Pre-diagnosis of flooding and drying in proton exchange membrane fuel cells by bagging ensemble deep learning models using long short-term memory and convolutional neural networks

Kim, Kyunghyun; Kim, Jaeyeon; Choi, Heesoo; Kwon, Obeen; Jang, Yujae; Ryu, Seungoh; Lee, Heeyun; Shim, Kyu–Hwan; Park, Taehyun; Won, Suk

doi:10.1016/j.energy.2022.126441

Cited by 17 publications

(2 citation statements)

References 54 publications

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“…Nevertheless, both simulation and experimental results showed the good accuracy of the proposed technique. Similar works can be found in [163][164][165].…”

Section: Residual-based Approachessupporting

confidence: 76%

“…Symptoms Consequences Diagnostics Recovering mechanism [104] operation at nominal power [112] low temperature, and poor air distribution [52] water droplets retained at the GDL [76] anode flooding (unoptimized exhaustion system) [112] excess of water at the anode [113] increased pressure drops [54,118] increased membrane resistance [156] high level of overall impedance [159] double layer effect affected [101] temperature decreasing rapidly (oscillating dewpoint) + increased cathode pressure [76] voltage degradation [169] internal humidity levels higher than 100% [169] high reactants pressure [174] reactants hygrometry higher than 1.1 [125] low air stoichiometry [100] decrease in electric power [118] increased imaginary and real part in EIS results-Nyquist plot-(cathode flooded) [118] decrease temperature (bigger EIS semi-circle diameter) [52,53] neutron imaging [101] online machine learning: ENN (cathode pressure residuals) [112] infrared spectroscopy [113] pressure, mass flow rate and humidity monitorization [114] anode to cathode pressure drop [115][116][117] EIS [118] empirical equivalent model parameter estimation [156] harmonic impedance measurement [159] online threshold around the nominal polarization curve (current interrupt method) [160] online signal based (EMD) [161] online machine learning: BN [163][164][165] online machine learning algorithm…”

Section: Causesmentioning

confidence: 99%

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A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

Andrade,

Laadjal,

Alcaso

et al. 2024

Energies

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The complexity of Fuel Cell (FC) systems demands a profound and sustained understanding of the various phenomena occurring inside of it. Thus far, FCs, especially Proton Exchange Membrane Fuel Cells (PEMFCs), have been recognized as being among the most promising technologies for reducing Green House Gas (GHG) emissions because they can convert the chemical energy bonded to hydrogen and oxygen into electricity and heat. However, their efficiency remains limited. To enhance their efficiency, two distinct factors are suggested. First, the quality of materials plays a significant role in the development of more robust and efficient FCs. Second, the ability to identify, mitigate, and reduce the occurrence of faults through the use of robust control algorithms is crucial. Therefore, more focused on the second point, this paper compiles, distinguishes, and analyzes several publications from the past 25 years related to faults and their diagnostic techniques in FCs. Furthermore, the paper presents various schemes outlining different symptoms, their causes, and corresponding fault algorithms.

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“…Nevertheless, both simulation and experimental results showed the good accuracy of the proposed technique. Similar works can be found in [163][164][165].…”

Section: Residual-based Approachessupporting

confidence: 76%