Dr. Jingyuan Zhao | Energy and Sustainability | Research Excellence Award
University of California Davis | United States
Jingyuan (Andy) Zhao, Ph.D., is an Assistant Professional Researcher and independent principal investigator at the University of California, Davis, internationally recognized for pioneering work in AI-enabled battery and energy systems. His research integrates multiphysics and multiscale modeling with advanced artificial intelligence to address battery safety, diagnostics, prognostics, and system-level optimization for electrified transportation. He has led or co-led major U.S. and international research projects supported by USDOT, Caltrans, CEC, CARB, and national science foundations in China, while also translating research into industrial impact through prior leadership roles in electric vehicle battery intelligence. His academic training spans mechanical and vehicle engineering, complemented by extensive postdoctoral research in the U.S. and China. Dr. Zhao has received numerous honors, including Elsevier–Stanford World’s Top 2% Scientist and ScholarGPS Top 0.5% Scholar distinctions. Through interdisciplinary scholarship, global collaboration, and mentorship, his work advances safe, intelligent, and scalable battery energy systems, bridging laboratory innovation with real-world deployment and shaping the future of sustainable mobility.
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Featured Publications
Review on Supercapacitors: Technologies and Performance Evaluation
J. Zhao, A.F. Burke, Journal of Energy Chemistry, 59, 276–291, 2021. (Citations: 602)
Autonomous Driving System: A Comprehensive Survey
J. Zhao, W. Zhao, B. Deng, Z. Wang, F. Zhang, et al., Expert Systems with Applications, 242, 122836, 2024. (Citations: 330)
Electrochemical Capacitors: Materials, Technologies and Performance
J. Zhao, A.F. Burke, Energy Storage Materials, 36, 31–55, 2021. (Citations: 202)
Electrochemical Capacitors: Performance Metrics and Evaluation by Testing and Analysis
J. Zhao, A.F. Burke, Advanced Energy Materials, 11(1), 2002192, 2021. (Citations: 182)
Machine Learning for Predicting Battery Capacity for Electric Vehicles
J. Zhao, H. Ling, J. Liu, J. Wang, A.F. Burke, Y. Lian, eTransportation, 15, 100214, 2023. (Citations: 171)