Parameter estimation of PEM fuel cells using metaheuristic algorithms

[Display omitted] •Necessary constraint is included when using GSSEM to avoid inconsistency.•An Improved Artificial Hummingbird Algorithm is employed to extract unknown parameters.•Seven PEMFC cases are taken to validate the proposed model and algorithm.•Comprehensive comparisons with other algorith...

Full description

Saved in:
Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation Vol. 237; p. 115302
Main Authors: Xuebin, Li, Zhao, Jin, Daiwei, Yu, Jun, Zhang, Wenjin, Zhang
Format: Journal Article
Language:English
Published: Elsevier Ltd 30.09.2024
Subjects:
ISSN:0263-2241
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:[Display omitted] •Necessary constraint is included when using GSSEM to avoid inconsistency.•An Improved Artificial Hummingbird Algorithm is employed to extract unknown parameters.•Seven PEMFC cases are taken to validate the proposed model and algorithm.•Comprehensive comparisons with other algorithms and models are carried out. Developing an accurate model for precise parameter estimation is crucial for the design of polymer electrolyte membrane fuel cells (PEMFC). While many studies can meet the evaluation criteria for parameter estimation, there is a significant physical inconsistency when applying the Generalized Steady State of Electrochemical Model (GSSEM) prediction in the optimization framework. This study aims to address this inconsistency by adding necessary constraints to the optimization model. It employs the Artificial Hummingbird Algorithm (AHA) and Lévy flight improvement to find unknown parameters by minimizing the sum of squared errors (SSE). The proposed method has been rigorously tested with seven PEMFC cases, demonstrating its robustness and superior accuracy compared to alternative approaches. Notably, the cathode charge transfer coefficient exhibits zero relative percent difference (RPD) between the two sources, confirming its physical consistency. Furthermore, the proposed constrained optimization model lays the groundwork for achieving consistent parameter estimation results for PEMFC when utilizing GSSEM.
ISSN:0263-2241
DOI:10.1016/j.measurement.2024.115302