Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells

Briceno-Mena, Luis A.; Arges, Christopher G.; Romagnoli, Jose A.

doi:10.1016/j.compchemeng.2023.108159

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DOE OSTI · 1923286

Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells

Briceno-Mena, Luis A.·Arges, Christopher G.·Romagnoli, Jose A.

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Abstract

Widespread adoption of high-temperature polymer electrolyte membrane electrochemical systems, such as fuel cells (HT-PEMFCs), requires models and computational tools for accurate optimization and guiding new materials for enhancing performance and durability. In this contribution, knowledge-based modelling and data-driven modelling are combined using Few-Shot Learning and implementing an Automated Machine Learning framework for the generation of Machine Learning-based surrogate models. Applicability of the resulting model for derivative-free optimization is demonstrated. Additionally, a way of considering extrapolation in the optimization task is presented. Results show that although extrapolation is needed to achieve better solutions during optimization, it can be monitored and managed. As a result, tuning the electrode ionomer binder's properties, such as ionic conductivity, in the fuel cell represents a promising pathway for improving HT-PEMFC performance.

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08 HYDROGEN AutoML data-driven modeling derivative free optimization high-temperature polymer electrolyte membrane fuel cells machine learning optimization surrogate models transfer learning

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Briceno-Mena, Luis A., Arges, Christopher G., Romagnoli, Jose A.. 2023-01-31. Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells. https://doi.org/10.1016/j.compchemeng.2023.108159

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