Online assessment of Solid Oxide fuel cell electric performance degradation by means of an innovative Discrete Wavelet Transform-based data-driven methodology

Solid Oxide technology, due to its capability for reversible operation and higher round-trip efficiency among hydrogen technologies, can contribute to the development of multi-vector renewable based power systems. The management of these systems, including energy storage plants, needs of accurate dynamic models and algorithms, which have to take into account components degradation. This paper presents a procedure to assess the State of Health of Solid Oxide Cells (SOCs) in real-time. Specifically, internal resistance is determined online through the elaboration of voltage measurements, gathered during SOCs normal operation. An aging test over more than 1000 h of operation is performed providing voltage and electrochemical impedance spectroscopy (EIS) measurements at regular time steps. Discrete Wavelet Transform is applied to measured voltage profiles and a correlation is identified to assess internal resistance based on the outcomes of voltage signal processing. The followed data-driven methodology allows an accurate online assessment of internal resistance. Root-mean squared error equals 0.1 % with respect to internal resistance values deduced from EIS measurements. The proposed method does not rely on SOCs mechanism model. Moreover, it doesn't need specialized and high-cost equipment as well as trained personnel, usually required by conventional methodologies to perform EIS measurements on installation sites. • Degradation is of main interest for reversible Solid Oxide Cells. • A new DWT-based algorithm is proposed for online prediction of internal resistance. • Internal resistance is assessed by elaborating voltage profiles measured during operation. • The method is developed over data gathered during 1000 h operation. • Internal resistance is assessed with 0.1 % Root Mean Squared Error.