Готові списки джерел за темами / Charging Station Placement

Добірка наукової літератури з теми "Charging Station Placement"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Charging Station Placement"

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Mortimer, Benedict J., Christopher Hecht, Rafael Goldbeck, Dirk Uwe Sauer, and Rik W. De Doncker. "Electric Vehicle Public Charging Infrastructure Planning Using Real-World Charging Data." World Electric Vehicle Journal 13, no. 6 (May 24, 2022): 94. http://dx.doi.org/10.3390/wevj13060094.

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Анотація:
The current increase of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage. To make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. An essential factor for the degree of utilization is the placement of a charging station. Therefore, the initial site selection plays a critical role in the planning process. This paper proposes a charging station placement procedure based on real-world data on charging station utilization and places of common interest. In the first step, we correlate utilization rates of existing charging infrastructure with places of common interest such as restaurants, shops, bars and sports facilities. This allows us to estimate the untapped potential of unexploited areas across Germany in a second step. In the last step, we employ the resulting geographical extrapolation to derive two optimized expansion strategies based on the attractiveness of locations for electric vehicle charging.
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El Hafdaoui, Hamza, Hamza El Alaoui, Salma Mahidat, Zakaria El Harmouzi, and Ahmed Khallaayoun. "Impact of Hot Arid Climate on Optimal Placement of Electric Vehicle Charging Stations." Energies 16, no. 2 (January 9, 2023): 753. http://dx.doi.org/10.3390/en16020753.

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Анотація:
Electric vehicles (EVs) are becoming more commonplace as they cut down on both fossil fuel use and pollution caused by the transportation sector. However, there are a number of major issues that have arisen as a result of the rapid expansion of electric vehicles, including an inadequate number of charging stations, uneven distribution, and excessive cost. The purpose of this study is to enable EV drivers to find charging stations within optimal distances while also taking into account economic, practical, geographical, and atmospheric considerations. This paper uses the Fez-Meknes region in Morocco as a case study to investigate potential solutions to the issues raised above. The scorching, arid climate of the region could be a deterrent to the widespread use of electric vehicles there. This article first attempts to construct a model of an EV battery on MATLAB/Simulink in order to create battery autonomy of the most widely used EV car in Morocco, taking into account weather, driving style, infrastructure, and traffic. Secondly, collected data from the region and simulation results were then employed to visualize the impact of ambient temperature on EV charging station location planning, and a genetic algorithm-based model for optimizing the placement of charging stations was developed in this research. With this method, EV charging station locations were initially generated under the influence of gas station locations, population and parking areas, and traffic, and eventually through mutation, the generated initial placements were optimized within the bounds of optimal cost, road width, power availability, and autonomy range and influence. The results are displayed to readers in a node-link network to help visually represent the impact of ambient temperatures on EV charging station location optimization and then are displayed in interactive GIS maps. Finally, conclusions and research prospects were provided.
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Islam, Md Mainul, Hussein Shareef, and Azah Mohamed. "Optimal Quick Charging Station Placement for Electric Vehicles." Applied Mechanics and Materials 785 (August 2015): 697–701. http://dx.doi.org/10.4028/www.scientific.net/amm.785.697.

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Анотація:
Environmental concerns, dependency on imported petroleum and lower cost alternative to gasoline always motivated policymakers worldwide to introduce electric vehicles in road transport system as a solution of those problems. The key issue in this system is recharging the electric vehicle batteries before they are exhausted. Thus, the charging station should be carefully located to make sure the vehicle users can access the charging station within its driving range. This paper therefore proposes a multi-objective optimization method for optimal placement of quick charging station. It intends to minimize the integrated cost of grid energy loss and travelling of vehicle to quick charging station. Due to contrary objectives, weighted sum method is assigned to generate reference Pareto optimal front and optimized the overture by genetic algorithm. The results show that the proposed method can find the optimal solution of quick charging station placement that can benefit electric vehicle users and power grid.
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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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Анотація:
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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Bacanli, Salih Safa, Enas Elgeldawi, Begümhan Turgut, and Damla Turgut. "UAV Charging Station Placement in Opportunistic Networks." Drones 6, no. 10 (October 9, 2022): 293. http://dx.doi.org/10.3390/drones6100293.

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Анотація:
Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including a key role within opportunistic wireless networks. These types of opportunistic networks are considered well suited for infrastructure-less areas, or urban areas with overloaded cellular networks. For these networks, UAVs are envisioned to complement and support opportunistic network performance; however, the short battery life of commercial UAVs and their need for frequent charging can limit their utility. This paper addresses the challenge of charging station placement in a UAV-aided opportunistic network. We implemented three clustering approaches, namely, K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and random clustering, with each clustering approach being examined in combination with Epidemic, Spray and Wait, and State-Based Campus Routing (SCR) routing protocols. The simulation results show that determining the charging station locations using K-means clustering with three clusters showed lower message delay and higher success rate than deciding the charging station location either randomly or using DBSCAN regardless of the routing strategy employed between nodes.
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Mishra, Partha, Eric Miller, Shriram Santhanagopalan, Kevin Bennion, and Andrew Meintz. "A Framework to Analyze the Requirements of a Multiport Megawatt-Level Charging Station for Heavy-Duty Electric Vehicles." Energies 15, no. 10 (May 21, 2022): 3788. http://dx.doi.org/10.3390/en15103788.

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Анотація:
Widespread adoption of heavy-duty (HD) electric vehicles (EVs) will soon necessitate the use of megawatt (MW)-scale charging stations to charge high-capacity HD EV battery packs. Such a station design needs to anticipate possible station traffic, average and peak power demand, and charging/wait time targets to improve throughput and maximize revenue-generating operations. High-power direct current charging is an attractive candidate for MW-scale charging stations at the time of this study, but there are no precedents for such a station design for HD vehicles. We present a modeling and data analysis framework to elucidate the dependencies of a MW-scale station operation on vehicle traffic data and station design parameters and how that impacts vehicle electrification. This framework integrates an agent-based charging station model with vehicle schedules obtained through real-world vehicle telemetry data analysis to explore the station design and operation space. A case study applies this framework to a Class 8 vehicle telemetry dataset and uses Monte Carlo simulations to explore various design considerations for MW-scale charging stations and EV battery technologies. The results show a direct correlation between optimal charging station placement and major traffic corridors such as cities with ports, e.g., Los Angeles and Oakland. Corresponding parametric sweeps reveal that while good quality of service can be achieved with a mix of 1.2-megawatt and 100-kilowatt chargers, the resultant fast charging time of 35–40 min will need higher charging power to reach parity with refueling times.
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Mohanty, Ajit Kumar, Perli Suresh Babu, and Surender Reddy Salkuti. "Fuzzy-Based Simultaneous Optimal Placement of Electric Vehicle Charging Stations, Distributed Generators, and DSTATCOM in a Distribution System." Energies 15, no. 22 (November 19, 2022): 8702. http://dx.doi.org/10.3390/en15228702.

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Анотація:
Electric vehicles (EVs) are becoming increasingly popular due to their inexpensive maintenance, performance improvements, and zero carbon footprint. The electric vehicle’s load impacts the distribution system’s performance as the electric vehicle’s adoption rises. As a result, the distribution system’s dependability depends on the precise location of the electric vehicle charging station (EVCS). The main challenge is the deteriorating impact of the distribution system caused by the incorrect placement of the charging station. The distribution system is integrated with the charging station in conjunction with the distribution static compensator (DSTATCOM) and distributed generation (DG) to reduce the impact of the EVCS. This paper presents a fuzzy classified method for optimal sizings and placements of EVCSs, DGs, and DSTATCOMs for 69-bus radial distribution systems using the RAO-3 algorithm. The characteristic curves of Li-ion batteries were utilized for the load flow analysis to develop models for EV battery charging loads. The prime objective of the proposed method is to (1) Reduce real power loss; (2) Enhance the substation (SS) power factor (pf); (3) Enhance the distribution network’s voltage profile; and (4) Allocate the optimum number of vehicles at the charging stations. The proposed fuzzified RAO-3 algorithm improves the substation pf in the distribution system. The fuzzy multi-objective function is utilized for the two stages and simultaneous placements of the EVCS, DG, and DSTATCOM. The simulation results reveal that the simultaneous placement method performs better, due to the significant reduction in real power loss, improved voltage profile, and the optimum number of EVs. Moreover, the existing system performances for increased EV and distribution system loads are presented.
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Houssein, Essam H., Sanchari Deb, Diego Oliva, Hegazy Rezk, Hesham Alhumade, and Mokhtar Said. "Performance of Gradient-Based Optimizer on Charging Station Placement Problem." Mathematics 9, no. 21 (November 6, 2021): 2821. http://dx.doi.org/10.3390/math9212821.

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Анотація:
The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.
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Ahmed, Ahmed Jassim, Mohammed H. Alkhafaji, and Ali Jafer Mahdi. "DECISION-MAKING METHOD FOR THE OPTIMUM ALLOCATION OF CHARGING STATIONS Of ELECTRIC VEHICLE IN DISTRIBUTION NETWORKS." Tekhnichna Elektrodynamika 2023, no. 1 (January 9, 2023): 67–75. http://dx.doi.org/10.15407/techned2023.01.067.

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Анотація:
Electric vehicles are becoming prominent nowadays and playing an important role in the transportation sector as conventional vehicles affect the environment. The rising number of vehicles requires increasing the charging stations, which affects the distribution network if placed randomly. Therefore, selecting the optimal place for these charging stations is very important to mitigate the effect on the distribution system. This paper presents a decision-making method to select the location of the charging station in a radial distribution system optimally. The fixed point algorithm was used for the analysis of load flow. The analysis was carried outand tested on the 33 bus IEEE and a real case study in Iraq was used for the study. The result of the charging station placement is compared with other research and showed its efficiency in work. The analysis showed the effectiveness of the proposed method in reducing the effect of charging stations on voltages and losses under different conditions. References 27, Figures 10, Tables 6.
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Kalakanti, Arun Kumar, and Shrisha Rao. "Charging Station Planning for Electric Vehicles." Systems 10, no. 1 (January 2, 2022): 6. http://dx.doi.org/10.3390/systems10010006.

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Анотація:
Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) the EV charging station placement (EVCSP) problem, and (ii) the CS need estimation problem for a region. In this work, different explainable solutions based on machine learning (ML) and simulation were investigated by incorporating quantitative and qualitative metrics. The solutions were compared with traditional approaches using a real CS area of Austin and a greenfield area of Bengaluru. For EVCSP, a different class of clustering solutions, i.e., mean-based, density-based, spectrum- or eigenvalues-based, and Gaussian distribution were evaluated. Different perspectives, such as the urban planner perspective, i.e., the clustering efficiency, and the EV owner perspective, i.e., an acceptable distance to the nearest CS, were considered. For the CS need estimation, ML solutions based on quadratic regression and simulations were evaluated. Using our CS planning methods urban planners can make better CS placement decisions and can estimate CS needs for the present and the future.
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Дисертації з теми "Charging Station Placement"

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Hertzberg, Samuel, and Daniel Dahlgren. "Optimal Placement of a Charging Station for a Robotic Vacuum Cleaner." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229763.

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Robot vacuum cleaners are used in many domestic and industrial appliances around the world today. The vacuum cleaners have a certain goal: To clean an area in a set amount of time. It does so by applying different techniques based on information from its different sensors.However, the efficiency might vary from robot to robot. The variations are a product of many things as the problem is fairly complex.This paper will measure the variations due to point of entry. In a statistical analysis on a simulated dataset results showed that point of entry does affect the robot vacuum cleaner in some cases. Of the two algorithms simulated in this paper, the random bump algorithm showed little to no benefit at all. Whereas the spiral algorithm showed up to a 20% increase in efficiency due to point of entry.
Robotdammsugare används i många hushåll och industriella tillämpningar runt om i värden idag. Robotdammsugare har ett visst mål: att städa en yta inom en viss tid. Den gör det genom att använda olika tekniker beroende på informationen det får från sina sensorer. Dock kan effektiviteten variera mellan robot och robot. Variationerna beror på många olika saker då problemet är komplext. Denna rapport kommer att mäta variationer som uppstår på grund av startposition. I en statistisk analys på ett genererat dataset visade resultaten att i några fall påverkar startpositionen robotdammsugaren. Med en av de två algoritmerna som simulerades i detta arbete, random bump algoritmen, visade det sig inte spela stor roll. Medans den andra algoritmen, spiralalgoritmen, kunde bli upp till 20% mer effektiv på grund av startposi-tionen.
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Mello, Igoor Morro. "Fast charging stations placement and electric network connection methodology for electric taxis in urban zones." Ilha Solteira, 2018. http://hdl.handle.net/11449/155892.

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Orientador: Antonio Padilha Feltrin
Abstract: In recent years, the use of electric vehicles in urban zones has been intensified. As a policy of increasing the penetration of electric vehicles and reducing air pollution, electric taxis have been introduced into transportation systems. They need special attention because of its different driving patterns. In contrast to private electric vehicles, which can be recharged for a long period, electric taxis need to recharge only for a short time due to their constant operation. Therefore, fast charging stations are required to meet the demand for recharging electric taxis and should be located at strategic places. In addition, an analysis must be performed to connect these stations in the electric network. To improve their allocation and connectivity, this work presents a methodology to help in decision making for installing fast charging stations, considering as criteria: locations with greater flow of electric taxis and low level of state of charge, the available physical space to carry out their recharge and cost functions for the connection of charging stations. The result of the proposal is a map with the location of fast charging stations and analysis of the lowest cost places for connection to the network. The methodology is tested in a medium-sized city in Brazil, showing the importance of this map and cost functions in decision making. The proposal is compared with another methodology, showing that the proposed method considers different criteria and creates a better s... (Complete abstract click electronic access below)
Resumo: Nos últimos anos, o uso dos veículos elétricos nas zonas urbanas tem se intensificado. Como política para o aumento na penetração de veículos elétricos e reduzir a poluição do ar, os táxis elétricos vem sendo introduzidos nos sistemas de transporte. Eles necessitam de atenção especial devido aos seus diferentes padrões de condução. Em contraste com os veículos elétricos privados, que podem ser recarregados por um longo período, táxis elétricos necessitam de recarga em um curto período de tempo devido a sua constante operação. Portanto, estações de recarga rápida são necessárias para receber a demanda de recarga dos táxis elétricos e devem estar localizadas em locais estratégicos. Além disso, uma análise deve ser realizada para a conexão destas estações com a rede elétrica. Para melhorar sua alocação e conectividade, este trabalho apresenta uma metodologia para auxiliar na tomada de decisão da instalação de estações de recarga rápida considerando como critérios: locais com maior fluxo de táxis elétricos e baixo nível de carga nas baterias, espaço físico disponível para realizar o carregamento e funções de custo para a conexão das estações de recarga. Os resultados da proposta são mapas com a localização das estações de recarga rápida e análise dos locais de menor custo para a conexão com a rede elétrica. A metodologia é testada em uma cidade de médio porte no Brasil, mostrando a importância dos mapas e funções de custo na tomada de decisão. A proposta é comparada com outras me... (Resumo completo, clicar acesso eletrônico abaixo)
Mestre
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3

Yang, Jun-Cheng, and 楊竣丞. "A Study on Strategic Placement of Electric Vehicle Charging Stations." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/88489835382241227901.

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碩士
國立高雄海洋科技大學
輪機工程研究所
101
With a small number of electric vehicles (EVs), the distribution system might not be significantly affected by the charging. However, with a large number of electric vehicles, the characteristics of the charging patterns could have a significant impact on the distribution system depending on the charging location. This may result in overloading and power losses. From a system planning point of view, this impact could be mitigated through strategically placing the charging stations in the distribution system. This thesis aims at presenting an algorithm dedicated to the EVs charging stations placement optimization in a given distribution system using genetic algorithms (GA), where daily time varying loads are considered together with random EVs charging patterns including starting time, duration, and power of charging. The problem is formulated as a non-differential combinational optimization problem, where the system losses to be minimized subject to capacity and system operation constraints. The placement alternatives considered are the installation of Level 2 single-phase slow chargers. In the GA evolutionary process, all individuals’ fitness is analyzed and for each feasible solution, a non-linear three phase power flow problem is solved and the system losses are calculated. A practical distribution system composed of 20 buses was used to validate the algorithm and demonstrate its applicability to large systems.
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Книги з теми "Charging Station Placement"

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Zverovich, Vadim. Modern Applications of Graph Theory. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198856740.001.0001.

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This book discusses many modern, cutting-edge applications of graph theory, such as traffic networks and Braess’ paradox, navigable networks and optimal routing for emergency response, backbone/dominating sets in wireless sensor networks, placement of electric vehicle charging stations, pedestrian safety and graph-theoretic methods in molecular epidemiology. Because of the rapid growth of research in this field, the focus of the book is on the up-to-date development of the aforementioned applications. The book will be ideal for researchers, engineers, transport planners and emergency response specialists who are interested in the recent development of graph theory applications. Moreover, this book can be used as teaching material for postgraduate students because, in addition to up-to-date descriptions of the applications, it includes exercises and their solutions. Some of the exercises mimic practical, real-life situations. Advanced students in graph theory, computer science or molecular epidemiology may use the problems and research methods presented in this book to develop their final-year projects, master’s theses or doctoral dissertations; however, to use the information effectively, special knowledge of graph theory would be required.
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Частини книг з теми "Charging Station Placement"

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Guo, Yudi, Junjie Yao, Jiaxiang Huang, and Yijun Chen. "Data Driven Charging Station Placement." In Web and Big Data, 260–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26075-0_20.

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Ouertani, Mohamed Wajdi, Ghaith Manita, and Ouajdi Korbaa. "Improved Genetic Algorithm for Electric Vehicle Charging Station Placement." In Intelligent Decision Technologies, 37–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2765-1_4.

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Liu, Chen, Hui Song, and Xinghuo Yu. "Optimal Electric Vehicle Charging Station Placement with Online Charging Navigation Strategy in Urban Areas." In Electric Transportation Systems in Smart Power Grids, 145–64. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003293989-6.

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Kunj, Tripti, and Kirti Pal. "EV Technology Trends & Placement of Electric Vehicle Charging Station: A Review." In Lecture Notes in Electrical Engineering, 303–14. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8892-8_23.

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Kameda, Hisashi, and Naoto Mukai. "Optimization of Charging Station Placement by Using Taxi Probe Data for On-Demand Electrical Bus System." In Knowledge-Based and Intelligent Information and Engineering Systems, 606–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23854-3_64.

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6

AbdelAzim, Ahmed Ibrahim. "Economic Placement of EV Charging Stations within Urban Areas." In Electric Vehicle Integration in a Smart Microgrid Environment, 295–311. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780367423926-13.

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7

Mohanty, Ajit Kumar, and P. Suresh Babu. "Optimal Placement of Electric Vehicle Charging Stations Using JAYA Algorithm." In Lecture Notes in Electrical Engineering, 259–66. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7994-3_23.

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8

Lima, Pablo M., and Carlos A. Castro. "Optimal Placement of EV Charging Stations Using a Dedicated, Two-Level Teaching-Learning-Based Optimization Algorithm." In Proceedings of the 7th Brazilian Technology Symposium (BTSym’21), 287–98. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08545-1_28.

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9

Zverovich, Vadim. "Graph Models for Optimization Problems in Road Networks." In Modern Applications of Graph Theory, 275–336. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198856740.003.0005.

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Анотація:
Here two applications of graph theory are considered. The first is devoted to pedestrian safety, and the focus is on pedestrian safety in urban areas with respect to pedestrian-vehicle crashes. In particular, an algorithm for automated construction of a graph model for pavement networks is discussed. Then, an algorithm for finding a user-optimal path in a given pavement network is presented. This algorithm is based on three criteria: path safety, distance, and path complexity. The second part of this chapter is devoted to optimizing the placement of charging stations for electric vehicles in road networks. The placement of charging stations in road networks is modelled as a multiple domination problem on reachability graphs. This model takes into account a threshold for the remaining battery charge and provides some minimal choice for a travel direction to recharge the battery. Experimental evaluation and simulations for the proposed facility location model are given for real road networks of the cities of Boston and Dublin.
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Тези доповідей конференцій з теми "Charging Station Placement"

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Lam, Albert Y. S., Yiu-Wing Leung, and Xiaowen Chu. "Electric vehicle charging station placement." In 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2013. http://dx.doi.org/10.1109/smartgridcomm.2013.6688009.

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Spieker, Helge, Alexander Hagg, Alexander Asteroth, Stefanie Meilinger, Volker Jacobs, and Alexander Oslislo. "Successive evolution of charging station placement." In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA). IEEE, 2015. http://dx.doi.org/10.1109/inista.2015.7276733.

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3

Kundu, Tanmoy, and Indranil Saha. "Charging Station Placement for Indoor Robotic Applications." In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8461006.

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Lu, Tianqi, Qiang Ma, and Zheng Gu. "EV Charging Station Placement Considering Traffic Flow." In 6th International Conference on Information Engineering for Mechanics and Materials. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icimm-16.2016.38.

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Dait, Wenkuan, Yuqing Lit, Xiaoying Gan, and Gongquan Xie. "Fast Charging Station Placement with Elastic Demand." In GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8647556.

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Hidalgo, Pablo A. Lopez, Max Ostendorp, and Markus Lienkamp. "Optimizing the charging station placement by considering the user's charging behavior." In 2016 IEEE International Energy Conference (ENERGYCON). IEEE, 2016. http://dx.doi.org/10.1109/energycon.2016.7513920.

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Aljaidi, Mohammad, Nauman Aslam, Xiaomin Chen, Omprakash Kaiwartya, and Yousef Ali Al-Gumaei. "Energy-efficient EV Charging Station Placement for E-Mobility." In IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2020. http://dx.doi.org/10.1109/iecon43393.2020.9255254.

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Deb, Sanchari, and Saad Alam. "Comprehensive Review of Planning Models for Charging Station Placement." In 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC). IEEE, 2021. http://dx.doi.org/10.1109/icsgsc52434.2021.9490453.

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Ouertani, Mohamed Wajdi, Ghaith Manita, and Ouajdi Korbaa. "Improved Antlion Algorithm for Electric Vehicle Charging Station Placement." In 2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT). IEEE, 2022. http://dx.doi.org/10.1109/setit54465.2022.9875614.

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"Energy Consumption Model and Charging Station Placement for Electric Vehicles." In 3rd International Conference on Smart Grids and Green IT Systems. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004859601500156.

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Звіти організацій з теми "Charging Station Placement"

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Kontou, Eleftheria, Yen-Chu Wu, and Jiewen Luo. Electric Vehicle Infrastructure Plan in Illinois. Illinois Center for Transportation, December 2022. http://dx.doi.org/10.36501/0197-9191/22-023.

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Анотація:
We study the allocation of dynamic electric vehicle charging investments from the policymaker’s perspective, which aims to meet statewide emission-reduction targets for the Illinois passenger vehicle sector. We determine statewide charging deployment trajectories over a 30-year planning horizon and estimate their emission reduction. Electric vehicle demand functions model the electrified vehicle market growth and capture network externalities and spatial heterogeneity. Our analysis indicates that most chargers need to be deployed in the first 10 to 15 years of the transition to allow benefits to accrue for electric vehicle drivers, availability of home charging influences consumers’ choice and drivers’ electrified travel distance, charging stations should be prioritized for frequent long-distance drivers, and spatial effects are crucial in accurately capturing the demand for electric vehicles in Illinois. We also develop a multi-criteria suitability map to site charging stations for electric vehicles based on economic, societal, and environmental justice indicators. We identify census tracts that should be prioritized during Illinois’ statewide deployment of charging infrastructure along with interstates and major highways that traverse them. Major interstates and highways I-90, I-80, I-55, and I-57 are identified as having high siting suitability scores for charging stations. Last, a novel location model was developed for equitable electric vehicle charging infrastructure placement in the Illinois interstate and major highway network. Two objectives were set to reduce detours and improve the ability to complete long-distance trips for low-income electric vehicle travelers and multi-unit dwelling residents. Our analysis indicates that if the system’s efficiency is the only consideration, low-income/multi-unit housing resident travelers are most likely to fail to complete their trips, while an equitable charging siting could mitigate this issue.
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