УДОСКОНАЛЕНИЙ НЕЧІТКИЙ МЕТОД ФОРМУВАННЯ ГРАФІКА ЗАРЯДЖАННЯ ЕЛЕКТРОМОБІЛІВ ПАРКІНГУ

Сергій Балюта; Людмила Копилова; Максим Жуков; Денис Жуков; Максим Власов; Петро Зінькевич

doi:10.20535/1813-5420.3.2026.370300

Vol. · Issue 3 · 2026 Oct 12, 2026 ENERGY SYSTEMS AND COMPLEXES

IMPROVED FUZZY METHOD FOR SCHEDULING ELECTRIC VEHICLE CHARGING IN A PARKING FACILITY

Serhii Balyuta Corresponding National University of Food Technologies , Ukraine

Lyudmila Kopylova National University of Food Technologies , Ukraine

Maksym Zhukov National University of Food Technologies , Ukraine

Denis Zhukov National University of Food Technologies , Ukraine

Maksym Vlasov National University of Food Technologies , Ukraine

Petro Zinkevych National University of Food Technologies , Ukraine

Pages28-36 PublishedOct 12, 2026 LicenseOpen Access

PDF (Ukrainian)

VOL · ISS 3 · 2026 85 View issue

Abstract

The purpose of the study is to develop and improve an interpretable fuzzy method for operational scheduling of electric vehicle charging in a parking facility under a limited number of charging ports, heterogeneous arrival and departure times, and different required states of charge. The basic method ranks connected vehicles using normalized state-of-charge deficit, remaining-time urgency, required-energy urgency, time-adequacy risk, and departure proximity. The improved version introduces adaptive amplification of the time-adequacy risk, a minimum guaranteed energy reserve for critical vehicles, departure-time protection, and a critical-priority mode that gradually shifts the allocation policy from fairness to deadline protection. The simulation model was implemented in MATLAB R2016a. A frozen benchmark matrix containing one reference case and 108 stress scenarios was used, while the physical evaluator, vehicle data structure, charging-port constraints, and time base remained unchanged. Eight methods were compared, including uncontrolled charging, first-come-first-served, equal-power allocation, urgency-based scheduling, the basic fuzzy method, the improved fuzzy method, an optimization reference, and mixed-integer linear programming. The improved fuzzy method increased the mean target-achievement coefficient from 0.27212 to 0.43853 and reduced mean departure-time violations from 9.2685 to 7.8241 while retaining a Jain fairness index of 0.94873. Mixed-integer linear programming achieved the best service indicators when a valid solution was obtained, but strict comparable solutions were available for only 72.22% of stress scenarios and required substantially longer computation. The results show that the improved fuzzy method provides a practical compromise between target achievement, fairness, maximum state-of-charge deficit, and computational scalability, making it suitable for real-time parking-facility charging control.

Keywords

#electric vehicle #parking facility #charging scheduling #fuzzy logic #state of charge #mixed-integer linear programming #fairness #real-time control

References

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R. Gauchotte, A. Oulamara, M. Ghogho, and M. Oudani, “Study of electric vehicle charging scheduling with renewable energy: Offline and stochastic online optimization,” European Journal of Operational Research, vol. 333, no. 1, pp. 295–319, 2026, doi: 10.1016/j.ejor.2026.01.015.
A. Khiar, M. E. A. Brahmia, A. Oulamara, and L. Idoumghar, “Multi-objective electric vehicle charging scheduling under stochastic duration uncertainty,” Omega, vol. 141, Art. no. 103506, 2026, doi: 10.1016/j.omega.2025.103506.
B. Wang and M. Xu, “A Fuzzy-Based Multi-Stage Scheduling Strategy for Electric Vehicle Charging and Discharging Considering V2G and Renewable Energy Integration,” Applied Sciences, vol. 16, no. 3, Art. no. 1166, 2026, doi: 10.3390/app16031166.
K. G. Firouzjah and J. Ghasemi, “A similarity-based predictive scheduling method for dynamic electric vehicle charging load management,” Scientific Reports, vol. 16, Art. no. 17560, 2026, doi: 10.1038/s41598-026-42594-w.
S. Faddel, A. T. Al-Awami, and M. A. Abido, “Fuzzy Optimization for the Operation of Electric Vehicle Parking Lots,” Electric Power Systems Research, vol. 145, pp. 166–174, 2017, doi: 10.1016/j.epsr.2017.01.008.

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§ 06 — Related

[1] C. Si, K. T. Chau, W. Liu, and Y. Hou, “Optimal scheduling for electric vehicle charging: A review of methods, technologies, and uncertainty management,” Journal of Energy Storage, vol. 131, Part A, Art. no. 117500, 2025, doi: 10.1016/j.est.2025.117500.

[2] J. Pasha, B. Li, Z. Elmi, A. M. Fathollahi-Fard, Y.-Y. Lau, A. Roshani, T. Kawasaki, and M. A. Dulebenets, “Electric vehicle scheduling: State of the art, critical challenges, and future research opportunities,” Journal of Industrial Information Integration, vol. 38, Art. no. 100561, 2024, doi: 10.1016/j.jii.2024.100561.

[3] A. Dolgui, S. Kovalev, and M. Y. Kovalyov, “Scheduling electric vehicle regular charging tasks: A review of deterministic models,” European Journal of Operational Research, vol. 325, no. 2, pp. 221–232, 2025, doi: 10.1016/j.ejor.2024.11.044.

[4] M. S. Aarabi, M. Khanahmadi, and A. Awasthi, “A Literature Review on Strategic, Tactical, and Operational Perspectives in EV Charging Station Planning and Scheduling,” World Electric Vehicle Journal, vol. 16, no. 7, Art. no. 404, 2025, doi: 10.3390/wevj16070404.

[5] S. Deb, M. Pihlatie, and M. Al-Saadi, “Smart Charging: A Comprehensive Review,” IEEE Access, vol. 10, pp. 134690–134703, 2022, doi: 10.1109/ACCESS.2022.3227630.

[6] O. Sadeghian, A. Oshnoei, B. Mohammadi-Ivatloo, V. Vahidinasab, and A. Anvari-Moghaddam, “A Comprehensive Review on Electric Vehicles Smart Charging: Solutions, Strategies, Technologies, and Challenges,” Journal of Energy Storage, vol. 54, Art. no. 105241, 2022, doi: 10.1016/j.est.2022.105241.

[7] B. P. Rimal, C. Kong, B. Poudel, Y. Wang, and P. Shahi, “Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues,” Energies, vol. 15, no. 5, Art. no. 1908, 2022, doi: 10.3390/en15051908.

[8] S. Wu and A. Pang, “Optimal scheduling strategy for orderly charging and discharging of electric vehicles based on spatio-temporal characteristics,” Journal of Cleaner Production, vol. 392, Art. no. 136318, 2023, doi: 10.1016/j.jclepro.2023.136318.

[9] Z. Zhao, C. K. M. Lee, X. Yan, and H. Wang, “Reinforcement learning for electric vehicle charging scheduling: A systematic review,” Transportation Research Part E: Logistics and Transportation Review, vol. 190, Art. no. 103698, 2024, doi: 10.1016/j.tre.2024.103698.

[10] H. Li, D. Son, and B. Jeong, “Electric vehicle charging scheduling with mobile charging stations,” Journal of Cleaner Production, vol. 434, Art. no. 140162, 2024, doi: 10.1016/j.jclepro.2023.140162.

[11] R. Gauchotte, A. Oulamara, M. Ghogho, and M. Oudani, “Study of electric vehicle charging scheduling with renewable energy: Offline and stochastic online optimization,” European Journal of Operational Research, vol. 333, no. 1, pp. 295–319, 2026, doi: 10.1016/j.ejor.2026.01.015.

[12] A. Khiar, M. E. A. Brahmia, A. Oulamara, and L. Idoumghar, “Multi-objective electric vehicle charging scheduling under stochastic duration uncertainty,” Omega, vol. 141, Art. no. 103506, 2026, doi: 10.1016/j.omega.2025.103506.

[13] B. Wang and M. Xu, “A Fuzzy-Based Multi-Stage Scheduling Strategy for Electric Vehicle Charging and Discharging Considering V2G and Renewable Energy Integration,” Applied Sciences, vol. 16, no. 3, Art. no. 1166, 2026, doi: 10.3390/app16031166.

[14] K. G. Firouzjah and J. Ghasemi, “A similarity-based predictive scheduling method for dynamic electric vehicle charging load management,” Scientific Reports, vol. 16, Art. no. 17560, 2026, doi: 10.1038/s41598-026-42594-w.

[15] S. Faddel, A. T. Al-Awami, and M. A. Abido, “Fuzzy Optimization for the Operation of Electric Vehicle Parking Lots,” Electric Power Systems Research, vol. 145, pp. 166–174, 2017, doi: 10.1016/j.epsr.2017.01.008.

IMPROVED FUZZY METHOD FOR SCHEDULING ELECTRIC VEHICLE CHARGING IN A PARKING FACILITY

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