DocumentCode :
1478297
Title :
Route Preview in Energy Management of Plug-in Hybrid Vehicles
Author :
Zhang, Chen ; Vahidi, Ardalan
Author_Institution :
Dept. of Mech. Eng., Clemson Univ., Clemson, SC, USA
Volume :
20
Issue :
2
fYear :
2012
fDate :
3/1/2012 12:00:00 AM
Firstpage :
546
Lastpage :
553
Abstract :
This brief evaluates the use of terrain, vehicle speed, and trip distance preview to increase the fuel economy of plug-in hybrid vehicles. Access to future information is classified into full, partial, or no future information and for each case an energy management strategy with the potential for a real-time implementation is proposed. With full knowledge of future driving conditions, dynamic programming (DP) provides a best-achievable benchmark. A partial preview level has access to future trip terrain and requires velocity estimation. Equivalent consumption minimization strategy (ECMS) is deployed as an instantaneous real-time minimization strategy with parameters adjusted by estimated future driving conditions and obtained either from DP or from a backward solution of ECMS. To reduce the requirement for future velocity and detailed terrain information, another partial preview level only assumes known trip distance to the next charging station and elevation changes (if available). In this level, the parameter of the real-time ECMS is estimated based on the remaining trip distance, the battery´s state-of-charge, and elevation changes if included. The results are evaluated against cases with no preview. Results from a number of simulation case studies indicate that the fuel economy can be substantially enhanced with only partial preview.
Keywords :
dynamic programming; energy management systems; fuel economy; hybrid electric vehicles; minimisation; driving conditions; dynamic programming; energy management; equivalent consumption minimization strategy; fuel economy; plug in hybrid vehicle; real-time ECMS; real-time minimization strategy; trip distance; trip distance preview; trip terrain information; vehicle speed; velocity estimation; Batteries; Electronic countermeasures; Engines; Fuels; Real time systems; System-on-a-chip; Vehicles; Energy management; optimization control; plug-in hybrid electric vehicle; predictive control;
fLanguage :
English
Journal_Title :
Control Systems Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
1063-6536
Type :
jour
DOI :
10.1109/TCST.2011.2115242
Filename :
5737780
Link To Document :
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