Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC

Performance degradation prediction is an effective method to improve the durability of proton exchange membrane fuel cell (PEMFC). In this study, a hybrid deep learning model based on two-dimensional convolutional neural network (CNN2D), gate recurrent unit (GRU), and improved manta ray foraging optimization (IMRFO) algorithm is proposed for performance degradation prediction of PEMFC. Firstly, the mutual information (MI) and the locally weighted scatterplot smoothing (LOESS) are used to preprocess the data in order to boost the sample quality and reduce the influence of insignificant and noisy data on the model prediction. Secondly, CNN2D is used to deeply explore the nonlinear degradation characteristics in the data. Thirdly, three strategies including half uniform initialization, exponential weight coefficient and fitness-distance balance (FDB) are added to the algorithm to improve the defect that the optimization algorithm is easy to fall into local optimum. Finally, the GRU model optimized by the improved MRFO algorithm is used to predict the degradation data and obtain the final prediction results. The experimental results show that the prediction accuracy of the proposed prediction model in this study is 99.79%, and the RMSE and MAE are 0.0072 and 0.0042, respectively. Therefore, the method can effectively explore the deep features in the data and improve the accuracy, reliability, and robustness of PEMFC performance degradation prediction.