Patterns (Feb 2021)
Machine learning for guiding high-temperature PEM fuel cells with greater power density
Abstract
Summary: High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) are enticing energy conversion technologies because they use low-cost hydrogen generated from methane and have simple water and heat management. However, proliferation of this technology requires improvement in power density. Here, we show that Machine Learning (ML) tools can help guide activities for improving HT-PEMFC power density because these tools quickly and efficiently explore large search spaces. The ML scheme relied on a 0-D, semi-empirical model of HT-PEMFC polarization behavior and a data analysis framework. Existing datasets underwent support vector regression analysis using a radial basis function kernel. In addition, the 0-D, semi-empirical HT-PEMFC model was substantiated by polarization data, and synthetic data generated from this model was subject to dimension reduction and density-based clustering. From these analyses, pathways were revealed to surpass 1 W cm−2 in HT-PEMFCs with oxygen as the oxidant and CO containing hydrogen. The bigger picture: Renewable energy and energy efficiency are crucial for achieving global sustainability goals. In this context, there is need for the development of new materials that realize high-performing and low-cost power sources. At the same time, advances in computational power, simulation, and Machine Learning enable researchers to explore large amounts of data, providing inspiration and tools for the design of new systems. In this work, we combined experiments with modeling and data analysis tools to build a framework for the study and development of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). The framework used Machine Learning tools (e.g., support vector regression, dimension reduction, and clustering) that seamlessly linked materials characteristics with fuel cell performance. This allowed for the accelerated discovery of material properties and fuel cell operating parameters that achieve greater power density while co-currently addressing costs.
