Fundamentals of robotics with an emphasis on mobile robots, which are intelligent integrated mechanical, electrical and computational systems functioning in the physical world will be covered. Topics include state-of-the-art technologies in mobile robotics, such as locomotion, sensing, control, communication, localization, mapping, navigation, etc. Advanced topics such as coordination of multiple mobile robots will also be explored. The course aims to provide both theoretical and practical experience to students through lectures and simulation software. Students will also complete independent projects or research on current topics covering mobile robotics technologies and related fields.

This course will provide introductory theories and technologies in artificial intelligence focusing on machine learning, covering a wide range of machine learning methods, concepts and applications. Machine learning studies algorithms that learn from large quantities of date, identify patterns and make predictions on new data. The major list of machine learning fields are computer vision, robotics, autonomous driving, voice/gesture recognition, and automated planning & scheduling, etc. Students will study the concepts that underlie intelligent systems and investigate advanced topics in intelligent systems through a course project.

This course covers modeling, simulation, and analysis of multivariable dynamic systems. Approaches to modeling a variety of dynamic physical systems are discussed using examples of dynamic systems taken from a variety of fields. The course places emphasis on modeling and analysis of electric vehicle systems and components. Transient and steady state behavior of power electronic circuits using state space models is included in this course. These systems are simulated using MATLAB simulation tools. Most of the course is devoted to the analysis of linear systems using now classical techniques based on linear algebra, state-space representations and the state transition matrix.

This graduate level course is designed to introduce critical digital signal/ image processing principles/theories and techniques applied to a variety of automotive engineering applications. Special focus is given to autonomous driving and NVH analysis. Examples include, but are not limited to, edge detection methods in traffic sign recognition and identification, Kalman filtering for vehicle state estimation, Modal analysis (frequency domain) and expansion to time-frequency domain analysis of dynamic response using techniques such as wavelets and Empirical Mode Decomposition (EMD). MATLAB will be heavily used for analysis and simulations.

This class will focus on applying students' knowledge of fundamental principles of control systems to a variety of systems within automobiles. Specific topics will include the control of the air-fuel ratio, spark timing, idle speed, transmissions, cruise and headway, lane-keeping, and active suspensions. Other topics that may be included are antilock brakes, traction control and vehicle stability control. Simulations will be used and students will be using MATLAB/Simulink for many of the assignments.

Methods of controlling electric machines and their applications in electric vehicles are discussed. Topics include solid-state devices; various switching schemes; types of drives; characteristics of motors; controlling motors including vector control; braking of motors; and dynamics of electric drives and applications.

This is an advanced class in power electronics. Advanced converter topologies, control methods, and analyses used in electric-vehicle and power-system domains will be discussed. topics include state-variable modeling of DC-DC converters for closed-loop control system design, isolated DC-DC converter topologies (half, full, and dual bridges) and resonant DC-DC converter topologies (series, parallel and series-parallel) for wireless power transfer and battery charging, soft-switching concepts and control methods for isolated DC-DC converters, single-phase and three-phase inverter design, inverter control methods including six-step, Sine PWM, Space Vector PWM, and Discontinuous PWM and the design and control of multilevel and modular multilevel inverters.

This course provides an overview of theoretical and practical background regarding the design and development of autonomous vehicles. Topics include an overview of autonomous vehicle systems, autonomous vehicle localization technologies, perception in autonomous driving, decision and planning, and control for autonomous driving. This course aims to cover the basics of autonomous driving through lectures, assignments, a term project, and readings on current related topics.

The ability to listen and craft well-written messages verbally, in writing, and within digital spaces are valued skills among employers regardless of industry. This course is designed to provide opportunities for students to sharpen writing, improve editing, hone critical thinking skills, and create effective persuasive messages. Course content also includes best practices for organizing, revising and presenting information in-person and remotely.

This course is designed for students to create an MSE ECE focused project applicable to current ECE applications especially related to electrification or advanced mobility. Throughout the course, students develop their proposal regarding an organization-based electrification or advanced mobility challenge; including identifying and incorporating all feedback from stakeholders. Students establish a team contract, identify deliverables, and collect and analyze data. At the end of the course, students develop and deliver a presentation with solutions to their organization’s challenge(s).