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Fast Diagnosis of Failure Mechanisms and Lifetime Prediction of Li Metal Batteries

  • Ningshengjie Gao
  • , Alexander W. Abboud
  • , Gerard S. Mattei
  • , Zhuo Li
  • , Adam A. Corrao
  • , Chengcheng Fang
  • , Boryann Liaw
  • , Ying Shirley Meng
  • , Peter G. Khalifah
  • , Eric J. Dufek
  • , Bin Li
  • Idaho National Laboratory
  • Stony Brook University
  • Brookhaven National Laboratory
  • University of California at San Diego

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Lithium (Li) metal serving as an anode has the potential to double or triple stored energies in rechargeable Li batteries. However, they typically have short cycling lifetimes due to parasitic reactions between the Li metal and electrolyte. It is critically required to develop early fault-detection methods for different failure mechanisms and quick lifetime-prediction methods to ensure rapid development. Prior efforts to determine the dominant failure mechanisms have typically required destructive cell disassembly. In this study, non-destructive diagnostic method based on rest voltages and coulombic efficiency are used to easily distinguish the different failure mechanisms—from loss of Li inventory, electrolyte depletion, and increased cell impedance—which are deeply understood and well validated by experiments and modeling. Using this new diagnostic method, the maximum lifetime of a Li metal cell can be quickly predicted from tests of corresponding anode-free cells, which is important for the screenings of electrolytes, anode stabilization, optimization of operating conditions, and rational battery design.

Original languageEnglish
Article number2000807
JournalSmall Methods
Volume5
Issue number2
DOIs
StatePublished - Feb 15 2021

Keywords

  • failure mechanisms
  • Li-metal batteries
  • lifetime prediction
  • rest voltages
  • synchrotron X-ray diffraction

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