Multi Reservoir Echo State Network for Proton Exchange Membrane Fuel Cell Remaining Useful Life prediction
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.
IECON 2018 44th Annual Conference of the IEEE Industrial Electronics Society