Dynamic Modeling and Optimization of Permanent Magnet Synchronous Electrical Machine Propulsion Powertrain at Different Modeling Levels

Abstract

Electric vehicles (EVs) have emerged as a compelling solution to mitigate environmental concerns and meet the growing demand for energy-efficient transportation systems. The careful selection of the electric motor is critical in determining the overall performance of the EV. This paper uses an EV from the University of Debrecen as a reference to comprehensively study the feasibility of using a permanent magnet brushless direct current (PMBLDC) motor in the vehicle. The optimal performance of the overall powertrain is realized based on a proportional integral and derivative (PID) controller. The advanced nonlinear dynamics of the system make the performance of the control algorithm unrealistic. The PID is optimized based on a genetic algorithm (GA-PID) to address this limitation and achieve optimal performance. The integral performance indices are used as a fitness value for the optimization problem. However, MATLAB/Simulink/Simscape is used to comprehensively investigate and compare the simplified and advanced models of a three- phase, four-pole, Y-connected PMBLDC motor in the EV application. The simulation results indicate that the proposed electrical machine is promising in EVs, achieving 90.90 % energy efficiency, thereby decreasing the energy consumption by 11.12 % compared to the measured real-world results. This research contributes significantly to energy efficiency, power efficiency, and thermal performance, offering invaluable insights into the optimal selection and modeling of PMBLDC motors at varying complexity levels in EVs. Ultimately, this study steers the industry towards a more sustainable and environmentally conscious trajectory.