By Topic

Model predictive control of velocity and torque split in a parallel hybrid vehicle

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Tae Soo Kim ; Department of Mechanical Engineering The University of Melbourne, Victoria, 3010 Australia ; Chris Manzie ; Rahul Sharma

Fuel economy of parallel hybrid electric vehicles is affected by both the torque split ratio and the vehicle velocity. To optimally schedule both variables, information about the surrounding traffic is necessary, but may be made available through telemetry. Consequently, in this paper, a nonlinear model predictive control algorithm is proposed for the vehicle control system to maximise fuel economy while satisfying constraints on battery state of charge, relative position and vehicle performance. Different scenarios are considered including allowing and disallowing overtaking; various hard and soft constraints; and computational aspects of the solution. The optimal control signal vector was found to be characterised by smooth changes in velocity and increases in the motor to engine power ratio as the vehicle accelerates. It was found that using feedforward information about traffic flow in the range of five to fifteen seconds has the potential for significant fuel savings over two urban drive cycles.

Published in:

Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on

Date of Conference:

11-14 Oct. 2009