Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

Abstract

This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode-and cathode stoichiometry is 100 % success rate. Overall global accuracy on the test data is 94.6 %.

Effects of Impurities on Pre-Doped and Post-Doped Membranes for High Temperature PEM Fuel Cell Stacks

“…In the interpretation of fitted resistances, it is generally accepted that the ohmic resistance (R ohm ) represents all the contact resistances, including the proton conduction across the electrolyte and its changes reflect the changes in proton conductivity [35,36]. High frequency resistance (R HF ) and intermediate frequency resistance (R IF ) are associated to charge transfer losses, with the former dominated by the anode charge transfer losses and the latter by those in the cathode [37]. Finally, the low frequency resistance (R LF ) is ascribed to the mass transport losses [38].…”

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

“…(Rahimzadeh et al, 2016) or SVM (Kheirandish et al, 2016). Use of AI-based methods have now exploded with use of methods like artificial neural networks both feed-forward and recurrent (Jeppesen et al, 2017;Zhao et al, 2019;Ma et al, 2018), extreme and deep learning methods (Yang et al, 2019b;Liu et al, 2019), clustering methods including support vector and k-means (Liu et al, 2018). The black-box methods are better suited for complex non-linear systems, but they require a large amount of experimental data (Petrone et al, 2013) and only the SVM-based ones are characterised by a high level of genericity.…”

System Design and Modeling of a High Temperature PEM Fuel Cell Operated with Ammonia as a Fuel

“…The higher operating temperatures increase the tolerance to impurities, for example, CO tolerance increases up to 3% compared to only few ppm in low temperature PEM fuel cells [19]. Due to this higher tolerance to impurities, HT-PEMFCs have been extensively studied in conjunction with methanol reformer, where the effects of the different resulting impurities, namely CO, CO 2 , and CH 3 OH have been reported with satisfactory tolerances for single cells and stacks [20][21][22]. Moreover, steam methane reforming and HT-PEMFC systems are also available in the literature [23][24][25].…”