Real-time Optimal Battery Thermal Management System Controller for Electric and Plug-in Hybrid Electric Vehicles
Loading...
Date
2017-01-24
Authors
Masoudi, Yasaman
Advisor
L. Azad, Nasser
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
The objective of this thesis is to propose a real-time model predictive control (MPC)
scheme for the battery thermal management system (BTMS) of given plug-in hybrid electric
and electric vehicles (PHEV/EVs). Although BTMS control in its basic form can be well
represented by a reference tracking problem, there exists only little research in the literature
taking such an approach. Due to the importance of a prediction component in thermal
systems, here the BTMS controller has been designed based on MPC theory to address
this gap in the literature. Application of the controller to the baseline vehicles is then
examined by several simulations with di erent optimization algorithms.
By comparing the results of the predictive controller with those of the standard rulebased
(RB) controller over a variety of driving scenarios, it is observed that the predictive
controller signi cantly reduces the power consumption and provides a better tracking behaviour.
Integrating trip prediction into the control algorithm is particularly important in
cases such as aggressive driving cycles and highly variable road-grades, where the standard
BTMS scheme does not perform as e ectively due to the load current pro le.
Moreover, based on the simulation results, the designed controller is observed to have a
turnaround time between 10 s to 1 ms, and is thus applicable to the real-time automotive
systems.
Prosperity of the proposed BTMS control methodology paves the way for the use of
model-based (MB) thermal management techniques, not only in future
Description
Keywords
Nonlinear Model Predictive Control