An Accurate Parameter Estimation Method of the Voltage Model for Proton Exchange Membrane Fuel Cells

Jian Mei, Xuan Meng, Xingwang Tang, Heran Li, Hany Hasanien, Mohammed Alharbi, Zhen Dong, Jiabin Shen, Chuanyu Sun (), Fulin Fan, Jinhai Jiang and Kai Song ()
Additional contact information
Jian Mei: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Xuan Meng: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Xingwang Tang: School of Automotive Studies, Tongji University, Shanghai 201804, China
Heran Li: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Hany Hasanien: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Mohammed Alharbi: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Zhen Dong: Suzhou SEEEx (Sustainable Electrical Energy Expert) Technology Company, Suzhou 215000, China
Jiabin Shen: General Motors Canada Company, Oshawa, ON L1J 0C5, Canada
Chuanyu Sun: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Fulin Fan: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Jinhai Jiang: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Kai Song: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China

Energies, 2024, vol. 17, issue 12, 1-21

Abstract: Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm 2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Keywords: metaheuristic algorithm; parameter estimation; tuna swarm optimization; proton exchange membrane fuel cell (PEMFC); Amphlett model; multi-strategy; convergence speed; objective function; accuracy; generalization ability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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