A key area of research in a promising renewable energy technology called Proton Exchange Membrane Fuel Cells (PEMFCs) focuses on identifying parameters not provided by manufacturers’ datasheets and developing highly accurate models for PEMFC voltage-current characteristics. In this regard, a precise model is crucial for designing effective PEMFC systems. This study aims to discover the seven unknown parameters of the steady-state model for PEMFCs using a recent optimization algorithm called Educational Competition Optimizer (ECO). The ECO is used to reduce the effects of local optimal stagnation and early convergence, commonly observed in literature approaches. The goal is to improve model parameter correctness by reducing errors between experimental and predicted polarization curves. A robust regression fitness function known as Huber loss is used to decrease inaccuracies between experimentally measured voltages and their corresponding calculated values. The present research examines three test cases of well-known commercial PEMFC units as benchmarks under various steady-state operation situations. The simulation results show that the suggested model is significantly more accurate than the best alternative technique and achieves high closeness to the experimental records. The article compares the ECO against current optimizers in the literature to assess its feasibility. Based on the findings of this study, the prospective Huber loss function increases the optimizer’s resilience and robustness compared to steady-state error.

中文翻译：

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利用 Huber 损失统计函数对质子交换膜燃料电池进行稳健参数估计


质子交换膜燃料电池 （PEMFC） 是一种前景广阔的可再生能源技术，其关键研究领域侧重于识别制造商数据表未提供的参数，并开发高精度的 PEMFC 电压-电流特性模型。在这方面，精确的模型对于设计有效的 PEMFC 系统至关重要。本研究旨在使用一种称为 Educational Competition Optimizer （ECO） 的最新优化算法来发现 PEMFC 稳态模型的七个未知参数。ECO 用于减少局部最优停滞和早期收敛的影响，这在文献方法中很常见。目标是通过减少实验极化曲线和预测极化曲线之间的误差来提高模型参数的正确性。使用称为 Huber loss 的稳健回归适应度函数来减少实验测量电压与其相应计算值之间的误差。本研究考察了知名商用 PEMFC 装置的三个测试用例，作为各种稳态运行情况下的基准。仿真结果表明，所建议的模型明显比最佳替代技术更准确，并且与实验记录高度接近。本文将 ECO 与文献中的当前优化器进行了比较，以评估其可行性。基于这项研究的结果，与稳态误差相比，预期的 Huber 损失函数提高了优化器的弹性和鲁棒性。