Automated Electrolyzer Control System for the Production, Accumulation, and Storage of Hydrogen for Refueling Vehicles

On-site hydrogen refueling stations (HRS) face significant operational challenges due to the stochastic nature of hydrogen demand, creating a severe supply–demand mismatch. Under traditional pressure-based hysteresis control, this volatility forces Proton Exchange Membrane (PEM) electrolyzers into frequent start–stop cycles, accelerating degradation and reducing efficiency. In response, this study introduces an automated control framework integrating macroscopic gas-state modeling with deep-learning-based demand prediction. First, a real-gas thermodynamic model was established. Monte Carlo simulations of 100 random filling scenarios identified a robust design benchmark of 4.5 kg per vehicle. A low filling stability coefficient (5.02%) confirmed that individual thermodynamic fluctuations are negligible, validating a traffic-flow-driven demand approach. Next, a deep Long Short-Term Memory (LSTM) network was developed to forecast short-term demand. Trained on an 8784 h dataset exhibiting “double-peak” traffic patterns, the model achieved high precision on the unseen test set, yielding a Root Mean Square Error (RMSE) of 6.75 kg and a normalized RMSE (nRMSE) of 0.0987, explaining 82% of the demand variance. Finally, an LSTM-informed demand-following control strategy was formulated to enable proactive, thermally bounded operation alongside a novel “Hot Standby” mechanism. Maintaining a minimal 3.0 kg/h holding current during idle periods sustains stack temperatures above 60 °C, effectively mitigating thermal stress. Comparative simulations over 1464 h demonstrated that the proposed framework reduces detrimental cold start–stop cycles by 98.4% (from 61 to 1) and suppresses power output fluctuations by 40.7% compared to the traditional baseline. These results confirm that data-driven control significantly enhances operational stability, facilitates grid integration, and extends core equipment service life.