Fuel Cell Degradation Prediction Using Machine Learning Models: A Study on Proton Exchange Membrane (PEM) Fuel Cell Dataset

Abstract

Abstract Proton Exchange Membrane (PEM) fuel cells offer great potential in terms of green energy solutions but degrade over a period of time based on diverse operation and material aspects. Based on the data obtained from PEM Fuel Cell Dataset with polarization as well as impedance at diverse operations, this study makes use of machine learning algorithms for predicting degradation of fuel cell. Following preprocessing by imputation of missing values via mean imputation, exploratory data analysis was performed via heatmaps and key visualizations. Fifteen machine learning models involving linear and nonlinear regressors, decision trees, ensemble models, and neural networks were trained to predict important performance metrics like cell voltage, power density, and impedance characteristics. Model performance was conducted with Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² Score, where the Extra Trees Regressor performed best with an MAE of 0.00099 and an R² score of 0.996. These results show the potential of machine learning to forecast fuel cell degradation, enabling proactive maintenance and increased system reliability. Future work will explore real-time deployment of predictive models for enhanced operational effectiveness in real-world systems.

Key findings

• ▸The Extra Trees Regressor model performed best with an MAE of 0.00099 and an R² score of 0.996.
• ▸Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² Score were used to evaluate model performance.
• ▸The study shows the potential of machine learning to forecast fuel cell degradation, enabling proactive maintenance and increased system reliability.

Keywords

Identifiers

DOI

10.21203/rs.3.rs-6710108/v1

Journal

Research Square

Year

2025