COURSES TAUGHT AT CU-ICAR
AuE 817 -- Alternative Energy Sources
This course is targeted at graduate engineering students and professionals interested or working in the area of energy conversion and utilization at the vehicular level, and more specifically towards hybrid electric vehicles (HEV) and Fuel Cell vehicles (FCV). This course focuses on fundamentals of alternative propulsion systems including hybrid-electric vehicles and covering reasons for hybridization, energy analysis architectures and components, unified energy modeling, vehicle simulation, energy optimization strategies, and supervisory control. The course also covers a technology overview of fuel cells stacks. Special emphasis will be given to the energetics of the whole process, including upstream fuel processing (i.e. “well-to-wheel” analysis).
The various facets of the course material are integrated though a progression of homework and projects leading to the simulation, supervisory control, and energy optimization of a complete HEV.
AuE 826 -- Diagnostics
This course covers the theory and application of fault diagnosis in multi-domain (mechatronic) dynamic systems. The course presents the basic principles of fault diagnosis in dynamic systems. Vehicle and powertrain system diagnostic case studies are considered throughout the course. The course culminates in the completion of a project in which students design and implement a diagnostic system in simulation.
The topic of system fault diagnosis is of growing importance in the automotive industry and in many other fields due to the desire to guarantee system availability and the requirement to satisfy government regulations related to safety and environmental impact.
The main features of the course are:
-- A system approach is used to cover various techniques for the development of diagnostic algorithms based on dynamic system models (analytic redundancy) and on the analysis of sensor signals.
-- Fault trees, failure mode and effects analysis (FMEA), hazard and fault analysis.
-- Design procedures for diagnostic observers and estimators, and diagnostic applications of signal processing, with special emphasis on application and implementation issues.
-- Projects from automotive powertrain and chassis systems. These include engine idle speed control, vehicle suspension and handling diagnostics, automotive alternator, and automotive battery.
AuE 893 -- Advanced Control
(Team led with Dr. Beshah Ayalew)
The course covers contemporary topics and tools for modeling, designing and analyzing advanced process control systems. This course consists of two parts. In the first part, the course covers the theory and application of variable structure control and sliding mode control in multi-domain (mechatronic) dynamic systems. The course presents the basic principles of sliding mode control for decoupling of design procedure and low sensitivity with respect to uncertainties in dynamic systems. Case studies of control of autonomous vehicles, vehicle systems, and hybrid electric vehicles components such as electric motors, and converters are considered throughout the course.
In the second part, the course discusses the suitability and limitations of lumped (ODEs) and distributed parameter models (PDEs and delay systems) for process control design. It summarizes existing results for diffusion, convection/transport, and/or reaction dominated processes. This includes results from in-domain and boundary control, state and parameter estimation, model reduction and identification, and model predictive control. The course introduces applications in advanced manufacturing and structural control. The course highlights available sensing and actuation options for the same.
AuE 893 -- Hybrid Powertrain Control Lab
(Team led with Dr. Beshah Ayalew)
This course is targeted at graduate engineering students and professionals interested in working in the area of hybrid electric powertrains and more specifically in the integration of low-level and high-level real-time implementable control for advanced vehicle powertrain architectures. The course focuses on techniques and tools to build Hardware-in-the-Loop (HIL) Simulation for evaluating hybrid powertrains components and architectures using programmable power supplies, electrical loads, dynamometers, and rapid control prototyping tools. Special emphasis is given to the use of such tools for component characterization, safely and efficiently interfacing electric machines and their controllers within the hybrid powertrain, accommodating accessory loads (disturbances) in hybrid powertrains, and conducting system diagnostics.
The various facets of the course are integrated through a progression of experimental educational laboratory modules and projects.
AuE 893 -- Control of Cyber-Physical Systems
This course highlights the central roles of control theory and systems thinking in developing the theoretical foundations of CPS. In particular, the course covers topics and tools for modeling, stability analysis and control design for large scale with particular focus on centralized, decentralized, and hierarchical control methods, and addresses topics related to structural and computational issues. Case studies of control of autonomous and connected vehicles, power systems, smart grid, traffic networks are considered throughout the course