Advancements in AI-Powered Electric Vehicle Routing: Multi-Constraint Optimization and Infrastructure Integration Approaches for Evolving EVs—A Survey

Electric vehicles (EVs) have become essential tools in the worldwide shift to sustainable transportation, driven by advances in battery technology, charging infrastructure, and routing algorithms. This paper presents a comprehensive investigation of the emerging EV ecosystem through three key domains: electric automobiles, routing algorithms, and charging infrastructure.The study uses a dual methodology combining automated data collection with manual filtering to ensure research quality. The investigation spans EV evolution from the 1820s to 2024+, tracking battery technology advancement from early Lead-acid systems (20-30km range) to future Solid-State batteries (800-1000km range), alongside the development of charging infrastructure from basic Level 1 systems to advanced wireless and vehicle-to-grid technologies. The research examines how routing algorithms have evolved from traditional approaches like Dijkstra’s and Bellman-Ford to advanced AI-driven methods. A key focus is multi-constraint optimization that simultaneously manages State of Charge,waiting times, traffic conditions, energy consumption, and charging needs. The study evaluates modern AI techniques including Graph Neural Networks, Transformers, Multi-Agent Reinforcement Learning, Deep Reinforcement Learning, and Neuro-Fuzzy systems, demonstrating their superior performance over conventional methods.Major EV challenges such as range anxiety, charging delays, poor station distribution, and traffic unpredictability are addressed through intelligent solutions. These include reinforcement learning for adaptive routing, predictive analytics for demand forecasting, and smart charging systems for grid optimization. Experimental validation shows significant improvements in energy efficiency, route optimization, and system adaptability. Future directions include Vehicle-to-Everything communication, multi-agent coordination, next-generation batteries, and renewable energy integration. This study establishes a multi-constraint optimization framework with hybrid AI integration, providing a foundation for scalable and intelligent electric vehicle ecosystems.