Vehicle Electronics and Controls

The increasing use of electrical systems and electronic sensors and devices in vehicles and automobiles has resulted in new developments in this field for vehicle application. With rapid progress in battery technology, it is envisaged that electric vehicles will become more affordable and more efficient. Electric drive control requires the use of power devices which are primarily high power electronic devices. Modern vehicles will rely on both analog and digital hardware for efficient operation of the vehicle. Engineers would be required to be well versed in the design of hybrid electrical and electronic systems.

The Vehicle Electronics certificate will introduce the participants to analog and digital electronics. Starting with simple diodes and rectifiers, students will be introduced to other solid state devices that are used in electronic circuits. Participants will learn the design of amplifiers, switches and other commonly used circuits. They will also receive instruction on digital logic and the use of microprocessors. Besides featuring hands-on laboratory practice, participants will be involved in several group design projects. (12 credit hours)

Certificate offered on Campus and via Distance Learning

Coursework

Please choose four courses to complete the required 12 credit hours.
AENG 510Vehicle Electronics I3
AENG 545Vehicle Ergonomics I3
ECE 505Intro to Embedded Systems3
ECE 515Vehicle Electronics II3
ECE 519Adv Topics in EMC3
ECE 531Intelligent Vehicle Systems3
ECE 532Auto Sensors and Actuators3
ECE 5462Elec Aspects of Hybrid Vehicle3
ECE 583Artificial Neural Networks3

AENG 510     Vehicle Electronics I     3 Credit Hours

Semiconductor diodes, junction transistors, FETS, rectifiers and power supplies, small signal amplifiers, biasing considerations, gain-bandwidth limitations, circuits models, automotive applications and case studies. (Not open to students with EE degree.)

Prerequisite(s): ECE 305

Restriction(s):
Can enroll if Level is Graduate or Rackham
Can enroll if College is Engineering and Computer Science

AENG 545     Vehicle Ergonomics I     3 Credit Hours

Overview of drive characteristics, capabilities, and limitations. Human variability and driver demographics, driver performance measurements. Driver information processing models, driver errors and response time. Driver sensory capabilities: vision, audition, and other inputs. Vehicle controls and displays. Driver anthropometry, biomechanical considerations.

Prerequisite(s): IMSE 442

Restriction(s):
Can enroll if Level is Rackham or Graduate
Can enroll if College is Engineering and Computer Science

ECE 505     Intro to Embedded Systems     3 Credit Hours

Introduction to modern digital computer logic. Numbers and coding systems; Boolean algebra with application to logic systems; examples of digital logic circuits; simple machine language programming and Assembly and C/C+ programming language; microprocessors programming (both assembly and C/C+) for input/output, interrupts, and system design. (May not be available to students with EE or CE degrees) Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Major is Electrical Engineering, Computer Engineering

ECE 515     Vehicle Electronics II     3 Credit Hours

This course discusses advanced topics in electronics with an emphasis on vehicle applications. It will include ignition systems and controls, amplifiers, frequency characteristics of electronic circuits, feedback in electronic systems and stability, power electronics and motor drive controls (DC/DC and DC/AC converters) and EMC issues. Selected examples include applications such as voltage regulators and battery chargers. Three lecture hours per week.

Prerequisite(s): AENG 510

ECE 519     Adv Topics in EMC     3 Credit Hours

This course covers the EMC requirements and EMC test methods for large systems. Examples involving various types of applications (automotive, communications, computers) will be discussed. Discussion of design practices used in large installation, including component segregation, cable routing, connectors, grounding, shielding, common impedance coupling, ground planes, screening and suppression. Classification of electromagnetic environments will also be discussed. Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate or Doctorate
Can enroll if Level is Graduate or Doctorate or Rackham
Can enroll if Major is Computer Engineering, Electrical Engineering

ECE 531     Intelligent Vehicle Systems     3 Credit Hours

The course covers important technologies relevant to intelligent vehicle systems including systems architecture, in-vehicle electronic sensors, traffic modeling and simulation. Students will design and implement algorithms and simulate driver-highway interactions.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Level is Graduate or Doctorate or Rackham
Can enroll if Major is Computer & Information Science, Software Engineering, Electrical Engineering, Computer Engineering

ECE 532     Auto Sensors and Actuators     3 Credit Hours

Study of automotive sensory requirements; types of sensors; available sensors and future needs. Study of functions and types of actuators in automotive systems. Dynamic models of sensors and actuators. Integrated smart sensors and actuators. Term project.

Restriction(s):
Can enroll if Class is Graduate

ECE 5462     Elec Aspects of Hybrid Vehicle     3 Credit Hours

To introduce fundamental concepts and the electrical aspects of HEV, including the design, control, modeling, battery and other energy storage devices, and electric propulsion systems. It covers vehicle dynamics, energy sources, electric propulsion systems, regenerative braking, parallel and series HEV design, practical design considerations, and specifications of hybrid vehicles. Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Major is Electrical Engineering, Computer Engineering

ECE 583     Artificial Neural Networks     3 Credit Hours

Students will gain an understanding of the language, formalism, and methods of artificial neural networks. The student will learn how to mathematically pose the machine learning problems of function approximation/supervised learning, associative memory and self-organization, and analytically derive some well-known learning rules, including backprop. The course will cover computer simulations of various neural network models and simulations. Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Level is Graduate or Rackham or Doctorate
Can enroll if Major is Computer Engineering, Electrical Engineering, Computer & Information Science, Software Engineering

 
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An asterisk denotes that a course may be taken concurrently.

Frequency of Offering

The following abbreviations are used to denote the frequency of offering: (F) fall term; (W) winter term; (S) summer term; (F, W) fall and winter terms; (YR) once a year; (AY) alternating years; (OC) offered occasionally