Advanced intelligent control for photovoltaic-vehicle-to-grid integration

• Дата: 2026
• Автори: Lachheb, A., Chrouta, J., Telmoudi, A. J., Zaafouri, A.
• Формат: Стаття
• Мова: Англійська
• Опубліковано: National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2026
• Теми: artificial neural network; terminal sliding mode control; fuzzy logic; maximum power extraction; renewable energy
• Онлайн доступ: https://eie.khpi.edu.ua/article/view/343893
• Назва журналу: Electrical Engineering & Electromechanics

Introduction. The increasing penetration of electric vehicles (EVs) and renewable energy has intensified concerns about grid stability and energy sustainability. Integrating photovoltaic (PV) systems with vehicle-to-grid (V2G) technology provides a promising solution but requires efficient energy management and robust control strategies. Problem. Conventional maximum power point tracking (MPPT) methods such as perturb & observe (P&O) suffer from oscillations and poor dynamic response under rapidly changing conditions. Likewise, existing V2G strategies lack adaptive management for optimal renewable utilization and battery protection. Goal. To design an intelligent hybrid control system that maximizes PV power extraction and optimizes EV charging/discharging while ensuring grid stability and extending battery lifespan. Methodology. A two-level hierarchical control architecture is developed. At the low level, an artificial neural network combined with terminal sliding mode control (ANN-TSMC) performs adaptive MPPT. At the high level, a fuzzy logic controller (FLC) manages charging/discharging cycles based on state of charge, grid demand and parking duration. The proposed framework is validated through MATLAB/Simulink simulations. Results. Compared to conventional P&O, the ANN-TSMC controller improves tracking efficiency by 3.6 %, achieves faster convergence (0.14 s), and reduces steady-state oscillations. The FLC reduces grid reliance by 20 % while maintaining a high charging efficiency of 94 %. Furthermore, optimized charging cycles extend battery lifespan by 18.5 %. Scientific novelty. Unlike previous studies limited to single-level control or computationally intensive optimization, this work combines ANN learning ability with TSMC robustness and integrates FLC-based adaptive energy management. Practical value. The proposed system enables resilient PV-based V2G charging stations, reducing grid dependence, improving renewable penetration, and enhancing battery lifetime. These findings support the development of sustainable and grid-friendly EV infrastructures. References 31, tables 2, figures 7.