Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network

Accurate evaluation of the health status lithium-ion batteries must be deemed as great significance, insofar utility and safety are concern. Lithium plating, in particular, is notoriously known to a chemical reaction that can cause deterioration in, or even fatal hazards to, batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such being fast non-destructive over its competitors, suffices detecting lithium plating thus been attracting increasing attention field battery management, but ability assessing quantitatively degree remains largely unexplored hitherto. On this point, work seeks narrow gap by proposing an EIS-based method quantify plating. The core conception eventually circumvent reliance on state-of-health measurement, use instead spectrum acquire estimate capacity loss. To do so, effects solid electrolyte interphase formation first decoupled, so mass quantified. Then, based parameters fractional equivalent circuit model (ECM) identified. These ECM received inputs artificial neural network, tasked with establishing correspondence between empirical part revolves around data collected from aging experiment, validity proposed truthfully attested dismantling batteries, otherwise not needed during actual uptake method.