Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System. - Sadiqa Jafari; Yung-Cheol Byun (IEEE Access, 2022)

XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries. - Sadiqa Jafari; Yung-Cheol Byun (Sensors, 2022)

Enhancing Predictive Battery Maintenance Through the Use of Explainable Boosting Machine. - Sadiqa Jafari; Yung-Cheol Byun (NiDS (2), 2023)

Optimizing Battery RUL Prediction of Lithium-Ion Batteries Based on Harris Hawk Optimization Approach Using Random Forest and LightGBM. - Sadiqa Jafari; Yung-Cheol Byun (IEEE Access, 2023)

A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning. - Sadiqa Jafari; Yung-Cheol Byun; Seok Jun Ko (IEEE Access, 2023)

A CNN-GRU Approach to the Accurate Prediction of Batteries' Remaining Useful Life from Charging Profiles. - Sadiqa Jafari; Yung-Cheol Byun (Comput., 2023)

Accurate remaining useful life estimation of lithium-ion batteries in electric vehicles based on a measurable feature-based approach with explainable AI. - Sadiqa Jafari; Yung Cheol Byun (J. Supercomput., 2024)

Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries. - Sadiqa Jafari; Jisoo Kim; Wonil Choi; Yung-Cheol Byun (IEEE Access, 2025)