Energy management for hydrogen refueling stations: A synergistic approach of cascade compression architecture and deep reinforcement learning

Abstract

With the growth of the fuel cell electric vehicles market, hydrogen refueling stations have become vital infrastructure bridging hydrogen production and dispensing. In hydrogen refueling stations, the compression process is a necessary and the most energy-intensive stage requiring further optimization aimed at reducing avoidable thermodynamic waste. In this study, a synergistic framework based on a deep reinforcement learning algorithm is integrated with a three-stage cascade compression architecture and four industrial technologies: variable speed drive, intelligent bypass control, dynamic intercooling, and adaptive pressure control. Progressive ablation studies demonstrate that the four-technology synergy yields a 75.7% profit improvement over naive setups, and the deep reinforcement learning control increases profit by 20.7% compared to the profit-oriented heuristic baseline. This study proves that equipping a three-stage architecture with industrial technologies under deep reinforcement learning outperforms traditional single-stage baselines, making it a potential approach to achieve high thermodynamic efficiency and supporting a growing market.