Udaya K. Madawala 
The University of Auckland
IEEE Fellow, Distinguished Lecturer of the IEEE PELS

Udaya K. Madawala graduated with a B.Sc. (Electrical Engineering) (Hons) degree from The University of Moratuwa, Sri Lanka, and received his PhD (Power Electronics) from The University of Auckland, New Zealand as a Commonwealth Doctoral Scholar.  At the completion of his PhD, he was employed as a Research and Development Engineer by Fisher & Paykel Ltd, New Zealand, to develop new technologies for PM motor drives.  At present as a Full Professor in the Department of Electrical, Computer & Software Engineering at University of Auckland, New Zealand, he leads a group of researchers focusing on a number of power electronics projects that are related to energy and wireless EV charging systems for V2X applications. 
Udaya is a Fellow of the IEEE,  and has both industry and research experience in the fields of power electronics and energy. He has served both the IEEE Power Electronics and Industrial Electronics Societies in numerous roles, relating to editorial, advisory, conferences, administrative & technical committees and chapter activities.  He was the General Chair of the 2nd IEEE Southern Power Electronics Conference (SPEC)- 2016, held in New Zealand, and is Distinguished Lecturer of the IEEE Industrial Electronic Society.  He is the recipient of the IEEE PELS Milan M. Jovanović Award for Power Electronics Emerging Technology and the University of Auckland Research Excellence Medal in 2024. Udaya, who has over 300 journal and conference publications, holds a family of global patents related to wireless power transfer (WPT) technology and power converters, and is a consultant to industry.

Abstract: To be added...

 Xiong Du  
Chongqing University, China

Xiong Du received his Bachelor's, Master's and Doctor's degrees from Chongqing University in 2000, 2002 and 2005 respectively. He was a lecturer at Chongqing University from 2005 to 2007 and a visiting scholar at the Department of Electrical Engineering at Rensselaer Polytechnic Institute from 2007 to 2008. He was an associate professor at Chongqing University from 2007 to 2010 and a professor at Chongqing University from 2010 to now. His research interests include HVDC system stability, new energy grid-connected system stability and power electronics equipment reliability.

Abstract: TBA

 Yun Wang  
ASME Fellow, RSC Fellow
University of California, USA

Yun Wang received his B.S. and M.S. degrees in Mechanics and Engineering Science from Peking University in 1998 and 2001, respectively. He went to the Pennsylvania State University where he earned his Ph.D degree in Mechanical Engineering in 2006. Dr. Wang joined the Mechanical and Aerospace Engineering department at the University of California, Irvine in 2006. He has produced over 100 publications in PEM fuel cell, Li-air battery, and other energy systems, including three books on PEM Fuel Cell and one on Thermal Fluid Science. Dr. Wang served as Track chair/co-chair, session chair/co-chair, conference chair and committee member for many international conferences on fuel cell, thermal energy, and machine learning. Dr. Wang received 2018 Reviewer of The Year from the Journal of Electrochemical Energy Conversion and Storage and is currently Professor at the UC Irvine, ASME fellow, RSC fellow, and associate editor for the journal of heat and mass transfer.

Abstract: Proton exchange membrane (PEM) fuel cells can play a pivotal role in a sustainable society through directly converting the chemical energy stored in hydrogen fuel to electricity at high efficiency (as high as 65%). Artificial intelligence (AI) has demonstrated significant efficacy in the research and development (R&D). AI methods have been proposed to optimize fuel cell structure, operating condition, porous materials, and surface properties by considering multi-physics transfer in fuel cells. In this talk, I will discuss the main physics in fuel cells, including power transients [1], cold-start fundamentals [2], electron/proton transfer [3], thermal management [4], and two-phase dynamics [5]; and explore advanced AI methods for fuel cell design, including artificial neural networks (ANNs), convolutional neural networks (CNNs), generative AI, and genetic algorithms (GAs) [6]. Future directions to advance next-generation fuel cell technology will be discussed.