Voltage Regulation in Polymer Electrolyte Fuel Cell Systems Using Gaussian Process Model Predictive Control

TL;DR

This paper presents a Gaussian process model predictive control approach for stabilizing voltage in polymer electrolyte fuel cell systems, effectively managing constraints and disturbances without requiring detailed system models.

Contribution

The study introduces a novel Gaussian process MPC method that captures PEFC dynamics and improves voltage regulation while reducing model dependency compared to traditional methods.

Findings

Effectively maintains 48 V output voltage under workload disturbances.

Achieves 43% higher overshoot and 25% slower response than traditional MPC.

Requires less system information and does not need the true system model.

Abstract

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.