Polymer electrolyte fuel cells (PEMFC) will play a central role in the establishment of a de-carbonized society. They are seen as a key technology to empower electric heavy-duty vehicles. However, the lifespan is still below the fixed standards for their commercialization. A way to increase their durability is to complement them during operation with a mitigation strategy enabling to avoid the arise of faulty states accelerating degradation processes. Nowadays, most of the proposed fault identification and isolation architectures rely on monitoring of the performance through electrochemical impedance spectroscopy (EIS). Nevertheless, the analysis of impedance data is often complicated, since the contribution of many processes in the EIS spectra are coupled with each other and, for this reason, difficult to identify. This constitutes a limiting factor for its employment as diagnostic tool. In the last years, in order to overcome such issues, the use of nonelectrical periodic inputs and/or outputs has been advanced. The idea motivating this research direction is the possibility to detect the contribution of selected processes on the performance of PEMFCs separating them from the others. Novel frequency response techniques involving back pressure, temperature and partial pressure have been developed [1].In this contribution, the so-called concentration frequency response analysis (CFRA) is considered [2, 3]. It consists on perturbing the cathode side of the cell with a feed characterized by a periodic partial pressure of oxygen and water, and the detection of the electric response, voltage or current. A test station has been designed and constructed in order to apply this technique and investigate its capabilities. A model based analysis of the measured CFRA spectra indicates the possibility to decouple the contributions of the water transport into Nafion and oxygen mass transport on the performance of the cell depending on the chosen concentration input and electrical output. In this way, the analysis and identification of the related power losses is simplified with respect to classic EIS.Moreover, the performance of a fault identification and isolation architecture employing CFRA as monitoring technique have been evaluated and compared to EIS. For such purpose, an identifiability analysis of parameters related to faulty states and obtained by model fitting to spectra of each technique has been performed. The results clearly show that the employment of CFRA based on water pressure input offers the most reliable parameter estimation and, consequently, constitutes the most preferable diagnosis method among the others analyzed.
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