Results on Dendrite Detection for Li-metal Batteries
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Author
Iurilli, P.
Luppi, L.
Brivio, Claudio
Hutter, Andreas
DOI
Abstract
We investigated six non-invasive detection techniques to anticipate failures in Lithium Metal Batteries (LMBs) and to lay the basis for innovative self-healing mechanisms. The novel methodology is based on: (i) defining detection parameters to track the evolution of cell aging, (ii) defining a detection algorithm and applying it to cycling data, and (iii) validating the algorithm in its capability to detect failure. The main outcomes of the work include the characterization results of the tested LMBs under different cycling conditions, the detection techniques performance evaluation, and a sensitivity analysis to identify the most performing parameter and its activation threshold.
Publication Reference
CSEM Scientific and Technical Report 2022, p. 124
Year
2022