摘要： High intensity lithium-ion (Li-Ion) batteries have been widely used in consumer electronics, electric vehicles (EV), and critical marine and space systems. Their reliability and performance play an important role in the entire engineering system. As a result, developing prognostics and health management (PHM) approaches for Li-Ion batteries has received increasing attention in recent years. This talk discusses a few model development for Li-Ion battery prognosis at different data and knowledge granularities. We use both simulation and real experiment data to demonstrate the features of different models. Through this research example, we want to highlight that statistical models, when fused with engineering knowledge, can lead to a more effective data analytics solution to practical problems. 

报告人简介：Nan Chen is an Assistant Professor in the Department of Industrial and Systems Engineering at National University of Singapore.  He obtained his B.S. degree in Automation from Tsinghua University, and M.S. degree in Computer Science, M.S. degree in Statistics, and Ph.D. degree in Industrial Engineering all from University of Wisconsin-Madison.  His research interests include statistical modeling and surveillance of engineering systems, simulation modeling design, condition monitoring and degradation modeling.  He is a member of INFORMS, IIE, and IEEE.