DefinePK

Abstract

The rapid evolution of electric vehicles (EVs) demands the development of high-efficiency and dynamically responsive traction motor systems that ensure optimal performance, energy utilization, and drivability. This research presents a comprehensive, multi-domain simulation framework and control-oriented modeling approach for advanced traction motor architectures tailored for EV applications. Leveraging the MATLAB/Simulink environment and Simscape Electrical toolbox, the study integrates electrical, mechanical, and thermal domains into a unified platform for the accurate representation of motor dynamics under real-world driving conditions. The proposed model supports various motor topologies, including Permanent Magnet Synchronous Motors (PMSMs) and Induction Motors (IMs), with implementation of field-oriented control (FOC), direct torque control (DTC), and sensorless estimation techniques. Emphasis is placed on system-level co-simulation to evaluate the impact of inverter switching behavior, load fluctuations, regenerative braking, and thermal interactions on motor performance. Key performance metrics such as torque ripple, efficiency maps, power losses, and transient response are extracted and analyzed to validate the robustness and adaptability of the control strategies under both steady-state and dynamic regimes. Furthermore, the simulation architecture incorporates speed and torque control loops, PWM inverter interfacing, battery dynamics, and load conditions based on standard drive cycles such as NEDC and WLTP. MATLAB’s embedded optimization and visualization tools are used to fine-tune controller parameters for different load and road conditions. The approach also allows plug-and-play integration with other EV subsystems, facilitating system-level performance testing and future upgrades. The results demonstrate the capability of MATLAB and Simscape Electrical to serve as a high-fidelity platform for design, analysis, and optimization of traction motors in EVs. This simulation framework provides a scalable foundation for future integration of intelligent control algorithms, digital twin development, and hardware-in-the-loop (HIL) testing for real-time validation of electric vehicle propulsion systems.