Degradation prediction model for proton exchange membrane fuel cells based on long short-term memory neural network and Savitzky-Golay filter

Proton exchange membrane fuel cell (PEMFC) as a promising green power source, can be applied to vehicles, ships, and buildings. However, the lifetime of the fuel cell needs to be prolonged in order to achieve a wide range of applications. Consequently, the prediction of the health state draws attention lately and is critical to improving the reliability of the fuel cell. Since the degradation mechanism of the fuel cell is not fully understood, the data driven method is very suitable for designing degradation prediction models. However, the data-driven method usually requires a lot of data, which is difficult to be obtained. To solve the issues, we propose a degradation prediction model for PEMFC based on long short-term memory neural network (LSTM) and Savitzky-Golay filter in this paper. First, we select the monitoring parameters for building the degradation prediction model by analyzing the degradation phenomenon of the fuel cell. Then, Savitzky-Golay filter is utilized to smooth out the selected data, and the sliding time window is used to generate training samples. Finally, the LSTM is applied to establish the degradation prediction model. Moreover, the dropout layer and mini-batch method are adopted to improve the model generalization ability. We use an actual aging data of the fuel cell to verified the proposed degradation prediction model. The results demonstrate that the proposed model can precisely predict the fuel cell degradation. It is worth mentioning that the determination coefficient (R-2) of the test set based on the model trained by 25% of data is 0.9065. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a PEMFC degradation prediction model based on LSTM and Savitzky-Golay filter. By selecting monitoring parameters, smoothing data, generating training samples with sliding time window, and adopting dropout layer and mini-batch method to improve model generalization, the model accurately predicts fuel cell degradation.