TY - JOUR
T1 - Identification of fractional-order models for condition monitoring of solid-oxide fuel cell systems
AU - Dolenc, Boštjan
AU - Nusev, Gjorgji
AU - Juricic, Ðani
AU - Subotic, Vanja
AU - Hochenauer, Christoph
AU - Boškoski, Pavle
PY - 2020
Y1 - 2020
N2 - With rising market deployment the condition monitoring of solid oxide fuel cell systems is gaining particular importance. The conventional approaches mainly use electrochemical impedance spectroscopy based on the repeated sinusoidal perturbation over a range of frequencies. One of the notable weaknesses of the approach is excessively long perturbation time needed to properly evaluate the impedance curve. In this paper, we propose a time-efficient approach in which, a short, persistently exciting and small-amplitude perturbation is used to excite all the relevant system eigenmodes. A model structure from a class of linear fractional order models is selected to describe the perturbed dynamics and to account for anomalous diffusion processes in the cells. Then, the model parameters are estimated directly from measured input and output records. The paper presents a computationally efficient parameter estimation procedure in which the numerical issues of differentiation of noisy signals are alleviated by using modulating functions. In practice, that means a combination of filtering and application of conventional least squares. The approach is applied on a case of health assessment of solid oxide fuel cells.
AB - With rising market deployment the condition monitoring of solid oxide fuel cell systems is gaining particular importance. The conventional approaches mainly use electrochemical impedance spectroscopy based on the repeated sinusoidal perturbation over a range of frequencies. One of the notable weaknesses of the approach is excessively long perturbation time needed to properly evaluate the impedance curve. In this paper, we propose a time-efficient approach in which, a short, persistently exciting and small-amplitude perturbation is used to excite all the relevant system eigenmodes. A model structure from a class of linear fractional order models is selected to describe the perturbed dynamics and to account for anomalous diffusion processes in the cells. Then, the model parameters are estimated directly from measured input and output records. The paper presents a computationally efficient parameter estimation procedure in which the numerical issues of differentiation of noisy signals are alleviated by using modulating functions. In practice, that means a combination of filtering and application of conventional least squares. The approach is applied on a case of health assessment of solid oxide fuel cells.
KW - Condition monitoring
KW - Fractional order systems
KW - Solid oxide fuel cell systems
KW - Time-domain identification
UR - http://www.scopus.com/inward/record.url?scp=85105077911&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.734
DO - 10.1016/j.ifacol.2020.12.734
M3 - Conference article
AN - SCOPUS:85105077911
SN - 2405-8963
VL - 53
SP - 12014
EP - 12019
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 2
T2 - 21st IFAC World Congress 2020
Y2 - 12 July 2020 through 17 July 2020
ER -