A Systematic Review of GAN-Based Data Augmentation for Lithium-Ion Battery State Estimation and Prognostics: State of Charge, State of Health, and Remaining Useful Life Prediction

OSF Preprints (OSF Preprints) 2026 Preprint

Authors: Md Alamgir Kabir, Md. Sulyman Islam Sifat
Cited by: 0

Abstract

Systematic literature review examining GAN-based data augmentation approaches for lithium-ion battery State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL) prediction. This review follows PRISMA 2020 guidelines and analyzes 31 primary studies to evaluate the effectiveness, challenges, and future directions of GAN-based data augmentation in battery prognostics.

Fields of Study & Subjects

Advanced Battery Technologies Research Advancements in Battery Materials Machine Fault Diagnosis Techniques

Publication details

Journal / source
OSF Preprints (OSF Preprints)
Publication date
9 January 2026
Document type
Preprint