Remaining useful life prediction for proton exchange membrane fuel cells using combined convolutional neural network and recurrent neural network.

The search for sustainable but environmentally friendly medium of harnessing energy for the automotive industry has led to the evolution of various energy generating and converting devices. One of such energy converting device is fuel cells. Despite the merits associated to the performance of proton exchange membrane (PEM) fuel cells, issues relating to the cost and remaining useful life prediction still persist hence impeding their further commercialization especially in the automotive industry. In spite of the progress made by the research community in developing various predictive models in order to mitigate these challenges, the accuracy of these developed models has lately become active research direction. The current study explored the accuracy of recurrent neural network, bi recurrent neural network, combined convolutional neural network and bi recurrent neural network in predicting the remaining useful life of a PEM fuel cell. The presence of the convolutional neural network was mainly to ensure pre – processing of the bi recurrent neural network for the extraction of high level features. To reduce the possibility of overfitting, a dropout approach coupled with callback technique is adopted. Validation of the model was executed based on an experimental data. The outcome of the investigation highlighted the key role of the convolutional neural network in improving the accuracy of the recurrent neural network. Comparing the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the present model with other models, the developed model yielded the least values indicating a higher accuracy. For instance, the relative error showed a least value of 0.12 for the combined convolutional neural network and bi recurrent neural network compared to the long short term memory with 2.61 reported in previous studies. • Evaluation of various existing predictive models for PEM fuel cells is studied. • Convolutional neural network accuracy in predicting the rate of degradation for PEM fuel cell is captured. • The developed predictive model have higher accuracy compared to other models. • Future studies should explore further combination of various predictive models and the effect on the accuracy. [ABSTRACT FROM AUTHOR]