PEM Fuel Cell Anode Purge Timing via Virtual Hydrogen Sensing

The patent transitions from static, fixed purge intervals to dynamic, adaptive purge scheduling. The machine learning model continuously adjusts the purge valve activation interval based on real-time operating conditions, predicting when purging will be necessary to maintain hydrogen concentration. This dynamic approach ensures purging occurs only when needed, minimizing hydrogen waste while maintaining fuel cell performance.

The patent introduces a machine learning model as an intermediary that indirectly measures hydrogen concentration by analyzing correlations between operating parameters (recirculation fan speed, electrical state, temperature, pressure) and hydrogen concentration. This virtual sensor approach eliminates the need for physical hydrogen concentration sensors in the anode path, removing mechanical interfaces and associated leakage risks while maintaining measurement capability through computational inference.

The patent creates a virtual copy of the hydrogen concentration sensor function using a machine learning model. Instead of installing physical sensors in the anode path, the system uses a trained model that replicates sensor functionality by processing readily available operating parameters. This software-based approach eliminates complex hardware installations while achieving the same measurement objective.