About the Presentation
Lithium-ion (Li-ion) batteries are commonly used in electric vehicles (EVs). However, the Li-ion battery performance degrades over time due to problems such as capacity degradation and impedance growth over time. A reliable battery health monitoring and prognostics system is very beneficial to diagnose the battery's state-of-health and predict the remaining-useful-life in order to improve battery performance. This speech presents a new technology to improve battery state-of-health monitoring and remaining useful life prediction. An enhanced particle filter technique is proposed to reduce the impact of sample degeneracy and impoverishment in state estimation. An evolving neurofuzzy predictor is developed to deal with the lack of new battery measurements during the prognostic period.
Dr. Wilson Wang received his BSc in Electromechanical Engineering (SIT in China), MSc in Mechanical Engineering (Northeastern University in China), MEng in Industrial Engineering (University of Toronto), and PhD in Mechatronics Engineering (University of Waterloo) in 2002. From 2002 to 2004, he was a Senior Scientist at Mechworks Systems Inc. in Waterloo, Ontario. He joined Lakehead University in 2004, and is currently a Professor in the Department of Mechanical Engineering. His research interests include mechatronics, artificial intelligence, machinery diagnostics, system state prognostics, and smart sensors. He received the LU Distinguished Research Award in 2017, and was a LU Research
Chair from 2019-2022. He has supervised 35 thesis-based MSc students, 16 PhD, and 10 postdoctoral fellows. He is also a fellow of International Society of Engineering Asset Management.