Machine learning optimization of operating parameters to achieve high power density and efficiency of polymer electrolyte membrane fuel cell

Abstract

The performance of PEMFCs is significantly influenced by operating conditions, including temperature, pressure, and flow rates. To optimize the complicated nonlinearity of operational conditions such as anode pressure (AP), cathode pressure (CP), cell temperature (Temp), anode flow rate (HFL), cathode stoichiometry (CS), and current density (CD) in experiments, this study employs five machine learning (ML) regression models: Artificial Neural Network, Decision Tree, Random Forest, Gradient Boosting, and Extreme Gradient Boosting to predict key performance indicators: voltage, power density, and efficiency, thereby optimizing the fuel cell’s operational parameters. Among these, the Gradient Boosting regressor demonstrated superior performance when validated with experimental data and was subsequently employed to fine-tune the operating parameters. Leveraging the prediction model, the performance of PEMFC in terms of power density and efficiency was further assessed by varying AP, CP and CS. The optimal conditions were identified with AP, CP at 2 bar, and CS at 2.50–2.75.