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

• Published in: International journal of hydrogen energy Vol. 42; no. 2; pp. 1472 - 1480
• Main Authors: Morando, Simon, Jemei, Samir, Hissel, Daniel, Gouriveau, Rafael, Zerhouni, Noureddine
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 12.01.2017; Elsevier
• Subjects: Artificial neural network; Automatic; Computer Science; Data Structures and Algorithms; Echo State Network; Electric power; Engineering Sciences; Fluid mechanics; Fuel cell; Mechanics; Physics; Prognostic; Reservoir computing; Thermics; Artificial neural network; Echo State Network; Reservoir computing; Fuel cell; Prognostic
• ISSN: 0360-3199; 1879-3487
• DOI: 10.1016/j.ijhydene.2016.05.286

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. •A new paradigm of Neural network is used for the proton exchange membrane fuel cell diagnosis.•This paradigm is named Reservoir Computing, due to the fact than the traditional hidden layers are replaced by a reservoir of neurons created randomly.•The Reservoir computing inputs are the signals obtained after a Short Time Fourier Transform (STFT).•This algorithm is applied on experimental data to verify the effectiveness of the method.•A statistical analysis of the influence of the learning database is realized.