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Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Zhou, S; Shearing, PR; Brett, DJL; Jervis, R; (2022) Machine learning as an online diagnostic tool for proton exchange membrane fuel cells. Current Opinion in Electrochemistry , 31 , Article 100867. 10.1016/j.coelec.2021.100867. Green open access

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Abstract

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications.

Type: Article
Title: Machine learning as an online diagnostic tool for proton exchange membrane fuel cells
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.coelec.2021.100867
Publisher version: https://doi.org/10.1016/j.coelec.2021.100867
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Proton exchange membrane fuel cell (PEMFC), Fault diagnosis, Water management failure, Machine learning, Data-driven
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
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
URI: https://discovery.ucl.ac.uk/id/eprint/10136905
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