3 papers
cs.LG2026
HyBattNet: Hybrid Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries
Khoa Tran, Tri Le, Bao Huynh +4
Accurate prediction of the Remaining Useful Life (RUL) is essential for enabling timely maintenance of lithium-ion batteries, impacting the operational efficiency of electric appli…
cs.LG2025
SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling
Khoa Tran, Hung-Cuong Trinh, Vy-Rin Nguyen +2
Accurate battery modeling is essential for reliable state estimation in modern applications, such as predicting the remaining discharge time and remaining discharge energy in batte…
cs.LG2025
HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction
Khoa Tran, Bao Huynh, Tri Le +4
Accurate prediction of the Remaining Useful Life (RUL) in Lithium ion battery (LIB) health management systems is essential for ensuring operational reliability and safety. However,…