Driving cycle recognition for hybrid electric vehicle

Author

Xing Jie ; Han Xuefeng ; Ye Hui ; Cui Yan ; Ye Huiping

Author_Institution

China North Vehicle Res. Inst., Beijing, China

fYear

2014

fDate

Aug. 31 2014-Sept. 3 2014

Firstpage

1

Lastpage

6

Abstract

Global optimization oriented energy control algorithms can improve fuel economy of HEV for certain driving cycle. But it can´t be used for the other driving cycles. This paper built a adaptive energy control strategy based on driving cycle recognition. Through driving cycle and microtrip studying, analysis was made on characteristic parameters of driving cycles, based on which, ten parameters oriented algorithm for driving cycle LVQ neural net recognition are proposed. The simulation result shows that the precision of driving cycle recognition is very good.

Keywords

adaptive control; fuel economy; hybrid electric vehicles; learning (artificial intelligence); neural nets; optimisation; power control; power engineering computing; vector quantisation; HEV fuel economy improvement; adaptive energy control strategy; driving cycle LVQ neural net recognition; global optimization oriented energy control algorithm; hybrid electric vehicle; learning vector quantization neural network; microtrip studying analysis; Acceleration; Batteries; Biology; Economic indicators; Ice; Road transportation; Simulation; HEV; LVQ neural net; driving cycle recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo

Conference_Location

Beijing

Print_ISBN

978-1-4799-4240-4

Type

conf

DOI

10.1109/ITEC-AP.2014.6940693

Filename

6940693