- Ph.D. Student in Mechanical Engineering – University of Illinois
- M.S. Mechanical Engineering – University of Illinois (December 2022)
- B.S. Mechanical Engineering – Clemson University (December 2019)
- Research Interests: Control Systems, Electro-Thermal Systems
This research is sponsored by the National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) and The National Science Foundation Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS).
Although fully electric vehicles have been used for small payload or limited range applications, the production of long range, high payload electric vehicles will require decades of research and development. To aid in bridging the gap between gas powered and fully electric vehicles, hybrid electric vehicles for marine and aviation applications can increase vehicle electrification while reducing the necessary technological leaps in areas such as energy density. These hybrid-electric vehicles incorporate turbine generators, drive motors, and energy storage systems which require decisions on optimal power distribution between components. Similar to electric vehicles, the electric drive components and vehicle power electronics also require thermal management to prevent the thermal related electronics failures prevalent in electrified systems.
The dynamics of these vehicles create control challenges in both the electrical and thermal domains, as the vehicle objectives (trajectory, cost, etc.) cannot be compromised to accommodate the constraints and requirements of the subsystems (maximum temperature, power required, etc.). Additionally, these subsystems operate on unique timescales, causing a centralized controller to be insufficient to provide optimal performance for each domain and subsystem. Past work within the Alleyne Research Group has demonstrated the success of hierarchical model predictive control for these multi-domain, varying-timescale control challenges, and a similar technique can be applied to hybrid-electric vehicle applications. A hierarchical model predictive control strategy allows for primary objectives and constraints of the system with slower dynamics, such as trajectory or motor temperature, to be governed by high-level controllers; however, domains with quicker dynamics, such as power distribution from an electrical bus, can be controlled by low-level, short-timescale controllers.
Reid has utilized the graph-based modeling framework to develop computationally efficient models for a hybrid electric aircraft powertrain and thermal management system. These models were used to facilitate the control of the aircraft propulsion, air cycle, and power generation systems using a nominal model predictive controller. To extract additional performance from the hybrid electric aircraft and improve disturbance rejection, Reid will develop control techniques which use mission information and past flight data to provide optimal control allocations for future missions.
- Smith, Reid. “Dynamic Modeling and Control of a Hybrid Electric Quadrotor Powertrain and Thermal Management System.” M.S. Thesis. Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign. 2022.
- Smith, Reid, and Alleyne, Andrew. “Dynamical Graph-Based Models of Brayton Cycle Systems.” American Control Conference. 2022. [Link]
- Smith, Reid, and Dutta, Sandip. “Conjugate Thermal Optimization with Unsupervised Machine Learning.” ASME Journal of Heat Transfer. 2021. [Link]
- Dutta, Sandip, and Smith, Reid. “Nonlinear Optimization of Turbine Conjugate Heat Transfer with Iterative Machine Learning and Sample Replacement.” Energies. 2020. [Link]
- Dutta, Sandip, and Smith, Reid. “Transfer Function Based Optimization of Film Hole Sizes with Conjugate Heat Transfer Analysis.” ASME Turbomachinery Technical Conference and Exposition. 2020. [Link]