A Study on Estimating Mechanical Properties of Solid State Electrolyte Using Machine Learning & Finite Element Analysis
Authors: Pyo Changmin , Kim Jaewoong , Lim Jinsub , Sim Jaesam , Kang Sungwook 
Published: December 2024

As environmental pollution worsens, related environmental regulations are becoming stricter. Recently, regulations on internal combustion engine vehicles have been tightening, leading to increased attention towards hydrogen and electric vehicles as alternatives. Especially within a decade, electric vehicles have emerged as prominent eco-friendly vehicles, with lithium-ion batteries garnering significant attention as energy storage devices. Among them, all solid-state batteries, boasting advantages such as high energy density and output, are continuously researched as the ultimate solution, free from issues like explosions. The battery manufacturing process typically comprises the pole plate process, assembly process, and formation process. To enhance battery performance and manufacturing efficiency, process optimization is essential, and simulation can reduce resource and time wastage.
Finite Element Method (FEM) is a prominent simulation technique that can simulate various processes to derive optimal designs or processes. Predicting problems that may occur during electrode manufacturing processes and deriving relevant alternatives can save time and costs.
Accurate simulation requires high-quality mechanical properties of materials. However, for sulfide-based solid electrolytes, exposure to air leads to material property changes, and their thickness, often below 100μm, makes deriving material properties through measurement nearly impossible.
To estimate electrode material properties, Xu derived force-deformation curves (F-D Curve) through measurements for the entire lithium-ion battery (LIB) and calculated the material properties of the anode-separator-cathode assembly as representative properties. Wierzbicki estimated the mechanical properties of cylindrical-shaped lithium-ion cells using homogenization techniques. However, in such cases, it is impossible to obtain data for each component, resulting in weaknesses in optimizing the process for all solid-state batteries.
To obtain data for each component, Cheng measured the elastic modulus using nanoindentation after sintering the NMC powder to derive the material properties of NMC, a key cathode material. However, for sulfide-based batteries, such as those using solid electrolytes, approaching through the above methods is impossible due to their reaction with air.
In this study, we utilized Crystal Graph Convolutional Neural Network (CGCNN), a machine learning technique, to derive the material properties of Li6PS5Cl, a representative material among sulfide-based solid electrolytes. Materials are composed of a crystal structure where atoms are arranged in a certain pattern. Due to variables such as the type of atoms, composition ratio, and arrangement, predicting outcomes is challenging without measurements. However, CGCNN inputs structural information of bulk materials to predict basic properties accurately, offering a promising alternative. After modeling the material structure, a certain strain was applied to calculate the bulk modulus and shear modulus. Using this data, CGCNN was trained to predict micro-scale bulk modulus and shear modulus, from which Young’s modulus and Poisson’s ratio, representing key mechanical properties, were derived.
Based on this study, utilizing CGCNN, the material properties of Li5PS5Cl were predicted, resulting in Young’s modulus of 28.07 MPa, Shear Modulus of 11.02 MPa, and Poisson’s ratio of 0.27. This study successfully derived the challenging-to-measure material properties of solid-state batteries. This serves as fundamental data for process optimization in the mass production process of solid-state batteries, contributing to enhanced productivity and cost reduction. This research is expected to significantly contribute to the ultimate goal of optimizing the manufacturing process of solid-state batteries and improving productivity, ultimately leading to cost reduction.
This work was supported by Technology Innovation Program (project name : Equipment technologies for 50cm2 all solid state battery cell, project Number : 20012349) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).
This research has been conducted with the support of the Ministry of Trade, Industry and Energy(MOTIE), Republic of Korea as “Development of a battery pack case for electric vehicles that deltas thermal runaway”(P0024522) in the small and medium business win-win innovation leap project.
This research was funded by the ‘New Faculty Research Support Grant’ at Changwon National University in 2024.