Chemo-Mechanical Model of SEI Growth on Silicon Electrode Particles

TL;DR

This paper introduces a comprehensive thermodynamic model that captures the coupled chemical, mechanical, and fracture processes of SEI growth on silicon electrodes, explaining aging behavior under different cycling conditions.

Contribution

It presents the first mechanistic model linking mechanical deterioration and growth of SEI on silicon electrodes, incorporating elastic, plastic deformation, and fracture effects.

Findings

Model predicts transition from square-root-of-time to linear growth of SEI.

Cycling rate significantly influences SEI deterioration and regrowth.

Model aligns with experimental observations of capacity fade.

Abstract

Silicon anodes promise high energy densities of next-generation lithium-ion batteries, but suffer from shorter cycle life. The accelerated capacity fade stems from the repeated fracture and healing of the solid-electrolyte interphase (SEI) on the silicon surface. This interplay of chemical and mechanical effects in SEI on silicon electrodes causes a complex aging behavior. However, so far, no model mechanistically captures the interrelation between mechanical SEI deterioration and accelerated SEI growth. In this article, we present a thermodynamically consistent continuum model of an electrode particle surrounded by an SEI layer. The silicon particle model consistently couples chemical reactions, physical transport, and elastic deformation. The SEI model comprises elastic and plastic deformation, fracture, and growth. Capacity fade measurements on graphite anodesand in-situ mechanical SEI measurements on lithium thin films provide parametrization for our model. For the first time, we model the influence of cycling rate on the long-term mechanical SEI deterioration and regrowth. Our model predicts the experimentally observed transition in time dependence from square-root-of-time growth during battery storage to linear-in-time growth during continued cycling. Thereby our model unravels the mechanistic dependence of battery aging on operating conditions and supports the efforts to prolong the battery life of next-generation lithium-ion batteries.