Title :
A neural network based SRM drive control strategy for regenerative braking in EV and HEV
Author :
Gao, Hongwei ; Gao, Yimin ; Ehsani, Mehrdad
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
The characteristics of the regenerative braking in EV and HEV are analyzed in this paper. A neural network based SRM drive control strategy is developed for satisfying the requirements of regenerative braking in EV and HEV when SRM is chosen as the power source of EV and HEV. The energy recovery efficiency of the proposed control strategy is also evaluated
Keywords :
electric vehicles; machine control; neurocontrollers; regenerative braking; reluctance motor drives; EV; HEV; SRM drive control; electric vehicles; energy recovery efficiency; hybrid electric vehicles; neural network based control; power source; regenerative braking; Friction; Hybrid electric vehicles; Intelligent networks; Kinetic energy; Neural networks; Propulsion; Reluctance machines; Reluctance motors; Torque; Wheels;
Conference_Titel :
Electric Machines and Drives Conference, 2001. IEMDC 2001. IEEE International
Conference_Location :
Cambridge, MA
Print_ISBN :
0-7803-7091-0
DOI :
10.1109/IEMDC.2001.939368
