Zhaomiao Guo
University of Texas at Austin, TX·Engineering·Early-career
As electric vehicles (EVs) and clean energy spread, roads and power grids are becoming tightly linked. EV charging changes when and where electricity is needed, and traffic data changes how people travel.
- Fields
Engineering
Mathematics & Statistics
Computer Science
- Topics
electric vehicles
transportation electrification
power systems
- Grants
- 2 known grants, through Jul 2028
What they work on
- Develop network models of how decentralized transportation and power system stakeholders with incomplete information interact across space and time as electric vehicles and clean energy expand.
- Design equity-aware market mechanisms for coupled transportation and power systems that use EVs and clean energy, solved with convexification, decomposition and variational analysis.
- Model how cars, autonomous and connected vehicles, buses and bicycles adapt to evolving travel information, and find the limits of positive and negative information on system efficiency and resilience.
- Determine what traffic information to share, when, where and with which groups to optimize performance, using stochastic multi-agent optimization and traffic network equilibrium models.
More about the research
Yet these systems are run by many separate players: drivers, utilities, transit agencies and charging operators, each with partial information and their own goals. The lab builds mathematical optimization models of how these independent decision-makers interact over large networks. It uses those models to design rules and information strategies that make the whole system work better.
Why it matters
This work could help utilities and transportation planning agencies plan for EV adoption and fold intermittent clean energy into the grid more smoothly and fairly. Better strategies for sharing traffic information could make city travel more efficient, reliable and resilient. The methods may also extend to other large infrastructure networks with many independent stakeholders.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Deng Cao
Central State University, OH·Engineering
Design a coordinated strategy for connected self-driving vehicles to decide when to send perception tasks to roadside edge computers, keeping the decisions manageable as vehicle numbers grow and robust to uncertain edge behavior.
Emiliano Dall'Anese
Boston University, MA·Engineering·Early-career
Design real-time controllers that approximate optimization-based safety filters using only local or limited measurements, keeping networked systems with partially known dynamics within operating limits.
Jiangfeng Zhang
Clemson University, SC·Engineering
Model electric vehicle fast-charging station management as a multi-objective optimization over charging protocols, power allocation, pricing and grid interaction, constrained by battery degradation, market prices, solar output, vehicle-to-grid service and demand response.
The school
University of Texas at Austin: 49 labs on PI Pages, $1.1B a year on research, 871 research doctorates a year.
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