Automotive System Dynamics and Control

Research Description

We are developing physics-based models of various automotive components and subsystems, including tires, suspensions, engines, catalytic converters, batteries, drivebelts, and torque converters. Model reduction methods are used to create “control-oriented” models for model-predictive controllers. For hybrid electric and fuel cell powertrains, we are using machine learning algorithms to develop neural network models from experimental CAN measurements during vehicle testing on our track or $10M Green and Intelligent Automotive (GAIA) Research Facility.

Student Researchers 

Amer Keblawi
Chris Shum 
Bryce Hosking (Alumnus)
Soroosh Hassanpoor (Alumnus)

Keywords and Themes

• Vehicle Dynamics Modelling
• Powertrain Systems Modelling and Control
• Model Reduction
• Model-Predictive Controllers
• Electric and Hybrid Electric Vehicles
• Tire and Battery Modelling