Neutron imaging experiments were carried out to measure the water content of an operating proton exchange membrane fuel cell (PEMFC) under varying conditions of current density and temperature. It was found that the water content of the PEMFC is strongly coupled to the current density and temperature of the cell. These measurements indicate that changes in water content lag changes in current density by at least 100 s, both when the current density was increased and decreased. Less liquid water was measured in the cells when operating at 80°C than at 40°C. At 60°C cell temperature, a peak in water content was observed around 650 mA/cm 2 and the water content was found to decrease with increasing current density. This is explained in the context of cell heating by performing a simple thermal analysis of an operating PEMFC so as to yield quantitative information on the waste heat and its effects on the liquid water contained in the cell.

Real-time imaging of liquid water in an operating proton exchange membrane fuel cell.

Review of hydrogen crossover through the polymer electrolyte membrane

Influence of Catalyst Layer and Gas Diffusion Layer Porosity in Proton Exchange Membrane Fuel Cell Performance

Measurement of solidification heat from supercooled water freezing during PEFC cold start and visualization of ice distribution

A new strategy of carbon – Pb composite as a bipolar plate material for unitized regenerative fuel cell system

Investigating the vapour transport phenomena inside the cathode gas diffusion layer media by controlling local temperature gradient inside an operating proton exchange membrane fuel cell

Water management is very important for more spread of polymer electrolyte fuel cell (PEFC). Water distribution inside the PEFC must be kept proper and it is largely changed with the temperature profile change inside the PEFC. Therefore, the interrelationship between the temperature gradient and water distribution in the cathode gas diffusion layer (GDL) must be investigated in detail. In this report, X-ray computed tomography (CT) was used to visualize inside the cathode GDL. And thin film thermocouple (TFTC), which width is 30 µm and thick is 6 µm, has been fabricated by using micro electro mechanical system (MEMS) technology. With X-ray CT and TFTC, the interrelationship between temperature gradient in the direction of through-plane of the cathode GDL and water distribution inside the cathode GDL was investigated. From the results, we considered that temperature gradient may largely affect on water distribution and the PEFC performance.

https://iopscience.iop.org/article/10.1149/09208.0161ecst/pdf

Visualization Analysis of Interrelationship between Temperature Profile and Water Distribution inside the PEFC

Acoustic emission (AE) based condition monitoring is a popular method to inspect a material health condition in many areas[1]. It monitors irreversible structure changes of material through radiation of acoustic waves. Typically, the structure change generates a typical spectrum of acoustic waves starting at 1 kHz, and falling off at several MHz, which can be detected and analyzed via an AE system. Meanwhile, AE signals convey massive information such as rise time, AE energy, AE peak frequency, which are expressive of the failure process. AE based condition monitoring technology exhibits excellent capability of detecting, positioning, and characterizing damage and has been wildly used in monitoring of inner structure changes in bridges, pressure containers, and pipeline systems. In the last decade, the AE technology has been applied to condition monitoring of power modules and shows a promising application scenario in the power electronics field, which help us acquire accurate failure information and making precise perdition of a failure in the power module[2].
 In this work, we firstly combined the AE monitoring and a deep learning model - Gated Recurrent Unit (GRU) to monitor and predict the failure of power module as shown in Figure 1[3]. The proposed method integrally analyzes and summarizes the patterns from the time-series data obtained during power cycle tests of power modules and predicts the failures. Specifically, the time-series data consisting of junction temperature, electric power, thermal resistance, and several AE parameters were obtained from AE sensor data and power cycling test. The proposed method (a) automatically extracts common or different time-series patterns among devices and estimates device states, and (b) effectively predicts failures by switching prediction models according to changes in device states.
 By time-series analysis of AE sensor data, it was confirmed that the AE parameters obtained from the AE sensor data capture the progression and signs of failures. In addition, by verifying the effectiveness of the time-series pattern detection, it was confirmed that the proposed method can accurately extract the timeseries patterns during normal conditions, the time-series patterns that show signs of failure, and the time-series patterns after failure. Furthermore, by verifying the prediction accuracy and learning efficiency, it was showed that the proposed method is capable of faster learning while retaining the same or better performance compared to existing methods.
 
 Reference
 
 [1] A. Nair and C. Cai, "Acoustic emission monitoring of bridges: Review and case studies," Eng Struct, vol. 32, no. 6, pp. 1704-1714, 2010.
 [2] S. Müller, C. Drechsler, U. Heinkel, and C. Herold, "Acoustic emission for state-of-health determination in power modules," in 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD), 2016: IEEE, pp. 468-471.
 [3] R. Dey and F. M. Salem, "Gate-variants of gated recurrent unit (GRU) neural networks," in 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), 2017: IEEE, pp. 1597-1600.

(Digital Presentation) Lifetime Prediction of SiC Power Module By Using Time-Series Analysis of Acoustic Emission during Power Cycling Test

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Investigating the vapour transport phenomena inside the cathode gas diffusion layer media by controlling local temperature gradient inside an operating proton exchange membrane fuel cell

Visualization Analysis of Interrelationship between Temperature Profile and Water Distribution inside the PEFC

(Digital Presentation) Lifetime Prediction of SiC Power Module By Using Time-Series Analysis of Acoustic Emission during Power Cycling Test