A multivariable sliding mode predictive control method for the air management system of automotive fuel cells

Bibliographic Details
Title: A multivariable sliding mode predictive control method for the air management system of automotive fuel cells
Authors: Duo Yang, Hanwen Fu, Junjun Li, Siyu Wang
Source: Measurement + Control, Vol 57 (2024)
Publisher Information: SAGE Publishing, 2024.
Publication Year: 2024
Collection: LCC:Technology (General)
Subject Terms: Control engineering systems. Automatic machinery (General), TJ212-225, Technology (General), T1-995
More Details: The proton exchange membrane fuel cell gas control has been one key point in fuel cell management systems. The complexity and coupling of the air management system make it difficult to achieve precise air intake adjustment. In this paper, an accurate joint control method for the air flow and pressure regulation is proposed. The nonlinear mathematical model of the air management system is developed to describe the output characteristic and state change. Based on this, the feedback linearization method is proposed to obtain the direct correspondence between control variables and controlled variables. In addition, to solve the problem that the controlled variables cannot be measured directly, an extended state observer is applied to estimate the stack cathode pressure. The sliding mode predictive control method is proposed to control the oxygen excess ratio and cathode pressure simultaneously. The relative order of the system is used to design the sliding mode surface, and the corresponding predictive model is proposed. The results obtained by simulation experiments show that pressure and mass flow have little effect on each other through decoupling. The proposed algorithm has been verified to have high precision, fast response, and robustness through comparative experiments.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 0020-2940
00202940
Relation: https://doaj.org/toc/0020-2940
DOI: 10.1177/00202940231195129
Access URL: https://doaj.org/article/95937c25b9cf4546ac516553526dc6ef
Accession Number: edsdoj.95937c25b9cf4546ac516553526dc6ef
Database: Directory of Open Access Journals
More Details
ISSN:00202940
DOI:10.1177/00202940231195129
Published in:Measurement + Control
Language:English