Deep Learning for Data‐Driven Vehicle‐To‐Grid and Grid‐To‐Vehicle Energy Management

Electric vehicles (EVs) have emerged as a sustainable alternative, offering low operational costs and nearly zero CO ₂ emissions, representing a greener solution for modern transportation needs. EVs with large battery packs are not just mobile loads; they can also serve as energy sources in bidirectional vehicle‐to‐grid (V2G) and grid‐to‐vehicle (G2V) interactions. This paper proposes a data‐driven approach, based on real‐world data on EV charging sessions and the power network, to optimise bidirectional power flow by matching EV charging demand with energy production. The study modelled, analysed and optimised the dynamic interaction between the EV fleet and the power grid. The impact of EV charging and discharging is analysed using high‐resolution temporal data and deep learning techniques. High‐resolution data are used to simulate realistic charging and discharging behaviour, including state‐of‐charge (SOC) dynamics and battery constraints. Further, the proposed approach generates a synthetic yet reliable profile of V2G power support, which is then incorporated into the grid. Advanced deep learning techniques predict and manage load adjustments utilising EVs, ensuring a balance between supply and demand while meeting EV users’ requirements. The framework demonstrates significant improvement by reducing annual peak demand by 8.6%, lowering the peak‐to‐average ratio from 1.42 to 1.33, decreasing load variance by 40.4% and reducing the demand‐generation mismatch by 43.3%. The K ‐means clustering identifies five distinct EV charging behaviours, while battery ageing analysis indicates a low degradation cost (≈$ 0.043/kWh of V2G throughput) with positive net owner benefits, and the enhanced deep neural network achieves high prediction accuracy (MAE = 40.7 MW, RMSE = 49.0 MW and R ² = 0.9943) with well‐calibrated 95% uncertainty intervals (98.7% empirical coverage). Further, this research validates the data‐driven integration of smart energy management strategies for EVs, such as peak shaving and valley filling, to enhance grid performance, stability and reliability.