Hydrogen fuel cell electric vehicles (FCEVs) present a promising solution to environmental challenges such as emissions, global warming, and fossil fuel depletion. By utilizing hydrogen as a clean energy source, FCEVs achieve zero tailpipe emissions while maintaining high efficiency and extended driving ranges. Their development focuses on optimizing fuel cell architecture, hydrogen storage systems, and advanced power management strategies to enhance energy conversion under varying operational conditions. This requires precise design, simulation, analysis, and system integration to improve performance, durability, and sustainability.
This study develops a high-fidelity mathematical model of a hydrogen Proton Exchange Membrane (PEM) fuel cell vehicle and implements advanced Energy Management Strategies (EMS). Using the forward approach method, the model accounts for the physical constraints of the powerplant. Detailed fuel cell system modeling accurately represents voltage loss mechanisms, while an electric motor (EM) with a Field-Oriented Controller (FoC) ensures precise torque regulation.
Energy management strategies play a critical role in optimizing power distribution among the fuel cell, battery, and motor, enhancing overall efficiency and performance. By dynamically controlling energy flow under varying driving conditions, these strategies extend driving range, reduce hydrogen consumption, and improve system longevity. This research develops and applies both online rule-based and offline Genetic Algorithm (GA) EMS to optimize vehicle performance, fuel efficiency, and powertrain operation. Simulation-based assessments refine energy allocation, ensuring optimal system efficiency. Online EMS strategies utilize real-time data for adaptive power distribution, while offline strategies leverage machine learning (ML) techniques to analyze historical driving patterns and predict optimal energy allocation. The GA approach demonstrates superior power distribution efficiency over rule-based strategies, resulting in improved fuel economy and energy utilization.
This work advances PEM FCEV technology by enhancing design, simulation, and optimization frameworks, providing a strong foundation for future developments in sustainable and efficient transportation solutions.