Deep Learning Approaches for Electric Vehicle Charging and Smart Grid Coordination: A Review

Abstract

The rapid growth of electric vehicles (EVs) has created major challenges for modern power systems, including increased energy demand, grid instability, and efficient charging management. Integrating EVs with smart grids and intelligent transportation systems requires advanced computational frameworks capable of handling dynamic and large-scale data. Artificial intelligence, particularly deep learning and optimization techniques, has emerged as an effective solution for optimizing EV charging operations. Parallel Convolutional Neural Networks (PCNNs) and hybrid architectures such as CNN-LSTM and reinforcement learning models have demonstrated strong performance in load forecasting, charging prediction, and real-time energy management.

AI-driven charging systems improve grid efficiency by enabling intelligent scheduling, demand response, and adaptive charging control. Optimization approaches significantly reduce peak load demand and minimize grid stress during large-scale EV integration. PCNN models enhance system performance through parallel processing and multi-dimensional feature extraction from IoT sensors, traffic systems, and smart grid data. Furthermore, reinforcement learning-assisted frameworks effectively manage uncertainties in EV charging behavior, improving decision-making in dynamic environments. These intelligent approaches support seamless coordination between smart grids and transportation systems, contributing to efficient, scalable, and sustainable EV charging infrastructures.