Reliable remaining useful life (RUL) prediction of lithium‑ion batteries underpins proactive maintenance and lifecycle optimization. Two pervasive issues compromise prediction fidelity: (1) domain heterogeneity caused by batch‑to‑batch variations, and (2) non‑monotonic capacity regeneration events. This talk focuses on our dual contributions: a robust transfer‑learning ensemble that leverages early‑cycle kernel regression with domain‑distance–based weighting and transfer component analysis for cross‑batch alignment; and a monotone decomposition technique that segregates the capacity signal into a monotonically decreasing component and a regeneration term, each forecasted via Gaussian processes and deep autoregression for uncertainty‑aware RUL estimates. Results on multiple datasets demonstrate the efficacy of our approaches in real‑world scenarios.

Dr. Piao Chen is currently an associate professor at the ZJU-UIUC Institute, Zhejiang University. He previously served as an assistant professor in statistics at TU Delft, the Netherlands. His research interests include quality and reliability, statistical learning, and decision optimization. He has published over 30 papers in leading journals across management, engineering, and statistics, such as Management Science, Production and Operations Management, and IEEE Transactions on Information Theory. His work has received several Best Paper Awards at international conferences, including INFORMS QSR and SRSE.