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Reliability modelling using Petri-Net simulation of polymer electrolyte membrane fuel cell degradation in automotive applications

Climate change concerns have increased in recent years, and technologies to reduce emissions from the transport industry have been put forward. Hydrogen fuel cells have the potential to mitigate emissions concerns as they only produce water vapor as an emission. However, their commercialization has been hindered due to reliability and lifetime concerns. They need to meet strict targets of 5000 hours of operation (equivalent to 150,000 miles), and currently struggle to do so. Reliability analysis of fuel cells can ascertain key information as to the reduction of current lifetimes due to degradation. Failure mode and effect analysis, and fault tree analysis was performed for a Polymer Electrolyte Membrane Fuel Cell (PEMFC) revealing interactions and relationships between failure modes which makes the use of FTA in this case unfeasible. Petri-Net simulation techniques have been pursued to alleviate these concerns and to develop an accurate degradation model of a PEMFC in an automotive context. The Petri-Net model is integrated into a PEMFC performance model for the purpose of incorporating key variables into each model. For example; membrane thickness is an input into the performance model, and this can be modified by the Petri-Net degradation model based upon failure modes such as radical attack of the membrane (which thins the membrane). Thus, outputs from the degradation relationships in the Petri-Net model directly feed into the running of the performance model. This work furthers the research in PEMFC reliability by providing an accurate degradation model of a PEMFC based upon Petri-Net simulation of interactions and relationships between failure modes. Integration of relationship concerns with a verified performance model of a PEMFC is invaluable to increase the accuracy of PEMFC degradation research. Future work will include PEMFC stack experimentation to fill holes in the literature regarding failure mode failure rates that are fed into the model.