abstract
- This article introduces a data-driven modeling approach for Proton-Exchange Membrane Fuel Cell (PEMFC) stacks. The proposed data-driven methodology leverages real-time operational data to refine a dynamic model of the fuel cell, capturing its performance under varying operating conditions. By using actual signal values obtained from the fuel cell, the proposed model can accurately replicate the dynamic behavior of the PEMFC stack, offering improved predictive capabilities over traditional modeling techniques. This refined model is validated against experimental data, demonstrating its efficacy in predicting the fuel cell's response to changes in operation. The proposed data-driven approach provides a robust framework for enhancing the design, control, and optimization of fuel cell systems, contributing to the advancement of sustainable energy technologies. © 2025 Hydrogen Energy Publications LLC