Voltage Regulation in Polymer Electrolyte Fuel Cell Systems Using Gaussian Process Model Predictive Control
Xiufei Li, Miao Yang, Miao Zhang, Yuanxin Qi, Zhuowei Li, Senbin Yu, Yuantao Wang, Linpeng Shen
Abstract
This study presents a novel approach using Gaussian process model predictive control (MPC) to stabilize the output voltage of a polymer electrolyte fuel cell (PEFC) by regulating hydrogen and airflow rates. Two Gaussian process models capture PEFC dynamics, accounting for constraints like hydrogen pressure and input change rates to reduce predictive control errors. The performance of the physical model and Gaussian process MPC in handling constraints and system inputs is compared. Simulations show that the proposed Gaussian process MPC maintains the voltage at 48 V while adhering to safety constraints, even with workload disturbances from 110-120 A. Compared to traditional MPC with detailed system models, Gaussian process MPC has similar overshoot and slower response time but requires less system information and no underlying true system model.
BibTeX
@inproceedings{iros2024_voltageregulatio,
title = {Voltage Regulation in Polymer Electrolyte Fuel Cell Systems Using Gaussian Process Model Predictive Control},
author = {Xiufei Li and Miao Yang and Miao Zhang and Yuanxin Qi and Zhuowei Li and Senbin Yu and Yuantao Wang and Linpeng Shen and Xiang Li},
booktitle = {IROS 2024},
year = {2024}
}