Title： Intelligent Energy Control and Management in Hybrid Electric Vehicles (HEV)

Location：27th Feb，18:30 pm

Location：A228 

Introduction：

Dr. Yi Lu Murphey received a M.S. degree in computer science from Wayne State University, Detroit, Michigan, in 1983, and a Ph.D degree with a major in Computer Engineering and a minor in Control Engineering from the University of Michigan, Ann Arbor, Michigan, in 1989. She is currently a Full Professor at the ECE (Electrical and Computer Engineering) department and the Associate Dean for Graduate Education and Research at the College of Engineering and Computer Science in the University of Michigan-Dearborn. Prior to her current position, she served as the chair of the Department of Electrical and Computer Engineering for seven years. She has authored over 150 publications in refereed journals and conference proceedings in the areas of areas of machine learning, pattern recognition, computer vision and intelligent systems with applications to intelligent vehicle systems, optimal vehicle power management, data analytics, automated and connected vehicles and robotic vision systems. She has received over $7 million in research grants and contracts over the last twenty years from US National Science Foundation, US Department of Defense, and many industrial companies. Currently her current research in machine learning, computer vision, and data science are funded by Ford Motor Company, ZF-TRW Automotive, University of Michigan Mobility Transformation Center (MTC), Michigan Institute of Data Science (MIDAS) and Toyota Research Institute. Dr. Murphey is a Distinguished Lecturer for the IEEE Society of Vehicular Technologies and a fellow of IEEE.

Content：

Energy control and management in Hybrid Electric Vehicles (HEV) has been actively investigated recently because of its potential to significantly improve fuel economy and emission control. Because of the multi-power-source nature and the complex configuration and operation modes in a HEV, energy management is more complicated but more important than in a conventional vehicle. In this lecture, I will introduce machine learning technologies combined with roadway types, traffic congestion levels, optimization algorithms to achieve quasi-optimal energy management in hybrid vehicles.