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<p>This paper presents an advanced and comparative study on the control of permanent magnet synchronous motors (PMSMs) in electric vehicle (EV) propulsion systems. A detailed simulation model is developed to evaluate PMSM performance in terms of dynamic response, energy efficiency, and controllability. The research introduces a novel integration of a fractional order PID (FO-PID) controller optimized using the ant lion optimizer (ALO), and contrasts its performance with traditional PI and Fuzzy-PI controllers under identical test scenarios. Unlike previous works that focused on isolated controllers or limited tuning approaches, this study implements a hybrid intelligent control scheme tailored to the nonlinear dynamics of PMSMs in EVs. Simulation results reveal that the ALO-optimized FO-PID controller significantly outperforms its counterparts, achieving a rise time of 0.0034&#xa0;s, peak time of 0.0067&#xa0;s, and settling time of 0.1041&#xa0;s. The integral absolute error (IAE) was minimized to 0.003291, indicating a 92.36% improvement over the Fuzzy-PI controller. Furthermore, the proposed controller demonstrated superior speed regulation, reduced torque and current ripples, and robust stability under diverse driving conditions. These findings highlight the potential of advanced fractional control in achieving next-generation EV performance benchmarks.</p>

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Advanced control of permanent magnet synchronous motor for electric vehicle by using fractional order PID controller with different techniques

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Bashar Reda,
  • Rosy Pradhan,
  • Motaz Altahhan

摘要

This paper presents an advanced and comparative study on the control of permanent magnet synchronous motors (PMSMs) in electric vehicle (EV) propulsion systems. A detailed simulation model is developed to evaluate PMSM performance in terms of dynamic response, energy efficiency, and controllability. The research introduces a novel integration of a fractional order PID (FO-PID) controller optimized using the ant lion optimizer (ALO), and contrasts its performance with traditional PI and Fuzzy-PI controllers under identical test scenarios. Unlike previous works that focused on isolated controllers or limited tuning approaches, this study implements a hybrid intelligent control scheme tailored to the nonlinear dynamics of PMSMs in EVs. Simulation results reveal that the ALO-optimized FO-PID controller significantly outperforms its counterparts, achieving a rise time of 0.0034 s, peak time of 0.0067 s, and settling time of 0.1041 s. The integral absolute error (IAE) was minimized to 0.003291, indicating a 92.36% improvement over the Fuzzy-PI controller. Furthermore, the proposed controller demonstrated superior speed regulation, reduced torque and current ripples, and robust stability under diverse driving conditions. These findings highlight the potential of advanced fractional control in achieving next-generation EV performance benchmarks.