Research Faculty/Areas of Expertise
Laura Forlano (DSGN): urban informatics, driverless and autonomous transportation systems
Carrie Hall (MMAE): dynamics of high-efficiency engines
Ron Henderson (ARCH): driverless and autonomous vehicles
Lulu Kang (MATH): statistical methodologies
Mahesh Krishnamurthy (ECE): HEV/EV; auto. powertrain/thermal mgmt.; energy storage; autonomous veh./robotics
Boris Pervan (MMAE): navigation systems, autonomous vehicles, fault detection and isolation
Paco Ruiz (MMAE): internal combustion engines, diesel injection
Mohammad Shahidehpour (ECE): power system operation and control, sustainable energy integration in transportation systems
Leon Shaw (MMAE): Li-ion and Na-ion batteries, hydrogen storage materials for fuel cell vehicles
John Shen (ECE): power electronics, inter-dependency with electricity infrastructure
Matthew Spenko (MMAE): autonomous vehicle localization safety
Qing-Chang Zhong (ECE): power electronics, motor drives, storage systems, and power architecture for electric vehicles, high-speed trains, more-electric aircraft, all-electric ships etc., transportation electrification
Selected Current Projects
Efficient and Clean Transportation (PI: Carrie Hall, MMAE)
This project deals with examining methods of enabling efficient and clean transportation through the implementation of high-efficiency internal combustion engine concepts and engines that leverage alternative fuels as well as the use of hybrid electric powertrains. While these systems have the potential to greatly improve vehicle efficiency and emissions, their more complex dynamics require more advanced control methodologies. This research group at IIT focuses on studying the dynamics of high-efficiency engines and developing control methodologies that can enable them to be viable in production vehicles. In addition, they also explore the influence of fuel properties, hybridization, and additional vehicle-to-vehicle sensor information on the performance that can be achieved in modern vehicles using more advanced control algorithms.
Autonomous Vehicle Localization Safety (PI: Matthew Spenko, MMAE)
The objective of this research is to ensure the integrity of vehicle position, heading, and velocity estimates that are used by self-driving cars as the basis for life-critical decisions such as the initiation and execution of hazard-avoidance maneuvers. Integrity, which is a measure of trust in a sensor's information, has been successfully implemented in commercial aircraft to guarantee the safety of maneuvers such as landing. This project addresses several obstacles in translating integrity from aviation applications to self-driving cars, including integrating the disparate sensor types used by ground vehicles; meeting the stringent demands of routine autonomous driving; accounting for the number, proximity, and high relative velocity of other vehicles on the road; and evaluating multiple, distinct, and mutually exclusive courses of action in a timely manner. Project subtasks include characterization of integrity for representative sensors, construction of appropriate models for uncertainty propagation, and experimental validation of the resulting integrity framework. The project will advance the larger research effort to realize the potential of self-driving cars for relieving congestion, reducing emissions, and saving lives.
Autonomous Vehicle Localization Safety (PI: Matthew Spenko, Co-PIs: Boris Pervan, Ron Henderson)
This research project will investigate how to reshape 20th-century transportation infrastructure (such as highways, intersections, roads, and sidewalks) for the 21st-century so that it may accommodate autonomous vehicles while addressing the needs of the entire community. To do so, it will explore trade-offs between three key elements: safety, usability, and aesthetics. It will then propose a suitable balance between these elements by developing a framework for reshaping the existing infrastructure. The resulting framework will serve to inform city planners, architects, and landscape architects how to plan and design cities in which autonomous vehicles safely interact with humans, and it will serve to educate roboticists on how to ensure that the technology they are developing has a positive societal impact.]
MRI: Acquisition of Large-Scale Real-Time Simulators for Next-Generation Smart Grids (PI: Qing-Chang Zhong, ECE)
Power systems are going through a paradigm change from the current power systems dominated by electric machines to the next-generation smart grid enabled by power electronics. Millions of active, intermittent, non-synchronous, variable, and distributed energy resources and flexible loads are being connected to power systems through power electronic converters. This brings an unprecedented challenge to grid stability and reliability. The electrification of transportation is making this even more challenging. To address this challenge, Illinois Institute of Technology (IIT) is pioneering a synchronized and democratized (SYNDEM) framework for next-generation smart grids, which enables all power electronics-interfaced suppliers and loads to behave like virtual synchronous machines (VSM). As a result, they can seamlessly integrate with the grid and actively maintain grid stability, following the synchronization mechanism of synchronous machines that has underpinned the operation and growth of power systems for over 100 years. This will significantly reduce/defer the infrastructure investment on transmission and distribution networks, reduce the required reserve, release the communication infrastructure from low-level control, enhance cybersecurity, and open up the prospect of achieving autonomous power systems. The objective of this project is to establish a large-scale real-time simulation facility to further advance the research, education, training and outreach activities of IIT in next-generation smart grid while benefiting some neighboring institutions and outreaching Chicago-area high-school students and others.