Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests

Behzad Najafi, Paolo Bonomi, Andrea Casalegno, Fabio Rinaldi and Andrea Baricci
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Behzad Najafi: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Paolo Bonomi: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Andrea Casalegno: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Fabio Rinaldi: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Andrea Baricci: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy

Energies, 2020, vol. 13, issue 14, 1-19

Abstract: 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.

Keywords: PEM fuel cells; fault diagnosis; electrochemical impedance spectroscopy; machine learning; feature selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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