In the field of Permanent Magnet Synchronous Motor (PMSM) control in electric vehicles, two advanced control methods, Adaptive Particle Swarm Optimization-Proportional-Integral-Derivative (APSO-PID) and adaptive Fuzzy logic - Proportional-Integral-Derivative (FL-PID), have demonstrated outstanding performance. APSO-PID adaptive control combines the Particle Swarm Optimization (PSO) algorithm and traditional PID, which automatically adjusts parameters and optimizes control performance. This method improves the response speed and accuracy of the Permanent magnet synchronous motor (PMSM), which is especially useful in situations of sudden load changes or complex environments. In contrast, FL-PID fuzzy logic adaptive control handles uncertain and nonlinear information, making the control system more flexible and effective in the face of unpredictable changes. FL-PID improves the stability, anti-interference ability, and fast response of PMSM drivers. Both APSO-PID and FL-PID methods optimize the performance and reliability of the drive system in electric vehicles, contributing to increased energy efficiency and enhanced driving experience. These techniques meet the strict requirements of the modern automotive industry by optimizing control parameters in driving cycles according to the world standards Artemis rural cycle (ARC), European extra-urban drive cycle (EUDC), and Highway Fuel Economy Test Cycle (HWFET).