Yu-Xin Zhang

A physics-informed CNN-LSTM for state-of-health reconstruction and remaining-useful-life prediction of lithium-ion batteries

Accurate remaining-useful-life (RUL) prediction is important for battery safety, maintenance planning, and second-life assessment, but models trained on a small set of cells can overfit degradation patterns that do not transfer to unseen cells or operating conditions. This paper revises and evaluates a physics-informed...