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

Kim, Tae Soo, Manzie, Chris and Sharma, Rahul (2009). Model predictive control of velocity and torque split in a parallel hybrid vehicle. In: Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics. 2009 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2009), San Antonio, TX, U.S.A., (2014-2019). 11-14 October 2009. doi:10.1109/ICSMC.2009.5346115

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Author Kim, Tae Soo
Manzie, Chris
Sharma, Rahul
Title of paper Model predictive control of velocity and torque split in a parallel hybrid vehicle
Conference name 2009 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2009)
Conference location San Antonio, TX, U.S.A.
Conference dates 11-14 October 2009
Proceedings title Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics   Check publisher's open access policy
Journal name Conference proceedings: IEEE International Conference on Systems, Man, and Cybernetics   Check publisher's open access policy
Place of Publication New York, NY, U.S.A.
Publisher IEEE
Publication Year 2009
Sub-type Fully published paper
DOI 10.1109/ICSMC.2009.5346115
Open Access Status
ISBN 9781424427932
1424427932
9781424427949
1424427940
ISSN 1062-922X
Start page 2014
End page 2019
Total pages 5
Language eng
Formatted Abstract/Summary
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.
Keyword Hybrid vehicle
Vehicle telematics
Model predictive control
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article number 5346115.

 
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Created: Mon, 09 Jan 2012, 14:16:23 EST by Rahul Sharma on behalf of School of Information Technol and Elec Engineering