Energy Regeneration during Deceleration of Direct Torque Control of Induction Motor Drive for Electric Vehicles

In this paper, an effective neuro-fuzzy controller (NFC) technique has been proposed to control the induction motor torque and flux. The NFC hybrid technique is the grouping of the neural network (NN) and fuzzy logic controller (FLC), which generated the target voltages with the corresponding input flux and torque. The novelty of the proposed hybrid technique is highly flexible in nonlinear loads, convenient user interface and logical intellectual and permitting for integrated controlling schemes. Here, the FLC generates the training dataset of the NN technique based on the logical rules. The generated dataset contains the information about the flux and torque deviation parameters and the corresponding reference voltage parameters. The NN has been trained based on training dataset and the testing time which produces the optimal reference voltage parameters depends on the variation of the torque and flux parameters. By using the output of the NFC technique, the space vector modulation (SVM) develops the appropriate control pulses to the five-level inverter and the inverter generates the output voltage signal to the induction motor. The proposed method is designed in the MATLAB/Simulink platform and the outputs are verified through the comparison analysis with the existing techniques.

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Energy Regeneration during Deceleration of Direct Torque Control of Induction Motor Drive for Electric Vehicles

Cited by 4 publications

References 8 publications

Regenerative energy fed to the grid by DC–AC converter during deceleration for direct torque control of induction motor drive

Regenerative energy fed to the grid by DC–AC converter during deceleration for direct torque control of induction motor drive

Review on Different Topologies of Power Electronic Converters and Control Strategies for Motors in Electric Vehicles

Neuro Fuzzy Controller for DTC of Induction Motor Using Multilevel Inverter with SVM

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