DocumentCode :
2523230
Title :
Optimal power management of plug-in HEV with intelligent transportation system
Author :
Gong, Qiuming ; Li, Yaoyu ; Peng, Zhong-Ren
Author_Institution :
Wisconsin Univ., Milwaukee
fYear :
2007
fDate :
4-7 Sept. 2007
Firstpage :
1
Lastpage :
6
Abstract :
Hybrid electric vehicles (HEV) have demonstrated their capability of improving the fuel economy and emission. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of HEV. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. In the past, the trip information has been considered as future information for vehicle operation and thus unavailable a priori. This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board geographical information systems (GIS), global positioning systems (GPS) and advanced traffic flow modeling techniques. In this paper, a new approach of optimal power management of PHEV in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the final time of the cycle. The vehicle model was based on a hybrid SUV. Only fuel consumption is considered for the current stage of study. Simulation results showed significant improvement in fuel economy compared with rule-based power management. Furthermore, simulations on several driving cycles using the proposed method showed much better consistency in fuel economy compared to the rule-based control.
Keywords :
dynamic programming; energy management systems; fuel economy; hybrid electric vehicles; optimal control; traffic engineering computing; GIS; GPS; battery power; charge state; charge-depletion control; driving cycle modeling; dynamic programming algorithm; emission; fuel economy; global positioning systems; historic traffic information; hybrid SUV; intelligent transportation system; on-board geographical information systems; optimal power management; plug-in hybrid electric vehicles; rule-based power management; traffic flow modeling; trip information; vehicle operation; Batteries; Energy management; Fuel economy; Geographic Information Systems; Hybrid electric vehicles; Intelligent transportation systems; Management information systems; Power system management; Power system modeling; Traffic control; Dynamic Programming; Intelligent Transportation System; Plug-in Hybrid Electric Vehicles; Power Management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced intelligent mechatronics, 2007 IEEE/ASME international conference on
Conference_Location :
Zurich
Print_ISBN :
978-1-4244-1263-1
Electronic_ISBN :
978-1-4244-1264-8
Type :
conf
DOI :
10.1109/AIM.2007.4412579
Filename :
4412579
Link To Document :
بازگشت