Multi-Reservoir Echo State Network for Proton Exchange Membrane Fuel Cell Remaining Useful Life prediction
Rania Mezzi, Simon Morando, Nadia Yousfi Steiner, Marie Cécile Péra, Daniel Hissel, Laurent Larger · 2018
In this paper, a Multi-Reservoir Echo State Network is used to estimate the Fuel Cell degradation, and its remaining useful lifespan. It proposes a methodology for predicting the fuel cell output voltage evolution with time. Echo State Network is a powerful Artificial Intelligence tool for time series predicting which main characteristics is the use of a reservoir of neurons, randomly created, instead of hidden layers such as for Artificial Neural Networks. Only the output layer is optimized by a multi-linear regression, resulting in a time reduced training phase. This leads to a possible increase of the reservoir size to preserve, even improve, its accuracy. However, the bottleneck linked to the use of this tool lies in its architecture optimization. This paper proposes a way to overcome the echo state network parameters optimization process by using a Multi-Reservoir Echo State Network. Then a comparison between an Echo State Network optimized algorithm and a Multi-Reservoir Echo State Network for fuel cell RUL prediction is proposed. In order to have a good prediction of the FC lifetime, an innovative approach based on the Multi-Reservoir Echo State Network is developed and validated using experimental data.