An Interpolation based Synthetic Battery Data Generation Technique

Global collaborative efforts to mitigate environmental pollution has led to increased efforts to electrify the automotive sector. Lithium-ion batteries, one of the most predominant sources of energy, has been the primary driver of this green revolution to curb automotive pollutants. Accurate State-of-Charge evaluation of such Lithium-ion batteries has been a long standing research problem among energy researchers. There have been a series of solutions proposed in literature to compute precise State-of-Charge estimates, all of which assume accessibility to labelled battery datasets. In reality, researchers lack access to diverse datasets and battery manufacturers are unwilling to share proprietary datasets. In this paper, we tackle the issue of lack of high quality data by introducing a hierarchic interpolation synthetic data generation approach. Some of the key contributions of our work are: a) synthetic data generation using reliable interpolation method, b) we highlight the effectiveness of this approach by comparing it with other state-of-the-art synthetic data generation techniques. Code can be found at this URL: https://github.com/vageeshmaiya/Hierarchical-Interpolation