An optimal multi-objective control architecture of PMSM drives

Area of Science:

• Electrical Engineering
• Control Systems
• Electric Vehicle Technology

Background:

• Salient permanent magnet synchronous motors (PMSM) are crucial for electric vehicle (EV) efficiency.
• Existing control methods face challenges with parameter variations and load fluctuations.
• Optimizing PMSM drives is key to enhancing EV performance and energy conservation.

Purpose of the Study:

• To propose an optimal multi-objective control architecture for salient PMSM drives.
• To enhance system performance and energy efficiency in EV applications.
• To improve robustness against parameter mismatches and load variations.

Main Methods:

• Vector Model Predictive Control (V-MPC) for enhanced robustness.
• Current-based rotor angle estimation for load torque variations.
• Recursion-based stator-current step ahead model to reduce ripples and harmonics.
• 4-sector modified voltage vector selection with current error optimization.

Main Results:

• Improved robustness during large load torque variations.
• Reduced enumeration process and enhanced drive performance.
• Minimized stator current ripples and harmonics.
• Maintained machine variables during parameter mismatching, increasing system performance.
• Theoretical stability analysis using Lyapunov energy function.

Conclusions:

• The proposed V-MPC architecture offers superior control for PMSM drives in EVs.
• The method effectively enhances robustness, efficiency, and performance.
• Validated feasibility in the Simulink MATLAB environment.