Sorrentino Antonio, Sundmacher Kai, Vidakovic-Koch Tanja
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Otto-von-Guericke University Magdeburg, Process Systems Engineering, Magdeburg, Germany.
iScience. 2024 Jun 13;27(7):110254. doi: 10.1016/j.isci.2024.110254. eCollection 2024 Jul 19.
In this study, we evaluated the effectiveness of various frequency response analysis (FRA) techniques for identifying fault states in the diagnosis of polymer electrolyte membrane fuel cells (PEMFCs). To this end, an identifiability analysis was conducted to determine the reliability of parameters obtained by fitting a previously validated PEMFC model to the spectra from different methods. Specifically, we focused on electrochemical impedance spectroscopy (EIS) and the newly introduced concentration frequency response analysis (CFRA). The identifiability analysis revealed that CFRA, when applied with water pressure as the input and voltage as the output, provides the most accurate parameters estimates related to mass transport in the cathode electrode and the Nafion electrolyte, yielding physically meaningful insights. Consequently, employing this input for PEMFC diagnosis emerges as a promising approach. Furthermore, our findings underscore the importance of meticulously evaluating the quality of parameter estimation, even when utilizing well-established techniques such as EIS.
在本研究中，我们评估了各种频率响应分析（FRA）技术在聚合物电解质膜燃料电池（PEMFC）诊断中识别故障状态的有效性。为此，进行了可识别性分析，以确定通过将先前验证的PEMFC模型拟合到不同方法的光谱而获得的参数的可靠性。具体而言，我们重点关注电化学阻抗谱（EIS）和新引入的浓度频率响应分析（CFRA）。可识别性分析表明，当以水压为输入、电压为输出应用CFRA时，能提供与阴极电极和Nafion电解质中质量传输相关的最准确参数估计，产生具有物理意义的见解。因此，将此输入用于PEMFC诊断成为一种有前景的方法。此外，我们的研究结果强调了即使在使用诸如EIS等成熟技术时，仔细评估参数估计质量的重要性。
