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Journal articles on the topic 'Charging Algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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9

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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11

Davydenko, Liudmyla, Nina Davydenko, Andrii Bosak, Alla Bosak, Agnieszka Deja, and Tygran Dzhuguryan. "Smart Sustainable Freight Transport for a City Multi-Floor Manufacturing Cluster: A Framework of the Energy Efficiency Monitoring of Electric Vehicle Fleet Charging." Energies 15, no. 10 (2022): 3780. http://dx.doi.org/10.3390/en15103780.

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This study focuses on the problem of the efficient energy management of an independent fleet of freight electric vehicles (EVs) providing service to a city multi-floor manufacturing cluster (CMFMC) within a metropolis while considering the requirements of smart sustainable electromobility and the limitations of the power system. The energy efficiency monitoring system is considered an information support tool for the management process. An object-oriented formalization of monitoring information technology is proposed which has a block structure and contains three categories of classes (informa

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Shi, Peijun, Guojian Ni, Rifeng Jin, et al. "Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty." Energies 18, no. 2 (2025): 241. https://doi.org/10.3390/en18020241.

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With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stat

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Su, Shangbin. "Method of Location and Capacity Determination of Intelligent Charging Pile Based on Recurrent Neural Network." World Electric Vehicle Journal 13, no. 10 (2022): 186. http://dx.doi.org/10.3390/wevj13100186.

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With the popularity of new energy vehicles, a large number of cities began to focus on the installation of electric vehicle charging piles. However, the existing intelligent charging piles have faced problems such as short supply, unreasonable distribution areas, and insufficient power supply. In response to these problems, this research proposes a recurrent neural network algorithm with an integrated firefly algorithm. Based on these two algorithms, a charging pile location and capacity model was established, and users' travel habits were analyzed according to the model. In the simulation exp

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Tazoe, Takashi, Soushi Yamamoto, Harunaga Onda, Hidetoshi Takeshita, Satoru Okamoto, and Naoaki Yamanaka. "EDA-Based Charging Algorithm for Plug-In Hybrid Electric Vehicle to Shift the Peak of Power Supply." Applied Mechanics and Materials 267 (December 2012): 46–50. http://dx.doi.org/10.4028/www.scientific.net/amm.267.46.

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This paper proposes a charging algorithm which is based on the Estimation of Distribution Algorithm (EDA) for Plug in Hybrid Electric Vehicle (PHEV). The proposed algorithm shifts the peak of power supply, satisfies the requested State of Charge (SoC [%]), and minimizes the charging cost. The proposed algorithm uses flexible weight for charging and upper charging limit to each PHEV to minimize the charge amount in peak time. The simulation result shows that the proposed algorithm shifts the peak of power supply in commute time zones, satisfies SoC of 93% PHEVs, and reduces charging cost by 31%

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15

Kujundžić, Goran, Drago Bago, and Alen Bernadić. "Comparison of Two Different Battery Charging Methods." B&H Electrical Engineering 17, no. 1 (2023): 17–25. http://dx.doi.org/10.2478/bhee-2023-0003.

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Abstract This paper describes charging of battery stack using model predictive control (MPC) algorithm. Temperature model and hybrid electrical model are used in algorithm. The battery stack consists of four serially connected valve-regulated lead-acid (VRLA) batteries. The objective of the optimization is to charge the proposed battery stack without violating the constraints in order to maintain the battery health. Validation of MPC algorithm for battery charging is performed using comparison between closed-loop MPC simulations and obtained characteristics of actual MPC charging. Previously,

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Selvaraj, Dhamodharan, and Dhanalakshmi Rangasamy. "Roof top PV for charging the EV using hybrid GWO-CSA." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 2 (2022): 1186. http://dx.doi.org/10.11591/ijpeds.v13.i2.pp1186-1194.

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In this paper, a novel idea of charging the vehicle on the go while using the solar panels on the roof of the vehicle is introduced. The use of electric vehicles has increased among people as the vehicles are affordable. Electric vehicle charging is one of the major problems faced by most manufacturers today. The PV panels take the energy from sunlight, and it can charge the vehicle battery. When the vehicle is moving on the road, the power extraction for charging may not be proper due to the partial shaded condition. To extract sufficient power for charging, a hybrid optimization algorithm ha

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Dhamodharan, Selvaraj, and Rangasamy Dhanalakshmi. "Roof top PV for charging the EV using hybrid GWO-CSA." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 2 (2022): 1186–94. https://doi.org/10.11591/ijpeds.v13.i2.pp1186-1194.

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In this paper, a novel idea of charging the vehicle on the go while using the solar panels on the roof of the vehicle is introduced. The use of electric vehicles has increased among people as the vehicles are affordable. Electric vehicle charging is one of the major problems faced by most manufacturers today. The PV panels take the energy from sunlight, and it can charge the vehicle battery. When the vehicle is moving on the road, the power extraction for charging may not be proper due to the partial shaded condition. To extract sufficient power for charging, a hybrid optimization algorithm ha

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18

Faria, João P. D., Ricardo L. Velho, Maria R. A. Calado, José A. N. Pombo, João B. L. Fermeiro, and Sílvio J. P. S. Mariano. "A New Charging Algorithm for Li-Ion Battery Packs Based on Artificial Neural Networks." Batteries 8, no. 2 (2022): 18. http://dx.doi.org/10.3390/batteries8020018.

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This paper shows the potential of artificial intelligence (AI) in Li-ion battery charging methods by introducing a new charging algorithm based on artificial neural networks (ANNs). The proposed charging algorithm is able to find an optimized charging current profile, through ANNs, considering the real-time conditions of the Li-ion batteries. To test and validate the proposed approach, a low-cost battery management system (BMS) was developed, supporting up to 168 cells in series and n cells in parallel. When compared with the multistage charging algorithm, the proposed charging algorithm revea

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Verbrugge, Boud, Abdul Mannan Rauf, Haaris Rasool, et al. "Real-Time Charging Scheduling and Optimization of Electric Buses in a Depot." Energies 15, no. 14 (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 o

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Li, Yongjing, Wenhui Pei, Qi Zhang, Di Xu, and Hao Ma. "Optimal Layout of Electric Vehicle Charging Station Locations Considering Dynamic Charging Demand." Electronics 12, no. 8 (2023): 1818. http://dx.doi.org/10.3390/electronics12081818.

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This paper proposes an optimization method for electric vehicle charging station locations considering dynamic charging demand. Firstly, the driving characteristics and charging characteristics of the electric vehicle are obtained based on the driving trajectory of the electric vehicle, and the charging demand is predicted using a Monte Carlo simulation. Then a mathematical model with the goal of minimizing the overall cost is constructed, and the impact on carbon emissions is considered in the model. In order to better solve the location model, an improved whale optimization algorithm based o

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Ahmed, Asad, Osman Hasan, Falah Awwad, Nabil Bastaki, and Syed Rafay Hasan. "Formal Asymptotic Analysis of Online Scheduling Algorithms for Plug-In Electric Vehicles’ Charging." Energies 12, no. 1 (2018): 19. http://dx.doi.org/10.3390/en12010019.

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A large-scale integration of plug-in electric vehicles (PEVs) into the power grid system has necessitated the design of online scheduling algorithms to accommodate the after-effects of this new type of load, i.e., PEVs, on the overall efficiency of the power system. In online settings, the low computational complexity of the corresponding scheduling algorithms is of paramount importance for the reliable, secure, and efficient operation of the grid system. Generally, the computational complexity of an algorithm is computed using asymptotic analysis. Traditionally, the analysis is performed usin

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Raval, Sanket, Thangadurai Natarajan, and Sanchari Deb. "A Novel Levy-Enhanced Opposition-Based Gradient-Based Optimizer (LE-OB-GBO) for Charging Station Placement." Electronics 12, no. 7 (2023): 1522. http://dx.doi.org/10.3390/electronics12071522.

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Transportation modes are shifting toward electric vehicles from conventional internal combustion engines to reduce pollution and dependency on conventional fuels. This reduces the fuel cost, while charging stations must be distributed across the locations to minimize range anxiety. Installing charging stations randomly across the distribution system can lead to violation of active power loss, voltage deviation, and reliability parameters of the power system. The problem of the optimal location of charging stations is a nonlinear optimization problem that includes the parameters of the distribu

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Han, Seongik. "Optimal Charging Current Protocol with Multi-Stage Constant Current Using Dandelion Optimizer for Time-Domain Modeled Lithium-Ion Batteries." Applied Sciences 14, no. 23 (2024): 11320. https://doi.org/10.3390/app142311320.

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This study utilized a multi-stage constant current (MSCC) charge protocol to identify the optimal current pattern (OCP) for effectively charging lithium-ion batteries (LiBs) using a Dandelion optimizer (DO). A Thevenin equivalent circuit model (ECM) was implemented to simulate an actual LiB with the ECM parameters estimated from the offline time response data obtained through a hybrid pulse power characterization (HPPC) test. For the first time, DO was applied to metaheuristic optimization algorithms (MOAs) to determine the OCP within the MSCC protocol. A composite objective function that inco

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Hou, Weicheng, Qingsong Luo, Xiangdong Wu, Yimin Zhou, and Gangquan Si. "Multiobjective Optimization of Large-Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station." Complexity 2021 (February 24, 2021): 1–17. http://dx.doi.org/10.1155/2021/8868617.

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With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of t

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Siron Anita Susan T and Nithya Balasubramanian. "A Hybrid Metaheuristic Algorithm for Stop Point Selection in Wireless Rechargeable Sensor Network." International Journal of Engineering and Technology Innovation 13, no. 4 (2023): 296–312. http://dx.doi.org/10.46604/ijeti.2023.11552.

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A wireless rechargeable sensor network (WRSN) enables charging of rechargeable sensor nodes (RSN) wirelessly through a mobile charging vehicle (MCV). Most existing works choose the MCV’s stop point (SP) at random, the cluster’s center, or the cluster head position, all without exploring the demand from RSNs. It results in a long charging delay, a low charging throughput, frequent MCV trips, and more dead nodes. To overcome these issues, this paper proposes a hybrid metaheuristic algorithm for stop point selection (HMA-SPS) that combines the techniques of the dragonfly algorithm (DA), firefly a

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Sun, Lijuan, Menggang Chen, Yawei Shi, et al. "Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System." Energies 15, no. 24 (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 electricit

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Flocea, Radu, Andrei Hîncu, Andrei Robu, et al. "Electric Vehicle Smart Charging Reservation Algorithm." Sensors 22, no. 8 (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 t

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Erdogan, Gülsah, and Wiem Fekih Hassen. "Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations." Energies 16, no. 18 (2023): 6656. http://dx.doi.org/10.3390/en16186656.

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The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by for

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Kok, Chiang Liang, Xuanyao Fu, Yit Yan Koh, and Tee Hui Teo. "A Novel Portable Solar Powered Wireless Charging Device." Electronics 13, no. 2 (2024): 403. http://dx.doi.org/10.3390/electronics13020403.

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This paper presents the development of a portable solar panel wireless charging device with an advanced charging algorithm. The device features a 6500 mAh Li-ion battery and is designed to efficiently charge smartphones and laptops. It incorporates a simulated solar panel, charging circuit, microcontroller, and wireless charging circuits. Rigorous testing has demonstrated its stable output voltage and current at 5 V/2 A, with high power transfer efficiency. The advanced charging algorithm optimizes power transfer efficiency and reduces charging time by adjusting the current and voltage to matc

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Nugraha, Candra Febri, Jimmy Trio Putra, Lukman Subekti, and Suhono Suhono. "Optimal Scheduling of Electric Vehicle Charging: A Study Case of Bantul Feeder 05 Distribution System." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 1 (2023): 36–48. https://doi.org/10.26555/jiteki.v9i1.25287.

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The growing popularity of electric vehicles (EVs) has the potential to complicate distribution network operations. When a large number of electric vehicles are charging at the same time, the system load can significantly increase. This problem is exacerbated when charging is done concurrently in the evening, which coincides with peak load times. To prevent the increase in peak load and distribution operation stress, EV charging must be coordinated to achieve financial and technical objectives. This study seeks to evaluate the impact of financially driven EV charging scheduling algorithms. The

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Li, Lanlan, Haipeng Dai, Chen Chen, Zilu Ni, and Shihao Li. "Scheduling Precedence Constraints among Charging Tasks in Wireless Rechargeable Sensor Networks." Electronics 13, no. 2 (2024): 346. http://dx.doi.org/10.3390/electronics13020346.

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The development of wireless power transfer (WPT) facilitates wireless rechargeable sensor networks (WRSNs) receiving considerable attention in the sensor network research community. Most existing works mainly focus on general charging patterns and metrics while overlooking the precedence constraints among tasks, resulting in charging inefficiency. In this paper, we are the first to advance the issue of scheduling wireless charging tasks with precedence constraints (SCPC), with the optimization objective of minimizing the completion time of all the charging tasks under the precedence constraint

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Sun, Ningbo. "Research on energy storage charging piles based on improved genetic algorithm." Journal of Physics: Conference Series 2703, no. 1 (2024): 012006. http://dx.doi.org/10.1088/1742-6596/2703/1/012006.

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Abstract Aiming at the charging demand of electric vehicles, an improved genetic algorithm is proposed to optimize the energy storage charging piles optimization scheme. Firstly, the characteristics of electric load are analyzed, the model of energy storage charging piles is established, the charging volume, power and charging/discharging timing constraints in the charging process are considered, and the optimization is carried out by the improved genetic algorithm, and the profit of charging piles and user charging electricity charges are calculated, so as to obtain the possible optimal param

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Ma, Tao, Junyong Lu, Xiao Zhang, Bofeng Zhu, Wenxuan Wu, and Xinlin Long. "Modeling and Design Optimization of Energy Transfer Rate for Hybrid Energy Storage System in Electromagnetic Launch." Energies 15, no. 3 (2022): 695. http://dx.doi.org/10.3390/en15030695.

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The battery-pulse capacitor-based hybrid energy storage system has the advantage of high-energy density and high-power density. However, to achieve a higher firing rate of the electromagnetic launch, a shorter charging time of the pulse capacitor from the battery is needed. A new optimization model by formulating the charging time problem as a constrained optimization problem is presented. Unlike existing algorithms, the proposed model can find the globally optimal solution. The circuit parameters are optimized through the Enumeration algorithm to minimize the total charging time of the pulse

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Jin, Hojun, Sangkeum Lee, Sarvar Hussain Nengroo, and Dongsoo Har. "Development of Charging/Discharging Scheduling Algorithm for Economical and Energy-Efficient Operation of Multi-EV Charging Station." Applied Sciences 12, no. 9 (2022): 4786. http://dx.doi.org/10.3390/app12094786.

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As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this

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García Álvarez, Jorge, Miguel González, Camino Rodríguez Vela, and Ramiro Varela. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm." Energies 11, no. 10 (2018): 2752. http://dx.doi.org/10.3390/en11102752.

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Scheduling the charging times of a large fleet of Electric Vehicles (EVs) may be a hard problem due to the physical structure and conditions of the charging station. In this paper, we tackle an EV’s charging scheduling problem derived from a charging station designed to be installed in community parking where each EV has its own parking lot. The main goals are to satisfy the user demands and at the same time to make the best use of the available power. To solve the problem, we propose an artificial bee colony (ABC) algorithm enhanced with local search and some mating strategies borrowed from g

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Nguyen, Phi Le, Van Quan La, Anh Duy Nguyen, Thanh Hung Nguyen, and Kien Nguyen. "An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning." Sensors 21, no. 16 (2021): 5520. http://dx.doi.org/10.3390/s21165520.

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In wireless rechargeable sensor networks (WRSNs), a mobile charger (MC) moves around to compensate for sensor nodes’ energy via a wireless medium. In such a context, designing a charging strategy that optimally prolongs the network lifetime is challenging. This work aims to solve the challenges by introducing a novel, on-demand charging algorithm for MC that attempts to maximize the network lifetime, where the term “network lifetime” is defined by the interval from when the network starts till the first target is not monitored by any sensor. The algorithm, named Fuzzy Q-charging, optimizes bot

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Panichtanakom, Supipat, and Kusumal Chalermyanont. "Electric Energy Management for Plug-in Electric Vehicles Charging in the Distribution System by a dual cascade scheduling algorithm." International journal of electrical and computer engineering systems 13, no. 1 (2022): 63–75. http://dx.doi.org/10.32985/ijeces.13.1.7.

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This paper presents an algorithm for plug-in electric vehicles (PEVs) charging in the three-phase distribution system for residential houses. It aims to prevent violent voltage level deviation and increasing losses on the three-phase distribution system due to uncontrolled charging and allocate power to each plug-in electric vehicle. The algorithm is comprised of two processes. The first process is power limitation and limited power of load imbalance by if-else rules, while the second process is power allocation to each PEV by the dual cascade scheduling algorithm which is the integration of t

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Fu, Zhihe, Yibiao Fan, Xiaowei Cai, Zhaohong Zheng, Jiaxiang Xue, and Kun Zhang. "Lithium Titanate Battery Management System Based on MPPT and Four-Stage Charging Control for Photovoltaic Energy Storage." Applied Sciences 8, no. 12 (2018): 2520. http://dx.doi.org/10.3390/app8122520.

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To overcome the unstable photovoltaic input and high randomness in the conventional three-stage battery charging method, this paper proposes a charging control strategy based on a combination of maximum power point tracking (MPPT), and an enhanced four-stage charging algorithm for a photovoltaic power generation energy storage system. This control algorithm ensures that the charging process is not affected by fluctuations in the photovoltaic power. The discharge bus waveform, push–pull discharge load switching waveform, push–pull circuit efficiency, and voltage and current regulation accuracie

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Zhao, Guo, Xueliang Huang, and Hao Qiang. "Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm." International Journal of Photoenergy 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/497697.

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Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell) genetic algorithm, an improved differential evolution cellular (IDECell) genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DEC

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Aoun, Alain, Mehdi Adda, Adrian Ilinca, Mazen Ghandour, and Hussein Ibrahim. "Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks." World Electric Vehicle Journal 15, no. 7 (2024): 324. http://dx.doi.org/10.3390/wevj15070324.

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The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV chargi

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Arfeen, Zeeshan Ahmad, Md Pauzi Abdullah, Usman Ullah Sheikh, et al. "Novel Supervisory Management Scheme of Hybrid Sun Empowered Grid-Assisted Microgrid for Rapid Electric Vehicles Charging Area." Applied Sciences 11, no. 19 (2021): 9118. http://dx.doi.org/10.3390/app11199118.

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The spread of electric vehicles (EV) contributes substantial stress to the present overloaded utility grid which creates new chaos for the distribution network. To relieve the grid from congestion, this paper deeply focused on the control and operation of a charging station for a PV/Battery powered workplace charging facility. This control was tested by simulating the fast charging station when connected to specified EVs and under variant solar irradiance conditions, parity states and seasonal weather. The efficacy of the proposed algorithm and experimental results are validated through simula

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KOSTIAN, Nataliia, Mirosław ŚMIESZEK, and Petro MATEICHYK. "COORDINATION OF OPTIMISATION TARGETS AT DIFFERENT LEVELS OF CHARGING INFRASTRUCTURE DEVELOPMENT MANAGEMENT." Humanities and Social Sciences quarterly 30, no. 4 - part 2 (2023): 153–62. http://dx.doi.org/10.7862/rz.2023.hss.69.

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The work considers the problem of optimising the urban charging infrastructure as a multi-channel mass service system with a queue. Queues at charging stations are due to the shortage of energy resources in the studied region. An algorithm has been developed and implemented to optimise a charging station for electric vehicles based on the criterion of meeting charging demand has been developed and implemented. At the same time, the main parameters of the system during peak hours were taken into account. The algorithm allows to determine the optimal number of chargers at the station depending o

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Zhao, Shuyi, Chenshuo Ma, and Zhiao Cao. "Improved Multi-Objective Strategy Diversity Chaotic Particle Swarm Optimization of Ordered Charging Strategy for Electric Vehicles Considering User Behavior." Energies 18, no. 3 (2025): 690. https://doi.org/10.3390/en18030690.

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With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user satisfaction to solve the charging and discharging scheduling problem of EVs. This article adds an objective function to quantify user satisfaction and addresses the issues of premature local optima and insufficient diversity in the MOPSO algorithm. Based on the performance of different particles, the algorithm assigns

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Xu, Di, Wenhui Pei, and Qi Zhang. "Optimal Planning of Electric Vehicle Charging Stations Considering User Satisfaction and Charging Convenience." Energies 15, no. 14 (2022): 5027. http://dx.doi.org/10.3390/en15145027.

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To solve the problem of layout design of charging stations in the early stage of the electric vehicle industry, the user’s satisfaction and the charging convenience are considered. An electric vehicle charging station site-selection model is established based on the kernel density analysis of the urban population. The goal of this model is maximum electric vehicle user satisfaction and the highest charging convenience. Then, according to model characteristics, the immune algorithm is designed and optimized to solve the model. The optimization of the immune algorithm includes two aspects. On th

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Muslimin, Selamat, Ekawati Prihatini, Nyayu Latifah Husni, et al. "Battery Current Estimation and Prediction During Charging with Ant Colony Optimization Algorithm." Digital 5, no. 1 (2025): 6. https://doi.org/10.3390/digital5010006.

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This paper presents an application of the Ant Colony Optimization (ACO) algorithm combined with the Logistic Regression (LR) method in the lead acid battery charging process. The ACO algorithm is used to obtain the best current pattern in the battery charging system to produce a smart charging system with a fast and safe charging current for the battery. The best current pattern is conducted gradually and repeatedly to obtain termination in the form of the best current pattern according to the ACO algorithm. The results of the algorithm design produce a current pattern consisting of 10 A, 5 A,

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Zhang, Mai, Cai Hong Zhao, Li Juan Tan, Li Liu, and Lu Lu Chen. "Research on EV Ordered Charging Considering TOU Price." Applied Mechanics and Materials 448-453 (October 2013): 3147–53. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3147.

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Large-scale of electric vehicles (EVs) for charging will affect grid operation, disordered charging will have significant impact on the grid. This paper presents ordered charging mathematical model considering the smallest charging fees for EV users, a genetic algorithm is used to get optimization solutions. Monte Carlo method is used to simulate EV charging user behavior, analysis of disordered and ordered charging simulation results are compared under different charging mode. Simulation results show that ordered charging has huge roles in reducing the cost of user charge, reducing the peak-v

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Guillet, Marianne, Gerhard Hiermann, Alexander Kröller, and Maximilian Schiffer. "Electric Vehicle Charging Station Search in Stochastic Environments." Transportation Science 56, no. 2 (2022): 483–500. http://dx.doi.org/10.1287/trsc.2021.1102.

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Electric vehicles are a central component of future mobility systems as they promise to reduce local noxious and fine dust emissions, as well as CO2 emissions, if fed by clean energy sources. However, the adoption of electric vehicles so far fell short of expectations despite significant governmental incentives. One reason for this slow adoption is the drivers’ perceived range anxiety, especially for individually owned vehicles. Here, bad user experiences (e.g., conventional cars blocking charging stations or inconsistent real-time availability data) manifest the drivers’ range anxiety. Agains

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Antarasee, Phiraphat, Suttichai Premrudeepreechacharn, Apirat Siritaratiwat, and Sirote Khunkitti. "Optimal Design of Electric Vehicle Fast-Charging Station’s Structure Using Metaheuristic Algorithms." Sustainability 15, no. 1 (2022): 771. http://dx.doi.org/10.3390/su15010771.

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The fast development of electric vehicles (EVs) has resulted in several topics of research in this area, such as the development of a charging pricing strategy, charging control, location of the charging station, and the structure within the charging station. This paper proposes the optimal design of the structure of an EV fast-charging station (EVFCS) connected with a renewable energy source and battery energy storage systems (BESS) by using metaheuristic algorithms. The optimal design of this structure aims to find the number and power of chargers. Moreover, the renewable energy source and B

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Kalk, Alexis, Lea Leuthner, Christian Kupper, and Marc Hiller. "An Aging-Optimized State-of-Charge-Controlled Multi-Stage Constant Current (MCC) Fast Charging Algorithm for Commercial Li-Ion Battery Based on Three-Electrode Measurements." Batteries 10, no. 8 (2024): 267. http://dx.doi.org/10.3390/batteries10080267.

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This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estim

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Li, Jinglin, Chengpeng Jiang, Jing Wang, Taian Xu, and Wendong Xiao. "Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks." Applied Sciences 13, no. 5 (2023): 2840. http://dx.doi.org/10.3390/app13052840.

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In wireless rechargeable sensor networks (WRSNs), a novel approach to energy replenishment is offered by the utilization of mobile chargers (MCs), which charge nodes via wireless energy transfer technology. However, previous research on mobile charging schemes has commonly prioritized charging efficiency as a performance index, neglecting the importance of quality of sensing coverage (QSC). As the network scale increases, the MC's charging power becomes unable to meet the energy needs of all nodes, leading to a decline in network QSC when nodes' energy is depleted. To solve this problem, we st

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