Electrochemical modeling is commonly used to model the characteristics of proton exchange membrane (PEM) electrolyzer cells where all losses caused during the electrolysis process are taken into account. The model has a nonlinear relationship between current density and voltage ( J–V ), with five model parameters that are subjected to change depending on the physical properties and chemical conditions of the PEM electrolyzer. In this article, a novel analytical approach based on the least square error method is proposed to estimate the model parameters and characterize the electrochemical behavior of the PEM electrolyzer under various operating conditions. The accuracy and validity of the proposed approach are tested under different case studies at various operating temperatures, output pressures, hydrogen production rates, and sizes of the dataset. Also, the relationship between the estimated parameters and the operating conditions of the PEM electrolyzer is explored. Finally, the superiority of the proposed approach is demonstrated by comparison to numerical and heuristic optimization parameter identification methods.

中文翻译：

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通过轨迹数据时空分析了解私家车聚集效应


电化学建模通常用于对质子交换膜 (PEM) 电解槽的特性进行建模，其中考虑了电解过程中造成的所有损失。该模型的电流密度和电压 (J–V) 之间存在非线性关系，五个模型参数会根据 PEM 电解槽的物理特性和化学条件而发生变化。本文提出了一种基于最小二乘误差法的新型分析方法来估计模型参数并表征 PEM 电解槽在各种操作条件下的电化学行为。该方法的准确性和有效性在不同的案例研究中、不同的工作温度、输出压力、氢气生产率和数据集大小下进行了测试。此外，还探讨了估计参数与 PEM 电解槽运行条件之间的关系。最后，通过与数值和启发式优化参数识别方法的比较，证明了该方法的优越性。