  This study introduces a novel approach utilizing Gaussian process model
predictive control (MPC) to stabilize the output voltage of a polymer
electrolyte fuel cell (PEFC) system by simultaneously regulating hydrogen and
airflow rates. Two Gaussian process models are developed to capture PEFC
dynamics, taking into account constraints including hydrogen pressure and input
change rates, thereby aiding in mitigating errors inherent to PEFC predictive
control. The dynamic performance of the physical model and Gaussian process MPC
in constraint handling and system inputs is compared and analyzed. Simulation
outcomes demonstrate that the proposed Gaussian process MPC effectively
maintains the voltage at the target 48 V while adhering to safety constraints,
even amidst workload disturbances ranging from 110-120 A. In comparison to
traditional MPC using detailed system models, Gaussian process MPC exhibits a
43\% higher overshoot and 25\% slower response time. Nonetheless, it offers the
advantage of not requiring the underlying true system model and needing less
system information.
