Constrained Least-Squares Parameter Estimation for a Double Layer Capacitor

Nayzel I. Jannif (), Rahul R. Kumar, Ali Mohammadi, Giansalvo Cirrincione and Maurizio Cirrincione
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Nayzel I. Jannif: School of Information Technology, Engineering, Mathematics and Physics, University of the South Pacific, Private Mail Bag, Suva, Fiji
Rahul R. Kumar: School of Information Technology, Engineering, Mathematics and Physics, University of the South Pacific, Private Mail Bag, Suva, Fiji
Ali Mohammadi: School of Information Technology, Engineering, Mathematics and Physics, University of the South Pacific, Private Mail Bag, Suva, Fiji
Giansalvo Cirrincione: Laboratory of Novel Technologies, University of Picardie Jules Verne, 80000 Amiens, France
Maurizio Cirrincione: School of Information Technology, Engineering, Mathematics and Physics, University of the South Pacific, Private Mail Bag, Suva, Fiji

Energies, 2023, vol. 16, issue 10, 1-19

Abstract: This paper presents an estimation of the parameters for a Double Layer Super Capacitor (DLC) that is modelled with a two-branch circuit. The estimation is achieved using a constrained minimization technique, which is developed off-line and uses a single constraint to write the matrix equation. The model is algebraically manipulated to obtain a matrix equation, and a signal processing system is developed to prepare the signals for the identification algorithms. The proposed method builds on the results obtained using an unconstrained ordinary least-squares (OLS) technique. The method is tested both in simulation and experimentally, using a specially-designed experimental rig. A current ramp input is used to generate the corresponding output voltage and its derivatives. The results obtained from the constrained off-line minimization algorithm are compared with those obtained using a traditional off-line estimation method. The discussion of the results shows that the proposed method outperforms the traditional estimation technique. In summary, this paper contributes to the field of DLC parameter estimation by introducing a new off-line constrained minimization technique. The results obtained from the simulations and experimental rig demonstrate the effectiveness of the proposed method with two of three parameters showing relative errors less than 5%.

Keywords: supercapacitors; signal processing; parameter identification; constrained minimization; least squares; Faranda method; 2-branch Super Capacitor model; energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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