Next-Generation Electric Vehicles Integrating AI and Machine Learning with Fuel Cell Technologies for Enhanced Performance and Energy Management

The study showcases the latest ideas regarding architectural and control strategies that juxtapose Artificial Intelligence and Machine Learning with fuel cell technologies to increase the efficiency, range, and reliability of electric vehicles and advance toward the next generation of vehicles. A new hybrid framework is proposed that unifies physics-based models of fuel cells and batteries with data-driven predictive algorithms. Within this framework, a hierarchical Energy Management System (EMS) is designed in which a reinforcement learning layer signals power distribution set points, while a model predictive controller ensures safe operation of the system in real time! The proposed system, using simulation results within multiple driving scenarios, shows improvement in energy efficiency and hydrogen consumption as compared to traditional EMS along with an overall reduction in battery wear. The study considers computing challenges while running ML models in Battery Management Systems (BMS), predictive maintenance for life extension of components, and deployment challenges regarding hydrogen infrastructure and safety certification. The study ends with suggestions for pilot implementations and possible pathways for future enhancements.