Using resistor network models to predict the transport properties of solid-state battery composites

Area of Science:

• Materials Science
• Electrochemistry
• Computational Modeling

Background:

• Solid-state batteries utilize composite electrodes, crucial for charge and heat transport.
• Optimizing composite composition is challenging due to vast experimental parameter space.

Purpose of the Study:

• To develop a simplified resistor network model for predicting transport phenomena in solid-state battery composites.
• To provide a tool for guiding experimental optimization of electrode materials.

Main Methods:

• A resistor network model was developed and validated against experimental conductivity data.
• The model was applied to LiNi$_{0.83}$Co$_{0.11}$Mn$_{0.06}$O$_{2}$-Li$_{6}$PS$_{5}$Cl positive electrode composites.
• Literature data were analyzed using the proposed model to demonstrate its broad applicability.

Main Results:

• The resistor network model accurately describes electronic, ionic, and thermal conductivity in composite electrodes.
• The model successfully benchmarks against experimental data for specific composite materials.
• The approach is computationally inexpensive and expandable.

Conclusions:

• The resistor network model offers a valuable, accessible tool for researchers.
• It streamlines the optimization process for solid-state battery composite electrodes.
• The model aids in understanding and improving charge and heat transport for enhanced battery performance and safety.