Tehnički vjesnik, Vol. 33 No. 1, 2026.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20250208002346
Maximizing Photovoltaic Power Output in Partial Shading Conditions for Electric Vehicle Applications: A Comparative Study of Intelligent Control Strategies
Nabil Mchirgui
; Département d'Informatique et d'Ingénierie, LIMA Research Laboratory, Université du Québec en Outaouais, 101, rue Saint-Jean-Bosco, Gatineau, Quebec, Canada J8Y 3G5
Ahmed Lakhssassi
; Department of Computer Science and Engineering, LIMA Laboratory, Université du Québec en Outaouais (UQO), 101, rue Saint-Jean-Bosco, Gatineau, Quebec, Canada J8Y 3G5
Habib Kraiem
orcid.org/0000-0003-3248-2430
; Center for Scientific Research and Entrepreneurship, Northern Border University, Arar 73213, Saudi Arabia
*
Mohamed Rahouti
; Department of Computer and Information Science, Fordham University, 113 W 60th Street, New York, NY 10023, USA
Hady Abdel Maksoud
; 1) Electrical Engineering Department, College of Engineering, Northern Border University, Arar 1321, Saudi Arabia 2) Electrical Engineering Department, Faculty of Engineering, Menoufia University, Shebin El-Kom 32511, Egypt
Nordine Quadar
; Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON 11 K7K 7B4, Canada
* Dopisni autor.
Sažetak
The increasing demand for sustainable transportation has positioned Electric Vehicles (EVs) as a key application area for photovoltaic (PV) systems. This study aims to enhance PV performance under Partial Shading Conditions (PSCs) by employing advanced Maximum Power Point Tracking (MPPT) algorithms. A comprehensive simulation model featuring a boost converter and battery integration is developed to evaluate and compare three MPPT techniques: the conventional Perturb and Observe (P&O) method, Grey Wolf Optimization (GWO), and Fuzzy Logic Control. Unlike previous studies, this work conducts a structured and comparative performance analysis across four distinct and progressively complex shading scenarios. Performance metrics include average power output, convergence time, and steady-state oscillations. Simulation results under shading patterns SP1 to SP4 indicate that the Fuzzy Logic approach achieves superior performance, with mean power outputs of 997.094 W (SP1) and 410.081 W (SP4), outperforming GWO by 1.7% and 0.7%, respectively. These findings offer quantitative evidence for the effectiveness of intelligent MPPT methods in enabling robust and efficient PV integration for EV applications.
Ključne riječi
electric vehicles (EVs); fuzzy; grey wolf optimization GWO; maximum power point tracking (MPPT); perturb and observe (P&O), photovoltaic system
Hrčak ID:
342657
URI
Datum izdavanja:
31.12.2025.
Posjeta: 369 *