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Intelligent Direct Torque Control of Brushless DC motors for hybrid electric vehicles

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4 Author(s)
Gupta, A. ; Automotive Syst. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA ; Taehyung Kim ; Taesik Park ; Cheol Lee

This paper investigates the application of neural networks for Direct Torque Control (DTC) of a Brushless DC (BLDC) motor with non-sinusoidal back EMF. Conventional DTC technique controls the torque directly by providing appropriate switching signals from a predefined switching table based on torque error, stator flux linkage error and the stator flux angle. Applying this method for hybrid electric vehicles, results in serious torque ripple and power loss due to several system limitations. An intelligent neural network based direct torque control of BLDC motors for hybrid electric vehicle applications is proposed in this paper. The proposed method decreases the torque ripple and the number of switching and hence the switching power loss. Both the conventional DTC method and neural network based DTC of BLDC motor are simulated in MATLAB/SIMULINK and the results are compared and discussed to verify the proposed control.

Published in:

Vehicle Power and Propulsion Conference, 2009. VPPC '09. IEEE

Date of Conference:

7-10 Sept. 2009