Summary Energy storage systems have been in the spotlight for the past decade as they offer tangible solutions to the ever‐growing pollution problem faced by the planet. These storage systems, primarily lithium‐ion based, power most of the mobile devices and electric vehicles (EVs). Substantial efforts are being made to electrify every mode of transportation to combat climate change. Accurate state‐of‐charge (SOC) and state‐of‐health (SOH) assessment of lithium‐ion batteries play an important role for determining the available range in EVs. Research in this area of capacity estimation demands extensive curated battery parameter data such as voltage, current, and temperature. Performing repeated experiments to collect these data is expensive and tedious. Also, lack of diversity in open access datasets has limited research in this area. This paper introduces a generative adversarial network (GAN)‐based approach for data augmentation. This technique enables expansion of sparse datasets in‐turn enhancing the learning capability of Neural Networks used for SOC/SOH estimation. A time series GAN is used in the present work to produce synthetic data. This technique was evaluated on two publicly available battery parameter datasets to test its effectiveness. A Kullback‐Leibler Divergence value of 0.2317 and 1.0572 was obtained for the battery dataset obtained from NASA prognostics repository and Oxford battery degradation dataset, respectively. The contributions of the paper include: (a) synthetic time‐series data augmentation of battery parameters, (b) high‐fidelity diverse data generation of battery profile data such as voltage, current, temperature, and SOC.

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Advent of multiple data‐driven techniques in the energy storage domain has resulted in the development of accurate battery capacity estimators. A major impediment to research in this domain is the limited availability of diverse datasets. Generation of synthetic data is vital when experimental data is unavailable. Moreover, privacy concerns and confidentiality restrictions have further fueled data scarcity in this domain. Present state‐of‐the‐art techniques for synthetic data generators are primarily built on Generative Adversarial Networks (GANs). Although the performance of GANs are exceptional, the high dimensional outputs of these models are difficult to interpret and the associated loss function is highly data specific and cannot be generalized. Moreover, GANs consist of two resource‐intensive neural networks (generator and discriminator) which renders them unsuitable for devices with limited computational resources. In this article, we introduce a simple deep learning‐based probabilistic time series model and we employ the forecasting ability of this model to generate synthetic data. The simplicity of this model makes it an effective candidate to produce synthetic data in resource‐constrained scenarios.

Objective:To evaluate the outcome of early nasal CPAPin premature neonates with neonatal respiratory distress syndrome. Methods: 100 babies of 28-34 weeks gestational age admitted in Neonatal ICU of Nalanda Medical College & Hospital, Patna (Bihar, India), with clinical diagnosis of HMD, requiring respiratory support were treated with early nasal CPAPand studied prospectively from 1st November 2014 to 31st October 2016. Results: We found a success rate of 80% in babies with HMD, who were managed with early nasal CPAPalone. Remaining 20% needed intubation and higher mode of ventilation. Mild and moderate grade HMD were effectively managed with early nasal CPAP (P<0.05). It was also found to be effective in babies of mothers who have received antenatal steroids (P<0.05). Conclusion: Prematurity is the commonest predisposing cause for HMD. Early nasal CPAPis safe, inexpensive and effective means of respiratory support in HMD. It is useful in mild and moderate grade disease. It may not be a replacement for assisted ventilation in severe disease. It is also found to be more effective in babies of mothers who have received antenatal steroids.