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.