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

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 frame