MS Engineering-ECE-Advanced Mobility

MS Engineering-ECE-Advanced Mobility

Drive the Future

Kettering University is shaping the future of automotive engineering and advanced mobility with a groundbreaking online program: the MS Engineering-ECE-Advanced Mobility. The first of its kind, this cutting-edge advanced engineering degree equips graduates with the technical expertise to play an integral role in the development of electric and autonomous vehicles, mobile robotics and other dynamic systems. This program is specifically designed to meet growing demands within the automotive and advanced mobility industry, and it focuses on systems essential to the future of transportation, such as:

  • Integration of electrical and computer systems for autonomous vehicles and other advanced mobility applications
  • Development of advanced mobility applications for electric, hybrid and autonomous vehicles, transportation systems, artificial intelligence and robotics as it applies to mobility
  • Design of dynamic systems that work to enhance and support autonomous functionality
  • Robotics enhanced by artificial intelligence

MS Engineering-ECE-Advanced Mobility Fast Facts

  • GRE waiver available for Kettering undergraduates or students with a minimum 3.0 GPA from an ABET undergraduate school

  • 100% online – ideal for working professionals

  • Complete in as few as 24 months
  • Accredited by the Higher Learning Commission and a member of the North Central Association of Colleges and Schools
  • Ranked 14th nationally in non-PhD engineering programs in the 2018 U.S. News and World Report Best Colleges edition
  • Kettering University is ranked first in the Midwest and 19th nationally in’s 2017 return on investment rankings

Online Master of Science Electrical and Computer Engineering Career Outlook

Intense competition among companies and a skills gap have created tremendous demand for top engineering talent related to advanced mobility:

  • Salaries in the San Francisco Bay Area for self-driving car engineers, including annual bonuses and equity, averaged $295,000 in 2017 – Forbes

  • In 2017, 5,000 software and electronics positions in the auto industry were unfilled in the U.S. – The Fuse
  • Connected and autonomous vehicle workers are needed across 49 different occupations – from IT and cybersecurity to civil and transportation systems engineering – Industry Week 

MS Engineering-ECE-Advanced Mobility Featured Courses

Set yourself apart in this high demand industry and earn your MSECE - Advanced Mobility Focus degree from Kettering University. Below is a sample list of courses you will take while enrolled in the online Electrical and Computer Engineering degree at Kettering University Online:

ECE-610 | Modeling of Dynamic Systems

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. Most of the course is devoted to the analysis of linear systems using classical techniques based on linear algebra, state-space representations, and the state transition matrix. 

ECE-630 | Digital Signal Processing Techniques for Automotive Engineering

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.

ECE-642 | Machine Drives for Electric Vehicles

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.

CE-652 | Artificial Intelligence for Autonomous Driving

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.

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