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Ever-increasing efforts in state of charge (SOC) of lithium-ion battery estimation techniques have been centered on machine learning-based methods, and a prerequisite guaranteeing the effectiveness of these methods demands an abundance of high-quality datasets. However, performing battery-related experiments is time-consuming and expensive, and manufacturers are reluctant to release their datasets due to confidentiality constraints. This limited data availability has hindered further research in this area. This paper proposes a time series augmentation model (TS-DCGAN) based on a deep convolutional generative adversarial framework. By converting the data generation problem into image generation and utilizing the advancements in GAN-based image generation, the proposed method can produce high-quality battery data. The distributional similarity and diversity of generated datasets are evaluated quantitatively and qualitatively with several metrics. The SOC estimator trained on the synthet
Battery-managementData-augmentationEep-convolutional-generative-adversarial-networksLithium-ion-batteriesState-of-chargeIme-series-generation