Accelerating parameter estimation in Doyle–Fuller–Newman model for lithium-ion batteries

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.