A Survey of Methods and Architectures for Energy Management System for Electric Vehicle with Solar and Wind Using Red Panda and Similarity-Navigated Graph Neural Network

Abstract

The rapid growth of electric vehicles and the increasing deployment of solar and wind energy systems have created a critical need for intelligent energy management frameworks capable of handling complex, dynamic, and stochastic energy interactions. These systems must efficiently coordinate power generation, storage, and consumption while optimizing cost, reliability, and environmental impact in real time. This paper presents a comprehensive review of advanced energy management approaches, focusing on the integration of the Red Panda Optimization Algorithm (RPOA) and Similarity-Navigated Graph Neural Networks (SN-GNN). The RPOA offers effective multi-objective optimization through adaptive exploration–exploitation strategies, while SN-GNN enhances learning by capturing complex spatial and temporal relationships within energy networks using similarity-based attention mechanisms. The hybrid framework enables improved forecasting, state estimation, and optimal power dispatch in renewable-integrated EV systems. Applications include vehicle-to-grid systems, renewable energy scheduling, battery management, and smart grid operations across microgrids and large-scale energy networks. Comparative studies demonstrate that hybrid optimization–learning frameworks outperform traditional methods in efficiency, adaptability, and robustness. However, challenges such as computational complexity, scalability, and real-time deployment remain. This review highlights the potential of combining metaheuristic optimization and graph-based deep learning to develop intelligent, scalable, and sustainable energy management systems for future clean energy ecosystems.