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Dong Cao

GE EPISCenter Professor in Aerospace Electrical Power Systems and Professor

Full-Time Faculty

School of Engineering: Department of Electrical and Computer Engineering

Contact

Email: Dong Cao
Phone: 937-229-3724
Kettering Laboratories Room 261A
Website: Visit Site

Bio

Dong Cao received his B.S. degree from Zhejiang University, Hangzhou China, in 2005, and his M.S. and Ph.D. degrees in electrical engineering from Michigan State University, East Lansing USA, in 2010 and 2012, respectively. He worked at Ford Motor Company as a core power electronics engineer for hybrid electric vehicle electrified driveline hardware development from 2012-2014. He was an assistant professor at North Dakota State University from Aug. 2014 to Aug. 2019. Dr. Cao joined the University of Dayton as GE EPISCenter Professor in Aerospace Electric Power Systems and Associate Professor in Aug. 2019.

Selected Publications

  • Lyu, X., Ren, N., & Cao, D. (2019). Instantaneous pulse power compensator for high-density single-phase inverters power electronics. IEEE Transactions.
  • Lyu, X., Ren, N., & Cao, D. (2019). Optimization of high-density and high efficiency switched-tank converter for data center applications industrial electronics. IEEE Transactions.
  • Lyu, X., Ren, N., & Cao, D. (2018). Optimal configuration of high-efficiency segmented linear LED driver with genetic algorithm. Journal of Emerging and Selected Topics in Power Electronics, IEEE.
  • Li, Y., Curuvija, B., Lyu, X., & Cao, D. (2018). A 98.55% efficiency 750W/in3 switched-tank converter for data center application. IEEE Transactions on Industry Applications.
  • Lyu, X., Li, Y., & Cao, D. (2017). DC-Link RMS current reduction by increasing paralleled 3-phase inverter module number for segmented traction drive. Journal of Emerging and Selected Topics in Power Electronics, IEEE.
  • Li, Y., Lyu, X., & Cao, D. (2017). A zero-current-switching high conversion ratio modular multilevel DC-DC converter. Journal of Emerging and Selected Topics in Power Electronics, IEEE. (Prize Paper Award)
  • Lyu, X., Ren, N., Li., Y., & Cao, D. (2016, September). A SiC-based high power density single-phase inverter with in-series and in-parallel power decoupling method. IEEE Journal of Emerging and Selected Topics in Power Electronics, 4(3), 893-901.
  • Lyu, X., Li., Y., Liu, G., & Cao, D. (2015). A high-efficiency linear LED driver with concave current control for low-power application. Journal of Emerging and Selected Topics in Power Electronics, IEEE.
  • Lei, Q., Cao, D., Peng, F. Z. (2014). Novel loss and harmonic minimized vector modulation for a current-fed quasi-Z-source inverter in HEV motor drive application. Power Electronics, IEEE Transactions, 29, 1344-1357.
  • Cao, D., Jiang, S., & Peng, F. Z. (2013). Optimal design of multilevel modular capacitor-clamped DC-DC converter. Power Electronics, IEEE Transactions, 28, 3816-3826.
  • Jiang, S., Cao, D., Li, Y., Liu, J., & Peng, F. Z. (2012). Low-THD, fast-transient, and cost-effective synchronous-frame repetitive controller for three-phase UPS inverters. Power Electronics, IEEE Transactions, 27, 2994-3005.
  • Jiang, S., Cao, D., Li, Y., & Peng, F. Z. (2012). Grid-connected boost-half-bridge photovoltaic microinverter system using repetitive current control and maximum power point tracking. Power Electronics, IEEE Transactions, 27, 4711-4722.
  • Qian, W., Cao, D., Cintron-Rivera, J. G., Gebben, M., Wey, D., & Peng, F. Z. (2012). A switched-capacitor DC-DC converter with high voltage gain and reduced component rating and count. Industry Applications, IEEE Transactions, 48, 1397-1406.
  • Liu, J., Jiang, S., Cao, D., & Peng, F. Z. (2012). A digital current control of quasi-Z-source inverter with battery. Industrial Informatics, IEEE Transactions.
  • Cao, D., Jiang, S., Yu, X., & Peng, F. Z. (2011). Low-cost semi-Z-source inverter for single-phase photovoltaic systems. Power Electronics, IEEE Transactions, 26, 3514-3523.
  • Cao, D., & Peng, F. Z. (2011). Multiphase multilevel modular DC-DC converter for high current high gain TEG application. Industry Applications, IEEE Transactions, 47, 1400-1408. (Prize Paper Award)
  • Cao, D., & Peng, F. Z. (2010). Zero-current-switching multilevel modular switched-capacitor DC-DC converter. Industry Applications, IEEE Transactions, 46, 2536-2544. (Prize Paper Award)

Selected Patent

  • Chen, L., Cao, D., Kozarekar, S. Zarei, S., Wu, H., & Tang, Y. (2016, April 26). Dynamic IGBT gate drive for vehicle traction inverters. U.S. Patent 9,322,852. Washington, DC: U.S. Patent and Trademark Office.

Selected Research and Work

  • Harnessing solar energy through noise barriers and structural snow fencing, Minnesota Department of Transportation (Co-PI ~45%), 2019-2021.
  • Development of a high temperature (500 C) and high-power testbed for GaN HEMT /SiC based switching power converter for long-duration Venus surface operations, NASA EPSCoR CAN (PI: 100%), 2019-2020, funding rate: 10/44.
  • Collecting accelerated test data for GaN/SiC device-based power converter for the converter lifetime prediction modeling as machine learning training sets, North Dakota EPSCoR (PI: 100%), 2018-019.
  • An ultra-efficient composite modularized power delivery architecture for solar farm and data center, National Science Foundation (PI: 100%), 2018-2021.
  • High power high efficiency variable voltage converter, Ford Motor Company (PI: 100%), 2018-2021.
  • Real-time monitoring and diagnosis for SiC inverter system, John Deere Electronics Solutions, CRQME and ECE department (PI 100%), 2018-(Annually Renewable with 1 Ph.D. student support).
  • ZER0H: Zero energy ready homes, (Co-PI 10%), National Science Foundation, 2017-2018.
  • Ultra-high-density power adapter using gate driver integrated GaN (PI: 100%,) Navitas Semiconductor, unrestricted gift, 2017-2018.
  • Ultra-high-density power conversion systems using gallium nitride power devices, (PI: 100%), Department of Commerce, North Dakota, 2017-2018.
  • High-density, high-efficiency power converter for datacenter, (PI: 100%), Maxir Venture Investments/Offerdahl, 2016.
  • High-demand power supply research, (PI: 100%,), Google Inc., 2016.
  • Reliability oriented design for DC-link capacitors in power electronic converters, (PI: 100%,), Center for Quality, Reliability and Maintainability Engineering CQRME and John Deere Electronic Solutions, 2016-2018.
  • Ultra-light and compact power delivery architecture in space, NASA EPSCoR, (PI: 100%), 2015-2016.
  • Grid connected photovoltaic inverter, Transphorm, Inc, (PI: 100%), 2015-2016.

Selected Honors and Awards

  • Early Career Research Excellence Award of North Dakota State University College of Engineering, 2019
  • Prize Paper Award from the IEEE Journal of Emerging and Selected Topics in Power Electronics, 2018
  • 2019 and 2016 NDSU College of Engineering Graduate Research Assistant Award as Adviser
  • Bold Leadership Award, Ford Motor Company, 2014
  • Prize Poster Award from IEEE Energy Conversion Congress & Exposition, 2011
  • Invited Project Demo for IEEE Energy Conversion Congress & Exposition, 2011, 2017, 2019
  • Prize Paper Award from the IEEE Industry Applications Society Industrial Power Converter Committee, 2011
  • Prize Paper Award from the IEEE Industry Applications Society Industrial Power Converter Committee, 2010
  • Outstanding Presentation Award at Applied Power Electronics Conference and Exposition, 2010
  • Graduate School Dissertation Completion Fellowship and GOF Fellowship from Michigan State University

Degrees

  • Ph.D., Michigan State
  • MSEE, Michigan State
  • BSEE, Zhejiang University, Hangzhou, China

Courses Taught

  • Power Electronics
  • Advanced Power Electronics

Professional Activities

  • Associated editor for the IEEE Journal of Emerging and Selected Topics in Power Electronics

Research Interests

  • Aerospace Electric Power System, Power Electronics
  • Emerging applications utilizing high power or high frequency wide bandgap devices e.g. SiC or GaN
  • High density power conversion using innovative topologies for data center and transportation electrification
  • Health monitoring and lifetime prediction of power converters using AI and Deep Learning