DocumentCode
3322466
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
fYear
2001
fDate
2001
Firstpage
571
Lastpage
575
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Electric Machines and Drives Conference, 2001. IEMDC 2001. IEEE International
Conference_Location
Cambridge, MA
Print_ISBN
0-7803-7091-0
Type
conf
DOI
10.1109/IEMDC.2001.939368
Filename
939368
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