Automatic initialization of a Complex Nonlinear Least Squares algorithm for impedance battery frequential identification

This paper deals with the identification of battery impedance parameters in the frequency domain using Electrochemical Impedance Spectroscopy (EIS) measurements and fractional modeling. Unlike other classical models used for frequency identification algorithms, fractional modeling allows to perform simulations also in the time domain offering interesting perspectives for identification in the time domain in future works. The objective of the paper is to propose an automatic initialization of a Complex Nonlinear Least Squares algorithm based on fractional modeling and EIS measurements in order to accurately estimate impedance parameters whatever the kind of diffusion involved: Finite Length Warburg (FLW) or Finite Space Warburg (FSW) diffusion. Fractional models are used to approximate non-integer orders and are based on a simplified Randles equivalent circuit. When no sufficient low frequency measurements are available, diffusion can be modeled thanks to a single fractional integrator whose order is close to 0.5 (Warburg zone). Otherwise, dedicated fractional models are proposed to take into account FLW or FSW diffusion. The method is validated using not only simulation data but also EIS measurements performed on a commercial 3.5 Ah Li-ion cell and open source experimental data.

Automated summary

This paper discusses the identification of battery impedance parameters using Electrochemical Impedance Spectroscopy (EIS) measurements and fractional modeling in the frequency domain. It proposes an automatic initialization method for a Complex Nonlinear Least Squares algorithm based on fractional modeling and EIS measurements to accurately estimate impedance parameters for different diffusion scenarios.