Development of machine learning based model for low-temperature PEM fuel cells

Abstract

Low-Temperature Proton Exchange Membrane Fuel Cells (LT-PEMFC) are favored as an alternative power source due to their high efficiency, rapid initialization, shut-down cycles, and zero emissions. Developing an effective model for LT-PEMFC is essential. In this study, machine learning models are created for LT-PEMFC, utilizing techniques such as Gradient Boosting Regression (GBR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) to predict cell voltage based on operating parameters. The dataset is generated using an in-house physics-based MATLAB model, complemented by experimental data from elsewhere. GBR exhibits superiority over XGBoost, LightGBM, and RF. These data-based models for LT-PEMFC, developed on generated datasets, achieve R2≥ 0.99 and MAPE ≤ 0.06 during testing. These models are further validated on experimental data with R2≥ 0.90 and MAPE ≤ 0.1. This underscores the ability to construct accurate data-based models and thus reducing reliance on extensive experimentation.