3D Pore-Scale Transport Model Incorporating Realistic Cathode Morphology and Peroxide Growth in Lithium-Air Batteries

Tuesday, October 13, 2015: 08:40
102-C (Phoenix Convention Center)
C. Andersen (Drexel University) and Y. Sun (Drexel University)
The lithium-air (Li-air) battery, with its usable energy density close to 1,700Wh/kg1, has captured worldwide attention as a promising battery solution for electric vehicles. However, a major hurdle facing the development of Li-air battery systems is their poor round-trip efficiency owing to the formation of electrically insulating lithium peroxide (Li2O2) at the cathode surface.

Computational modeling has proven to be an indispensable tool in the analysis of battery systems, however, all existing simulations thus far treat the electrode/Li2O2 matrix as a homogenous continuum and utilize simply-shaped electrode morphologies, such as spheres and rods, to construct volume-averaged expressions (see Fig. 1a) for porosity and surface area and are insufficient to probe the effect of precise electrode microstructures and Li2O2 growth3,4. Utilizing a pore-scale transport-resolved model of the Li-air battery, the complex electrode and Li2O2morphologies can be directly incorporated and their effects on the system-level performance can be evaluated.

In this work, we present a pore-scale transport resolved model (see Fig. 1b) of the Li-air battery that fully accounts for the electrode microstructure and peroxide growth. This approach requires no empirical correlations regarding the electrode morphology. 3D reconstructed images of real carbon fiber cathode (Fig. 2) are used as geometric inputs to the model. The primary drawback of a pore-scale approach is the large amount of computational resources required in order to resolve the appropriate length and times scales for peroxide growth in the pore-scale framework. However, recent advancements in high performance computing have shown Graphics Processing Units (GPUs) to be a powerful alternative to the traditional parallel computation techniques for solving problems in computational fluid dynamics5and in this work multiple GPUs are used together to overcome the large computational challenge.

The model incorporates a thickness-dependent conductivity of Li2O2 based on inputs from the density functional theory and rate-dependent growth morphology of Li2O26. Results obtained from our pore-scale model agree well with experiments and the validated model is then used to predict the galvanostatic discharge behavior of a Li-air cell for a variety of electrode morphologies and design parameters. Fig. 3 shows the cell voltage versus specific capacity curves for electrodes with different nanostructure designs from our previous 2D pore-scale model7. The cell discharge capacity is limited by the spacing between nanostructures, which may lead to pore blocking and hence the reduction of active surface area.

Through extensive 3D simulations, we systematically examine the effect of drawing current, ORR rate coefficient, oxygen solubility, fiber and pore sizes on Li2O2growth and cell performance as to better understand the underlying physics of capacity fading during cycling.  The methodology presented here can be applied to other electrochemical systems that include an insoluble product formation as a result of the reaction process and will be a valuable tool for rational design of electrode microstructures for improved cell performance. 


[1]           G. Girishkumar, B. McCloskey, A. C. Luntz, S. Swanson, and W. Wilcke, "Lithium−Air Battery: Promise and Challenges," The Journal of Physical Chemistry Letters, 1, 2193, (2010).

[2]           J. Read, "Characterization of the Lithium/Oxygen Organic Electrolyte Battery," Journal of The Electrochemical Society, 149, A1190, (2002).

[3]           P. Andrei, J. P. Zheng, M. Hendrickson, and E. J. Plichta, "Some Possible Approaches for Improving the Energy Density of Li-Air Batteries," Journal of The Electrochemical Society, 157, A1287, (2010).

[4]           Y. Wang, "Modeling discharge deposit formation and its effect on lithium-air battery performance," Electrochimica Acta, 75, 239, (2012).

[5]           P. Zaspel and M. Griebel, “Solving incompressible two-phase flows on multi-GPU clusters”, Computers & Fluids, 80, 356, (2012)

[6]           B. Horstmann, B. Gallant, R. Mitchell, W. Bessler, Y. Shao-Horn, and M. Bazant, "Rate-Dependent Morphology of Li2O2 Growth in Li–O2 Batteries,"  The Journal of Physical Chemistry Letters, 4,  4217, (2013).

[7]           C. Andersen, H. Hu, G. Qiu, V. Kalra, and Y. Sun, "Pore-Scale Transport Resolved Model Incorporating Cathode Microstructure and Peroxide Growth in Lithium-Air Batteries," Journal of The Electrochemical Society, 162, A1135, (2015).