Changjun Kuang
ABSTRACT
The Proton Exchange Membrane Fuel Cell (PEMFC), a form of environmentally friendly energy conversion technology, has garnered significant interest and widespread adoption. Nevertheless, the durability of PEMFC has persistently posed a major hurdle to its practical implementation. This study aimed to compare the effect of neural networks with different layers on the lifetime prediction of proton exchange membrane fuel cells (PEMFC). This paper uses the Levenberg-Marquardt algorithm and Bayesian regularization to carry out experiments on 4, 10, and 15-layer neural networks. The basic working principle of PEMFC is introduced. In terms of content, this paper draws some important conclusions by analyzing the performance of different algorithms in predicting the life of PEMFC. The results show that the 15-layer neural network combined with the Levenberg-Marquardt algorithm achieves the best prediction performance. This study has important implications for optimizing PEMFC design and increasing its lifetime, guiding future fuel cell technology development.
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