Fractional-order model identification for state of health assessment of solid-oxide fuel cells

Abstract

Solid-oxide fuel cells (SOFCs) represent a group of electrochemical conversion devices that utilise hydrogen rich fuels and are characterised by their high efficiency of energy conversion. For optimal exploitation of SOFCs, accurate and online state-of-health (SoH) assessment is of utmost importance. SoH assessment is usually performed through the frequency domain analysis by characterising the changes of the Nyqvist curves, a process also known as electrochemical impedance spectroscopy. Such an approach is time consuming and suffers from low accuracy particularly at low frequencies. Methodologies for time-domain identification of fractional-order systems offer a way of resolving these issues. Using an algebraic approach to identification, the complete fractional-order transfer function is identified from a series of several step responses. The SoH can be estimated from the identified parameters of the fractional-order transfer function either in a form of a Nyqvist curve or by directly linking particular parameter values to electrochemical processes. The proposed approach was validated on a 300 W SOFC using pure hydrogen fuel.