Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm

Andrew J. Riad, Hany M. Hasanien (), Rania A. Turky and Ahmed H. Yakout
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Andrew J. Riad: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Hany M. Hasanien: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Rania A. Turky: Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt
Ahmed H. Yakout: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt

Sustainability, 2023, vol. 15, issue 5, 1-17

Abstract: The artificial rabbits optimization (ARO) algorithm is proposed in this article to find the optimum values for uncertain parameters for the proton exchange membrane fuel cell (PEMFC) model. The voltage–current polarization curve of the PEMFC is nonlinear, and the model used in this paper to describe it is Mann’s model, which has seven uncertain parameters. The sum of square errors (SSE) between the ARO-based estimated voltages of the model and the measured voltages of the fuel cell defines the objective function. The simulation results show that the ARO technique has the best SSE compared to other optimization techniques. The precision of the ARO model is evaluated by comparing the optimized model’s power–current and voltage–current curves with the measured curves of three stacks which are NedStack PS6, BCS stack 500 W, and Ballard Mark V. The results show that the estimated curves and measured curves are very close which, means a high accuracy is achieved. Moreover, the ARO method shows a fast convergence curve with a minimal standard deviation. Furthermore, the PEMFC-optimized model is studied at different temperature and pressure operating conditions.

Keywords: PEMFC; ARO; optimization techniques; modeling; parameters extraction; uncertain parameter; polarization curve; sum of square error (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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