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Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

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Abstract

This article presents an artificial neural network (ANN) for loss minimization of direct torque-controlled induction motor (IM)-driven electric vehicles. The IM drive can consume more power than it needs to be, especially when it is operating under conditions under full load. The proposed architecture and its control strategies use ANN to control the amplitude starting current and save more power. The performance of these controllers was verified by simulation using the MATLAB/SIMULINK package, and the results showed good and high performance in the time domain response and rapid rejection of the system disturbance compared to the conventional proportional–integral derivative (PID) controller. Thus, the core loss of IM greatly reduces, thus improving the efficiency of the driving system. Finally, the experimental results were validated which were highly consistent with the simulation results using the DSPACE MicroLabBox controller.

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Authors and Affiliations

  1. Electrical Department, Faculty of Technology and Education, Sohag University, Sohag, 82524, Egypt

    Hedra Saleeb & Rasha Kassem

  2. Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag, 82524, Egypt

    Khairy Sayed

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  1. Hedra Saleeb
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  2. Rasha Kassem
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  3. Khairy Sayed
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Correspondence to Hedra Saleeb.

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Saleeb, H., Kassem, R. & Sayed, K. Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system. Electr Eng 104, 1769–1780 (2022). https://doi.org/10.1007/s00202-021-01418-y

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  • DOI: https://doi.org/10.1007/s00202-021-01418-y

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