  We presents an approach for early cycle classification of lithium-ion
batteries into high and low-performing categories, coupled with the prediction
of their remaining useful life (RUL) using a linear lasso technique.
Traditional methods often rely on extensive cycling and the measurement of a
large number of electrochemical impedance spectroscopy (EIS) frequencies to
assess battery performance, which can be time and resource consuming. In this
study, we propose a methodology that leverages specific EIS frequencies to
achieve accurate classification and RUL prediction within the first few cycles
of battery operation. Notably, given only the 20 kHz impedance response, our
support vector machine (SVM) model classifies batteries with 100\% accuracy.
Additionally, our findings reveal that battery performance classification is
frequency agnostic within the high frequency ($<20$ kHz) to low-frequency (32
mHz) range. Our model also demonstrates accurate RUL predictions with
$R^2>0.96$ based on the out of phase impedance response at a single high (20
kHz) and a single mid-frequency (8.8 Hz), in conjunction with temperature data.
This research underscores the significance of the mid-frequency impedance
response as merely one among several crucial features in determining battery
performance, thereby broadening the understanding of factors influencing
battery behavior.
