UC Irvine

Proton exchange membrane fuel cells' (PEMFCs') degradation is a main problem that must be solved for their commercialization. In this thesis, we employ data-driven prognostic models to forecast PEMFC voltage degradation utilizing artificial neural networks (ANN), deep neural networks (DNN), and long short-term memory (LSTM) techniques. The suggested models are developed and tested using experimental information obtained through PEMFC stack testing, and their performance is assessed using a variety of metrics, such as root mean square error (RMSE). The findings show that the DNN technique performs better than the other methods, indicating that the model can reliably and precisely forecast the PEMFC stack voltage decline. In addition, we examined the models' performance in terms of the neural network designs' number of neurons and layers. The results demonstrate a trade-off between prediction accuracy and computational complexity, with an increase in neurons and layers able to enhance prediction accuracy. In conclusion, the suggested data-driven prognostic models may offer precise and trustworthy forecasts of PEMFC stack voltage degradation, which can aid in optimizing system design and operation. In order to improve the precision and effectiveness of the prediction process, future research can concentrate on investigating the applicability of these models to specific degradation modes, such as catalyst and membrane degradation, and developing more sophisticated data-driven prognostic models. Moreover, combining data-driven and physics-based models can offer a thorough strategy for PEMFC degradation prognostics.Keywords: Proton exchange membrane fuel cell, Degradation, Prognostics, Artificial neural network, Deep neural network, Long short-term memory, Data-driven model.