Developing a precise semiempirical mathematical model based on multi-nonlinear equations for the proton-exchange membrane fuel cell (PEMFC), which guarantees suitable and accurate simulation of the electrical characteristics of typical PEMFC stacks under various operating scenarios, is the main target of this study. The unknown parameters of the PEMFC model are extracted using a novel efficient optimization technique called coyote optimization algorithm (COA). To validate the effectiveness of the proposed COA-based PEMFC model, two different cases of seven and ten unknown parameters are performed on a commercial PEMFC taken from literature. The sum of squared errors (SSE) between the experimentally measured data and the corresponding computed ones is considered as the objective function. Besides, the effectiveness of the developed algorithm is validated under different operating conditions. Moreover, the results obtained by the application of the proposed COA have been compared with other recent optimization methods reported in the literature, and very competitive results have been provided. Furthermore, parametric and nonparametric statistical analyses are presented to evaluate the accuracy and viability of the developed COA-based PEMFC model.

中文翻译：

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开发用于提取质子交换膜燃料电池模型参数的土狼优化算法

本研究的主要目标是为质子交换膜燃料电池 (PEMFC) 开发基于多重非线性方程的精确半经验数学模型，以保证对典型 PEMFC 电池堆在各种操作场景下的电气特性进行适当和准确的模拟。 . 使用称为土狼优化算法 (COA) 的新型高效优化技术提取 PEMFC 模型的未知参数。为了验证所提出的基于 COA 的 PEMFC 模型的有效性，在从文献中提取的商业 PEMFC 上执行了两个不同的 7 个和 10 个未知参数案例。实验测量数据与相应计算数据之间的平方误差总和 (SSE) 被视为目标函数。除了，所开发算法的有效性在不同的操作条件下得到验证。此外，通过应用所提出的 COA 获得的结果已与文献中报道的其他最近的优化方法进行了比较，并提供了非常有竞争力的结果。此外，还提出了参数和非参数统计分析，以评估开发的基于 COA 的 PEMFC 模型的准确性和可行性。