Yue Cao is a sixth-year assistant professor in energy systems at Oregon State University. Before joining OSU, he was a research scientist in the propulsions team at Amazon Prime Air in Seattle, WA. He has been a power electronics or power systems intern with special projects group at Apple Inc., Cupertino, CA; Halliburton Company, Houston, TX; Flanders Electric, Evansville, IN; Oak Ridge National Laboratory, TN; and Memphis Light Gas Water – Utility, TN. His research interests include power electronics, motor drives, and energy storage with applications in renewable energy integration and transportation electrification. He has been a PI or co-PI of multiple projects sponsored by NSF, DOE ARPA-E, DOE EERE, NAVFAC, Portland General Electric, Amazon Prime Air, and Grainger Foundation. He is a Senior Member of IEEE.
Cao is the 2023 OSU Promising Scholar awarded by the faculty senate. He received the 2022 NSF CAREER award. He is selected into National Academy of Engineering Frontier of Engineering Class of 2022. He won the Oregon State Learning Innovation Grant for transformative education in 2020. He received the Myron Zucker student award from the IEEE Industry Applications Society in 2010. He was a national finalist of the USA Mathematical Olympiad in 2006 and 2007. Cao is currently the Vice Chair of IEEE Power Electronics Society (PELS) TC11 – Aerospace Power. He was the Special Sessions Chair of IEEE ECCE 2022 (Energy Conversion Congress Expo) and the Tutorials Chair of ECCE 2021. Since 2019, he has served as an invited panelist for multiple DOE and NSF proposal reviews. He is currently an associate editor of IEEE Transactions on Transportation Electrification and an associate editor of IEEE Transactions on Industry Applications.
ECE 431/531 POWER ELECTRONICS
ECE 438/538 ELECTRIC VEHICLES
ECE 535 ADJUSTABLE SPEED DRIVES
ECE 539 ADVANCED POWER ELECTRONICS
- Power electronics
- Motor drives
- Energy storage
- Modeling, control, optimization
- Transportation electrification (truck, aircraft, UAV, EV)
- Renewable energy (hydrokinetic, wave, solar)
- Microgrids and energy efficient buildings
- Machine learning enabled design optimization
- Electric-thermal integrated systems