A Framework Integrating Multi-Physic and Data-Driven Models and Optimization Approaches to Maximize Electrical Efficiency and Power of Pemfc

Abstract

The performance of open-cathode air-cooled proton exchange membrane fuel cells (PEMFCs) is critically dependent on fan characteristics, which can significantly impact electrical efficiency and output. This paper proposes a framework to address the low efficiencies in existing air-cooled stacks caused by fan operation. By combining a surrogate model, driven by a database containing 120,000 data sets, with an optimization algorithm, this framework aims to maximize the electrical efficiency and output power of an open-cathode air-cooled PEMFC stack at various currents. Simultaneously, the optimal power combinations for fan operation are identified within the stack. The results indicate that fan operation has multiple interactive effects on the stack. The developed data-driven surrogate model demonstrates excellent predictive performance, validated by three robust evaluation metrics. By adjusting the operating power of the fans, the maximum increments in electrical efficiency of the stack at 6 A, 27 A and 48 A are 49.79%, 10.00% and 5.91%, respectively. For the stack rated at 1 kW, the maximum output power reached up to 236.52 W, 839.43 W and 1,091.04 W at 6 A, 27 A, and 48 A respectively, with the maximum increments in output power of 4.14 W, 64.80 W and 59.52 W.

Keywords: Open-cathode PEMFC, Electrical efficiency, large database, Mathematical and surrogate model, Optimization strategy