Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection

For optimizing the performance of the proton exchange membrane fuel cells (PEMFCs), the I-V polarization curve is generally used as an important evaluation metric, which can represent many important properties of PEMFCs such as current density, specific power, etc. However, a vast number of experiments for achieving I-V polarization curves are conducted, which consumes a lot of resources, since the membrane electrode assembly (MEA) in PEMFCs involves complex electrochemical, thermodynamic, and hydrodynamic processes. To solve the issues, this paper utilizes deep learning (DL) to design a performance prediction method based on the random forest algorithm (RF) and convolutional neural networks (CNN), which can reduce unnecessary experiments for MEA development. In the proposed method, to improve the high quality of the training dataset, the RF algorithm is adopted to select the important factors as the input feature of the model, and the selected factors are further verified by the previous studies. CNN is used to construct the performance prediction model which outputs the IV polarization curve. In particular, batch normalization and dropout methods are applied to enhance model generalization. The effectiveness of the CNN-based prediction model is evaluated on the real I-V polarization curve dataset. Experiment results indicate that the prediction curves of the proposed model have good agreement with the real curves. Our study demonstrates the deep learning technologies are powerful complements for optimizing the PEMFCs.

Automated summary

This paper utilizes deep learning techniques to design a performance prediction method for proton exchange membrane fuel cells, reducing unnecessary experiments and improving prediction accuracy to optimize performance. The proposed method combines random forest algorithm and convolutional neural networks, achieving good agreement with real data. This study demonstrates the effectiveness of using deep learning technologies for optimizing PEMFCs.