Improved indirect instantaneous torque control based torque sharing function approach of SRM drives in EVs using...

Area of Science:

• Electrical Engineering
• Automotive Engineering
• Control Systems

Background:

• Switched reluctance motors (SRMs) are crucial for electric vehicle (EV) propulsion.
• Existing indirect instantaneous torque control (IITC) methods face challenges in optimizing performance parameters.
• Accurate modeling of SRM magnetic characteristics is essential for effective control.

Purpose of the Study:

• To propose an improved IITC method for SRMs in EVs using a hybrid optimization technique.
• To enhance torque sharing function (TSF) for precise torque control and minimal ripple.
• To optimize control parameters for maximum torque per ampere (MTPA) and improved speed range.

Main Methods:

• A hybrid optimization approach combining Reptile Search Algorithm (RSA) and Honey Badger Algorithm (HBA), termed Enhanced RSA (ERSA).
• Measurement of SRM magnetic features for precise system specification.
• Implementation of a modified TSF to compensate for torque errors and minimize flux linkage rate of change.
• Utilizing the ERSA method to determine optimal control parameters for the IITC of SRMs.

Main Results:

• The ERSA method was implemented and validated on the MATLAB platform.
• Performance comparison with existing systems demonstrated superior results.
• Mean Squared Error (MSE) achieved was as low as 0.01093 for Case 1 and 0.01095 for Case 2.
• Voltage deviation was consistently low at 5 for both cases.
• Power factor improvements were noted, reaching 50 for Case 1 and 40 for Case 2.

Conclusions:

• The proposed ERSA-based IITC method offers significant improvements for SRM drives in EVs.
• The hybrid optimization effectively addresses torque ripple, efficiency, and MTPA requirements.
• The method provides a robust solution for precise control and enhanced performance of electric vehicle powertrains.