Work

A hierarchical reinforcement learning framework for high-efficiency control of turbine-assisted fuel cell air supply systems

Year: 2026

Type: article

Abstract: The integration of turbine expanders into fuel cell systems (FCS) can significantly improve system efficiency. However, it also increases the complexity of operating parameter optimization and real-time control of the air supply system. To address these challenges, this study proposes a hierarchical reinforcement learning framework, termed GA-RDDPG, which combines a genetic algorithm (GA)-based efficiency-oriented feasible control baseline with deep deterministic policy gradient (DDPG)-based online residual correction to improve transient performance while maintaining air-supply safety. A progressive curriculum-learning strategy is incorporated into the DDPG training process to improve learning efficiency and reduce exploration in unsafe operating regions. The results demonstrate that GA-RDDPG achieves coordinated control of compressor speed and turbine nozzle opening, while effectively balancing air supply energy consumption and stack performance to improve overall FCS efficiency. At 0.6 A cm −2 , the average cell voltage reaches 0.77 V and the system efficiency reaches 54.61 %, representing improvements of 5.5 % and 1.35 %, respectively, compared with the baseline strategy. Under a 70 kW s −1 power variation, the GA-RDDPG agent exhibits excellent transient response and robustness, with cathode pressure and airflow stabilizing within 2 s. Under demanding vehicular operating conditions, the GA-RDDPG agent also demonstrates strong generalization. Although moderate transient overshoots in turbine nozzle opening are observed, the controller maintains stable air-supply regulation.

Source: Fuel

DOI: 10.1016/j.fuel.2026.141556