Computational Model for Predicting Particle Fracture During Electrode Calendering

Abstract

In the context of calling for low carbon emissions, lithium‐ion batteries (LIBs) have been widely concerned as a power source for electric vehicles, so the fundamental science behind their manufacturing has attracted much attention in recent years. Calendering is an important step of the LIB electrode manufacturing, and associated changes in the electrode microstructure and mechanical properties are worthy of study. In this work, we report the observed cracking of active material (AM) particles due to calendering pressure under ex situ X‐ray nano tomography experiments. We developed an innovative 3D discrete element method (DEM) model with bonded connections to physically mimic the calendering process using real AM particle shapes derived from the tomography experiments. The DEM model can well predict the change of the morphology of the dry electrode under pressure, and the changes of the applied pressure and porosity are consistent with the experiments. The model is also able to simulate the secondary AM particles cracking by the fracture of the bond under force. Our model is the first of its kind that can predict the fracture of the secondary particles along the calendering process. This work provides a tool for guidance in the manufacturing of optimized LIB electrodes.

Opportunities and Challenges of Calendering Sulfide‐Based Separators for Solid‐State Batteries

“…Consequently, it is assumed that a spring back effect by fast elastic recovery probably mainly caused by the binder after tension release could have a negative impact on the microstructure of the separator in terms of lithium‐ion transportation. The spring back effect after calendering has already been discussed for conventional electrodes [24,63–65] …”

“…The spring back effect after calendering has already been discussed for conventional electrodes. [24,[63][64][65] Sedlmeier et al [29] also compacted LPSCl (different supplier) separator sheets based on an HNBR binder different from the one used in this study with a content of 5 wt % in the separator layer. They described a 2-fold higher ionic conductivity for a separator non-compacted measured at a stack pressure of 590 MPa compared to a sample compacted at 590 MPa and measured at a stack pressure of 70 MPa despite having the same porosity of �3 %.…”

Time‐Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction

“…A porosity limit of ≈20% agrees with experimental reports which mention the challenge of going under it due to the fracture of secondary NMC particles. [15,[48][49] Regarding the diffusivity, it decreases as the CD increases on both DEM and DL models. It is observed that the major drop in diffusivity occurs between 20 to 30% CD.…”

“…While slurry and drying stages are equally crucial to the final electrode microstructure, calendering has a more direct influence since it is the last stage. [15][16] Therefore, the study of the calendering process is important to understand the performance of the fabricated electrode. In that line, besides experimental efforts, computational methodologies have been proposed and used.…”

“…[23] In parallel to experimental studies, computational methods such as the Discrete Element Method (DEM) have been applied to understand calendering effects on final electrode properties. [10,15,[24][25][26] Furthermore, the development of high-performance computing (HPC), for instance, supercomputers and cloud computing services, coupled with a sub-field of Artificial Intelligence (AI) such as Deep Learning (DL), are resources that can be used to accelerate the battery manufacturing process optimization as a whole. [25][26] For example, Shodiev et al, in our group, predicted electrolyte flow infiltration over time by using a multi-layer perceptron (MLP) approach.…”