Title :
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
fDate :
Aug. 31 2014-Sept. 3 2014
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;
Conference_Titel :
Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-4240-4
DOI :
10.1109/ITEC-AP.2014.6940693
