Energy Optimal Route Planning Framework for Electric Vehicles Using Hybrid Artificial Intelligence Models

The rapid proliferation of electric vehicles (EVs) has necessitated intelligent route planning systems that prioritize energy efficiency over mere distance or travel time. This study proposes a novel Energy Optimal Route Planning Framework that integrates multi-source spatio-temporal data with hybrid artificial intelligence (AI) models to enhance navigation decisions for EVs. The artificial intelligence approach includes the graph-based representation of roads, physics-directed neural differentiable representations of real-time energy prediction, and quantum-inspired optimization of multi-objective cost optimization. An extraordinary hybrid learning system is introduced that integrates Deep Reinforcement Learning (DRL) and Swarm-Based Global Search to enable the learning to be dynamic in response to the traffic, elevation, and charging constraints. The system also involves Gated Graph-Temporal Convolutional Networks (G-GTCNs) to predict traffic trends in order to proactively route traffic. A city-wide experimental validation shows that 26.3% energy efficiency is improved, 14.8% route detour time reduction, and 91.72 route selection accuracy improve compared to the traditional shortest-path and time-based navigation models. The future of smarter EV routing systems is that the framework has real-time flexibility, predictive intelligence, and multi-objective optimization. This study provides a foundation of intelligent transportation systems that are energy-sensitive, infrastructure-sensitive and appropriate in the real-world application in the highly populated or infrastructure-restrained.