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.

Preview

Text Zhou_Revised_No_highlight.pdf - Accepted Version Download (993kB) | Preview

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.

Download activity - last month

Export as CSVJSONXML

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as CSVJSONXML

Archive Staff Only

View Item