The normal electric grid loss becomes irregular as a result of climatic variations, demanding an effective technique for power. Fuel cell (FC) technologies have been developed to alleviate the shortcomings of conventional backup power alternatives. The automobiles that function with FC technologies also entered into the smartphone application. The FCs may be classified into numerous categories with respect to the type of electrodes employed. Among these, the proton exchange membrane fuel cell (PEMFC) is the most extensively deployed kind. Because of its high-power density at low temperatures and rapid responsiveness to electrodynamic processes, the PEMFC has piqued the interest of many research groups. Optimal modelling of PEMFC can increase the overall efficiency of the cell in diverse applications of smart microgrids. Since the extraction of optimum parameter values in the PEMFC is an optimization issue, it may be tackled by the construction of metaheuristic algorithms. For this purpose, this work provides an optimum parameter estimate of proton exchange membrane fuel cells utilizing the enhanced red fox optimizer (OPEMFC-IRFO) method for sustainable energy management. The fundamental objective of the OPEMFC-IRFO method is to estimate the optimal parameter values of the PEMFC systems. The OPEMFC-IRFO algorithm is essentially based on the notions of the stimulating behaviour of red foxes, and the performance of the OPEMFC-IRFO algorithm may be improved by the use of Levy flight. Besides, the OPEMFC-IRFO method develops an objective function for the reduction of the sum of square variation between measured and optimal estimated voltages. In addition, the unknown parameters involved in the PEMFC may be ideally calculated by the application of the OPEMFC-IRFO method. The performance validation of the OPEMFC-IRFO algorithm indicates the positive outcomes over its previous state-of-art methodologies.

由于气候变化，正常的电网损耗变得不规则，需要一种有效的电力技术。已经开发了燃料电池 (FC) 技术来缓解传统备用电源替代方案的缺点。搭载FC技术的汽车也进入了智能手机应用。就所用电极的类型而言，FC 可分为许多类别。其中，质子交换膜燃料电池（PEMFC）是应用最广泛的一种。由于其在低温下的高功率密度和对电动势的快速响应过程中，PEMFC 引起了许多研究小组的兴趣。PEMFC 的优化建模可以提高电池在各种智能微电网应用中的整体效率。由于 PEMFC 中最优参数值的提取是一个优化问题，因此可以通过构建元启发式算法来解决。为此，这项工作利用增强型红狐优化器 (OPEMFC-IRFO) 方法为可持续能源管理提供了质子交换膜燃料电池的最佳参数估计。OPEMFC-IRFO 方法的基本目标是估计最优参数值PEMFC 系统。OPEMFC-IRFO算法本质上是基于红狐刺激行为的概念，使用Levy飞行可以提高OPEMFC-IRFO算法的性能。此外，OPEMFC-IRFO 方法开发了一个目标函数，用于减少测量电压和最佳估计电压之间的平方变化之和。此外，PEMFC 中涉及的未知参数可以通过应用 OPEMFC-IRFO 方法进行理想计算。OPEMFC-IRFO 算法的性能验证表明了其先前最先进方法的积极成果。