DocumentCode
848313
Title
Power management strategy for a parallel hybrid electric truck
Author
Lin, Chan-Chiao ; Peng, Huei ; Grizzle, Jessy W. ; Kang, Jun-Mo
Author_Institution
Dept. of Mech. Eng., Univ. of Michigan, Ann Arbor, MI, USA
Volume
11
Issue
6
fYear
2003
Firstpage
839
Lastpage
849
Abstract
Hybrid vehicle techniques have been widely studied recently because of their potential to significantly improve the fuel economy and drivability of future ground vehicles. Due to the dual-power-source nature of these vehicles, control strategies based on engineering intuition frequently fail to fully explore the potential of these advanced vehicles. In this paper, we present a procedure for the design of a near-optimal power management strategy. The design procedure starts by defining a cost function, such as minimizing a combination of fuel consumption and selected emission species over a driving cycle. Dynamic programming (DP) is then utilized to find the optimal control actions including the gear-shifting sequence and the power split between the engine and motor while subject to a battery SOC-sustaining constraint. Through analysis of the behavior of DP control actions, near-optimal rules are extracted, which, unlike DP control signals, are implementable. The performance of this power management control strategy is studied by using the hybrid vehicle model HE-VESIM developed at the Automotive Research Center of the University of Michigan. A tradeoff study between fuel economy and emissions was performed. It was found that significant emission reduction could be achieved at the expense of a small increase in fuel consumption.
Keywords
dynamic programming; hybrid electric vehicles; optimal control; power control; regenerative braking; dynamic programming; fuel economy; gear-shifting sequence; hybrid vehicle model HE-VESIM; hybrid vehicle techniques; near-optimal rules; optimal control; parallel hybrid electric truck; power management strategy; powertrain control; Automotive engineering; Cost function; Dynamic programming; Energy management; Fuel economy; Intelligent vehicles; Land vehicles; Power engineering and energy; Road vehicles; Vehicle driving;
fLanguage
English
Journal_Title
Control Systems Technology, IEEE Transactions on
Publisher
ieee
ISSN
1063-6536
Type
jour
DOI
10.1109/TCST.2003.815606
Filename
1255660
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