Diagnosing Improper Membrane Water Content in Proton Exchange Membrane Fuel Cell Using Two-Dimensional Convolutional Neural Network

In existing proton exchange membrane fuel cell (PEMFC) applications, improper membrane water management will cause PEMFC performance decay, which restricts the reliability and durability of PEMFC systems. Therefore, diagnosing improper water content in the PEMFC membrane is the key to taking appropriate mitigations to guarantee its operating safety. This paper proposes a novel approach for diagnosing improper PEMFC water content using a two-dimensional convolutional neural network (2D-CNN). In the analysis, the collected PEMFC voltage signal is transformed into 2D image data, which is then used to train the 2D-CNN. Data enhancement and pre-processing techniques are applied to PEMFC voltage data before the training. Results demonstrate that with the trained model, the diagnostic accuracy for PEMFC membrane improper water content can reach 97.5%. Moreover, by comparing it with a one-dimensional convolutional neural network (1D-CNN), the noise robustness of the proposed method can be better highlighted. Furthermore, t-distributed Stochastic Neighbor Embedding (t-SNE) is used to visualize the feature separability with different methods. With the findings, the effectiveness of using 2D-CNN for diagnosing PEMFC membrane improper water content is explored.

Automated summary

This paper proposes a novel approach for diagnosing improper water content in proton exchange membrane fuel cell (PEMFC) using a two-dimensional convolutional neural network (2D-CNN). The trained 2D-CNN model achieves a diagnostic accuracy of 97.5% for PEMFC membrane improper water content. The proposed method exhibits better noise robustness compared to a one-dimensional convolutional neural network (1D-CNN). Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) is used to visualize the feature separability. Overall, the effectiveness of using 2D-CNN for diagnosing PEMFC membrane improper water content is explored.