Our approach differentiates from the prior art in two important ways: First, we employ an information-rich electrical signal -EIS -which captures the response of the cell across different timescales without any knowledge of the cycling history. This is in contrast to most existing methods which employ features from the charging-discharging curve -a significantly more coarse-grained signal -as input to machine learning models. Our results suggests significant improvements in battery management systems abound by incorporating circuitries that measure electrochemical impedance.