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

• Sohail R. Reddy ^[3] ; Matthias K. Scharrer ^[3] ; Franz Pichler ^[3] ; Daniel Watzenig ^[1] ; George S. Dulikravich ^[2]
  1. [1] Graz University of Technology

     Graz University of Technology

     Graz, Austria

     
  2. [2] Florida International University

     Florida International University

     Estados Unidos

     
  3. [3] VIRTUAL VEHICLE Research Center, Austria

  Mostrar afiliaciones +
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 38, Nº 5, 2019, págs. 1533-1544
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • 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-FullerNewman (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.