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.

[1]  Daniel Favrat,et al.  Simulation of thermal stresses in anode-supported solid oxide fuel cell stacks. Part I: Probability of failure of the cells , 2009 .

[2]  S. Barnett,et al.  Impact of pore microstructure evolution on polarization resistance of Ni-Yttria-stabilized zirconia , 2011 .

[3]  Joongmyeon Bae,et al.  Fast performance degradation of SOFC caused by cathode delamination in long-term testing , 2010 .

[4]  Dimitris Sarantaridis,et al.  Redox Cycling of Ni‐Based Solid Oxide Fuel Cell Anodes: A Review , 2007 .

[5]  M. Khaleel,et al.  Three-dimensional thermo-fluid electrochemical modeling of planar SOFC stacks , 2003 .

[6]  Stefano Rossi,et al.  FDI Oriented Simulation of an MSR Reactor for Application in SOFC Systems , 2015 .

[7]  Loredana Magistri,et al.  Reformer faults in SOFC systems: Experimental and modeling analysis, and simulated fault maps , 2014 .

[8]  Abhijit Chandra,et al.  Modeling of thermal stresses and lifetime prediction of planar solid oxide fuel cell under thermal cycling conditions , 2010 .

[9]  Meilin Liu,et al.  Characterization of sulfur poisoning of Ni–YSZ anodes for solid oxide fuel cells using in situ Raman microspectroscopy , 2007 .

[10]  P. Hendriksen,et al.  Corrosion stability of ferritic stainless steels for solid oxide electrolyser cell interconnects , 2010 .

[11]  Ting Hei Wan,et al.  Influence of the Discretization Methods on the Distribution of Relaxation Times Deconvolution: Implementing Radial Basis Functions with DRTtools , 2015 .

[12]  Linda Barelli,et al.  Diagnosis methodology and technique for solid oxide fuel cells: A review , 2013 .

[13]  Meilin Liu,et al.  Sulfur Poisoning and Regeneration of Ni-Based Anodes in Solid Oxide Fuel Cells , 2007 .

[14]  YangQuan Chen,et al.  Fractional-order Systems and Controls , 2010 .

[15]  M. Caputo.  Linear Models of Dissipation whose Q is almost Frequency Independent-II , 1967 .

[16]  T. F. Connolly.  Electrical properties of solids , 1972 .

[17]  Hiroki Muroyama,et al.  Degradation of nickel–yttria-stabilized zirconia anode in solid oxide fuel cells under changing temperature and humidity conditions , 2014 .

[18]  P. Voorhees,et al.  Observing the microstructural evolution of Ni-Yttria-stabilized zirconia solid oxide fuel cell anodes , 2016 .

[19]  K. Sasaki,et al.  SOFC Durability against Standby and Shutdown Cycling , 2014 .

[20]  Rolf Isermann,et al.  Identification of Dynamic Systems: An Introduction with Applications , 2010 .