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JCR Impact Factor: 0.800
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Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  4/2021 - 3

Intelligent Charging Control of Power Aggregator for Electric Vehicles Using Optimal Control

ALKAWAZ, A. N. See more information about ALKAWAZ, A. N. on SCOPUS See more information about ALKAWAZ, A. N. on IEEExplore See more information about ALKAWAZ, A. N. on Web of Science, KANESAN, J. See more information about  KANESAN, J. on SCOPUS See more information about  KANESAN, J. on SCOPUS See more information about KANESAN, J. on Web of Science, MOHD KHAIRUDDIN, A. S. See more information about  MOHD KHAIRUDDIN, A. S. on SCOPUS See more information about  MOHD KHAIRUDDIN, A. S. on SCOPUS See more information about MOHD KHAIRUDDIN, A. S. on Web of Science, CHOW, C. O. See more information about  CHOW, C. O. on SCOPUS See more information about  CHOW, C. O. on SCOPUS See more information about CHOW, C. O. on Web of Science, SINGH, M. See more information about SINGH, M. on SCOPUS See more information about SINGH, M. on SCOPUS See more information about SINGH, M. on Web of Science
 
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Download PDF pdficon (1,684 KB) | Citation | Downloads: 804 | Views: 1,329

Author keywords
battery chargers, electric vehicle, energy consumption, lithium batteries, optimal control

References keywords
electric(18), vehicles(13), charging(11), control(9), vehicle(8), plug(8), optimal(8), smart(7), grid(7), energy(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-11-30
Volume 21, Issue 4, Year 2021, On page(s): 21 - 30
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.04003
Web of Science Accession Number: 000725107100003
SCOPUS ID: 85122254802

Abstract
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Electric Vehicles (EVs) have been shown to be better for the environment since they emit lesser air pollutants compared to fuel-based vehicles. High penetration of EVs in the distribution network contributes to the increment of capital investment in smart grid technologies. This is because EVs' charging operation involves a considerably high level of electricity due to the size of EVs' battery charging period. Poor scheduling of EVs charging operation will lead to an increment in electricity consumption. This will then lead to overloading of distribution network, voltage quality degradation, power loss increment, and dispatch of uneconomical energy sources. Hence, coordinated, and smart charging schemes are vital in order to reduce charging costs. This paper proposes an optimized EV battery charging and discharging scheduling model using an ordinary differential equation (ODE) based on three charging scenarios. The daily charging and discharging schedule of EVs are optimized using optimal control. The result shows that the proposed optimized charging and discharging scheduling model reduces the charging cost up to approximately 50%.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 4
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Cited-By CrossRef

[1] Day-Ahead Electricity Price Forecasting Based on Hybrid Regression Model, Alkawaz, Ali Najem, Abdellatif, Abdallah, Kanesan, Jeevan, Khairuddin, Anis Salwa Mohd, Gheni, Hassan Muwafaq, IEEE Access, ISSN 2169-3536, Issue , 2022.
Digital Object Identifier: 10.1109/ACCESS.2022.3213081
[CrossRef]

[2] Training Multilayer Neural Network Based on Optimal Control Theory for Limited Computational Resources, Alkawaz, Ali Najem, Kanesan, Jeevan, Khairuddin, Anis Salwa Mohd, Badruddin, Irfan Anjum, Kamangar, Sarfaraz, Hussien, Mohamed, Baig, Maughal Ahmed Ali, Ahammad, N. Ameer, Mathematics, ISSN 2227-7390, Issue 3, Volume 11, 2023.
Digital Object Identifier: 10.3390/math11030778
[CrossRef]

[3] Adaptive Self-Organizing Map Using Optimal Control, Alkawaz, Ali Najem, Kanesan, Jeevan, Badruddin, Irfan Anjum, Kamangar, Sarfaraz, Hussien, Mohamed, Ali Baig, Maughal Ahmed, Ahammad, N. Ameer, Mathematics, ISSN 2227-7390, Issue 9, Volume 11, 2023.
Digital Object Identifier: 10.3390/math11091995
[CrossRef]

[4] Smart and Coordinated Charging Using Ensemble Model for Plug-in Electric Vehicle, Najem, Ali, Abdellatif, Abdallah, Kanesan, Jeevan, Khairuddin, Anis Salwa Mohd, Kairi, Muhammad Izhar, 2023 Innovations in Power and Advanced Computing Technologies (i-PACT), ISBN 979-8-3503-2518-8, 2023.
Digital Object Identifier: 10.1109/i-PACT58649.2023.10434577
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
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