Multi-objective real-time energy management optimization for autonomous plug-in fuel cell electric vehicles

With increasing concerns over climate change and the urgent need to reduce carbon emissions, electric vehicles offer a promising solution. However, challenges around efficiency and durability persist. Autonomous vehicles, equipped with advanced technologies, have the potential to revolutionize transportation. This paper presents a novel and comprehensive study of a real-time energy management system for the energy storage system of an autonomous plug-in fuel cell electric vehicle, which integrates a battery, a fuel cell system, and a supercapacitor. The research frames this as an optimization problem, aiming to minimize fuel cell and battery degradation while reducing hydrogen and electricity costs. The strategy employs a moving horizon approach, using quadratic programming to optimize power distribution. Basic GPS data is used to preplan the vehicle’s state of charge trajectory, which is also optimized using quadratic programming. Additionally, the system incorporates a simple adaptive cruise control based on model predictive control to bridge speed and power optimization, ensuring safe driving along the planned route. Validation through various battery charge levels and trajectory planning scenarios demonstrates the method’s robustness and efficiency, with results showing up to 99% optimality compared to other state of charge planners. This highlights the method’s ability to consistently deliver optimal power allocation under diverse driving conditions.