Hydrogen fuel cells, and notably the polymer electrolyte fuel cell (PEFC), present an important opportunity to reduce greenhouse gas emissions within a range of sectors of society, particularly for transportation and portable products. Despite several decades of research and development, there exist three main hurdles to full commercialisation; namely infrastructure, costs, and durability. This thesis considers the latter of these. The lifetime target for an automotive fuel cell power plant is to survive 5000 hours of usage before significant performance loss; current demonstration projects have only accomplished half of this target, often due to PEFC stack component degradation. Health management techniques have been identified as an opportunity to overcome the durability limitations. By monitoring the PEFC for faulty operation, it is hoped that control actions can be made to restore or maintain performance, and achieve the desired lifetime durability. This thesis presents fault detection and diagnosis approaches with the goal of isolating a range of component degradation modes from within the PEFC construction. Fault detection is achieved through residual analysis against an electrochemical model of healthy stack condition. An expert knowledge-based diagnostic approach is developed for fault isolation. This analysis is enabled through fuzzy logic calculations, which allows for computational reasoning against linguistic terminology and expert understanding of degradation phenomena. An experimental test bench has been utilised to test the health management processes, and demonstrate functionality. Through different steady-state and dynamic loading conditions, including a simulation of automotive application, diagnosis results can be observed for PEFC degradation cases. This research contributes to the areas of reliability analysis and health management of PEFC fuel cells. Established PEFC models have been updated to represent more accurately an application PEFC. The fuzzy logic knowledge-based diagnostic is the greatest novel contribution, with no examples of this application in the literature.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:763521 |
| Date | January 2018 |
| Creators | Davies, Benjamin |
| Publisher | Loughborough University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://dspace.lboro.ac.uk/2134/36322 |
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