A Study on Estimating Mechanical Properties of Solid State Electrolyte Using Machine Learning & Finite Element Analysis

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 o