Lithium-ion (Li-ion) batteries are critically important for portable electronics, electric vehicles, and grid-level energy storage. The development of next-generation Li-ion batteries requires high-capacity electrodes with a long cycle life. However, the high capacity of Li storage is usually accompanied by large volume changes, dramatic morphological evolution, and mechanical failures in the electrodes during charge and discharge cycling. To understand the degradation of electrodes and resulting loss of capacity, this thesis aims to develop mechanistic-based models for predicting the chemo-mechanical processes of lithiation and delithiation in high-capacity electrode materials. To this end, we develop both continuum and atomistic models that simulate mass transport, interface reaction, phase and microstructural evolution, stress generation and damage accumulation through crack or void formation in the electrodes. The modeling studies are tightly coupled with in-situ transmission electron microscopy (TEM) experiments to gain unprecedented mechanistic insights into electrochemically-driven structural evolution and damage processes in high-capacity electrodes. Our models are successfully applied to the study of the two-phase lithiation and associated stress generation in both crystalline and amorphous silicon anodes, which have the highest known theoretical charge capacity, as well as the lithiation/sodiation-induced structural changes and mechanical failures in silicon-based multilayer electrodes. The modeling studies have uncovered unexpected electrochemical reaction mechanisms and revealed novel failure modes in silicon-based nanostructured anodes. Our modeling research provides insights into how to mitigate electrode degradation and enhance capacity retention in Li-ion batteries. More broadly, our work has implications for the design of nanostructured electrodes in next-generation energy storage systems.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/53915 |
| Date | 21 September 2015 |
| Creators | Fan, Feifei |
| Contributors | Zhu, Ting |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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