This study investigates the performance and simulation analysis of a larger-scale proton exchange membrane fuel cell model using a lower-order model. With model order reduction studies, the present work aims to demonstrate the effectiveness of the proposed Genetic algorithm technique in terms of various reduced order transfer functions. The proposed methodology can assist the higher-order model in meeting its requirements, including stability, computation complexity, and the issue of local optima entrapment. This study demonstrates the efficacy of the proposed method by comparing it to other reduction techniques, such as the factor division method, the stability method, and the truncation method, utilizing a variety of performance metrics, including error indices, non-parametric tests, through in frequency, and time-domain analyses. The proposed method for model reduction offers advantages in terms of perturbation, stability, and parameter uncertainty.

中文翻译：

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基于遗传算法的质子交换膜燃料电池高阶模型降阶

本研究使用低阶模型研究了大规模质子交换膜燃料电池模型的性能和仿真分析。通过模型降阶研究，目前的工作旨在证明所提出的遗传算法技术在各种降阶传递函数方面的有效性。所提出的方法可以帮助高阶模型满足其要求，包括稳定性、计算复杂性和局部最优陷阱问题。本研究通过将所提出的方法与其他缩减技术（例如因子划分法、稳定性法和截断法）进行比较，利用各种性能指标（包括误差指数、非参数测试）来证明所提出方法的有效性，通过在频率和时域分析中。