Title: Prognostics and Health Management (PHM) of Lithium-Ion Energy Storage and Renewable Energy Systems
Abstract: Failures of engineered systems could not only result in enormous repair/replacement costs, but also cause life-threatening consequences such as explosion and fire. Since the 1980s, major industries and government agencies worldwide have faced increasing challenges in ensuring the reliability and safety of engineered systems, including the emerging lithium-ion energy storage and renewable energy systems (e.g., wind turbine and solar panel). In response to these challenges, a number of prognostics and health management (PHM) techniques have recently been developed. These techniques utilize sensory signals collected from an operating system to monitor the system’s health condition, predict its remaining useful life, and ultimately prevent catastrophic failures from occurring. This talk will give an overview of PHM and discuss several core techniques for health prognostics. Three industrial applications, namely i) model-based prognostics of lithium-ion battery, ii) data-driven prognostics of electric cooling fan, and iii) integrated diagnostics and prognostics of bearing in wind turbine gearbox, will be used for the demonstration of the techniques.
Research Areas of Interest: Dr. Chao Hu received his B.E. degree in Engineering Physics from Tsinghua University in Beijing, China in 2007 and the Ph.D. degree in Mechanical Engineering at the University of Maryland, College Park in Maryland in 2011. He worked as a Principle Scientist at Medtronic, Inc. in Minnesota from 2011 to 2015. He is currently an Assistant Professor in the Department of Mechanical Engineering at the Iowa State University. His research interests are engineering design under uncertainty, design of lithium-ion energy storage systems, and prognostics and health management (PHM). Dr. Hu has received several awards and recognitions for his research, including: the Highly Cited Research Paper 2012-2013 in the Journal of Applied Energy in 2015; the Star of Excellence Individual Award at Medtronic in 2014; the Best Paper Award in the ASME Design Automation Conferences (DAC) in 2013; a nomination for the 2013 Eni Award Renewable Energy Prize, known as “Nobel Prize in Energy Sector”; the Best Paper Award in the IEEE PHM Conference in 2012; and two-time receipts of the Top 10 Best Paper Award in the ASME DACs in 2011 and 2012. His research work has led to more than 50 peer-reviewed publications in the above areas.