Position Title
Assistant Professional Researcher, Sustainable Freight Research Program; Energy Futures Research Program
Position Title
Assistant Professional Researcher, Sustainable Freight Research Program; Energy Futures Research Program
Bio
Dr. Jingyuan Zhao is an Assistant Professional Researcher (Independent PI) at the Institute of Transportation Studies, UC Davis. His research focuses on the integration of AI-driven modeling, physics-based methods, and system-level analysis to advance next-generation energy and transportation systems.
His work centers on three tightly connected research thrusts:
- AI-driven battery modeling and diagnostics, including transformer-based approaches for health estimation, lifetime prediction, and cross-domain generalization
- Battery safety and degradation mechanisms, with emphasis on thermal runaway processes, fault evolution, and physically grounded risk characterization
- System-level modeling of zero-emission transportation, covering infrastructure dynamics, energy systems, and techno-economic transitions under uncertainty
His research on machine learning for battery systems develops advanced architectures (Predictive Pretrained Transformers) across supervised and self-supervised paradigms, with a focus on robustness, cross-domain generalization, and deployment under real-world operating conditions. These approaches are supported by large-scale operational datasets and integrated with physics-informed modeling to enhance interpretability and reliability.
At the system level, his work is guided by a unified perspective on battery intelligence, spanning materials, mechanisms, modeling, metrics, monitoring, management, manufacturing, mobility, maintenance, and market integration (M10). In parallel, he has contributed to the FORMAL framework, which organizes battery and energy systems into six interconnected layers—Foundation, Observation, Recording, Mobilization, Architecture, and Lifecycle—to enable consistent modeling, sensing, and decision-making across scales.
He develops the IMPACT (Integrating Market Penetration and Cost Technologies) framework, which links vehicle cost modeling, technology adoption dynamics, and infrastructure deployment within a unified analytical structure. The framework captures interactions among technology evolution, market behavior, and policy constraints, and has been applied to evaluate transition pathways for battery-electric and fuel-cell systems across transportation applications.
His research involves collaborations with academic institutions, national laboratories, and industry partners, and emphasizes the integration of fundamental modeling, real-world data, and decision-oriented system analysis. More broadly, his work contributes to the development of scalable, data-driven strategies for zero-emission transportation and energy system decarbonization.