Predicting Proton Exchange Membrane Fuel Cell Performance through Advanced Machine Learning Techniques: A Comparative Analysis of Ensemble Techniques

Proton exchange membrane fuel cells (PEMFCs) are a major player in the conversion of hydrogen energy and are essential for the realization of an environmentally friendly society. However, their cost and performance have yet to meet the requirements for widespread commercial use or adoption. Hence, this research aims to expand our understanding of PEMFCs performance by investigating the complex association between different operational factors and the real part of impedance (z_real). The principal objective is to predict z_real based on a comprehensive set of input variables, utilizing advanced machine learning techniques. The impedance, representing the fuel cell's opposition to electric current flow, is a complex quantity comprising real and imaginary components for understanding the complex polarization process of PEMFCs, especially from the viewpoint of frequency analysis. Obtaining frequency impedance that shows dynamic losses from signals recorded by sensors without using expensive impedance measuring gear is beneficial. Using this information, the impedance data may be utilized to assess the internal condition of the fuel cell and enhance system control. Unlike existing studies leveraging machine learning for similar predictions, this research introduces a novel dimension by undertaking a rigorous comparative analysis of ensemble techniques. While prior research has applied machine learning to forecast fuel cell behaviour, none have systematically evaluated and compared the performance of diverse ensemble methods in this specific task. Ensemble techniques, known for their capability to enhance predictive accuracy by combining multiple models, offer a promising prospect for achieving more robust predictions of z_real. The methodology employed in this study involves the rigorous exploration of a rich dataset derived from Nafion 112 membrane standard tests and Membrane Electrode Assembly (MEA) activation experiments. The dataset comprises Polarization and Impedance curves, providing a diverse perspective of the fuel cell's response across various pressures of H2/O2 gas, different voltages, and humidity conditions. Leveraging this dataset, the study employs machine learning algorithms, including ensemble methods such as Random Forest, Gradient Boosting, and Bagging, to predict the elusive z_real. The outcomes of this research stretch beyond mere prediction; they incorporate a nuanced understanding of how distinct factors influence the complex impedance behaviour of PEMFCs. Furthermore, the comparative analysis of ensemble techniques focuses to elucidate which method or combination produces the most accurate predictions. This study provides valuable insights not only to the evolving field of fuel cell optimization but also adds a unique perspective to the application of ensemble techniques in predicting critical electrochemical parameters. The discoveries are ready to advance the existing knowledge of PEMFCs dynamics and encourage a more informed approach to enhancing their performance in diverse operational conditions.