BATTERY CELL DEFECTIVENESS AND FIRE EARLY DIAGNOSIS METHOD AND APPARATUS BASED ON NEURAL NETWORK

The present invention relates to a battery cell defectiveness and fire early diagnosis method and apparatus based on a neural network. A method for performing early diagnosis of battery cell defectiveness and fire through neural network-based self-supervised learning by a battery cell defectiveness and fire early diagnosis apparatus for predicting battery cell defectiveness comprises the steps in which the battery cell defectiveness and fire early diagnosis apparatus: collects data including at least one of chemical composition, current, voltage, or temperature data measured at predetermined time intervals within each charge/discharge cycle while charging and discharging a plurality of batteries; inputs the collected data to the neural network to train the neural network; and predicts a battery deviating from a main cluster of the neural network as being defective.