Model-Based Fault Detection and Isolation of PEM fuel cells using Bond Graphs

Vasilyev, Andrey; Dunnett, Sarah J.; Jackson, Lisa M.

doi:10.1201/b19094-324

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(Damour et al, 2015), employ complex mathematical equations to simulate the systems behaviour. Recent approach use advanced multi-domain bond graph models to consider the complexities of fuel cell operation, and provide the diagnostic signatures for each failure mode (Jha et al, 2016;Vasilyev et al, 2015). Models of overall fuel cell system performance have also been provided with Petri-nets using Monte Carlo simulations (Wieland et al, 2009;Whiteley et al, 2015;Fecarotti et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Tsalapati

Johnson

Jackson

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

Polymer electrolyte membrane fuel cells (PEMFC) are a promising technology for economic and environmentally friendly energy production. However, they haven't reached their full potential in the market yet as only few reliable PEMFC systems have successfully passed the prototyping face. A drawback of the current diagnostic tools is that only a select few are of high genericity, reliability and can perform efficiently on-line at the same time. Furthermore, there is only limited research identifying both PEMFC stack faults and ancillary system faults simultaneously. While none of the existing tools can be interrogated by the end-user. In this research, we develop novel artificial intelligence-based technologies to overcome these existing barriers, i.e., i) a semantically enriched integrating schema (ontology) of the overall operation and structure of the PEMFC that allows automatic inference engines to automatically deduce fault detection; ii) a knowledgebased, light-weight, on-line fuel cell system diagnosis (FuCSyDi) platform. FuCSyDi detects and provides the location of failures by considering only the data from the reliable sensors. Additionally, it provides the reasons underpinning any forthcoming failures and enables the end-user to interrogate the platform for further information regarding its operation and structure. Our platform is validated by performing tests against common automotive stress conditions. This innovative approach enhances the reliability of the fuel cell system diagnosis and, hence, its lifetime performance.

show abstract