Development of a simulation model of a hydrogen vehicle and a subsystem for request control

BACKGROUND: Hydrogen technologies enable a significant reduction in greenhouse gas emissions in urban environments since their only byproducts are air and water. This aligns with global trends aimed at combating climate change and reducing air pollution in major cities. Hydrogen can be produced from various sources, including renewable energy. These factors justify integrating a hydrogen fuel cell system into vehicles. Developing power control algorithms for hydrogen fuel cells is an important scientific and technical challenge, as it increases vehicle range and enhances hydrogen fuel utilization efficiency. AIM: Development and selection of the most effective algorithm for implementation within a model of the hydrogen fuel cell’s power request control using various energy consumption control strategies and comparing the operating parameters of the power distribution system were compared based on an analysis of simulation results. METHODS: This paper presents the development of a simulation model for hybrid hydrogen-powered vehicles using the Simscape library in the MATLAB/Simulink environment. Using basic Simulink blocks, models and control algorithms for hydrogen fuel cell power request control were created, as well as a vehicle simulation model capable of testing these algorithms. RESULTS: During the study, the strategy for controlling fuel cell power was developed. The algorithm incorporates several strategies, including a PID controller, a state machine, an equivalent consumption minimization algorithm, and a fuzzy logic controller. Additionally, requirements for the vehicle system have been outlined, and a simulation model for a hybrid hydrogen vehicle has been developed. As a result of comparing the operating parameters of the power distribution system, the most efficient power request control algorithm was identified. CONCLUSION: The practical significance of this research lies in analyzing power request control algorithms and identifying the most effective among them. This significantly accelerates the process of selecting control algorithms for fuel cells in hydrogen vehicles.