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Published on: July 20, 2017
Particle Tracking Methods for Battery Precipitation Reactions.
Trent Koberna1, Steven C DeCaluwe1
1Colorado School of Mines Department of Mechanical Engineering, 1500 Illinois St, Golden, Colorado 80401, United States.
Summary
This study addresses particle size distribution in battery deposition reactions. We present an improved algorithm that accurately tracks particle growth, crucial for battery performance and longevity.
Area of Science:
- Electrochemistry
- Materials Science
- Computational Modeling
Background:
- Precipitation and deposition reactions at solid-liquid interfaces are critical in various battery chemistries, such as lithium-ion, anode-free, zinc-based, and lithium-sulfur batteries.
- Existing literature lacks detailed numerical algorithms for tracking the temporal evolution of particle size distribution in deposits on electrode surfaces during heterogeneous nucleation and growth.
Purpose of the Study:
- To examine and compare different numerical approaches for discretizing and tracking particle size distribution in battery electrode deposits.
- To identify and demonstrate the limitations of common algorithms, specifically anomalous flattening of the particle size distribution.
- To present a novel algorithm that accurately preserves the particle size distribution during particle growth.
Main Methods:
- Review and analysis of existing numerical methods for particle size distribution tracking.
- Implementation and testing of various discretization and tracking algorithms.
- Development and validation of a new algorithm designed to overcome limitations of current methods.
Main Results:
- Commonly used numerical approaches for tracking particle size distribution were found to lead to anomalous flattening.
- The study demonstrates the inaccuracies inherent in prevalent algorithms for modeling deposit growth.
- A new algorithm was developed and shown to effectively preserve the correct particle size distribution.
Conclusions:
- Accurate tracking of particle size distribution is essential for understanding and optimizing battery performance.
- The developed algorithm offers a significant improvement over existing methods for modeling precipitation and deposition in batteries.
- This work provides a more reliable computational tool for battery research and development.
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