Modeling and Control of Permanent Magnet Synchronous Motor Based Electric Vehicle

This study provides a comprehensive examination of the modeling and control of Permanent Magnet Synchronous Motors (PMSM) utilized in electric vehicle applications. The research focuses on the design and optimization of Fractional Order PID (FOPID) controllers, leveraging Genetic Algorithm (GA) and Hybrid Reinforcement Genetic Algorithm-Recursive Backpropagation Learning (GA-RBL) techniques to enhance tuning performance. The PMSM, known for its high efficiency and reliability, is mathematically modeled, and its control dynamics are analyzed under various operating conditions. A novel approach to FOPID controller tuning is introduced, utilizing the robustness of hybrid algorithms. Our proposed Hybrid GA-RBL optimized FOPID controller achieved a peak overshoot of 4.0 mm at 900 rpm, settling time of 1.65 s, and steady-state error of 0.6%. Additionally, error metrics were significantly improved, with Integral of Squared Error (ISE) of 0.003 mm 2 s, Integral of Absolute Error (IAE) of 0.045 mm s, and Integral of Time-weighted Absolute Error (ITAE) of 0.021 mm s 2 . Compared to conventional controllers such as Ziegler-Nichols and Cohen-Coon PID tuning methods, the proposed model demonstrated superior performance in terms of faster response time, enhanced stability, and improved energy efficiency. These findings contribute to advancing control strategies for electric vehicles, setting a benchmark for future research and development in PMSM control optimization.