performing a battery test by applying a plurality of pulses on a particular battery cell during a battery cycle to obtain battery test data for at least a portion of the battery cycle of the particular battery cell, the battery test data comprising data of at least one battery cell property in the battery test during the particular battery cycle;

determining, based at least on the battery test data and using a machine learning model, whether the particular battery cell has a likelihood to experience catastrophic fade that is above a threshold, wherein the machine learning model has been trained using training battery test data for battery cells that experienced catastrophic fade; and

taking an action at least partially based on the determination of whether the particular battery cell has a likelihood to experience catastrophic fade that is above the threshold,

wherein the at least one battery cell property comprises an internal resistance of the particular battery cell, the battery test data comprises values of a plurality of internal resistances of the particular battery cell, each of the plurality of internal resistances corresponds to a respective pulse of the plurality of pulses, and wherein each of the plurality of pulses has a respective importance level, a value of each of the plurality of internal resistances in the data has a respective weight in the machine learning model, and the respective weight of the internal resistance is based on an importance level of a corresponding pulse of the plurality of pulses associated with the internal resistance.