Citation: Riad, A.J.; Hasanien, H.M.; Turky, R.A.; Yakout, A.H. Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm. Sustainability 2023, 15, 4625. https://doi.org/10.3390/ su15054625 Academic Editors: Shuhua Fang, Andrei Ceclan, Levente Czumbil and Constantin B ˇ arbulescu Received: 11 January 2023 Revised: 21 February 2023 Accepted: 3 March 2023 Published: 5 March 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm Andrew J. Riad 1 , Hany M. Hasanien 1, * , Rania A. Turky 2 and Ahmed H. Yakout 1 1 Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt 2 Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt * Correspondence: hanyhasanien@ieee.org 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 1. Introduction Due to the decrease of fossil fuels worldwide and their impact on the environment, the need for clean, renewable, and continuous energy sources increases every day [1]. However, the power generation from renewable energy sources such as wind and solar energy is intermittent as they depend on weather conditions, location, and time [2]. Fuel cells do not have this problem as they produce electrical power as long as they are supplied with sufficient fuel. One of its types is the proton exchange membrane fuel cell (PEMFC) which uses the chemical reaction of oxygen and hydrogen gases to produce water and electrical energy [3,4]. Not only is the PEMFC a clean source of energy; it also has high efficiency (30–60%), no waste material, high reliability, fast startup, lightweight, and low operating temperature and pressure [4–6]. However, the PEMFC has some disadvantages: it needs an expensive metal catalyst, is highly sensitive to carbon monoxide, and needs complex water and thermal management [7]. Moreover, the electrolyte membranes of PEMFC suffer from a decrease in proton conductivity as the environment’s relative humidity decreases or the temperature rises [8]. The PEMFC is used in many applications such as micro- combined heat and power applications [9,10], microgrid applications [11], and domestic applications [12]. It is also used in feeding switched reluctance motors [13,14] and in power systems as distributed generation [15]. Other fuel cell types, such as alkaline fuel cells (AFC), molten carbonate fuel cells (MCFC), phosphoric acid fuel cells (PAFC), and solid oxide fuel cells (SOFC), are categorized based on the electrolyte used [7,16]. Sustainability 2023, 15, 4625. https://doi.org/10.3390/su15054625 https://www.mdpi.com/journal/sustainability