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DTSTAMP:20260921T095608Z
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SUMMARY:MAE Graduate Seminar Speaker - Dr. Yan Wang
DESCRIPTION:Dr. Yan Wang is a propulsion technology expert with more than 2
 5 years of experience advancing sustainable transportation throughinnovati
 ve energy and powertrain technologies. Throughout his career at Ford Motor
  Company\, Nikola Motor Company\, and Cummins\, he hasled the development 
 of advanced propulsion solutions across gasoline\, diesel\, hybrid\, batte
 ry-electric\, and fuel-cell-electric vehicle platforms. Dr.Wang's expertis
 e spans combustion systems\, electrified powertrains\, controls\, calibrat
 ion\, diagnostics\, optimization\, and intelligent energymanagement. He ha
 s developed model-based and data-driven engineering approaches that integr
 ate machine learning\, virtual development\,and connected vehicle technolo
 gies to improve vehicle efficiency\, emissions performance\, reliability\,
  and development productivity. An inventor ofmore than 50 U.S. patents and
  author of over 60 technical publications\, Dr. Wang is Chair of the IEEE 
 Control Systems Society TechnicalCommittee on Automotive Control for Elect
 ric and Hybrid Vehicles. His work continues to focus on enabling sustainab
 le\, efficient\, and intelligenttransportation systems through the converg
 ence of advanced controls\, electrification\, artificial intelligence\, co
 nnectivity\, and digital engineering. Intersection of model based powertra
 in controls with AI / ML Traditional model-based powertrain control system
 s have been the foundation of vehicle operation for decades\, evolving con
 tinuouslyto deliver safe\, efficient\, and comfortable transportation. As 
 powertrain architectures become increasingly complex and regulatory requir
 ementscontinue to tighten\, however\, achieving globally optimal control f
 or maximum efficiency using conventional approaches has become increasingl
 ychallenging. While AI/ML has seen widespread deployment in perception-int
 ensive applications such as advanced driver assistance systems(ADAS) and a
 utomated driving\, its adoption in production powertrain control has been 
 more selective. This presentation explores how AI/ML cancomplement—not r
 eplace—established model-based control frameworks to enhance powertrain 
 performance. We will discuss practicalapplications in offline calibration\
 , online adaptive control\, and digital twin development\, highlighting ho
 w AI/ML can improve efficiency\,robustness\, and scalability while leverag
 ing decades of existing control expertise.
LOCATION:Dow Environmental Sciences and Engineering Building 641\, 1400 Tow
 nsend Drive\, Houghton\, MI\, 49931\, United States
URL:https://www.qmeet.net/EventDetails?EventID=55023
STATUS:CONFIRMED
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