AI-Augmented Engineering for EV NVh: From Physics and Measurement to Conversational Workflows
Date: Thursday May 13, 2027
Time: 9:30 AM - 5:45 PM
Price: $900 per student, includes coffee and lunch
Instructor: Prof. Taner Onsay, University of Michigan
Overview:A rigorous full-day course integrating contemporary Electric Vehicle noise, vibration, and harmony (NVh) with AI-Augmented and Conversational Engineering. The course is ~ 60% EV NVh (h-harmony) and ~40% AI-augmented workflow, integrated throughout, while emphasizing that validation, judgment, and accountability remain with the engineer.
- EV NVh Physics, Sources & Paths. Changing EV acoustic/vibration signatures; EM excitation, inverter/PWM effects, orders and harmonics, torque ripple, gears/bearings, cooling, road/tire and aerodynamic inputs; airborne and structure-borne source–path–receiver behavior.
- Measurement, Diagnostics & Interpretation. Microphones, accelerometers and e-drive/operational signals; spectra, waterfalls and order tracking; linking measured signatures to physical mechanisms and developing defensible root-cause hypotheses.
- Modeling, Simulation & Design. Structural dynamics and acoustics; physics-based/data-supported models; FEA, SEA, multibody and coupled/multiphysics concepts; test–simulation correlation, assumptions and boundary conditions, sensitivity studies, design trade-offs, passive/active control, sound quality and NVh harmony.
- AI-Augmented & Conversational Engineering. Problem → Model → Compute → Analyze → Validate → Improve. AI as technical companion, computational assistant, analyst, modeling partner and critic/validator; conversational interaction with measurements, plots, models, simulations, code and documentation; rapid what-if and multimodal engineering exploration.
- Critical Thinking, Validation & “Break the AI.” AI output is not engineering truth. Participants examine failures involving assumptions, models, units, boundary conditions and interpretation, then independently validate with first principles, measurements, alternate calculations/models, dimensional checks and engineering experience. AI assists → Human verifies → Engineer decides.
- Integrated EV NVh Application. AI-Augmented measurements and operating data, order/source/path identification, physical hypotheses, conversational computation and design exploration, comparison of alternatives, validation against physics and measurement, and a defensible engineering recommendation that addresses uncertainty and trade-offs.
Who Should Attend
This course is intended for engineers, technical specialists, researchers, and engineering leaders working in electric-vehicle development, NVh (h-harmony), acoustics, sound quality, e-drive and powertrain systems, testing and measurement, CAE, simulation, and computational engineering. It will also benefit technical managers and engineering educators interested in incorporating AI-augmented and conversational workflows into professional engineering practice. No prior expertise in NVh or AI is required; NVh is covered rigorously, AI is introduced in the context of real EV NVh engineering and integrated with physics, measurement, modeling, computation, validation, and engineering judgment. The course is particularly suited to experienced NVH practitioners seeking to understand how emerging AI capabilities can augment established engineering methods while maintaining rigorous technical validation and human responsibility.
About the Author:
Dr. Taner Onsay, Ph.D. — Author / Presenter
Taner Onsay, Ph.D. is a mechanical engineer, educator, and former automotive engineering leader with 28 years of industry experience in vehicle noise, vibration, and acoustics, including technical and leadership roles in NVH development, testing, noise control, and laboratory operations. He has also taught undergraduate and graduate engineering courses at the University of Michigan–Dearborn for more than 20 years, where he currently serves as Professor of Practice. His work spans engineering dynamics, vibration and acoustics, computational modeling, vehicle development, and experimentation. His current focus is AI-Augmented Engineering and Conversational Engineering, integrating physical modeling, computation, validation, AI collaboration, and engineering judgment into modern engineering workflows. He is Co-Founder of AI First Engineering LLC.