PEMFC FLOODING FAULT IDENTIFICATION METHOD BASED ON CNN-BILSTM DUAL-CHANNEL FEATURE FUSION

Zhao Xuyang; Yuan Yupeng; Tong Liang; Zhu Xiaofang; Li Xiao

doi:10.19912/j.0254-0096.tynxb.2023-2028

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 30-38. DOI: 10.19912/j.0254-0096.tynxb.2023-2028

PEMFC FLOODING FAULT IDENTIFICATION METHOD BASED ON CNN-BILSTM DUAL-CHANNEL FEATURE FUSION

Zhao Xuyang^1,2, Yuan Yupeng^1,3, Tong Liang^1,3, Zhu Xiaofang³, Li Xiao⁴

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Abstract

In order to identify the flooding fault of proton exchange membrane fuel cell （PEMFC） in a timely and accurate manner, a PEMFC flooding fault identification method based on the dual-channel feature fusion of convolutional neural network （CNN） and bidirectional long short-term memory network （BiLSTM） is proposed. Firstly, normalization is used to eliminate the effect of dimension of the original features. On this basis, random forest （RF） is used to evaluate the feature importance of the data. The CNN and the BiLSTM are used to extract spatial features and temporal features, respectively. Then, these features are fused in series. Finally, the support vector machine （SVM） is used to identify the flooding fault. The case analysis shows that the proposed method can quickly and accurately identify the normal state and the flooding fault state of PEMFC, with an overall classification accuracy of 99.08% and a test time of 0.0929 s, which can effectively improve the accuracy of fault classification.

Key words

proton exchange membrane fuel cells / fault diagnosis / convolutional neural networks / long short-term memory / random forests / support vector machines

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Zhao Xuyang, Yuan Yupeng, Tong Liang, Zhu Xiaofang, Li Xiao. PEMFC FLOODING FAULT IDENTIFICATION METHOD BASED ON CNN-BILSTM DUAL-CHANNEL FEATURE FUSION[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 30-38 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2028

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