TY  - JOUR
AB  - 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.
VL  - 38
IS  - 5
SN  - 0332-1649
DO  - 10.1108/COMPEL-12-2018-0533
UR  - https://doi.org/10.1108/COMPEL-12-2018-0533
AU  - Reddy Sohail R.
AU  - Scharrer Matthias K.
AU  - Pichler Franz
AU  - Watzenig Daniel
AU  - Dulikravich George S.
PY  - 2019
Y1  - 2019/01/01
TI  - Accelerating parameter estimation in Doyle–Fuller–Newman model for lithium-ion batteries
T2  - COMPEL - The international journal for computation and mathematics in electrical and electronic engineering
PB  - Emerald Publishing Limited
SP  - 1533
EP  - 1544
Y2  - 2024/09/19
ER  -