Exploring Model Predictive Direct Torque Control for In-Wheel Switched Reluctance Motors in Electric Vehicle Propulsion

The in-wheel switched reluctance motor (IWSRM) is becoming increasingly important in the ongoing transition toward electrification in the automotive industry. This technology offers high efficiency due to its simple design and lack of permanent magnets, making it a cost-effective option compared to other polyphase machines. Moreover, integrating the motor into the wheel eliminates the need for complex transmission systems, reducing the overall size and weight of the vehicle while potentially increasing interior space. However, a significant challenge of IWSRM lies in controlling its complexity to optimize performance, particularly in terms of torque ripple reduction. To address this challenge, this paper proposes an enhanced model predictive direct torque control (MPDTC) approach, which involves selecting candidate voltage vectors in an optimized switching sequence. This strategy aims to mitigate torque ripple across various speeds and loads. Additionally, the sector arrangement selects voltage vectors with predictive control parameters to further enhance performance under different load conditions. The 12/16 IWSRM is developed using finite element analysis and the identified parameters are utilized to implement the control algorithm. The proposed model is validated and simulated using MATLAB/Simulink, analyzing motor performance in terms of speed, torque, and current under different vehicle propulsion conditions. Furthermore, a hardware setup is included to verify the real-time complexity of the system.