@article{LI2026, 
author = {Jianlin LI and Xinlong ZHENG and Haoyuan SUN and Zhonghao LIANG},
title = {Dynamic performance control technology of PEMEL hydrogen production based on IAW-MOPSO},
year = {2026},
journal = {Electric Power Engineering Technology},
volume = {45},
number = {5},
pages = {115-126},
keywords = {proton exchage membrane electrolyzer (PEMEL), PEMEL model building, improved adaptive weighted multi-objective particle swarm optimization (IAW-MOPSO), hydrogen production performance optimization of PEMEL, linear quadratic regulator (LQR), PEMEL temperature control},
url = {https://www.sciopen.com/article/10.12158/j.2096-3203.2026.05.011},
doi = {10.12158/j.2096-3203.2026.05.011},
abstract = {With the implementation of the "double carbon" policy, electrolyzers are gradually becoming a core component in the future hydrogen energy market and playing an increasingly important role in the energy transition. The research on the dynamic characteristics and performance of electrolyzers has also become a key academic focus. The previous research has primarily focused on system-level integration, such as wind-solar-hydrogen storage systems, while in-depth investigations into the intrinsic hydrogen production characteristics of electrolyzers remain relatively scarce. Therefore, proton exchage membrane electrolyzer (PEMEL) is taked as the research object, MATLAB/Simulink-based simulation model is developed that captures key performance indicators including hydrogen production efficiency, hydrogen generation rate, and operating voltage. An improved adaptive weighted multi-objective particle swarm optimization (IAW-MOPSO) algorithm is then employed to simultaneously optimize hydrogen production efficiency and rate, aiming to identify the optimal operating temperature and current density under varying working conditions. The proposed strategy is validated using real-world operational data from a specific region. Following parameter optimization, precise temperature control becomes essential for ensuring system stability and efficiency. To this end, the IAW-MOPSO algorithm is further utilized to optimize the weighting matrices of a linear quadratic regulator (LQR), which is subsequently applied to PEMEL temperature regulation. Simulation results demonstrate that the IAW-MOPSO tuned LQR controller significantly outperforms conventional proportional integral derivative (PID) control in terms of temperature tracking accuracy, dynamic response speed, and robustness against disturbances.}
}