Title :
Intelligent Energy Management in a Low Cost Hybrid Electric Vehicle Power System
Author :
Murphey, Yi L. ; Jungme Park ; Masrur, Md Abul
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA
Abstract :
This paper presents our research in vehicle energy optimization for a low-cost HEV power system that only allows the control of engine on/off and driving at three different speed limits. We present algorithms for modeling vehicle energy flow and optimization and machine learning of optimal control settings generated by Dynamic Programming on real-world drive cycles, and an intelligent energy controller designed for online energy control. Experimental results show the intelligent controller has the capability of 11% fuel saving.
Keywords :
electric vehicles; energy management systems; intelligent control; learning (artificial intelligence); machine control; optimal control; power control; dynamic programming; engine on-off control; intelligent energy control; intelligent energy management; low cost hybrid electric vehicle power system; machine learning; online energy control; optimal control setting; vehicle energy flow; vehicle energy optimization; Batteries; Engines; Fuels; Hybrid electric vehicles; Neural networks; Optimization;
Conference_Titel :
Vehicular Technology Conference (VTC Fall), 2013 IEEE 78th
Conference_Location :
Las Vegas, NV
DOI :
10.1109/VTCFall.2013.6692245
