Dynamic performance prediction of PEM electrolyzers combining an electrochemical neural network model and a lumped thermal capacitance model

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ABSTRACT The proton exchange membrane (PEM) electrolyzer electrochemical models based on conventional analytical and empirical approaches require numerous parameters, which are difficult to obtain accurately, leading to lower dynamic performance prediction accuracy while the parameters of the lumped thermal capacitance model are relatively simple and easily identifiable. This study proposes an integrated modeling framework, which combines a backpropagation (BP) neural network model for electrochemical performance and a lumped thermal capacitance model for the assessment of thermal performance. The neural network model is trained on polarization data collected from 44°C to 76°C. The BP neural network, optimized through iterative hyperparameter tuning, achieves high predictive accuracy with three hidden layer nodes and tansig and purelin activation functions, exhibiting less than 1% error in polarization curve predictions at four additional temperatures and current densities from 0.04 to 2.6 A/cm2. The thermal model, calibrated with experimental data, predicts thermal-balance temperatures with root mean square error (RMSE) of 0.08°C to 0.52°C under four different operating conditions. The dynamic performance of the integrated model is validated under three different operating conditions. This comprehensive model enables detailed analysis of critical operating variables, providing actionable insights for selecting and optimizing operating conditions in practical applications.