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Review of Capacity Fade Models for Lithium-Ion Batteries - Numerical Implications of SEI Layer Growth

Wednesday, 16 May 2018: 14:40
Room 619 (Washington State Convention Center)
M. Fan (University of Washington), S. B. Lee (University of Washington, Seattle), M. Pathak, Y. Qi (University of Washington), J. Chen, and V. R. Subramanian (University of Washington, Seattle)
Lithium-ion battery applications, such as EVs and PHEVs, require long battery life. However, capacity fade always occurs due to unwanted side reactions including electrolyte oxidation at the positive electrode, lithium deposition at the negative electrode, electrolyte decomposition processes, and formation of the Solid-Electrolyte Interphase (SEI) layer1. To model the capacity fade, different models have been proposed in the literature2-4.

In this presentation, we model the capacity fade by assuming the SEI layer formation to be the dominant mechanism. We address how to efficiently simulate SEI layer growth and modify the simulation approach for the solid phase equations if needed. Even within SEI layer models, several different expressions have been used in many papers5-8. In most cases, the solid particles are assumed to be spherical, and the intercalation process is modeled using Fick’s law of diffusion in the radial direction. For solving the solid-phase diffusion in the radial-dimension, many efficient methods to reformulate and simulate have been proposed in the past9-12.

The SEI forming side reaction with the Single Particle Model (SPM) is first studied. The partial differential equation of the SPM is discretized using the finite difference method in radial direction and solved in time using the numerical method of lines approach. An assumption of a parabolic profile in the radial direction is another efficient approximation9-12. After this, we combine the SEI layer model with the pseudo two-dimensional model to predict the performance of the battery.

Acknowledgements

The authors thank the Department of Energy (DOE) for providing partial financial support for this work, through the Advanced Research Projects Agency (ARPA-E) award number DE-AR0000275.

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