PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

TL;DR

This paper introduces a transfer learning approach combining knowledge-based and data-driven modeling to accurately predict high-temperature PEM electrochemical systems, reducing data requirements and enabling cross-system application.

Contribution

It presents a novel Few-Shot Learning method that leverages simulated data from a knowledge-based model to train accurate models for different high-temperature PEM systems with limited experimental data.

Findings

High accuracy achieved in modeling HT-PEMFCs and HT-PEM ECHPs.

Simulated data effectively pretrains models for different systems.

Transfer learning reduces the need for extensive experimental datasets.

Abstract

Widespread adoption of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) and HT-PEM electrochemical hydrogen pumps (HT-PEM ECHPs) requires models and computational tools that provide accurate scale-up and optimization. Knowledge-based modeling has limitations as it is time consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling on the other hand, is easier to implement, but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are uniquely combined by implementing a Few-Shot Learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFC was used to generate simulated data (887,735 points) and used to pretrain a neural network source model. Furthermore, the source model developed for HT-PEMFCs was successfully applied to HT-PEM ECHPs - a different electrochemical system that utilizes similar materials to the fuel cell. Experimental datasets from both HT-PEMFCs and HT-PEM ECHPs with different materials and operating conditions (~50 points each) were used to train 8 target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE between 1.04 and 3.73% for HT-PEMCs and between 6.38 and 8.46% for HT-PEM ECHPs).