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Energies
Volume 19
Issue 3
10.3390/en19030864
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Article

A Neural Network Integration of Virtual Synchronous Motor-Based EV Charging Stations Control Performance and Plant Stability Enhancement

by
Kabir Momoh
1,
Shamsul Aizam Zulkifli
1,*,
Mohammed F. Allehyani
2,
Husam S. Samkari
2,3,*,
Abdulgafor Alfares
4,
Petr Korba
5,
Mohd Zamri Che Wanik
6 and
Muhamad Syazmie Sepeeh
7
1
Department of Electrical Power Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia
2
Department of Electrical Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia
3
Artificial Intelligence and Sensing Technologies Research Center, University of Tabuk, Tabuk 47512, Saudi Arabia
4
Department of Electrical Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia
5
School of Engineering, ZHAW Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland
6
Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha 34110, Qatar
7
Department of Electrical and Electronic Engineering, Universiti Pertahanan Nasional Malaysia, Sungai Besi 57000, Malaysia
*
Authors to whom correspondence should be addressed.
Energies 2026, 19(3), 864; https://doi.org/10.3390/en19030864
Submission received: 15 December 2025 / Revised: 24 January 2026 / Accepted: 3 February 2026 / Published: 6 February 2026
(This article belongs to the Special Issue Advances in Power Distribution Systems: 2nd Edition)
Versions Notes

Abstract

Control techniques for neural-network-based charging stations (CSs) are attracting attention worldwide. This popularity is due to the emergent need for alternative intelligent and adaptive control solutions for attaining a CS with stabilized power transfer and voltage control at the point of common coupling. This paper demonstrates novel neural-network-based improved virtual synchronous motor (NN-i-VSM) control through the mechanism of the charging voltage feedback in conjunction with a trained neural network model to adaptively produce field excitation (MN) that mimics a virtual flux model. The MN adaptively generates an electromotive force based on the trained NN output to control the rectifying converter response of the CS for power quality enhancement during multiple-CS operation. Simulation results in the scenario of multiple CSs at 750 kW (5 × 150 kW) with varying capacities showed significant improvement in voltage variable tracking capacity of up to 500 V as well as power response overshot reduction and grid voltage response tracking improvement compared with an i-VSM-based CS model. A comprehensive CS efficiency assessment and plant stability analysis, including Bode plot evaluation, further confirmed the superior dynamic response performance and robustness of the NN-i-VSM model over the i-VSM model. The proposed model offers scalable applicability in smart mobility and wireless CS integration, signifying a new control advancement for future generations of multiple-grid-friendly charging infrastructure for penetration of batteries at varying capacities.
Keywords: neural networks; charging stations; virtual synchronous motor; charging voltage neural networks; charging stations; virtual synchronous motor; charging voltage

Share and Cite

MDPI and ACS Style

Momoh, K.; Zulkifli, S.A.; Allehyani, M.F.; Samkari, H.S.; Alfares, A.; Korba, P.; Che Wanik, M.Z.; Sepeeh, M.S. A Neural Network Integration of Virtual Synchronous Motor-Based EV Charging Stations Control Performance and Plant Stability Enhancement. Energies 2026, 19, 864. https://doi.org/10.3390/en19030864

AMA Style

Momoh K, Zulkifli SA, Allehyani MF, Samkari HS, Alfares A, Korba P, Che Wanik MZ, Sepeeh MS. A Neural Network Integration of Virtual Synchronous Motor-Based EV Charging Stations Control Performance and Plant Stability Enhancement. Energies. 2026; 19(3):864. https://doi.org/10.3390/en19030864

Chicago/Turabian Style

Momoh, Kabir, Shamsul Aizam Zulkifli, Mohammed F. Allehyani, Husam S. Samkari, Abdulgafor Alfares, Petr Korba, Mohd Zamri Che Wanik, and Muhamad Syazmie Sepeeh. 2026. "A Neural Network Integration of Virtual Synchronous Motor-Based EV Charging Stations Control Performance and Plant Stability Enhancement" Energies 19, no. 3: 864. https://doi.org/10.3390/en19030864

APA Style

Momoh, K., Zulkifli, S. A., Allehyani, M. F., Samkari, H. S., Alfares, A., Korba, P., Che Wanik, M. Z., & Sepeeh, M. S. (2026). A Neural Network Integration of Virtual Synchronous Motor-Based EV Charging Stations Control Performance and Plant Stability Enhancement. Energies, 19(3), 864. https://doi.org/10.3390/en19030864

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