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

Heng Zhang, Zhongyong Liu, Weilai Liu and Lei Mao
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Heng Zhang: School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China
Zhongyong Liu: Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China
Weilai Liu: Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China
Lei Mao: Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China

Energies, 2022, vol. 15, issue 12, 1-15

Abstract: 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.

Keywords: PEMFC; 2D-CNN; improper membrane water content; flooding; dehydration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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