About
Xinfan Lin is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California Davis, part of the College of Engineering. His research focuses on dynamic system modeling, diagnostics, and control, with a particular emphasis on machine learning, data analytics, and control-integrated design optimization. His work addresses applications in intelligent battery management systems, electric vehicles, unmanned aerial systems (UAS), electric-vertical-takeoff and landing (eVTOL) aircraft, and spacecraft. He leads the Lin Research Lab, which combines multi-physical domain knowledge, control theories, and machine learning to advance transportation and aerospace technologies.
Lin has received the NSF CAREER Award and was elected as an IEEE Senior Member. His research interests include battery electrochemical dynamics, system-level estimation and control, and energy systems optimization. He has contributed to advancing methodologies for battery health monitoring, energy-efficient UAV mission planning, and hybrid physics-based machine learning models.
Key applications of his work include improving battery management for electric vehicles, optimizing multirotor drone efficiency, and enhancing aerospace systems through integrated system modeling. His recent publications highlight advancements in energy-optimal trajectory planning, reinforcement learning for battery diagnostics, and lightweight electrochemical modeling techniques.
