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. Cell metrics such as resistance and thermal performance can be quickly calculated in a pseudo-two-dimensional (P2D) framework. 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. One approach to circumvent costly experiments is to use molecular dynamics to calculate electrolyte transport properties. We demonstrate how cell modeling using simulated transport properties enables predictions of cell level metrics, allowing for experiment-free component screening. We also show how the variation in transport property predictions from molecular dynamics affects the final cell level performance predictions.