TY  - INPR
KW  - polymer electrolyte membrane fuel cellP
KW  -  EMFC1D
KW  -   convolutional neural network
KW  -  AC voltage responsewater management failuresfault diagnosis
AV  - public
ID  - discovery10155711
N2  - 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.
UR  - https://doi.org/10.1016/j.xcrp.2022.101052
N1  - © 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/)
A1  - Zhou, Shangwei
A1  - Tranter, Tom
A1  - Neville, Tobias P
A1  - Shearing, Paul R
A1  - Brett, Dan JL
A1  - Jervis, Rhodri
Y1  - 2022/09/09/
TI  - Fault diagnosis of PEMFC based on the AC voltage response and 1D convolutional neural network
JF  - Cell Reports Physical Science
PB  - Elsevier BV
SN  - 2666-3864
ER  -