Control strategies for alkaline water electrolysis hydrogen production: a comprehensive review and future perspectives

Driven by the global energy transition and carbon neutrality targets, alkaline water electrolysis has emerged as a key technology for coupling variable renewable generation with clean hydrogen production, offering considerable potential for absorbing surplus power and enhancing grid flexibility. However, conventional control architectures typically treat the power converter and electrolyzer as independent units, neglecting their dynamic interactions and thereby limiting overall system performance under practical operating conditions. This review critically examines existing control approaches, ranging from classical proportional-integral schemes to model predictive control, fuzzy-logic algorithms, and data-driven methods, evaluating their effectiveness in managing dynamic response, multivariable coupling, and operational constraints as well as their inherent limitations. Attention is then focused on the performance requirements of the hydrogen-production converter, including current ripple suppression, rapid transient response, adaptive thermal regulation, and stable power delivery. An integrated co‑control framework is proposed, aligning converter output with electrolyzer demand across steady-state operation, variable renewable input, and emergency shutdown scenarios to achieve higher efficiency, extended equipment lifetime, and enhanced operational safety. Finally, prospects for advancing unified control methodologies are outlined, with emphasis on constraint-aware predictive control, machine-learning-enhanced modeling, and real‑time co‑optimization for future alkaline electrolyzer systems.