Artificial Intelligence Techniques for Energy Management System for Electric Vehicle with Solar and Wind Using Red Panda and Similarity-Navigated Graph Neural Network: Trends and Challenges

Abstract

The global transition toward sustainable energy systems and electric mobility has created a critical need for intelligent energy management systems capable of coordinating electric vehicles with solar and wind energy sources. The intermittent nature of renewable energy and the dynamic behavior of electric vehicle demand introduce complex, nonlinear, and stochastic challenges that require advanced computational frameworks beyond conventional methods. This paper presents a comprehensive review of artificial intelligence-based energy management approaches, focusing on the integration of the Red Panda Optimization Algorithm (RPOA) and Similarity-Navigated Graph Neural Networks (SN-GNN). The RPOA provides efficient multi-objective optimization through adaptive exploration–exploitation strategies, while SN-GNN captures complex spatial and temporal dependencies within energy networks using similarity-based attention mechanisms. Together, they form a hybrid framework capable of accurate 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. Comparative analysis shows that hybrid optimization–learning frameworks outperform traditional techniques in efficiency, adaptability, and robustness. However, challenges such as computational complexity, scalability, and real-time implementation 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 next-generation electric transportation.