Research Description
We are developing physics-based models of different automotive components and subsystems, including tires, suspensions, engines, catalytic converters, batteries, drivebelts, and torque converters. Model reduction is used to create “control-oriented” models for model-predictive controllers. For hybrid electric and fuel-cell powertrain systems, we are using machine learning algorithms to develop neural networks from experimental CAN measurements during vehicle testing on our outdoor track or $10M Green and Intelligent Automotive (GAIA) Research Facility.
Student Researchers
• Chris Shum
• Amer Keblawi (Alumnus)
• 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
Related Publications
• Batra M, McPhee J, and Azad NL. (2018). Anti-Jerk Dynamic Modeling and Parameter Identification of an Electric Vehicle Based on Road Tests. ASME Journal of Computational and Nonlinear Dynamics. DOI: 10.1115/1.4040870.
• Batra M, Maitland A, McPhee J, and Azad NL. (2018). Non-Linear Model Predictive Anti-Jerk Cruise Control for Electric Vehicles with Slip-Based Constraints. American Control Conference (ACC). DOI: 10.23919/ACC.2018.8431389.
• Hassanpour S and McPhee J. (2017). A Control-Oriented Modular One-Dimensional Model for Wall-Flow Diesel Particulate Filters. International Journal of Engine Research. DOI: 10.1177/1468087417702018.
