Relevant bibliographies by topics / Charging Algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Charging Algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Charging Algorithm"

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Wang, Wei, Haoran Jing, Junhua Liao, Feng Yin, Ping Yuan, and Liangyin Chen. "A Safe Charging Algorithm Based on Multiple Mobile Chargers." Sensors 20, no. 10 (2020): 2937. http://dx.doi.org/10.3390/s20102937.

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A safe charging algorithm in wireless rechargeable sensor network ensures the charging efficiency and the electromagnetic radiation below the threshold. Compared with the current charging algorithms, the safe charging algorithm is more complicated due to the radiation constraint and the mobility of the chargers. A safe charging algorithm based on multiple mobile chargers is proposed in this paper to charge the sensor nodes with mobile chargers, in order to ensure the premise of radiation safety, multiple mobile chargers can effectively complete the network charging task. Firstly, this algorith
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Gao, Weixin, Yuxiang Li, Tianyi Shao, and Feng Lin. "An On-Demand Partial Charging Algorithm without Explicit Charging Request for WRSNs." Electronics 12, no. 20 (2023): 4343. http://dx.doi.org/10.3390/electronics12204343.

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Wireless rechargeable sensor networks provide an effective solution to the energy limitation problem in wireless sensor networks by introducing chargers to recharge the nodes. On-demand charging algorithms, which schedule the mobile charger to charge the most energy-scarce node based on the node’s energy status, are one of the main types of charging scheduling algorithms for wireless rechargeable sensor networks. However, most existing on-demand charging algorithms require a predefined charging request threshold to prompt energy-starved nodes with energy levels lower than this threshold to sub
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Chrysocheris, Ilias, Asimakis Chatzileontaris, Christos Papakitsos, Evangelos Papakitsos, and Nikolaos Laskaris. "Pulse-Charging Techniques for Advanced Charging of Batteries." Mediterranean Journal of Basic and Applied Sciences 08, no. 01 (2024): 22–36. http://dx.doi.org/10.46382/mjbas.2024.8104.

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Batteries are remarkable devices. Nowadays, they power devices everywhere, from small children toys to IoT devices, cellphones and automobiles, especially rechargeable ones. The need to have healthy batteries ready to be reused in a very short time is essential. Unfortunately, charging a battery is a trivial task that can lead to battery degradation and wear, even thermal escape and fire. The faster the charging process, the more the problems that arise in the charging battery. In this work, several charging algorithms and noteworthy, although mostly unknown, methods are presented and commente
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Cai, Junpeng, Dewang Chen, Shixiong Jiang, and Weijing Pan. "Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles." Sustainability 12, no. 18 (2020): 7343. http://dx.doi.org/10.3390/su12187343.

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With the increasing popularization and competition of electric vehicles (EVs), EV users often have anxiety on their trip to find better charging stations with less travel distance. An intelligent charging guidance strategy and two algorithms were proposed to alleviate this problem. First, based on the next destination of EV users’ trip, the strategy established a dynamic-area model to match charging stations with users’ travel demand intelligently. In the dynamic area, the Dijkstra algorithm is used to find the charging station with the shortest trip. Then, the area extension algorithm and the
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Lu, Yiqi, Yongpan Li, Da Xie, et al. "The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load." Energies 11, no. 11 (2018): 3207. http://dx.doi.org/10.3390/en11113207.

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To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of hist
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Chang, Hsiao-Ching, Hsing-Tsung Lin, and Pi-Chung Wang. "Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas." Future Internet 15, no. 9 (2023): 301. http://dx.doi.org/10.3390/fi15090301.

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With the rapid development of the Internet of Things, the number of wireless devices is increasing rapidly. Because of the limited battery capacity, these devices may suffer from the issue of power depletion. Radio frequency (RF) energy harvesting technology can wirelessly charge devices to prolong their lifespan. With the technology of beamforming, the beams generated by an antenna array can select the direction for wireless charging. Although a good charging-time schedule should be short, energy efficiency should also be considered. In this work, we propose two algorithms to optimize the tim
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Smith, Theron, Joseph Garcia, and Gregory Washington. "Smart Electric Vehicle Charging via Adjustable Real-Time Charging Rates." Applied Sciences 11, no. 22 (2021): 10962. http://dx.doi.org/10.3390/app112210962.

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This paper presents a plug-in electric vehicle (PEV) charging control algorithm, Adjustable Real-Time Valley Filling (ARVF), to improve PEV charging and minimize adverse effects from uncontrolled PEV charging on the grid. ARVF operates in real time, adjusts to sudden deviations between forecasted and actual baseloads, and uses fuzzy logic to deliver variable charging rates between 1.9 and 7.2 kW. Fuzzy logic is selected for this application because it can optimize nonlinear systems, operate in real time, scale efficiently, and be computationally fast, making ARVF a robust algorithm for real-wo
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Wang, Xiaoli, Zhiyu Zhang, Mengmeng Jiang, Yifan Wang, and Yuping Wang. "Scheduling Model and Algorithm for Transportation and Vehicle Charging of Multiple Autonomous Electric Vehicles." Mathematics 13, no. 1 (2025): 145. https://doi.org/10.3390/math13010145.

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Autonomous electric vehicle (AEV) services leverage advanced autonomous driving and electric vehicle technologies to provide innovative, driverless transportation solutions. The biggest challenge faced by AEVs is the limited number of charging stations and long charging times. A critical challenge is maximizing passenger travel satisfaction while reducing the AEV idle time. This involves coordinating passenger transport and charging tasks via leveraging the information from charging stations, passenger transport, and AEV data. There are four important contributions in this paper. Firstly, we i
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Deng, Jiawen, Junqing Li, Chengyou Li, et al. "A hybrid algorithm for electric vehicle routing problem with nonlinear charging." Journal of Intelligent & Fuzzy Systems 40, no. 3 (2021): 5383–402. http://dx.doi.org/10.3233/jifs-202164.

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This paper investigates the electric vehicle routing problem with time windows and nonlinear charging constraints (EVRPTW-NL), which is more practical due to battery degradation. A hybrid algorithm combining an improved differential evolution and several heuristic (IDE) is proposed to solve this problem, where the weighted sum of the total trip time and customer satisfaction value is minimized. In the proposed algorithm, a special encoding method is presented that considers charging stations features. Then, a battery charging adjustment (BCA) strategy is integrated to decrease the charging tim
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Tang, Qinghua, Demin Li, Yihong Zhang, and Xuemin Chen. "Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks." Applied Sciences 13, no. 9 (2023): 5476. http://dx.doi.org/10.3390/app13095476.

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With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push–pull communication m
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Dissertations / Theses on the topic "Charging Algorithm"

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Sörme, Jacob. "Intelligent Charging Algorithm for Electric Vehicles." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280808.

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Electric vehicles play an important role in creating a fossil free transport sector. Making the vehicles efficient involves many new areas outside the manufacturing process, such as chargers, power grids and electricity markets. This thesis models the charging of electric vehicles using a Markov Decision Process and uses Reinforcement Learning solution models to derive an intelligent charging algorithm. This algorithm can take concepts such as electricity price, battery degradation and electrical losses into account in order to minimise the overall operational costs, and add more value to the
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Segelsjö, Duvernoy Rebecca, and Johanna Lundblad. "Development of a Smart Charging Algorithm for Electric Vehicles at Home." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384707.

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The purpose of this bachelor thesis is to develop a smart charging algorithm for electric vehicles (EVs) and examine the potential of the smart charging scheme, compared to uncontrolled charging scheme at residential houses with an installed photovoltaic (PV) system. The thesis examines if smart charging can increase the photovoltaic self-consumption and self-sufficiency of houses. Also, the thesis will evaluate if the smart charging scheme can reduce the household peak loads compared to the uncontrolled charging scheme. The presented results show that the implementation of the proposed algori
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Aloqaily, Osama. "Charging and Discharging Algorithms for Electric Vehicles in Smart Grid Environment." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34562.

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Power demands will increase day-by-day because of widely adopting of Plug-in Electric Vehicles (PEVs) in the world and growing population. Finding and managing additional power resources for upcoming demands is a challenge. Renewable power is one of the alternatives. However, to manage and control renewable resources, we need suitable Energy Storage System (ESS). PEVs have a large battery pack that is used mainly to supply electric motor. Moreover, PEV battery could be used as an ESS to store power at a certain time and use it at another time. Nevertheless, it can play the same role with elect
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Wang, Dian. "Microgrid based on photovoltaic energy for charging electric vehicle stations : charging and discharging management strategies in communication with the smart grid." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2584.

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Le développement rapide des véhicules électriques (EVs) augmente la demande de puissance, ce qui provoque une charge supplémentaire sur le réseau public et augmente les fluctuations de la charge. Par conséquent, la forte pénétration des EVs est freinée. Un algorithme simulé en temps réel et basé sur des règles est élaboré pour les bornes de recharge des EVs alimentées par un micro-réseau DC afin de faire face aux incertitudes du comportement des utilisateurs des EVs. L'algorithme prend en considération les choix arbitraires et aléatoires proposés via l'interface homme-machine. Les résultats de
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Omareen, Mustafa. "Optimized Integration of Electric Vehicles into the Smart Grid : V2G and Smart Charging Adaptive Algorithm." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99009.

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Electric Vehicles (EVs) reduce dependency on oil and carbon emissions. An upsurge in demand for EVs could lead to negative impacts on the grid. However, charging strategies, such as supporting the grid using vehicle-to-grid (V2G) and smart charging technology, can go a long way to reducing the impacts on the electrical load curve. The thesis presents a number of aspects which relate to the interconnection between EVs and the electric grid for achieving an optimized integration. An adaptive algorithm has been developed to perform load peak shaving by V2G and smart charging, while a hypothetical
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Andreasson, Erik, and Amanda Axelsson. "Comparing technologies and algorithms behind mapping and routing APIs for Electric Vehicles." Thesis, Tekniska Högskolan, Jönköping University, JTH, Datateknik och informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-50019.

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The fast-developing industry of electric vehicles is growing, and so is the driver community, which puts pressure on the electric charging grid. The purpose of this thesis is to simplify for the drivers of electric cars to charge their cars during trips. The research questions investigated are” How do the technologies and algorithms behind navigation APIs differ from each other?” and “What information is provided by the charging station APIs and how do they collect data about new stations?”. Information for the thesis was collected by reading and analyzing both documentation and previous work,
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Goga, Eda. "Progettazione ed analisi di algoritmi di mobilità controllata per reti mesh aeree con vincoli di copertura e persistenza." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.

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In questo lavoro di tesi verrà presentata l’implementazione di due algoritmi di Deployment e gestione di uno sciame di dispositivi UAV (Unmanned Aerial Vehicles). L’interesse scientifico su cui si fonda quest'analisi ha origine nelle enormi potenzialità degli UAV che garantiscono un'esplorazione aerea di aree pericolose in contesti di emergenze quali ad esempio scenari post catastrofe. La problematica principale affrontata sarà quella della gestione continuativa dell'area disastrata fornendo un algoritmo di schedulazione della cooperazione degli UAV alternando periodi attivi con quelli di rica
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Hamilton, Christopher. "Digital control algorithms : low power wind turbine energy maximizer for charging lead acid batteries." Honors in the Major Thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1280.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Engineering and Computer Science<br>Electrical Engineering and Computer Science
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Wu, Rentao. "Integration of electric vehicles in a flexible electricity demand side management framework." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33261.

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Recent years have seen a growing tendency that a large number of generators are connected to the electricity distribution networks, including renewables such as solar photovoltaics, wind turbines and biomass-fired power plants. Meanwhile, on the demand side, there are also some new types of electric loads being connected at increasing rates, with the most important of them being the electric vehicles (EVs). Uncertainties both from generation and consumption of electricity mentioned above are thereby being introduced, making the management of the system more challenging. With the proportion of
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Basma, Hussein. "Energy management strategies for battery electric bus fleet." Thesis, Université Paris sciences et lettres, 2020. http://thesesprivees.mines-paristech.fr/2020/2020UPSLM036_archivage.pdf.

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Les bus électriques à batterie (BEB) représentent une solution prometteuse pour remplacer les flottes de bus diesel actuelles consommant des énergies fossiles grâce à leur efficacité énergétique élevée et à leur potentiel de réduction des émissions de gaz à effet de serre et à l’absence d’émissions de polluants atmosphériques locaux. Cependant, cette technologie doit faire face à plusieurs défis, en particulier le coût total de possession (TCO) élevé et des contraintes opérationnelles comme l’autonomie des bus, le temps et le lieu de recharge. Cette thèse présente une méthodologie systématique
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Books on the topic "Charging Algorithm"

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Ma, Zhongjing, and Suli Zou. Efficient Auction Games: Theories, Algorithms and Applications in Smart Grids & Electric Vehicle Charging. Springer, 2020.

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Ma, Zhongjing, and Suli Zou. Efficient Auction Games: Theories, Algorithms and Applications in Smart Grids and Electric Vehicle Charging. Springer Singapore Pte. Limited, 2021.

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Book chapters on the topic "Charging Algorithm"

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Ibanez, Al Ann, Younggin Hong, and Younchan Jung. "Charging Algorithm for Quality-Based VoIP Charging Mechanism." In Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24082-9_5.

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Tang, Wanrong, and Ying Jun Zhang. "ORCHARD Algorithm for PEV Charging." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45862-5_2.

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Muslimin, Selamat, Zainudin Nawawi, Bakti Yudo Suprapto, and Tresna Dewi. "Smart Charging Model Design with ACO Algorithm for Determining Current Charging Pattern." In Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023). Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-386-3_35.

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Wang, Shuaifeng, Fuzhong Wang, Guangqiang Tian, and Li Zhang. "Orderly Charging Guidance Strategy for Photovoltaic Charging Stations Based on Genetic Algorithm Optimization." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6328-4_76.

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Zhang, Jia, Tao Geng, Jun Xu, Yang Li, and Chen Zhang. "Electric Vehicle Charging Robot Charging Port Identification Method Based on Multi-algorithm Fusion." In Intelligent Robotics and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89134-3_62.

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Abrahamsson, Henrik, Bengt Ahlgren, Juan Alonso, Anders Andersson, and Per Kreuger. "A Multi-path Routing Algorithm for IP Networks Based on Flow Optimisation." In From QoS Provisioning to QoS Charging. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45859-x_13.

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Anjali, Tricha, Caterina Scoglio, Jaudelice C. de Oliveira, et al. "A New Path Selection Algorithm for MPLS Networks Based on Available Bandwidth Estimation." In From QoS Provisioning to QoS Charging. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45859-x_20.

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Xiong, Rui. "Low-Temperature Heating and Optimal Charging Methods for Lithium-Ion Batteries." In Battery Management Algorithm for Electric Vehicles. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0248-4_7.

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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. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2765-1_4.

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Shen, Zhao, Qianqian Wang, Wenchao Lu, and Weilun Peng. "Joint Optimization of Intelligent Parking and Automated EV Charging with Advanced Orderly Charging Scheduling Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3977-9_44.

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Conference papers on the topic "Charging Algorithm"

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Patil, B. P., Pankaj Doodhwal, Prince Kumar, Rakesh Kumar Dudhawal, and Abhay Khonje. "Smart Algorithm for Wireless EV Charging." In 2024 4th International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET). IEEE, 2024. https://doi.org/10.1109/icefeet64463.2024.10866513.

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Kumar, Alok, and Atul Kelkar. "Electric Vehicle Optimal Charging Algorithm using Reinforcement Learning." In 2024 10th International Conference on Electrical Engineering, Control and Robotics (EECR). IEEE, 2024. http://dx.doi.org/10.1109/eecr60807.2024.10607262.

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Farman, Mohd, Duli Chand Meena, and Narendra Kumar. "Real Time Pricing Algorithm Considering EV Charging Station Queuing." In 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE, 2024. http://dx.doi.org/10.1109/sefet61574.2024.10717972.

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Raj, Divya Bharathi, and Venkatakirthiga Murali. "A Novel Hybrid Algorithm for Charging Station Planning Problem." In 2024 23rd National Power Systems Conference (NPSC). IEEE, 2024. https://doi.org/10.1109/npsc61626.2024.10986982.

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Hsu, Yuan-Yu, Rei-Heng Cheng, Chang Wu Yu, Yea-Shuan Huang, and Wen Ouyang. "An Onion-Peeling Like Wireless Charging Pad Deployment Algorithm." In 2025 IEEE 26th International Conference on High Performance Switching and Routing (HPSR). IEEE, 2025. https://doi.org/10.1109/hpsr64165.2025.11038915.

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Shao, Junyan, Baoze Wei, Qian He, Pegah Alaee, Josep M. Guerrero, and Juan C. Vasquez. "Coordinated Path Planning and Charging Scheduling in a Charging Station System Using Greedy Best-First Search Algorithm." In 2024 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2024. https://doi.org/10.1109/ecce55643.2024.10860744.

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Chen, Wuxiao, Qiang Ye, Yuqing Cai, Han Lin, Zhijun Jiang, and Chenhan Zhang. "Intelligent Algorithm Based Interactive Optimization Charging Strategy for Vehicle Network." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847527.

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Medrano-Hermosillo, Jesús A., Abraham E. Rodríguez-Mata, Larbi Djilali, Oscar J. Suarez-Sierra, and Victor A. González Huitron. "Solar-Powered Electric Vehicle Charging Station with Fuzzy MPPT Algorithm." In 2024 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI). IEEE, 2024. http://dx.doi.org/10.1109/colcaci63187.2024.10666570.

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Yan, Chunwang, and Chunheng Yan. "Charging station layout analysis based on improved particle swarm algorithm." In 2024 4th International Conference on Energy, Power and Electrical Engineering (EPEE). IEEE, 2024. https://doi.org/10.1109/epee63731.2024.10875166.

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Anwar, Sohel, Sourav Pramanik, and Ali Amini. "A Lithium-Ion Battery Fast Charging Algorithm Based on Electrochemical Model: Experimental Results." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-145181.

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Abstract Lithium-Ion batteries have become the principal battery technology for EVs to date. However, one of the principal factors limiting the widespread usage of the EVs is the length of charging times for the lithium-ion battery packs. The appropriate charging algorithm is critical to shorten the battery charging times while keeping the battery safe. In our earlier work, we proposed a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model using A performance index that aimed at achieving a faster charging rate while maintaining safe limits for var
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Reports on the topic "Charging Algorithm"

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Radvand, Tina, and Alireza Talebpour. A Quantum Optimization Algorithm for Optimal Electric Vehicle Charging Station Placement for Intercity Trips. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-028.

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Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure, particularly to support long-distance travel. Identifying optimal charging station locations in large transportation networks presents a well-known NP-hard combinatorial optimization problem, as the search space grows exponentially with the number of potential charging station locations. This report introduces a quantum search-based optimization algorithm designed to enhance the efficiency of solv
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