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Physics-Based Modeling of Lithium Plating and Dendrite Growth for Prediction of Extreme Fast-Charging

Wise, Matthew J
http://rave.ohiolink.edu/etdc/view?acc_num=osu1650325148735621

Abstract Details

2022, Master of Science, Ohio State University, Mechanical Engineering.
The Department of Energy has prioritized extreme fast charging (XFC) for customer adoption of Electric Vehicles (EVs). Range anxiety is a major public concern when purchasing an EV, so comparable refueling time to conventional vehicles is highly sought after. There are multiple barriers to achieving XFC at all levels: infrastructure, vehicle systems, and battery cell. A major concern is on a cell degradation phenomenon known as lithium plating, a side reaction that is favored at high C-rates and low temperatures. Lithium plating can significantly reduce the cell capacity and can also pose serious safety concerns due to potential internal shorting and thermal runaway. For these reasons, accurate prediction of the physical mechanisms related to plating is extremely important and can be used to mitigate effects and prevent occurrence of plating during charging. This work is focused on developing physics-based modeling techniques and integrating them with experimental testing to predict the cell behavior associated with lithium plating. Fast charge testing was performed at various charging C-rates using different charging methods (CC vs. CC-CV). Reference Performance Tests (RPTs) were conducted between each fast charge cycle to track cell performance after lithium plating. The collected data was used to calibrate a pre-existing lithium plating model within a DFN electrochemical model. After calibration, specific issues with prediction in voltage degradation and capacity loss were addressed. Using differential capacity analysis of the RPTs, different aging effects onset by lithium plating were identified, showing both a loss of cyclable lithium (LCL) and a loss of anode active material (LAM). Optimization of electrochemical model parameters to RPT data confirms this and provides ideal changes in capacity related parameters after each fast charge cycle. A new model was then developed to attribute the LAM to dendrite growth behavior, which relies on results from phase-field theory to model the isolation of the active material surface sites caused by the growth of dendrites. The addition of this model, combined with RPT data analysis and electrochemical model optimization has produced a plating model calibration procedure that can accurately predict degradation in cell performance due to lithium plating.
Marcello Canova (Advisor)
Jung-Hyun Kim (Committee Member)
152 p.
Mechanical Engineering
lithium-ion battery; fast charge; lithium plating; dendrite growth; battery model; XFC; loss of active material; loss of cyclable lithium; anode aging mechanisms

Recommended Citations

Citations

  • Wise, M. J. (2022). Physics-Based Modeling of Lithium Plating and Dendrite Growth for Prediction of Extreme Fast-Charging [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1650325148735621

    APA Style (7th edition)

  • Wise, Matthew. Physics-Based Modeling of Lithium Plating and Dendrite Growth for Prediction of Extreme Fast-Charging. 2022. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1650325148735621.

    MLA Style (8th edition)

  • Wise, Matthew. "Physics-Based Modeling of Lithium Plating and Dendrite Growth for Prediction of Extreme Fast-Charging." Master's thesis, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1650325148735621

    Chicago Manual of Style (17th edition)

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© 2022, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.
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