Electrochemical Impedance Spectroscopy (EIS) is a powerful tool to assess and understand the degradation of Li-ion battery (LIB) performances during operation (upon charge and discharge cycles). The full interpretation of EIS spectra of LIB can be however tedious because these systems are composed of porous electrodes and complex electrolyte. Moreover, side reactions, such as the formation of an insulating layer around the particles of the porous electrodes, can occur in battery operation modifying the impedance response.

A way to improve the interpretation of impedance data of LIB is the use of modeling. Most of the time, data interpretation is limited to the use of Electrical Equivalent Circuits (EEC) but the link between EEC and the physical processes involved in the system is ambiguous and difficult to establish. However, in the 60’s, Newman et al. [1] devised a model for porous battery electrodes. Their model describes the whole battery system thanks to equations that are directly related to the physical phenomena occurring in the composite electrodes. This model is still widely used in the battery field to simulate galvanostatic charge and discharge. Based on their work, and inspiring by works already done on modeling of EIS [2], it is possible to devise a model by linearizing all the equations [3]. The model obtained can then be directly connected to the physical phenomena occurring inside the porous electrodes which will greatly help to interpret EIS spectra.

In this work, we developed an analytical solution for the electrochemical impedance of a porous electrode composed of spherical intercalation particles in contact with concentrated electrolyte. The proposed model which is based on the concentrated solution theory but which also considers kinetics limitations and both solution-phase and solid-phase diffusion limitations, can predict the EIS response of a porous electrode at any state of charge using a limited set of parameters. Moreover, the developed model can predict the optimal porosity of the electrode to be targeted during the manufacturing process for the best cycling performances depending on the composition of the electrode (volume fractions of active material, binder, and conductive agent).

(1) Newman, J.S.; Tobias, C.W. Journal of the Electrochemical Society 1962, 109(12), page 1183-1191.

(2) Meyers, J.P. et al. ; Journal of the Electrochemical Society 2000, 147(8), page 2930-2940.

(3) Huang, J.; Zhang, J.B. Journal of the Electrochemical Society 2016, 163(9), page A1983-A2000.