Leveraging Molecular Dynamics to Improve Porous Electrode Theory Modeling Predictions of Lithium-Ion Battery Cells

Abstract

Lithium-ion battery cell modeling using physics-based approaches such as porous electrode theory is a powerful tool for battery design and analysis. Performance metrics such as cell power and resistance can be quickly calculated in a pseudo-two-dimensional (P2D) framework and when coupled with thermal models, component level detail of heating and temperature rise can be evaluated1. For engineering of electric vehicle batteries, speed and fidelity of electrochemical models is paramount in a competitive landscape. Physics-based models allow for high fidelity but require detailed knowledge of the cell component material properties. Acquiring these material characteristics typically requires time-consuming and expensive experiments limiting the ability to quickly screen through cell designs. Therefore, engineering assumptions of the materials properties must be made based on prior tests or literature sources necessarily limiting model accuracy. One approach to improve model accuracy and circumvent costly experiments is to use atomistic modeling to calculate battery cell material properties such as kinetics, OCVs, and transport properties without the need of experiments2–4.In this contribution we use molecular dynamics to calculate electrolyte conductivity and lithium-ion diffusion coefficients for a commercial electrolyte. We first validate our MD modeling approach by reproducing experimental measures of electrolyte conductivity for several test systems. We then construct a porous electrode theory model using the computed electrolyte transport values for the commercial system. Using this model, cell resistance and power is calculated and compared to experimental values measured from small-format pouch cells using the same electrolyte. We show how the variability in the MD modeling impacts cell performance metrics and demonstrate the robustness of this approach to improve cell-level model accuracy.