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Research Papers

# Insights Into Lithium-Ion Battery Degradation and Safety Mechanisms From Mesoscale Simulations Using Experimentally Reconstructed Mesostructures

[+] Author and Article Information
Scott A. Roberts

Thermal/Fluid Component Sciences Department,
Sandia National Laboratories,
Albuquerque, NM 87185
e-mail: sarober@sandia.gov

Hector Mendoza

Thermal/Fluid Component Sciences Department,
Sandia National Laboratories,
Albuquerque, NM 87185
e-mail: hmendo@sandia.gov

Victor E. Brunini

Thermal/Fluid Science and Engineering Department,
Sandia National Laboratories,
Livermore, CA 94550
e-mail: vebruni@sandia.gov

Thermal/Fluid Component Sciences Department,
Sandia National Laboratories,
Albuquerque, NM 87185
e-mail: btremba@sandia.gov

David R. Noble

Fluid and Reactive Processes Department,
Sandia National Laboratories,
Albuquerque, NM 87185
e-mail: drnoble@sandia.gov

Anne M. Grillet

Thermal/Fluid Component Sciences Department,
Sandia National Laboratories,
Albuquerque, NM 87185
e-mail: amgrill@sandia.gov

Manuscript received May 16, 2016; final manuscript received July 6, 2016; published online October 20, 2016. Assoc. Editor: Partha Mukherjee.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Electrochem. En. Conv. Stor. 13(3), 031005 (Oct 20, 2016) (10 pages) Paper No: JEECS-16-1068; doi: 10.1115/1.4034410 History: Received May 16, 2016; Revised July 06, 2016

## Abstract

Battery performance, while observed at the macroscale, is primarily governed by the bicontinuous mesoscale network of the active particles and a polymeric conductive binder in its electrodes. Manufacturing processes affect this mesostructure, and therefore battery performance, in ways that are not always clear outside of empirical relationships. Directly studying the role of the mesostructure is difficult due to the small particle sizes (a few microns) and large mesoscale structures. Mesoscale simulation, however, is an emerging technique that allows the investigation into how particle-scale phenomena affect electrode behavior. In this manuscript, we discuss our computational approach for modeling electrochemical, mechanical, and thermal phenomena of lithium-ion batteries at the mesoscale. We review our recent and ongoing simulation investigations and discuss a path forward for additional simulation insights.

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## Figures

Fig. 1

Two-dimensional illustration of the particle-scale geometry considered in this work, showing the particle, conductive binder, and electrolyte phases. Each of the three particles is shown to have a different crystallographic orientation.

Fig. 2

Two-dimensional images of cathode mesostructures from nano-CT: (a) LCO, image courtesy Yan et al. [7] and (b)NMC (Reprinted with permission from Ebner et al. [31]. Copyright 2013 by John Wiley and Sons.)

Fig. 3

Three-dimensional visualization of a mesostructure reconstruction of LCO. The shades (color online) represent particles that have been separated and individually surface-meshed.

Fig. 4

Illustration of the CDFEM process for a reconstructed LCO mesostructure, visualized on a 2D plane: (a) uniform background mesh (gray) with the curved outlines representing the intersection of the STL-described particles and (b) decomposed mesh after CDFEM, with the electrolyte phase represented by gray elements and the separate particles phases in distinct shades

Fig. 5

Visualization of the computational mesh resulting from the CDFEM process for a reconstructed LCO mesostructure: (a) particles and (b) electrolyte

Fig. 6

Solution verification of the CDFEM approach to calculate effective electrical conductivity and modulus on an LCO mesostructure

Fig. 7

Concentration of electrical current as it flows through the contact between two spheres. The color field is the magnitude of the current density, forming a singularity near the edge of the particle contact. The curved arrows are streamlines of current.

Fig. 8

Cross section of a coupled electrochemical–mechanical simulation on an LCO mesostructure showing how the interplay between the complex particle shapes and network arrangement affects lithium intercalation (a), strain (b), and stress (c). Simulation is of battery charging at 1C, with this snapshot in the fully charged state. (a) Lithium fraction, (b) von Mises equivalent strain and displacement vectors, and (c) von Mises stress (Reprinted with permission from Mendoza et al. [47]. Copyright 2016 by Elsevier.).

Fig. 9

Role of system flexibility (stiffness) on the partitioning of volume change between porosity and macroscopic swelling. The curve with square markers represents volume change of the particle phase (representing the total volume change), the curve with circular markers shows the volume change that reduces porosity, and the curve with triangle markers the volume change from macroscopic swelling. The dashed curve with left triangle markers (corresponding to the right ordinate axis) is the partition coefficient of the fraction of the total volume change that goes into porosity reduction. The vertical dashed line indicates the effective stiffness of the particle network [47].

Fig. 10

Electrical conduction through an LCO cathode including a 100 nm layer of polymeric binder on the outside of the particles. For an uncycled cathode (left), the binder conductivity is 10× that of the LCO and a vast majority of the current travels through the binder. Even when the conductivities of the two phases are equal, the peak current densities are localized in the binder at the contact points between particles (Reproduced with permission from Grillet et al. [50]. Copyright 2016 by The Electrochemical Society.).

Fig. 11

Contour maps of the effective electrical conductivity (a)–(c) and the cathode voltage drop (d)–(f) for a range of particle and binder conductivity values at three binder thicknesses. The large arrows on (a) and (d) show how the results change with charging and cycling. (a) Conductivity, 10 nm binder, (b) conductivity, 32 nm binder, (c) conductivity, 100 nm binder, (d) voltage drop, 10 nm binder, (e) voltage drop, 32 nm binder, and (f) voltage drop, 100 nm binder.

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