energy conversion devices in AC and DC power networks [13; 14]. These units can convert chemical energy to electrical energy besides heat while they use oxygen and hydrogen as fuel. It is a low-temperature form that uses a noble metal catalyst, such as platinum, at reaction sites. In addition, energy releases during the reaction [15; 16; 17]. The fuel cell chemical reactions produce dc electricity, heat, and liquid water [18; 19]. The parameter estimation of fuel cells has attracted research interest in recent years. A PEMFC is considered one of the most promising devices that convert chemical energy fuels into electrical energy based on electrochemical responses [15; 16; 17; 18]. Specifically, the PEMFCs have numerous rewards, e.g., good electrical efficiency, little emission, and flexibility in fuel, that make them applicable to diverse applications [20; 21; 22; 23; 24]. For instance, they can apply in the combined tough issue owing to PEMFC being a compound multivariable powerfully coupled scheme. For getting precise values for these parameters, numerous methods have been introduced in the literature. Most importantly, the usage of meta-heuristics has arisen as a viable and talented choice by reason of their robustness, ease of application, simplicity, and derivative-free generation of both electric power as well as heat. To additionally enhance the PEMFC performance, numerous efforts have been made by researchers that are carried out to guess the parameters of the PEMFC, which can be summarized as follows:  
* Ref. [16] applies particle swarm optimization (PSO) method for PEMFC parameter extraction;
* Ref. [25] used hybrid adaptive differential evolution procedure for the extraction process of PEMFC parameters;
* Ref. [26] develops grasshopper optimization algorithm for PEMFC parameter extraction;
* Ref. [27] proposed multi-verse optimizer for parameter PEMFC extraction. * The hybrid artificial bee colony technique for estimating the parameter of PEMFC in [28];
* The estimation of PEMFC parameters was obtained by a combination between backtracking search technique combined with [PERSON]'s chaotic map in [29];
* A P systems-based optimizer for parameter estimation of PEMFC model in [30];
* Another optimization called biogeography-based optimization technique improved by adding with mutations to estimate the parameter of two renewable energy cells solar and fuel cells in [31];
* The parameter estimation problem based on the fuel cell polarization curve using bird mating optimization algorithm in [32];
* Refs. [18; 33; 34] proposed a hybrid genetic algorithm for estimation the PEMFC parameters;
* In [35], the harmony search optimizer was developed for parameter identification of a PEMFC model;
* Due to the recent development in machine learning methods [36; 37], the authors of [38] the convolutional neural network have been used for the PEMFC parameters identification;
* In [39], the PEMFC parameters are extracted based on the polarization curve and provide the behavior analysis considering the various temperature impacts;
* In [5], the Matlab/Simulink-based model was proposed in conjunction with a waste heat recovery system; and
* In [40], the parameters of PEMFC are identified by using a new modified version of the differential algorithm associated with the collective guidance factor. In recent years, many optimization techniques were developed for several engineering problems. In particular, COA is a recent method for global optimization problems. It is a population-based metaheuristic technique. The COA has been classified as both swarm intelligence and evolutionary heuristic and it is based on the coyotes' behavior. The fitness function is reduced using the social structure and exchanging experiences between the coyotes instead of hunting only. In this regard, we propose a novel method to detect the unidentified parameter of the electrochemical model for PEM fuel cells in this paper. Specifically, a new swarm-based algorithm called coyote COA is developed to determine the accurate parameter of PEM fuel cells.