published journal article
published journal article
published journal article
conference paper
Carbon Nanotubes to Improve Short Glass Fiber Composites
published journal article
conference paper
As traffic systems are genuinely nonlinear with uncongested and congested patterns, advanced control and management strategies are needed to improve their safety, mobility, and environmental impacts. In this study, we consider the control design problem for a traffic system upstream to a lane-drop bottleneck regulated by variable speed limits. The design goal is to mitigate the impacts of capacity drop by maximizing the out-flux and minimizing the delay as well as stabilizing the system at an ideal equilibrium state. Through analyzing the equilibrium states and their stability properties of an open-loop switched system, we first design the set point for density and demonstrate the hysteretic relation between the speed limit and the equilibrium state. For the closed-loop switched system with a PI-controller, we derive the region of the controller parameters for the system to be asymptotically stable and derive a Poincar6 map to prove the existence of stable limit cycles when the system is unstable. Through this study, we have the following findings for a system with excessive demands: without control, the system converges to the congested equilibrium state with reduced out-fluxes; with open-loop control, the ideal uncongested equilibrium state is introduced, but the congested equilibrium state still exists and is asymptotically stable; with a well-designed PI feedback controller, the congested equilibrium state is removed, and the system can be stablized at the ideal uncongested equilibrium state with maximum out-fluxes. With numerical simulations, we verify the analytical results and findings.
published journal article
conference paper
research report
conference paper
Optimal energy management of hybrid electric vehicles has previously been shown to increase fuel economy (FE) by approximately 20% thus reducing dependence on foreign oil, reducing greenhouse gas (GHG) emissions, and reducing Carbon Monoxide (CO) and Mono Nitrogen Oxide (NOx) emissions. This demonstrated FE increase is a critical technology to be implemented in the real world as Hybrid Electric Vehicles (HEVs) rise in production and consumer popularity. This review identifies two research gaps preventing optimal energy management of hybrid electric vehicles from being implemented in the real world: sensor and signal technology and prediction scope and error impacts. Sensor and signal technology is required for the vehicle to understand and respond to its environment; information such as chosen route, speed limit, stop light locations, traffic, and weather needs to be communicated to the vehicle. Since optimal control requires accurate prediction of the vehicle environment and drive cycle, prediction scope and error impact analysis is needed to understand the required accuracy of sensor and signal information received by the vehicle as well as the accuracy of the optimal control computed. This review presents the current state of research and solutions in development for each of these research gaps. Once these research gaps have been filled, HEVs may have the potential to substantially increase the FE standard and remove ICE vehicles as the leading consumer of petroleum and leading contributor of GHG, CO, and NOx emissions.