In-situ estimation of nitrogen concentration in fuel cell systems via anode pressure drop modeling

Abstract

Optimizing hydrogen supply control is critical to enhancing the efficiency and lifespan of fuel cell systems. Nitrogen permeation across the membrane dilutes hydrogen concentration and increases the risk of hydrogen starvation. However, the absence of real-time, cost-effective methods to monitor or estimate nitrogen concentration hinders efforts to optimize hydrogen utilization and mitigate hydrogen starvation. To address these challenges, this study establishes an anode pressure drop model incorporating key operational parameters, including nitrogen concentration. Then, a series of experiments under various operating conditions are conducted on a 130 kW full-scale fuel cell system to validate the model, with the ultrasonic sensor employed to measure the flow rate and gas concentration within the hydrogen recirculation loop. Finally, a nitrogen concentration estimation algorithm based on the model is proposed and experimentally verified. Results demonstrate that the mean absolute error of the estimated nitrogen concentration is around 1 vol% under steady-state and dynamic conditions. This work employs a mechanistic model based on the relationship between gas composition and viscosity to elucidate the coupled variation of anode pressure drop and nitrogen concentration. Compared with existing solutions, the proposed nitrogen concentration estimation algorithm features high accuracy, low cost, and robustness against stack degradation, and can be implemented in controllers for in-situ nitrogen concentration estimation. These advancements enable predictive hydrogen supply regulation, which is anticipated to improve the system's durability and efficiency.