Accelerating parameter estimation in Doyle–Fuller–Newman model for lithium-ion batteries. Issue 5 (2nd September 2019)

• Record Type: Journal Article
• Authors: Reddy, Sohail R.; Scharrer, Matthias K.; Pichler, Franz; Watzenig, Daniel; Dulikravich, George S.
• Is Part Of: Compel. Volume 38:Issue 5(2019)
• Page Start: 1533
• Page End: 1544
• Publication Date: 2019-09-02
• Subjects: Multiphysics -- Differential evolution -- Optimal design -- Finite element method -- Evolution strategies -- Material modelling; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-12-2018-0533
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : Purpose: This paper aims to solve the parameter identification problem to estimate the parameters in electrochemical models of the lithium-ion battery. Design/methodology/approach: The parameter estimation framework is applied to the Doyle-Fuller-Newman (DFN) model containing a total of 44 parameters. The DFN model is fit to experimental data obtained through the cycling of Li-ion cells. The parameter estimation is performed by minimizing the least-squares difference between the experimentally measured and numerically computed voltage curves. The minimization is performed using a state-of-the-art hybrid minimization algorithm. Findings: The DFN model parameter estimation is performed within 14 h, which is a significant improvement over previous works. The mean absolute error for the converged parameters is less than 7 mV. Originality/value: To the best of the authors' knowledge, application of a hybrid optimization framework is new in the field of electrical modelling of lithium-ion cells. This approach saves much time in parameterization of models with a high number of parameters while achieving a high-quality fit.