Lithium-ion battery application time series data augmentation based on generative adversarial network for training deep learning algorithm

In this paper, a safety accident data augmentation method is proposed to determine the battery failure situation. For data augmentation, overcharging battery electrical abuse accident data is used. A black box-based generative advertising network (GAN) is applied to increase the time series of battery failure data. The effectiveness of the augmented algorithm designed by comparing the time series patterns of overdischarge data generated through GAN with the actual overcharged data is demonstrated. Finally, it is verified that the GAN-based data augmentation method that may be used for abnormality detection of the battery management system (BMS) may be efficiently applied.