Zhou, Shangwei;
Tranter, Tom;
Neville, Tobias P;
Shearing, Paul R;
Brett, Dan JL;
Jervis, Rhodri;
(2022)
Fault diagnosis of PEMFC based on the AC voltage response and 1D convolutional neural network.
Cell Reports Physical Science
, Article 101052. 10.1016/j.xcrp.2022.101052.
(In press).
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Abstract
Real-time diagnosis is required to ensure the safety, reliability, and durability of the polymer electrolyte membrane fuel cell (PEMFC) system. Two categories of methods are (1) intrusive, time consuming, or require alterations to the cell architecture but provide detailed information about the system or (2) rapid and benign but low-information-yielding. A strategy based on alternating current (AC) voltage response and one-dimensional (1D) convolutional neural network (CNN) is proposed as a methodology for detailed and rapid fuel cell diagnosis. AC voltage response signals contain within them the convoluted information that is also available via electrochemical impedance spectroscopy (EIS), such as capacitive, inductive, and diffusion processes, and direct use of time-domain signals can avoid time-frequency conversion. It also overcomes the disadvantage that EIS can only be measured under steady-state conditions. The utilization of multi-frequency excitation can make the proposed approach an ideal real-time diagnostic/characterization tool for fuel cells and other electrochemical power systems.
| Type: | Article |
|---|---|
| Title: | Fault diagnosis of PEMFC based on the AC voltage response and 1D convolutional neural network |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.xcrp.2022.101052 |
| Publisher version: | https://doi.org/10.1016/j.xcrp.2022.101052 |
| Language: | English |
| Additional information: | © 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
| Keywords: | polymer electrolyte membrane fuel cellP, EMFC1D, convolutional neural network, AC voltage responsewater management failuresfault diagnosis |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10155711 |
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