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

Li, Xiufei LU ; Yang, Miao ; Zhang, Miao ; Qi, Yuanxin LU ; Li, Zhuowei ; Yu, Senbin ; Wang, Yuantao ; Shen, Linpeng and Li, Xiang (2024) 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024 p.11456-11461
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... (More)

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.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024
conference location
Abu Dhabi, United Arab Emirates
conference dates
2024-10-14 - 2024-10-18
external identifiers
  • scopus:85216484018
ISBN
9798350377705
DOI
10.1109/IROS58592.2024.10802243
language
English
LU publication?
yes
id
93ec8dd4-33a8-498e-8d47-044732a1be78
date added to LUP
2025-06-03 09:08:30
date last changed
2025-06-03 09:09:47
@inproceedings{93ec8dd4-33a8-498e-8d47-044732a1be78,
  abstract     = {{<p>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.</p>}},
  author       = {{Li, Xiufei and Yang, Miao and Zhang, Miao and Qi, Yuanxin and Li, Zhuowei and Yu, Senbin and Wang, Yuantao and Shen, Linpeng and Li, Xiang}},
  booktitle    = {{2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024}},
  isbn         = {{9798350377705}},
  language     = {{eng}},
  pages        = {{11456--11461}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Voltage Regulation in Polymer Electrolyte Fuel Cell Systems Using Gaussian Process Model Predictive Control}},
  url          = {{http://dx.doi.org/10.1109/IROS58592.2024.10802243}},
  doi          = {{10.1109/IROS58592.2024.10802243}},
  year         = {{2024}},
}