Recent Advances in Energy Management System for Electric Vehicle with Solar and Wind Using Red Panda and Similarity-Navigated Graph Neural Network: A Systematic Review

Abstract

The global shift toward sustainable transportation and renewable energy integration has intensified the need for intelligent energy management systems capable of coordinating electric vehicles, solar photovoltaic systems, wind energy, and grid infrastructure. 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 systematic review of advanced energy management approaches, focusing on the integration of the Red Panda Optimization algorithm with Similarity-Navigated Graph Neural Networks. The Red Panda algorithm provides efficient global optimization through adaptive exploration–exploitation strategies, while the graph neural network captures spatial and temporal dependencies within energy systems. The similarity-based attention mechanism enhances prediction accuracy by leveraging relevant historical patterns, enabling improved forecasting, state estimation, and decision-making 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 studies demonstrate that hybrid optimization–learning frameworks outperform conventional methods in adaptability, efficiency, and robustness. However, challenges such as computational complexity, data uncertainty, and real-time deployment remain. This review highlights the potential of combining metaheuristic optimization and graph-based deep learning to develop scalable, intelligent, and sustainable energy management systems.