Proton exchange membrane fuel cell ageing forecasting algorithm based on Echo State Network

Regarded as a promising technology, proton exchange membrane fuel cell (PEMFC) are not far from a large-scale deployment. However, some improvements are still needed to extend the lifetime of these systems. The discipline of PHM (Prognostic and health management) seems like a great solution to help against this problem. The objective is to predict the evolution of the behavior of a system using algorithms to estimate in advance when a fault occurs. This knowledge of the default before its occurrence allows to anticipate a decision, often by using a fault-tolerant control. Different methodologies exist to make a prognostic algorithm: model based, data based or a hybridization between these two previous methodologies. This paper will focus on the data based prognosis, mainly due to the fact that all of the phenomena involved in the degradation of a PEMFC are not yet fully known, thus not yet modeled. The first innovation of this paper concern the use of a new neural network paradigm, the Echo State Network, which is a part of Reservoir Computing methods. This new paradigm gives very interesting results, with a mean average percentage error less than 5% in our study case. The other contribution is the definition of a filtering method, regarding to the test bench, by evaluating the Hurst exponent of the signal filtered by wavelet.