Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, novel meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Harris Hawks’ optimization (HHO) and atom search optimization (ASO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W PEM, 500W SR-12PEM and 250W stack, under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with that obtained in studies. Moreover, the I/V curves obtained by the application of HHO and ASO showed a clear matching with data sheet curves for all the studied cases. Finally, PEMFC model based on HHO technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed.

质子交换膜燃料电池（PEMFC）被认为是环保能源的有利解决方案。PEMFC的精确模型可以准确识别其极化曲线并深入了解其所有工作特性，吸引了许多研究人员的兴趣。在本文中，新的元启发式优化方法已成功应用于评估PEMFC模型的未知参数，尤其是Harris Hawks优化（HHO）和原子搜索优化（ASO）技术。在三种不同的商用PEMFC电池组（即BCS 500-W PEM，500W SR-12PEM和250W电池组）上，在各种操作条件下都对所提出的优化算法进行了测试。通过应用估计参数获得的结果与燃料电池组的实验测量结果之间的平方误差之和（SSE）被视为优化问题的目标函数。为了验证所提出方法的有效性，将结果与研究中的结果进行了比较。而且，通过HHO和ASO的应用获得的I / V曲线与所有研究案例的数据表曲线均显示出明显的匹配。最后，基于HHO技术的PEMFC模型在求解精度和收敛速度方面均优于所有比较算法。