The loss of electrochemical active surface area (ECSA) at the cathode is one of the main causes of performance degradation in Polymer Electrolyte Membrane Fuel Cells (PEMFCs). In order to investigate the catalyst degradation and the influence of the operating conditions we develop a multiscale degradation model which includes the formation and reduction of platinum oxides, platinum dissolution, particle growth due to Ostwald ripening, platinum ion transport through the ionomer and platinum band formation in the membrane. This degradation model is coupled with a 2D PEMFC performance model and predictions regarding ion concentration, ECSA evolution and particle growth are validated with dedicated experiments and literature data. Degradation under several AST protocols and under steady state operation are compared and discussed. The importance of a spatially resolved catalyst degradation model is conveyed by the occurrence of a depletion zone in the catalyst layer close to the membrane due to the platinum migration into the membrane. By comparing the correlation between platinum mass loss in the catalyst layer and the ECSA loss we conclude that catalyst degradation under AST conditions with nitrogen is not representative for the degradation under normal operation.

The present paper is focused on proposing and implementing a methodology for robust and rapid diagnosis of PEM fuel cells’ faults using Electrochemical Impedance Spectroscopy (EIS). Accordingly, EIS tests have been first conducted on four identical fresh PEM fuel cells along with an aged PEMFC at different current density levels and operating conditions. A label, which represents the presence of a type of fault (flooding or dehydration) or the regular operation, is then assigned to each test based on the expert knowledge employing the cell’s spectrum on the Nyquist plot. Since the time required to generate the spectrum should be minimized and considering the notable difference in the time needed for carrying out EIS tests at different frequency ranges, the frequencies have been categorized into four clusters (based on the corresponding order of magnitude: >1 kHz, >100 Hz, >10 Hz, >1 Hz). Next, for each frequency cluster and each specific current density, while utilizing a classification algorithm, a feature selection procedure is implemented in order to find the combination of EIS frequencies utilizing which results in the highest fault diagnosis accuracy and requires the lowest EIS testing time. For the case of fresh cells, employing the cluster of frequencies with $f>10$ Hz, an accuracy of $98.5\%$ is obtained, whereas once the EIS tests from degraded cells are added to the dataset, the achieved accuracy is reduced to $89.2\%$ . It is also demonstrated that, while utilizing the selected pipelines, the required time for conducting the EIS test is less than one second, an advantage that facilitates real-time in-operando diagnosis of water management issues.