ECS Student Achievement Award of the IEEE Division Integration of a 2+1D Kinetic Monte Carlo Algorithm with Continuum Models for SEI Layer Analysis of Lithium-Ion Batteries
Authors: Northrop Paul W. C. , Subramanian Venkat R. , Braatz Richard D. 
Published: February 2020

Rechargeable lithium-ion batteries have been extensively used in mobile communication and portable instruments due to their high volumetric and gravimetric energy density and low self-discharge rate, leading many scientists and engineers to work towards developing lithium-ion batteries with improved performance and longer life.

In normal operation, an active solid-electrolyte-interface (SEI) layer forms in the first cycle of charging, due to reactions of lithium and solvent at the active surface (1,2). The SEI layer provides a barrier between the electrolyte and electrode to prevent further reaction while allowing lithium ions to permeate through the SEI layer in order to intercalate into and out of the bulk solid to store and release energy. However, the SEI layer continues to grow under repeated cycling. This growth increases the diffusion resistance of the lithium ions to the bulk active material, and removes cyclable lithium from the system, reducing the efficiency of the battery (1,2). This increased resistance can produce high temperature and can result in thermal runaway (3-5) within the battery. The properties and chemical composition of the SEI layer (active and passive) has been a subject of intense research due to its importance in the safety, capacity fade, and cycle life of Li-ion secondary batteries (6-12).

Examination of the growth of the SEI layer as a mechanism for capacity fade has been performed using Kinetic Monte Carlo (KMC) simulations (13). This has provided valuable insight into the growth of the SEI layer and has shown the effects of high rates of charging on SEI layer passivation. These KMC simulations can be used to study the surface heterogeneity of the electrode surface as the passive layer is formed.

The KMC model used will be expanded to a 2+1D model which tracks the thickness of the SEI layer over a representative surface of the solid particle. This will give a sense of the heterogeneity that arises in the SEI layer and will be coupled with the pseudo 2D model to simulate full battery operation. The P2D model will be converted from a system of partial differential equations to a system of ordinary differential equations by using reformulation techniques developed for the continuum model (14).

The KMC model studies the events that occur in the SEI layer and the particle surface. For example, diffusion of the solvent in the SEI layer is modeled using the KMC approach. The reactions at the active material surface are modeled by considering the relative rate constants and using a random number generator to determine the event which occurs. In contrast, the continuum P2D model explains what happens in the bulk phases, and is deterministic.

This coupled model will perform continuum calculations simultaneously with the KMC calculations. As passivation is considered in the KMC model, the capacity of the battery will fade with time, allowing for life time studies to be performed. This will lead to better understanding of the conditions which lead to capacity fade and better operation of lithium-ion batteries.

Acknowledgements

The authors acknowledge financial support by the National Science Foundation under grant numbers CBET-0828002, CBET-0828123, and CBET-1008692, and the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275.

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