Abstract
This paper investigates the polymer electrolyte membrane (PEM) fuel cell internal behaviour variation at different operating condition, with characterization test data taken at predefined inspection times, and uses the determined internal behaviour evolution to predict the future PEM fuel cell performance. For this purpose, a PEM fuel cell behaviour model is used, which can be related to various fuel cell losses. By matching the model to the collected polarization curves from the PEM fuel cell system, the variation of fuel cell internal behaviour can be obtained through the determined model parameters. From the results, the source of PEM fuel cell degradation during its lifetime at different conditions can be better understood. Moreover, with determined fuel cell internal behaviour, the future fuel cell performance can be obtained by predicting the future model parameters. By comparing with prognostic results using adaptive neuro fuzzy inference system (ANFIS), the proposed prognostic analysis can provide better predictions for PEM fuel cell performance at dynamic condition, and with the understanding of variation in PEM fuel cell internal behaviour, mitigation strategies can be designed to extend the fuel cell performance.
Highlights
In the last few decades, many efforts have been devoted to the innovative energy generation sources to reduce the emissions
It should be mentioned that since ioc is considered as a constant value the overvoltage due to activation loss is a constant value. It can be found from the above figure that in the polymer electrolyte membrane (PEM) fuel cell durability tests at steady state condition, the mass transport loss accounts for 61% of PEM fuel cell performance degradation, and activation loss accounts for 24% of PEM fuel cell performance degradation, 12% PEM fuel cell degradation results from ohmic loss, only 3% from fuel crossover loss, this is consistent with the results from previous study [20]
In order to further study the effectiveness of PEM fuel cell prognostics using predicted model parameters, it is compared with the prognostic results using adaptive neuro fuzzy inference system (ANFIS), which has been widely used in fuel cell prognostics [14e17]
Summary
In the last few decades, many efforts have been devoted to the innovative energy generation sources to reduce the emissions. Prognostic results from the black-box models cannot provide the understanding of PEM fuel cell internal behaviour, it is difficult to design maintenance strategies to extend the PEM fuel cell lifetime based on the prognostic results. On this basis, it is necessary to study the variation of the PEM fuel cell internal behaviour during its lifetime, and predict the future PEM fuel cell performance using the evolution of fuel cell internal behaviour. By matching the model parameters to the collected polarization curves with certain interval, the variation of fuel cell internal behaviour can be obtained, which can be used to analyse the source for the PEM fuel cell degradation at both steady state and dynamic conditions. The prognostic results are compared with those using ANFIS at both steady state and dynamic conditions, results demonstrate that the proposed prognostic analysis can provide better predictions at PEM fuel cell dynamic condition
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