An Echo State Network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile

•Data driven method is used for Fuel Cell prognostics under a stationary load profile.•The parameters of the Echo State Network are optimized via an optimization algorithm.•Cycle repetetion and markov chains are used to generate the stationary load profile.•The remaining useful lifetime is estimated using the developed approach.•The prognostics performance is validated using experimental data. Improving Proton Exchange Membrane Fuel Cell durability is a key that paves the way to its large scale industrial deployment. During the last five years, the prognostics discipline emerged as an interesting field for Proton Exchange Membrane Fuel Cell state of health prediction and lifetime estimation. The information provided by the prognostic module is crucial for optimizing the control strategy to extend the fuel cell lifetime. In this paper, an approach based on Echo State Network for fuel cell prognostics under a variable load is developed. The novelty of this paper is to perform prognostics under a variable load profile without prior knowledge of this latter. Two solutions are developed in this work. The first one consists of evaluating the remaining useful lifetime under a repeated load cycle. The second one is based on using Markov chains to generate estimations of the future load profile, allowing thus to overcome the need of real future load profile prior knowledge. Both proposed solutions give accurate prediction results of proton exchange membrane fuel cell remaining useful lifetime, with low uncertainties.