A data-driven digital-twin model and control of high temperature proton exchange membrane electrolyzer cells

Abstract

The high temperature proton exchange membrane electrolyzer cells (HT-PEMEC) are promising for hydrogen generation from fluctuating and intermittent renewable energy. In this study, a data-driven method is developed to study the dynamic behavior of HT-PEMEC. This method combines multiphysics simulation and nonlinear system identification, avoiding expensive experimental costs and time-consuming full multiphysics calculations. Dynamic models for predicting the power consumption, hydrogen production and temperature are identified, and the verified fit is 96.31%, 97.87%, 87.73%, respectively, which demonstrated the accuracy of the identification model. Subsequently, the identification model was used to predict the dynamic behavior of HT-PEMEC and design control strategies. Fuzzy control strategy and neural network predictive control strategy are implemented to alleviate overshoot and suppress fluctuations so as to improve the durability of the electrolyzer. Moreover, compared with the fuzzy control strategy, the neural network predictive control strategy reduces the power overshoot by approximately 92%. This data-drive digital-twin model can not only guide dynamic experimental research, but also can be extended to study the dynamic behavior of various fuel cells and electrolyzer cells.