In a significant stride toward optimizing electric vehicle (EV) ecosystems, researchers have published a comprehensive survey in the journal “Published in the IEEE Journal of Access” that delves into the intricate world of AI-powered EV routing. Led by P. Anandha Prakash from the School of Electrical Engineering at Vellore Institute of Technology in Chennai, India, the study explores how advanced algorithms and infrastructure integration are revolutionizing sustainable transportation.
The research traces the evolution of EVs from their inception in the 1820s to the cutting-edge technologies of 2024 and beyond. “We’ve seen a remarkable transformation in battery technology, from early Lead-acid systems with a range of just 20-30 kilometers to the promising Solid-State batteries that could offer 800-1000 kilometers,” Prakash explains. This evolution is paralleled by advancements in charging infrastructure, moving from basic Level 1 systems to sophisticated wireless and vehicle-to-grid technologies.
At the heart of the study is the development of routing algorithms, which have progressed from traditional methods like Dijkstra’s and Bellman-Ford to sophisticated AI-driven approaches. The research highlights the importance of multi-constraint optimization, which simultaneously manages factors such as State of Charge, waiting times, traffic conditions, energy consumption, and charging needs. “Modern AI techniques like Graph Neural Networks, Transformers, Multi-Agent Reinforcement Learning, Deep Reinforcement Learning, and Neuro-Fuzzy systems have shown superior performance over conventional methods,” Prakash notes.
The study addresses major challenges in the EV sector, including range anxiety, charging delays, poor station distribution, and traffic unpredictability. Intelligent solutions such as reinforcement learning for adaptive routing, predictive analytics for demand forecasting, and smart charging systems for grid optimization are presented as key innovations. Experimental validation demonstrates significant improvements in energy efficiency, route optimization, and system adaptability.
Looking ahead, the research points to future directions like 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,” Prakash concludes.
The implications for the energy sector are profound. As EVs become more prevalent, the need for efficient routing and charging infrastructure becomes critical. This research not only enhances the commercial viability of EVs but also paves the way for a more sustainable and interconnected transportation network. By leveraging AI and advanced algorithms, the energy sector can optimize resources, reduce costs, and improve user experience, ultimately driving the global shift toward sustainable mobility.