The Control, Optimization, Robotics, and Electrification (CORE) Lab at UC Davis focuses on advancing intelligent decision-making, optimization, and control strategies for modern transportation systems. Our research integrates game theory, optimal control, data-driven modeling, and machine learning to address complex challenges in autonomous vehicles, electric powertrains, and sustainable mobility solutions.
We develop advanced control algorithms for autonomous and multi-agent systems, ensuring real-time adaptability in dynamic environments. Our work on game-theoretic control frameworks enables interactive-aware planning and robust decision-making under uncertainty. In addition, we explore optimization-based motion planning, leveraging Model Predictive Control (MPC) for energy-efficient and intelligent mobility.
Our lab is at the forefront of electric and hybrid vehicle technology, focusing on thermal management, powertrain hybridization, and energy optimization. We investigate the interaction between component sizing and control to enhance system efficiency and reliability. Moreover, we analyze EV charging infrastructure, studying battery health and long-term performance to support the widespread adoption of electrified transportation.
Beyond control and optimization, we also engage in sustainability and cost analysis, conducting life cycle assessments (LCA) for infrastructure components and evaluating energy storage solutions for various applications.
By combining theoretical advancements with real-world implementation, the CORE Lab contributes to the next generation of intelligent, efficient, and sustainable transportation technologies.