Deep Learning and Optimization Approaches in Energy Management System for Electric Vehicle with Solar and Wind Using Red Panda and Similarity-Navigated Graph Neural Network: A Review

Abstract

The rapid advancement of deep learning and bio-inspired optimization has significantly enhanced the development of intelligent energy management systems for integrated electric vehicle, solar, and wind energy networks. These systems must address complex, dynamic, and uncertain interactions while optimizing energy efficiency, cost, battery health, and grid stability in real time.

This paper presents a comprehensive review of hybrid intelligent frameworks combining advanced deep learning architectures with metaheuristic optimization techniques. The study explores models such as convolutional neural networks, recurrent networks, transformers, and graph neural networks, including the Similarity-Navigated GNN, for accurate forecasting and state estimation. It also examines optimization approaches, particularly the Red Panda Optimization algorithm, which provides effective global search through adaptive exploration–exploitation strategies for solving multi-objective scheduling problems in renewable-integrated EV systems.

Applications include vehicle-to-grid systems, renewable energy scheduling, battery management, and demand response in smart grids and microgrids. Comparative analysis demonstrates that hybrid learning–optimization frameworks outperform traditional methods in adaptability, efficiency, and robustness. However, challenges such as computational complexity, data uncertainty, and scalability remain. This review highlights the potential of integrating deep learning and metaheuristic optimization to develop intelligent, adaptive, and sustainable energy management systems for next-generation power networks.