Summary A vast majority of human population around the planet has limited availability to electricity. Inexpensive energy storage systems are crucial for solving this critical issue. Rapid rise in global pollutants has also sparked widespread efforts to limit fossil fuelled automobiles. Lithium‐ion batteries continue to be a commanding force in the energy storage domain. Lithium‐ion batteries have been consistently used in distributed solar‐battery modules as well. Precise prognostics of battery health and remaining shelf life of the battery is a challenging task. Sufficient access to high‐quality battery dataset is important to compute accurate State‐of‐Charge estimations and also to predict end‐of‐life of the storage system. A data‐driven approach is the need of the hour to derive useful insights into battery health and cycle life. Researchers have limited access to battery data chiefly due to privacy concerns. Commercial manufacturers rarely share data, which leads to data scarcity issues. Lack of data leads to ineffective State‐of‐Charge evaluations as well. This paper aims to provide reasonable solutions to resolve the limited data difficulty faced by battery researchers. The primary contributions of our paper include: (a) we employ a seasonal‐trend contrastive learning approach to generate reliable battery synthetic data, (b) heterogeneity of the synthetic data is preserved which helps to improve battery health forecasting. We also release the code implementation details relevant to this research work to enable battery scientists to reproduce and develop upon our work.