Functional Data-Driven Framework for Fast Forecasting of Electrode Slurry Rheology Simulated by Molecular Dynamics

TL;DR

This paper introduces a data-driven framework combining PCA and KNN to rapidly forecast electrode slurry rheology in lithium-ion batteries, significantly reducing simulation time while maintaining high accuracy.

Contribution

The novel framework accelerates mechanistic electrode simulations by 11 times using functional data analysis, enabling efficient prediction of simulation outcomes with high accuracy.

Findings

Simulation speed increased 11-fold.

High predictive accuracy with F1 score of 0.90.

R^2 score of 0.96 indicating strong correlation.

Abstract

Computational modeling of the manufacturing process of Lithium-Ion Battery (LIB) composite electrodes based on mechanistic approaches, allows predicting the influence of manufacturing parameters on electrode properties. However, ensuring that the calculated properties match well with experimental data, is typically time and resources consuming In this work, we tackled this issue by proposing a functional data-driven framework combining Functional Principal Component Analysis and K-Nearest Neighbors algorithms. This aims first to recover the early numerical values of a mechanistic electrode manufacturing simulation to predict if the observable being calculated is prone to match or not, \textit{i.e} screening step. In a second step it recovers additional numerical values of the ongoing mechanistic simulation iterations to predict the mechanistic simulation result, \textit{i.e} forecasting step. We demonstrated this approach in context of LIB manufacturing through non-equilibrium molecular dynamics (NEMD) simulations, aiming to capture the rheological behavior of electrode slurries. We discuss in full details our novel methodology and we report that the expected mechanistic simulation results can be obtained 11 times faster with respect to running the complete mechanistic simulation, while being accurate enough from an experimental point of view, with a $F1_{score}$ equals to 0.90, and a $R^2_{score}$ equals to 0.96 for the learnings validation. This paves the way towards a powerful tool to drastically reduce the utilization of computational resources while running mechanistic simulations of battery manufacturing electrodes.