Abstract
This work explores various data-driven approaches for battery SOH estimation, leveraging easily obtainable voltage data, either in full or partial segments, to infer battery health. It investigates different data augmentation techniques and employs diverse machine learning methods across different datasets. Experimental results demonstrate the efficacy of the proposed methods, yielding promising accuracy levels. These findings offer valuable insights and practical implications for potential implementation in industrial applications.