Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method
Journal article, 2024

Proton exchange membrane fuel cell (PEMFC) models are conventionally established with a set of parameters identified under steady-state operating conditions. However, such an approach is insufficient to accurately capture the dynamic characteristics of multi-parameter changes in real-world scenarios. This paper develops a semi-empirical model for a 110-kW commercial PEMFC system based on its dynamic operation data to remedy the defects. To improve the fitting accuracy of the semi-empirical PEMFC model, an improved grey wolf optimization (IGWO) algorithm is proposed for model parameter identification. The IGWO algorithm adopts chaotic mapping to optimize the initial population distribution, and a random walk strategy is incorporated to boost the local search ability of the traditional grey wolf optimization (GWO) algorithm. The effectiveness of this IGWO algorithm in optimizing the semi-empirical model is experimentally verified on the 110-kW PEMFC system under highly dynamic operating conditions. Results show that the proposed IGWO algorithm can effectively identify the semi-empirical model’s parameters, establishing a stable and robust model that outperforms those based on traditional metaheuristic algorithms such as GWO, particle swarm optimization, and genetic algorithm. The demonstrated improvement renders it as better suited for optimizing PEMFC semi-empirical models under real-world operating conditions.

Grey wolf algorithm

parameter identification

Proton exchange membrane fuel cell

Chaotic mapping

fuel cell

Random walk strategy

Author

Hongxu Zhou

Xihua University

Xiaohua Wu

Xihua University

Yang Li

Chalmers, Electrical Engineering, Systems and control

Zhanfeng Fan

Xihua University

Weishan Chen

Xihua University

Jianwei Mao

Xihua University

Pengyi Deng

Xihua University

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

Fuel

0016-2361 (ISSN)

Vol. 357 Part A 129589

Driving Forces

Sustainable development

Areas of Advance

Transport

Energy

Subject Categories

Energy Engineering

Control Engineering

DOI

10.1016/j.fuel.2023.129589

More information

Latest update

6/28/2024