Fast Diagnosis of Failure Mechanisms and Lifetime Prediction of Li Metal Batteries. Issue 2 (3rd November 2020)

• Record Type: Journal Article
• Authors: Gao, Ningshengjie; Abboud, Alexander W.; Mattei, Gerard S.; Li, Zhuo; Corrao, Adam A.; Fang, Chengcheng; Liaw, Boryann; Meng, Ying Shirley; Khalifah, Peter G.; Dufek, Eric J.; Li, Bin
• Is Part Of: Small methods. Volume 5:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-03
• Subjects: failure mechanisms -- lifetime prediction -- Li‐metal batteries -- rest voltages -- synchrotron X‐ray diffraction; Nanotechnology -- Methodology -- Periodicals; Nanotechnology -- Periodicals; Periodicals; 620.5028
• DOI: 10.1002/smtd.202000807
• Languages: English
• ISSNs: 2366-9608

Abstract

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. Abstract : A simple non‐destructive electrochemical diagnostic method is demonstrated, which can quickly and reliably identify the origin of cell capacity loss. This method is deeply understood and well validated, enabling the early detection of cell failure and prediction of maximum cell lifetime as well as the rapid development of durable, reliable, and safe Li metal batteries for practical applications.