Diagnosis of Polymer Electrolyte Fuel Cells Failure Modes (Flooding & Drying out) by Neural Networks Modeling

Fault diagnosis and durability of Polymer Electrolyte Fuel Cells (PEFCs) have been identified among the critical issues that need to be overcome for a commercial viability of these power sources. Fuel cells fault diagnosis requires the knowledge of a number of fundamental parameters such as applied current, air inlet flow rate Q, stack temperature and dew point temperature that usually need a special monitoring system and a specifically adapted fuel cell geometry. This might be difficult and even impossible in many fuel cell stacks. Such a constraint could only be possible in a laboratory setup and is not adapted to real application. Moreover, for the transportation application, which aims at minimizing the embedded instrumentation, simple diagnosis methods involving non-intrusive and easy-to-monitor parameters are highly desired. This paper presents a diagnosis procedure of water management issues in fuel cell, namely flooding and drying out, based on a limited number of parameters that are, besides, easyto-monitor. This procedure uses a black-box model based on neural networks that simulates, in case of healthy operation, the evolution of pressure drop at the cathode as well as fuel cell voltage. Two residuals are generated from the comparison between the actual operation of the fuel cell and the parameters calculated by a neural network in case of normal operation. The two residuals analysis permits the detection (by the means of comparison with a predetermined threshold) and the classification of fuel cell’s states-of-health between flooding, drying out or normal operation

Monitoring the degradation of a solid oxide fuel cell stack during 10,000h via electrochemical impedance spectroscopy

International audienc

SOFC modelling based on discrete Bayesian network for system diagnosis use

International audienceWe propose in this paper a diagnosis method that is aimed to detect and isolate SOFC system fault by using the FC stack as a sensor. A discrete Bayesian network (BN) was established to illustrate the input-output causal relations of the stack. In order to examine the generalizability of the network structure, the BN was parameterized to fit the experimental data from two different SOFC systems. Themodels showed reasonable accuracy of state estimation for 6 operating variables. Finally, the BN model was experimented for diagnosing a specified system fault

Selective electrocatalysis imparted by metal-insulator transition for durability enhancement of automotive fuel cells

Repetitive start-up and shut-down events in polymer electrolyte membrane fuel cells for automotive applications lead to serious corrosion of the cathode due to an instantaneous potential jump that results from unintended air leakage into the anodic flow field followed by a parasitic oxygen reduction reaction (ORR) on the anode. Here we report a solution to the cathode corrosion issue during the start-up/shut-down events whereby intelligent catalyst design is used to selectively promote the hydrogen oxidation reaction (HOR) while concomitantly suppressing the ORR on the anode. Platinum thin layers supported on hydrogen tungsten bronze (Pt/HxWO3) suppressed the ORR by converting themselves into an insulator following exposure to oxygen, while selectively promoting the HOR by regaining metallic conductivity following subsequent exposure to hydrogen. The HOR-selective electrocatalysis imparted by a metal-insulator transition in Pt/HxWO3 demonstrated a remarkably enhanced durability of membrane electrode assemblies compared to those with commercial Pt/C catalysts