEB operators. Therefore, this paper performs a detailed reliability assessment to study the impact of smart and bidirectional (V2X) charging on the lifetime of SiC-based high-power off-board charging infrastructure used for BEBs in a depot for overnight charging. In this paper, four different charging current profiles, generated by a smart charging algorithm, are considered. In addition, an electro-thermal model of the charging system is developed to accurately estimate the junction temperature of the switching devices when subjected to the applied charging current profiles. The thermal stress is converted into a number of cycles to failures and accumulated damage by means of a rainflow cycle counting algorithm, a lifetime model and Miner’s damage rule. Finally, a Monte Carlo analysis and a Weibull probability function fit are applied to obtain the system reliability. The results have demonstrated that smart charging strategies can improve the lifetime of the charging system by at least a factor of three compared to conventional uncoordinated charging. Moreover, an uncoordinated charging strategy fails to fulfill the lifetime requirements in the parts per million range, while bidirectional charging could even further enhance the lifetime with a factor of one and a half.
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R, Gowtham, Guganesh P, Ilanchelvan Harikumar V, Ramachandran K, Udhaya Kumar C, and Priyadharshini A. "IoT-Based Smart Booking of Electric Vehicle Charging Station." Journal of ISMAC 5, no. 2 (June 2023): 100–112. http://dx.doi.org/10.36548/jismac.2023.2.002.
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The Immense growth in the implementation of electric vehicles (EVs), has established a path in the advancements of charging infrastructure. Interlinking and connecting the charging stations to a common server is essential for future EV adoption, and this can reduce the user's anxiety. The existing charging stations have not implemented charging slot availability and time scheduling management. The objective of this research is to enhance the current electric vehicle (EV) charging infrastructure by gathering and sharing real-time information about the availability of reserve slots at charging stations. This information is made available to users through an interactive application created with Android Studio, allowing them to locate the closest charging station for their EV. The application utilizes the State of Charge of the EV to assign available charging slots. The proposed real-time predictive charging server infrastructure avoids latency, effectively manages time scheduling, and avoids draining electric vehicle batteries. This proposed system uses the real-time traffic database to estimate the minimum distance path to reach the charging station. The system utilizes soil moisture sensors, Potassium, sodium and phosphorus sensors, and amount of water needed for crops. The system is designed to be energy-efficient, cost-effective, and easy to use. It can be remotely controlled and monitored via a mobile application, allowing farmers to have real-time access to their irrigation systems. This system has the potential to improve crop yields, conserve water resources, and reduce labor costs associated with manual irrigation practices.
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Mitova, Stefania, and Rudy Kahsar. "Urban energy system impact analysis: integration of household solar panels and electric vehicles into smart cities via storage and smart charging." Renewable Energy and Environmental Sustainability 7 (2022): 25. http://dx.doi.org/10.1051/rees/2022013.
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Smart charging and battery storage can improve the integration of electric vehicles (EV's) and photovoltaic solar panels (PV's) into the residential buildings of a smart city. The impact of those two solutions can vary across households with an EV, PV, both, or no technologies. Therefore, it is unclear how smart charging and storage impact the energy, economic, and environmental benefits of each technology adoption group. To address this problem, an urban energy system dynamics model compares two smart charging scenarios that optimize PV energy consumption and carbon emissions as well as one scenario that optimizes storage. The results show that in general storage reduces carbon emissions and increases solar energy use more effectively than smart charging. Specifically, it reduces emissions at a rate of 17% and smart charging at 7%; it also increases PV self-consumption at a rate of 45% and smart charging at 28%. The main reason for this difference is that storage is able to shift a larger electricity load than smart charging without compromising user convenience. However, expenditures decline at a faster rate in the smart charging scenario (–91%) than the storage scenario (–52%), due to the ratio of Value of Solar to residential tariffs. Therefore, this article recommends storage as a solution to all technology adoption groups; furthermore, cities are encouraged to invest in energy storage solutions in the short term as well as smart devices in the long term, so that eventually smart charging could shift a larger share of the loads as well. The contribution of this study is that it compares several experimental groups across the energy, emission, and economic benefits derived from their respective clean energy technologies; it also provides specific guidelines for parties interested in optimizing the benefits of their technologies.
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Anandh, B. A., A. Shankar Ganesh, R. Sakthivel, D. MaheshKumar, and E. Prem Kumar. "Smart Battery Management System Using LiFePO4 Battery for Offline UPS." Journal of Physics: Conference Series 2040, no. 1 (October 1, 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2040/1/012007.
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Abstract Power integrated circuits (ICs) have developed solutions for charging systems and power management system for battery powered devices. The main purpose of a battery management system comprises handling the charging cycle to limit the charging time without focusing on the battery. It also monitors the present status of the battery and reports the fault conditions if any malfunction or error occurs. This system is based on a smart LiFePO4 battery for offline UPS, which is used to power up a home AC application with 230VAC/50Hz. The main reason to select the LiFePO4 battery life cycle, it is made up of non-toxic material and this battery is not overheated or catches fire in case of overcharging or overload. In this system, the Arduino UNO microcontroller is used and it takes care of the parameters like battery charging voltage, charging current, and high voltage cutoff at backup mode. It also monitors the battery voltage, battery current, low voltage cutoff, and overload protection at inverter mode.
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Richardson, et al., Chase. "Multiple Smart Phones Inductive Charging Station System." International Journal of Computing and Digital Systems 7, no. 6 (November 1, 2018): 347–54. http://dx.doi.org/10.12785/ijcds/070603.
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Wang, Yue, David Infield, and Simon Gill. "Smart charging for electric vehicles to minimise charging cost." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 231, no. 6 (January 23, 2017): 526–34. http://dx.doi.org/10.1177/0957650916688409.
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This paper assumes a smart grid framework where the driving patterns for electric vehicles are known, time variations in electricity prices are communicated to householders, and data on voltage variation throughout the distribution system are available. Based on this information, an aggregator with access to this data can be employed to minimise electric vehicles charging costs to the owner whilst maintaining acceptable distribution system voltages. In this study, electric vehicle charging is assumed to take place only in the home. A single-phase Low Voltage (LV) distribution network is investigated where the local electric vehicles penetration level is assumed to be 100%. Electric vehicle use patterns have been extracted from the UK Time of Use Survey data with a 10-min resolution and the domestic base load is generated from an existing public domain model. Apart from the so-called real time price signal, which is derived from the electricity system wholesale price, the cost of battery degradation is also considered in the optimal scheduling of electric vehicles charging. A simple and effective heuristic method is proposed to minimise the electric vehicles’ charging cost whilst satisfying the requirement of state of charge for the electric vehicles’ battery. A simulation in OpenDSS over a period of 24 h has been implemented, taking care of the network constraints for voltage level at the customer connection points. The optimisation results are compared with those obtained using dynamic optimal power flow.
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Sun, Lijuan, Menggang Chen, Yawei Shi, Lifeng Zheng, Songyang Li, Jun Li, and Huijuan Xu. "Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System." Energies 15, no. 24 (December 10, 2022): 9364. http://dx.doi.org/10.3390/en15249364.
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As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles.
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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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Fernandez, Daniel, Ann Sebastian, Patience Raby, Moneeb Genedy, Ethan C. Ahn, Mahmoud M. Reda Taha, Samer Dessouky, and Sara Ahmed. "Roadway Embedded Smart Illumination Charging System for Electric Vehicles." Energies 16, no. 2 (January 11, 2023): 835. http://dx.doi.org/10.3390/en16020835.
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Inspired by the fact that there is an immense amount of renewable energy sources available on the roadways, such as mechanical pressure, this study presents the development and implementation of an innovative charging technique for electric vehicles (EVs) by fully utilizing the existing roadways and state-of-the-art nanotechnology and power electronics. The developed Smart Illuminative Charging is a novel wireless charging system that uses LEDs powered by piezoelectric materials as the energy transmitter source and thin film solar panels placed at the bottom of the EVs as the receiver, which is then poised to deliver the harvested energy to the vehicle’s battery. The piezoelectric materials were tested for their mechanical-to-electrical energy conversion capabilities and the relatively large-area EH2N samples (2 cm × 2 cm) produced high output voltages of up to 52 mV upon mechanical pressure. Furthermore, a lab-scale prototype device was developed to testify the proposed mechanism of illuminative charging (i.e., “light” coupled pavement and vehicle as a wireless energy transfer medium).
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Galbis, Alberto Zambrano, Moisés Antón García, Ana Isabel Martínez García, Stylianos Karatzas, Athanasios Chassiakos, Vasiliki Lazari, and Olympia Ageli. "Smart Tool Development for Customized Charging Services to EV Users." World Electric Vehicle Journal 13, no. 8 (August 3, 2022): 145. http://dx.doi.org/10.3390/wevj13080145.
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E-mobility is a key element in the future energy systems. The capabilities of EVs are many and vary since they can provide valuable system flexibility services, including management of congestion in transmission grids. According to the literature, leaving the charging process uncontrolled could hinder some of the present challenges in the power system. The development of a suitable charging management system is required to address different stakeholders’ needs in the electro-mobility value chain. This paper focuses on the design of such a system, the TwinEV module, that offers high-value services to electric vehicles (EV) users. This module is based on a Smart Charging Tool (SCT), aiming to deliver a more user-central and cooperative approach to the EV charging processes. The methodology of the SCT tool, as well as the supportive optimization algorithm, are explained thoroughly. The architecture and the web applications of TwinEV module are analyzed. Finally, the deployment and testing results are presented.
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Mazhar, Tehseen, Rizwana Naz Asif, Muhammad Amir Malik, Muhammad Asgher Nadeem, Inayatul Haq, Muhammad Iqbal, Muhammad Kamran, and Shahzad Ashraf. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods." Sustainability 15, no. 3 (February 1, 2023): 2603. http://dx.doi.org/10.3390/su15032603.
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Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well.
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Zalnidzam, Wan Iqmal Faezy Wan, Hasmaini Mohamad, Nur Ashida Salim, Hazlie Mokhlis, and Zuhaila Mat Yasin. "Optimal Charging Schedule Coordination of Electric Vehicles in Smart Grid." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (July 1, 2018): 82. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp82-89.
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The increasing penetration of electric vehicle (EV) at distribution system is expected in the near future leading to rising demand for power consumption. Large scale uncoordinated charging demand of EVs will eventually threatens the safety operation of the distribution network. Therefore, a charging strategy is needed to reduce the impact of charging. This paper proposes an optimal centralized charging schedule coordination of EV to minimize active power losses while maintaining the voltage profile at the demand side. The performance of the schedule algorithm developed using particle swarm optimization (PSO) technique is evaluated at the IEEE-33 Bus radial distribution system in a set time frame of charging period. Coordinated and uncoordinated charging schedule is then compared in terms of active power losses and voltage profile at different level of EV penetration considering 24 hours of load demand profile. Results show that the proposed coordinated charging schedule is able to achieve minimum total active power losses compared to the uncoordinated charging.
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Khan, Prince Waqas, and Yung-Cheol Byun. "Blockchain-Based Peer-to-Peer Energy Trading and Charging Payment System for Electric Vehicles." Sustainability 13, no. 14 (July 16, 2021): 7962. http://dx.doi.org/10.3390/su13147962.
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The world is moving rapidly from carbon-producing vehicles to green transportation systems. Electric vehicles (EV) are a big step towards a friendly mode of transport. With the constant rise in the number of electric vehicles, we need a widespread and seamless charging infrastructure that supports seamless charging and billing. Some users generate electricity using solar panels and charge their electric vehicles. In contrast, some use charging stations, and they pay for vehicle charging. This raises the question of trust and transparency. There are many countries where laws are not strictly enforced to prevent fraud in payment systems. One of the preeminent problems presently existing with any of the trading systems is the lack of transparency. The service provider can overcharge the customer. Blockchain is a modern-day solution that mitigates trust and privacy issues. We have proposed a peer-to-peer energy trading and charging payment system for electric vehicles based on blockchain technology. Users who have excess electricity which they can sell to the charging stations through smart contracts. Electric vehicle users can pay the charging bills through electronic wallets. We have developed the electric vehicle’s automatic-payment system using the open-source platform Hyperledger fabric. The proposed system will reduce human interaction and increase trust, transparency, and privacy among EV participants. We have analyzed the resource utilization and also performed average transaction latency and throughput evaluation. This system can be helpful for the policymakers of smart cities.
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Park, Hwi-Geun, and Hyun-Sik Kim. "Mechanism Development and Position Control of Smart Buoy Robot." Journal of Ocean Engineering and Technology 35, no. 4 (August 31, 2021): 305–12. http://dx.doi.org/10.26748/ksoe.2021.043.
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There is a gradual increase in the need for energy charging in marine environments because of energy limitations experienced by electric ships and marine robots. Buoys are considered potential energy charging systems, but there are several challenges, which include the need to maintain a fixed position and avoid hazards, dock with ships and robots in order to charge them, be robust to actions by birds, ships, and robots. To solve these problems, this study proposes a smart buoy robot that has multiple thrusters, multiple docking and charging parts, a bird spike, a radar reflector, a light, a camera, and an anchor, and its mechanism is developed. To verify the performance of the smart buoy robot, the position control under disturbance due to wave currents and functional tests such as docking, charging, lighting, and anchoring are performed. Experimental results show that the smart buoy robot can operate under disturbances and is functionally effective. Therefore, the smart buoy robot is suitable as an energy charging system and has potential in realistic applications.
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El-Fedany, Ibrahim, Driss Kiouach, and Rachid Alaoui. "A smart management system of electric vehicles charging plans on the highway charging stations." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (August 1, 2021): 752. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp752-759.
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Electric vehicles (EVs) are seen as one of the principal pillars of smart transportation to relieve the airborne pollution induced by fossil CO2 emissions. However, the battery limit, especially where the journey is with a long-distance road remains the most formidable obstacle to the large-scale use of EVs. To overcome the issue of prolonged waiting charging time due to the large number of EVs may have a charging plan at the same charging station (CS) along the highway, we propose a communication system to manage the EVs charging demands. The architecture system contains a smart scheduling algorithm to minimize trip time including waiting time, previous reservations and energyare needed to reach the destination. Moreover, an automatic mechanism for updating reservation is integrated to adjust the EVs charging plans. The results of the evaluation under the Moroccan highway scenario connecting Rabat and Agadir show the effectiveness of our proposal system.<br /><div> </div>
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Ree, Jason Jihoon, and Kwangsoo Kim. "Smart Grid R&D Planning Based on Patent Analysis." Sustainability 11, no. 10 (May 22, 2019): 2907. http://dx.doi.org/10.3390/su11102907.
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A smart grid employs information and communications technology to improve the efficiency, reliability, economics, and sustainability of electricity production and distribution. The convergent and complex nature of a smart grid and the multifarious connection between its individual technology components, as well as competition between private companies, which will exert substantial influences on the future smart grid business, make a strategic approach necessary from the beginning of research and development (R&D) planning with collaborations among various research groups and from national, industry, company, and detailed technological levels. However, the strategic, technological, business environmental, and regulatory barriers between various stakeholders with collaborative or sometimes conflictive interests need to be clarified for a breakthrough in the smart grid field. A strategic R&D planning process was developed in this study to accomplish the complicated tasks, which comprises five steps: (i) background research of smart grid industry; (ii) selection of R&D target; (iii) societal, technological, economical, environmental, and political (STEEP) analysis to obtain a macro-level perspective and insight for achieving the selected R&D target; (iv) patent analysis to explore capabilities of the R&D target and to select the entry direction for smart grid industry; and, (v) nine windows and scenario planning analyses to develop a method and process in establishing a future strategic R&D plan. This R&D planning process was further applied to the case of a Korean company holding technological capabilities in the sustainable smart grid domain, as well as in the sustainable electric vehicle charging system, a global consumer market of smart grid. Four plausible scenarios were produced by varying key change agents for the results of this process, such as technology and growth rates, policies and government subsidies, and system standards of the smart grid charging system: Scenario 1, ‘The Stabilized Settlement of the Smart Grid Industry’; Scenario 2, ‘The Short-lived Blue Ocean of the Smart Grid Industry’; Scenario 3, ‘The Questionable Market of the Smart Grid’; and, Scenario 4, ‘The Stalemate of the Smart Grid Industry’. The R&D plan suggestions were arranged for each scenario and detailed ways to cope with dissonant situations were also implied for the company. In sum, in this case study, a future strategic R&D plan was suggested in regard to the electric vehicle charging technology business, which includes smart grid communication system, battery charging duration, service infrastructure, public charge station system, platform and module, wireless charging, data management system, and electric system solution. The strategic R&D planning process of this study can be applicable in various technologies and business fields, because of no inherent dependency on particular subject, like electric vehicle charging technology based on smart grid.
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Alanazi, Fayez, and Mamdouh Alenezi. "Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles." Applied Sciences 13, no. 15 (July 28, 2023): 8741. http://dx.doi.org/10.3390/app13158741.
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As the process of urbanization continues to accelerate, the demand for sustainable cities has become more critical than ever before. The incorporation of electric vehicles (EVs) is a key component in creating sustainable cities. However, the development of smart cities for EVs entails more than just the installation of charging stations. Software engineering plays a crucial role in realizing smart cities for electric vehicles. This paper examines the role of software engineering in the creation of smart cities for electric vehicles, the techniques utilized in electric vehicle charging infrastructure, the obstacles faced by software engineers, and the future of software engineering in sustainable cities. Specifically, the paper explores the significance of software engineering in integrating EVs into the transportation system, including the design of smart charging and energy management systems, and the establishment of intelligent transportation systems. Additionally, the paper offers case studies to demonstrate successful software engineering implementations for smart cities. Finally, the paper concludes with a discussion of the challenges that software engineers encounter in implementing intelligent transportation systems for EVs and provides future directions for software engineering in sustainable cities.
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Flocea, Radu, Andrei Hîncu, Andrei Robu, Stelian Senocico, Andrei Traciu, Baltariu Marian Remus, Maria Simona Răboacă, and Constantin Filote. "Electric Vehicle Smart Charging Reservation Algorithm." Sensors 22, no. 8 (April 7, 2022): 2834. http://dx.doi.org/10.3390/s22082834.
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The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impeding this process, ranging from insufficient charging infrastructure, battery capacity, and long queueing and charging times, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fuelled by the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that bypasses the limitations of the “reserve now” function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station’s reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations and the occupation of several stations without queues, assuring, thus, a proper distribution of the available energy resources, while increasing end-user satisfaction. Our solution is independent from the OCPP reservation method; therefore, the authentication and reservation processes performed by the proposed algorithm run only through the central system, authorizing only the creator of the reservation to start the charging transaction.
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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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A. Elkasrawy, Mohamed, Ahmed Hassan, Sameh O. Abdellatif, Gamal Abdel Shafy Ebrahim, and Hani A. Ghali. "Prototyping Design and Optimization of Smart Electric Vehicles/Stations System using ANN." International journal of electrical and computer engineering systems 13, no. 6 (September 1, 2022): 485–91. http://dx.doi.org/10.32985/ijeces.13.6.8.
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This paper demonstrates an experimental attempt to prototype electric vehicle charging station’s (EVCS) decision-making unit, using artificial neural network (ANN) algorithm. The algorithm acts to minimize the queuing delay in the station, with respect to the vehicle state of charge (SOC), and the expected arrival time. A simplified circuit model has been used to prototype the proposed algorithm, to minimize the overall queuing delay. Herein, the worst-case scenario is considered by having number of electric vehicles arriving to the station at the same time greater than the charging points available in the station side. Accordingly, the optimization technique was applied to reduce the mean charging time of the vehicles and minimize queuing delay. Results showed that this model can help in reducing the queuing delay by around 20% of the mean charging time of the station, while referring to a bare model without ANN algorithm as a reference.
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Guneser, M. Tahir, Mohamed Elweddad, and Cevat Ozarpa. "An Energy Management Approach for Solar Charge Stations in Smart Cities." Academic Perspective Procedia 3, no. 1 (October 25, 2020): 410–17. http://dx.doi.org/10.33793/acperpro.03.01.84.
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The rapid development of intelligent transportation system in smart cities offers great opportunities to improve the performance of energy management strategies (EMSs) for connected electric vehicles (EV) charging system. An electric vehicle charging station integrating Solar Photovoltaic Panel (PV), a Battery Energy Storage System (BESS) and based utility grid is designed for many scenarios. In the proposed system, the PV is the primary energy source with the battery for storing excessive energy to deliver the lowest possible electricity bill with the grid-tied to ensure power delivery| (power continuity). An efficient design of charging solar station with control strategy is developed for the optimal power management between solar, BESS, grid. By taking dynamic charging process of EVs battery, the power management flow in the station is formulated and validated using MATLAB. Machine learning methods will be applied to optimize proposed energy management system.
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Govidan, N., and M. Rajasekaran Indra. "Smart fuzzy-based energy-saving photovoltaic burp charging system." International Journal of Ambient Energy 39, no. 7 (May 30, 2017): 671–77. http://dx.doi.org/10.1080/01430750.2017.1318780.
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Rozman, Matjaz, Augustine Ikpehai, Bamidele Adebisi, Khaled M. Rabie, Haris Gacanin, Helen Ji, and Michael Fernando. "Smart Wireless Power Transmission System for Autonomous EV Charging." IEEE Access 7 (2019): 112240–48. http://dx.doi.org/10.1109/access.2019.2912931.
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Aqeel, Muhammad, Hammad Shahab, Muhammad Naeem, Muhammad Sikander Shahbaz, Faizan Qaisar, and Muhammad Ali Shahzad. "Intelligent Smart Energy Meter Reading System Using Global System for Mobile Communication." International Journal of Intelligent Systems and Applications 15, no. 1 (February 8, 2023): 35–47. http://dx.doi.org/10.5815/ijisa.2023.01.04.
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The innovation of e-metering (Electronic Metering) has experienced fast mechanical progressions and there is expanded interest in a solid and effective Automatic Meter Reading (AMR) framework. GSM Based shrewd vitality meter perusing framework replaces conventional meter perusing techniques. It empowers remote access to the existing vitality meter by the vitality provider. A GSM-based remote correspondence module is incorporated with the electronic vitality meter of every element to have remote access to the utilization of power. A PC with a GSM recipient at the opposite end, which contains the database goes about as the charging point. Live meter perusing from the GSM-empowered vitality meter is sent back to this charging point intermittently and these subtle elements are refreshed in a focal database. The total month-to-month utilization and the due bill are informed back to the client after handling this information. So, GSM-based remote AMR framework is a more successful approach for a traditional charging framework. This framework additionally gives specialists to power organizations to take activities against tolerant clients who have a remarkable contribution; generally, the organization has the ideal to detach the power supply, and it can reconnect the control supply after the affidavit of duty. So, we thought about building such an automatic system. This research is GSM-Based on a smart energy meter reading system to eliminate the conventional way of the reading system. In this paper, the GSM module sends reading information through SMS to the related Authority. There are no chances of any unethical mistake by using this modern technique.
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J, Gangadhar. "Consumer Interface Smart Energy Meter." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2603–6. http://dx.doi.org/10.22214/ijraset.2023.52209.
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Abstract: It is realized that one of the defective subsystems adding to the tremendous budgetary loss in Power Supply Company is the conventional metering and charging framework. Mistakes get presented at each phase of charging the energy rates, similar to blunders with conventional meters, reading errors by human while noticing the consumed energy; and blunder during the preparation of paid and the due bills. The solution for this downside is a prepaid charging or billing framework of consumed energy. Most of the developing countries are shifting their conventional energy management practices to the modern one by replacing the old and conventional energy meters with the smart meters outfitted with the prepaid facility to quantify the power consumption so as to decrease the income deficits looked by utilities because of customer unwillingness to make consumed energy payments on time. Our proposed design embedded with Arduino and GSM technology is advancement over conventional energy meter, which enables consumer to effectively manage their electricity usage. The system performance is good with the acquired results. An earlier charging will undoubtedly get rid of the issues of unpaid bills and human mistakes in meter readings, along these lines guaranteeing justified income for the utility.
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Chung, Ching-Yen, Aleksey Shepelev, Charlie Qiu, Chi-Cheng Chu, and Rajit Gadh. "Mesh Network for RFID and Electric Vehicle Monitoring in Smart Charging Infrastructure." Journal of Communications Software and Systems 10, no. 2 (June 23, 2014): 114. http://dx.doi.org/10.24138/jcomss.v10i2.132.
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With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of drivers and those of the local distribution grid. However, the current approach to charging is not well suited to scaling with the PEV market. If PEV adoption continues, charging infrastructure will have to overcome its current shortcomings such as unresponsiveness to grid constraints, low degree of autonomy, and high cost, in order to provide a seamless and configurable interface from the vehicle to the power grid. Among the tasks a charging station will have to accomplish will be PEV identification, charging authorization, dynamic monitoring, and charge control. These will have to be done with a minimum of involvement at a maximum of convenience for a user. The system proposed in this work allows charging stations to become more responsive to grid constraints and gain a degree of networked autonomy by automatically identifying and authorizing vehicles, along with monitoring and controlling all charging activities via an RFID mesh network consisting of charging stations and in-vehicle devices. The proposed system uses a ZigBee mesh network of in-vehicle monitoring devices which simultaneously serve as active RFID tags and remote sensors. The system outlined lays the groundwork for intelligent charge-scheduling by providing access to vehicle’s State of Charge (SOC) data as well as vehicle/driver IDs, allowing a custom charging schedule to be generated for a particular driver and PEV. The approach presented would allow PEV charging to be conducted effectively while observing grid constraints and meeting the needs of PEV drivers.
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Hsaini, Sara, Mounir Ghogho, and My El Hassan Charaf. "An OCPP-Based Approach for Electric Vehicle Charging Management." Energies 15, no. 18 (September 15, 2022): 6735. http://dx.doi.org/10.3390/en15186735.
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This paper proposes a smart system for managing the operations of grid-connected charging stations for electric vehicles (EV) that use photovoltaic (PV) sources. This system consists of a mobile application for EV drivers to make charging reservations, an algorithm to optimize the charging schedule, and a remote execution module of charging operations based on the open charge point protocol (OCPP). The optimal charging schedule was obtained by solving a binary integer programming problem. The merits of our solution are illustrated by simulating different charging demand scenarios.
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Kostenko, A. P. "Overview of european trends in electric vehicle implementation and the influence on the power system." System Research in Energy 2022, no. 1 (December 27, 2022): 62–71. http://dx.doi.org/10.15407/srenergy2022.01.062.
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An overview of the trends in the development of electric transport in European countries is presented, including the state and tax policy of supporting electric transport owners, the dynamics of the growth of the European fleet of electric vehicles, the development of charging infrastructure and the main principles of the interaction between electric vehicles and power system. The experience of countries that have successfully developed electric transport was reviewed, and it was found that the demand for electric cars mostly depends on the price of electric cars for the end user, infrastructure development and government incentive programs. The review also showed that, in practice, the wide spread of electric vehicles should occur simultaneously with the development of the corresponding infrastructure, as well as the development of systems for their interaction with the energy system and compliance with global environmental standards. The growing number of electric vehicles certainly requires special attention from network and power system operators, because the appearance of charging stations of various manufactures and capacities, installed in residential areas, private and apartment buildings, can lead to the number of problems, the emergence of which is associated with electromagnetic compatibility, overloading of electric cables and distribution transformers, safety of operation, that as a result can negatively affect the reliability and quality of power supply. There is also the potential risk that with a certain number of EVs, there will not be enough existing generation capacity and capacity of the electricity grid to charge EV batteries. The analysis of the principles of interaction between electric vehicles and power system in Europe showed that they are based on the concept of Smart Grid - smart or intelligent power supply networks, by means of which the electric vehicle charging system can be managed by the energy supplying company in order to solve a number of energy system tasks: increasing the efficiency of network infrastructure using, peak load shift and the development of smart grid infrastructure. Keywords: electric vehicle, electric charging station, charging infrastructure, power system, environmental requirements, the Smart Grid concept
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Lo Franco, Francesco, Mattia Ricco, Riccardo Mandrioli, and Gabriele Grandi. "Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization." Energies 13, no. 19 (September 23, 2020): 5003. http://dx.doi.org/10.3390/en13195003.
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In the context of electric vehicle (EV) development and positive energy districts with the growing penetration of non-programmable sources, this paper provides a method to predict and manage the aggregate power flows of charging stations to optimize the self-consumption and load profiles. The prediction method analyzes each charging event belonging to the EV population, and it considers the main factors that influence a charging process, namely the EV’s characteristics, charging ratings, and driver behavior. EV’s characteristics and charging ratings are obtained from the EV model’s and charging stations’ specifications, respectively. The statistical analysis of driver behavior is performed to calculate the daily consumptions and the charging energy request. Then, a model to estimate the parking time of each vehicle is extrapolated from the real collected data of the arrival and departure times in parking lots. A case study was carried out to evaluate the proposed method. This consisted of an industrial area with renewable sources and electrical loads. The obtained results show how EV charging can negatively impact system power flows, causing load peaks and high energy demand. Therefore, a charging management system (CMS) able to operate in the smart charging mode was introduced. Finally, it was demonstrated that the proposed method provides better EV integration and improved performance.
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Verbrugge, Boud, Abdul Mannan Rauf, Haaris Rasool, Mohamed Abdel-Monem, Thomas Geury, Mohamed El Baghdadi, and Omar Hegazy. "Real-Time Charging Scheduling and Optimization of Electric Buses in a Depot." Energies 15, no. 14 (July 9, 2022): 5023. http://dx.doi.org/10.3390/en15145023.
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To improve the air quality in urban areas, diesel buses are getting replaced by battery electric buses (BEBs). This conversion introduces several challenges, such as the proper control of the charging process and a reduction in the operational costs, which can be addressed by introducing smart charging concepts for BEB fleets. Therefore, this paper proposes a real-time scheduling and optimization (RTSO) algorithm for the charging of multiple BEBs in a depot. The algorithm assigns a variable charging current to the different time slots the charging process of each BEB is divided to provide an optimal charging schedule that minimizes the charging cost, while satisfying the power limitations of the distribution network and maintaining the operation schedule of the BEBs. A genetic algorithm is used to solve the formulated cost function in real time. Several charging scenarios are tested in simulation, which show that a reduction in the charging cost up to 10% can be obtained under a dynamic electricity price scheme. Furthermore, the RTSO is implemented in a high-level charging management system, a new feature required to enable smart charging in practice, to test the developed algorithm with existing charging infrastructure. The experimental validation of the RTSO algorithm has proven the proper operation of the entire system.
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Zhu, Chuangao, Ao Wang, Lutong Yang, and Jia Li. "Design and application of smart-microgrid in industrial park." ITM Web of Conferences 47 (2022): 03011. http://dx.doi.org/10.1051/itmconf/20224703011.
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Due to the uncertain and randomness of both wind power photovoltaic output of power generation side and charging load of user side, a set of wind-solar-storage-charging multi-energy complementary smart microgrid system in the park is designed. Through AC-DC coupled, green energy, such as wind energy, distributed photovoltaic power and battery echelon utilization energy storage power, can be supplemented as factory power. While alleviating the power consumption pressure in the plant, it also realizes functions such as smoothing the fluctuation green energy power generation, and peak loading shifting. Vehicle DC super and fast charging are also integrated in this station. The system realizes real-time state monitoring of different energy sources, energy storage, power distribution, and loads, which can guarantee green, smooth, efficient and economic operation of the multi-energy complementary system in the plant.
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Khan, Muhammad Hilal, Azzam Ul Asar, Nasim Ullah, Fahad R. Albogamy, and Muhammad Kashif Rafique. "Modeling and Optimization of Smart Building Energy Management System Considering Both Electrical and Thermal Load." Energies 15, no. 2 (January 13, 2022): 574. http://dx.doi.org/10.3390/en15020574.
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Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.
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Ahsan, Syed Muhammad, Hassan Abbas Khan, Sarmad Sohaib, and Anas M. Hashmi. "Optimized Power Dispatch for Smart Building and Electric Vehicles with V2V, V2B and V2G Operations." Energies 16, no. 13 (June 22, 2023): 4884. http://dx.doi.org/10.3390/en16134884.
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The operation of smart buildings (with solar, storage and suitable power routing infrastructure) can be optimized with the addition of parking stations for electric vehicles (EVs) with vehicle-to-everything (V2X) operations including vehicle-to-vehicle (V2V), vehicle-to-building (V2B) and vehicle-to-grid (V2G) operations. In this paper, a multi-objective optimization framework is proposed for the smart charging and discharging of EVs along with the maximization of revenue and savings of smart building (prosumers with solar power, a battery storage system and a parking station) and non-primary/ordinary buildings (consumers of electricity without solar power, a battery storage system and parking station). A mixed-integer linear program is developed to maximize the profits of smart buildings that have bilateral contracts with non-primary buildings. The optimized charging and discharging (V2X) of EVs at affordable rates utilizing solar power and a battery storage system in the smart building helps to manage the EV load during on-peak hours and prevent utility congestion. The results indicate that in addition to the 4–9% daily electricity cost reductions for non-primary buildings, a smart building can achieve up to 60% of the daily profits. Further, EVs can save 50–69% in charging costs while performing V2X operations.
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Vyas, Hari Dutt. "Review of the Impact of Electric Vehicle Charging on Distribution Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 102–6. http://dx.doi.org/10.22214/ijraset.2023.53581.
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Abstract: Electric vehicles (EVs) have gained significant popularity, leading to concerns about their impact on distribution networks and the integration of renewable energy sources.In response, researchers have proposed various strategies and methodologies. These include adaptive charging coordination strategies that aim to minimize the influence of EV chargingon distribution networks while considering uncertainties in renewable energy generation and charging demands. Optimizationbased approaches have been employed to balance loads, alleviate network congestion, and coordinate EV charging schedules with renewable energy generation and distribution network demand. The studies have also highlighted potential issues such as distribution system overloading, voltage stability problems, and the need to account for different charging infrastructure scenarios and standards. To address these challenges, control strategies integrating EV charging and battery energy storage systems (BESS) have been proposed. These strategies aim to reduce peak power demands, optimize the utilization of renewable energy sources, and shift charging loads away from peak periods. How- ever, there are still gaps to be addressed, including scalability forlarge-scale deployment, user convenience, and economic viability.Further research is needed to explore these areas and consider the impact of user behavior, smart grid technologies, and cost- effectiveness in accommodating EV charging and plug-in hybrid electric vehicles (PHEVs) in residential distribution grids
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EL-Fedany, Ibrahim, Driss Kiouach, and Rachid Alaoui. "A smart system combining real and predicted data to recommend an optimal electric vehicle charging station." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (April 1, 2023): 394. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp394-405.
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The electric vehicle (EV) is considered an attractive alternative to a conventional vehicle, due to its potential beneficiation in decreasing carbon emission. But the battery range anxiety is a key challenge to its wide adoption and also the EV drivers spend so much time in public charging stations (CS) to charge especially during peak times. In this paper, we propose a charging station selected system (C3S) to control and manage EVs charging plans. Moreover, the C3S system proposed consists of a set of algorithms that are proposed to recommend a suitable CS for EV charging requests. The CS selection is based on minimizing travel time and takes into account in real-time the queuing time at CS, EVs' charging reservations, and the predicted time of EVs' future charging requests. Besides, we proposed three different strategies for predicting the EVs incoming and controlling the uncertainty matter of the dynamic arrival of EV charging requests. As part of the Helsinki City scenario, the evaluation results demonstrate the performance, especially at peak times, of our proposed C3S with regard to the CS recommendation which has the minimum total trip duration.
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Tamay, Pablo, and Esteban Inga. "Charging Infrastructure for Electric Vehicles Considering Their Integration into the Smart Grid." Sustainability 14, no. 14 (July 6, 2022): 8248. http://dx.doi.org/10.3390/su14148248.
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Concern about environmental problems, including the greenhouse effect, directly related to the gases produced by vehicles, has led governments and various organizations to promote standards on fuel use and gas emissions. Therefore, promoting the development of electric vehicles is fundamental to facing the challenges of climate change and achieving sustainable transportation. For this reason, this article presents a bibliometric analysis based on databases to elaborate on the state of the art that integrates successful cases in the design and implementation of charging stations for electric vehicles. Once the results are presented, a methodological model is proposed for the design of charging stations considering restrictions and recommendations issued by international standardization organizations. The Digsilent PowerFactory software analyzes the behavior of active and reactive power, voltage, and current. Finally, a system for different charging modes and powers is proposed. The charging system is tested through the insertion of phase–ground, phase–phase, and three-phase short circuits located at the connection points of the charging stations, and the faults are inserted and released in different periods for the analysis of system stability. As a result, it has a stable system for all proposed scenarios, as long as the fault is released; otherwise, the system remains unstable. The three-phase fault is the most severe for the load system, making the performance of the protections assigned to each circuit essential.
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Alsharif, Abdulgader, Tan Chee Wei, Razman Ayop, Kwan Yiew Lau, and Abba Lawan Bukar. "A Review of the Smart Grid Communication Technologies in Contactless Charging with Vehicle to Grid Integration Technology." Journal of Integrated and Advanced Engineering (JIAE) 1, no. 1 (April 30, 2021): 11–20. http://dx.doi.org/10.51662/jiae.v1i1.8.
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Power needs to be transferred from the source to the load (electric vehicle). Transmitting electricity through the air gap for charging using electromagnetic waves as one of the smart grid technologies called Wireless Power Transfer (WPT), or Inductive Power Transfer. This paper presents the fulfilment of future gird that addresses the issues of Greenhouse Gas emission, and transportation and industries emissions known as the smart grid with a complex system. The complexity of the smart grid communication system is the motivation to be an open area of research issue. The main contribution of this paper is to close the gap between this research and other researches by delivering a comprehensive review and update the recent state-of-the-art of smart grid communication technologies with the integration of vehicle-to-grid (V2G) technology using the contactless charging method. Smart grid communication technologies with their pros and cons, topologies of wireless communication, challenges of the V2G, WPT challenges, and standards are discussed. Therefore, this study is expected to be a significant guide to engineers and researchers studying in the field of smart grid communication technologies and contactless charging for electric vehicles.
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Mies, Jerome, Jurjen Helmus, and Robert van den Hoed. "Estimating the Charging Profile of Individual Charge Sessions of Electric Vehicles in The Netherlands." World Electric Vehicle Journal 9, no. 2 (June 22, 2018): 17. http://dx.doi.org/10.3390/wevj9020017.
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The mass adoption of Electric Vehicles (EVs) might raise pressure on the power system, especially during peak hours. Therefore, there is a need for delayed charging. However, to optimize the charging system, the progression of charging from an empty battery to a full battery of the EVs, based on real-world data, needs to be analyzed. Currently, many researchers view this charging profile as a static load and ignore the actual charging behavior during the charging session. However, this study investigates how different factors influence the charging profile of individual EVs based on real-world data of charging sessions in The Netherlands, and thereby enable optimization analysis of EV smart charging schemes.
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Chaikovskaya, Eugene. "Development of smart grid technology to maintain the functioning of photoelectric charging stations." Eastern-European Journal of Enterprise Technologies 3, no. 8(111) (June 30, 2021): 14–24. http://dx.doi.org/10.15587/1729-4061.2021.235120.
Full textAbstract:
An integrated Smart Grid system has been developed for matching the production and consumption of electric power based on a prediction of changes in the battery capacity. Advanced decisions on the change in power transmission capacity have made it possible to regulate voltage in the distribution system by maintaining the power factor of the photoelectric charging station. Voltages at the input to the hybrid inverter and in the distribution system were measured to assess their ratio. Comprehensive mathematical and logical modeling of the photoelectric charging station was performed based on the mathematical substantiation of architecture and operation maintenance. A dynamic subsystem including such components as mains, a photoelectric module, a hybrid inverter, batteries, a two-way counter Smart Meter and a charger formed the basis of the proposed technological system. Time constants and coefficients of mathematical models of dynamics in terms of estimation of changes in the battery capacity and power factor of the photoelectric charging station were determined. A functional estimate of changes in the battery capacity and power factor of the photoelectric charging station was obtained. Maintenance of voltage in the distribution system was realized based on resulting operation data to estimate a change in the battery capacity. Advanced decision-making has made it possible to raise the power factor of the photoelectric charging station up to 40 % due to matching the electric power production and consumption. Maintenance of operation of the photoelectric charging station using the developed Smart Grid technology has enabled prevention of peak loading of the power system due to a 20 % reduction of power consumption from the network.
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Sirisumrannukul, Somporn. "Charging Management of Electric Vehicles on Loading Capability of Distribution System Equipment, Voltage Quality, and Energy Loss by Monte Carlo Simulation and Linear Programming." International Transactions on Electrical Energy Systems 2023 (April 19, 2023): 1–37. http://dx.doi.org/10.1155/2023/1195648.
Full textAbstract:
This paper presents the impact of uncoordinated and coordinated charging management of electric vehicles (EVs) on the loading capability of major distribution system equipment, voltage quality, and energy loss in a distribution system. The main emphasis is given to the overloading of distribution transformers, primary feeders, and a substation transformer. The voltage quality of load points along the feeders and the system energy loss are also underlined. The load profile for uncoordinated EV charging is simulated by a Monte Carlo method with several deterministic and stochastic variables involved. To mitigate the overloading of the system components, a coordinated charging (also known as smart charging) model formulated as a linear programming problem is proposed with the objective of maximizing the total amount of energy consumption by EVs and the sum of all individual final states of charge (SoCs), and minimizing the sum of the absolute deviation of individual SoCs from the overall average SoC. The optimization problem is subject to equipment capability loading and planning criteria constraints with low, medium, and high EV penetration levels. The voltage quality problem and energy loss are also analyzed by an unbalanced three-phased power flow model. A case study of a real and practical 115/22 kV distribution system of the Provincial Electricity Authority (PEA) with a 50 MVA substation transformer, 5 feeders, and 732 distribution transformers shows that the possibility of overloaded system components, voltage drops along the feeders, and the system energy loss can be identified in the uncoordinated charging scenario and offer the readiness for equipment replacement and network reinforcement planning. The proposed smart charging model allows the distribution system to accommodate more EVs by appropriately managing the power and the start times of charging for the individual EVs over the timeslots of a day. The study results confirm no violation of the system components and voltage regulation imposed by the system planning guidelines. In addition, the system peak demand and the system energy loss are significantly lower in the smart charging scenario, thus deferring investment upgrades, offering better asset utilization, and retaining network security and service quality.