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<p>Recently, electric vehicle (EVs) development for the automotive industry has become great attention to reducing internal combustion engine vehicles, due to tailpipe air pollution, and demand for fuel crises make high cost. The EVs come at a high price, mainly because EV motors use rare earth materials. Therefore, many researchers focus on low-cost switched reluctance motors (SRM) for EV driving. The main drawback of SRM for EVs is the high commutation torque ripple in the unsaturated region. Many conventional control methods reduce the torque ripples are insufficient to use SRM in EVs. The proposed field-oriented control (FOC) approach adaptive neuro-fuzzy inference system (ANFIS) modelled based 8/6 SRM minimizing the torque ripple with the advantage of hybrid control techniques. The main objective of the proposed controller is to achieve optimal current and speed control loops to reduce the torque and flux error at the SRM- -asymmetrical half-bridge (AHB) converter. The proposed model was simulated and validated using MATLAB/Simulink individually with fuzzy logic control (FLC), artificial neural network (ANN), and ANFIS performance. Additionally, the proposed model experimental setup results are verified, minimizing the flux and torque ripple in suitable EV applications.</p>

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ANFIS-based SRM speed control and torque ripple mitigation for electric vehicle applications

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • M. Deepak,
  • S. Devakirubakaran

摘要

Recently, electric vehicle (EVs) development for the automotive industry has become great attention to reducing internal combustion engine vehicles, due to tailpipe air pollution, and demand for fuel crises make high cost. The EVs come at a high price, mainly because EV motors use rare earth materials. Therefore, many researchers focus on low-cost switched reluctance motors (SRM) for EV driving. The main drawback of SRM for EVs is the high commutation torque ripple in the unsaturated region. Many conventional control methods reduce the torque ripples are insufficient to use SRM in EVs. The proposed field-oriented control (FOC) approach adaptive neuro-fuzzy inference system (ANFIS) modelled based 8/6 SRM minimizing the torque ripple with the advantage of hybrid control techniques. The main objective of the proposed controller is to achieve optimal current and speed control loops to reduce the torque and flux error at the SRM- -asymmetrical half-bridge (AHB) converter. The proposed model was simulated and validated using MATLAB/Simulink individually with fuzzy logic control (FLC), artificial neural network (ANN), and ANFIS performance. Additionally, the proposed model experimental setup results are verified, minimizing the flux and torque ripple in suitable EV applications.