A Gaussian Process-Based Crack Pattern Modeling Approach for Battery Anode Materials Design

Abstract

Silicon-based anodes are one of the promising candidates for the next generation high-power/energy density lithium ion batteries (LIBs). However, a major drawback limiting the practical application of the Si anode is that Si experiences a significant volume change during lithiation/delithiation, which induces high stresses causing degradation and pulverization of the anode. This study focuses on crack initiation within a Si anode during the delithiation process. A multi-physics-based finite element (FE) model is built to simulate the electrochemical process and crack generation during delithiation. On top of that, a Gaussian process (GP)-based surrogate model is developed to assist the exploration of the crack patterns within the anode design space. It is found that the thickness of the Si coating layer, TSi, the yield strength of the Si material, σFc, the cohesive strength between Si and the substrate, σFs, and the curvature of the substrate, ρ, have large impacts on the cracking behavior of Si. This coupled FE simulation-GP surrogate model framework is also applicable to other types of LIB electrodes and provides fundamental insights as building blocks to investigate more complex internal geometries.

Machine learning toward advanced energy storage devices and systems

“…In (Li et al, 2019b) (Jain et al, 2014). Moreover, the Gaussian process is also applied to assist in constructing crack pattern model for LIB with silicon-based anode (Zheng et al, 2020), to assist the design of anode material. Specifically, the data points generated by FEM are applied to train their GP-based surrogate model.…”

Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology

“…Zheng et al. [ 149 ] developed a surrogate GPR model trained to reproduce the results of multi‐physics FE simulations of electrochemical and crack generation processes during anode delithiation. The data for training were generated using an adaptive sampling procedure.…”

“…The crack formation resulting from a significant volume change during anode delithiation is known to be a major limitation of the use of silicon anodes in Li-ion batteries. Zheng et al [149] developed a surrogate GPR model trained to reproduce the results of multi-physics FE simulations of electrochemical and crack generation processes during anode delithiation. The data for training were generated using an adaptive sampling procedure.…”

Uncertainty Quantification Analysis on Mechanical Properties of the Structured Silicon Anode via Surrogate Models

“…In the previous study, we have developed a GP based surrogate model to assist the exploration of the crack initiation phenomenon in the Si anode over the design space. 40 It is found that the GP model can efficiently predict the formation of the lithiationinduced cracks in the Si layer without losing fidelity.…”

“…Numerical simulation models like FE analysis have been widely adopted in the LIB research field, [31][32][33][34][35][36][37][38][39][40] which are built upon specific physics of failure and can be used to investigate the electrochemicalthermal and electrochemical-mechanical couplings, as well as the capacity degradation failure mechanisms, etc. These models can precisely estimate the electrochemical reactions, Li diffusion, thermal response, and cycle dependent structural changes and stresses and provide valuable insights in understanding the characterizations of sophisticated electrode microstructures, which might be otherwise difficult to study experimentally.…”