Development of Efficient Energy Management Strategy to Mitigate Speed and Torque Ripples in SR Motor Through Adaptive Supervisory Self-Learning Technique for Electric Vehicles

Abstract

Switched reluctance motors (SRM) are receiving extensive attention from the industry because of their simple construction, high reliability and efficiency. Antithetically, controlling SRM is challenging due to its nonlinearities and variable parameters such as speed, torque, flux, etc. In this study, a novel approach is used to control the speed and torque of SRM with various controllers. This methodology integrates model-in-loop (MIL) and hardware-in-loop (HIL) simulations to evaluate the efficiency of SRM in electric vehicles (Evs). To reduce the speed variations in the SRM, PID, intelligent, hybrid, and adaptive supervisory self-learning control approaches (ASSC) are used. The mathematical models of the aforementioned controllers were created in MATLAB/Simulink, and these control approaches are used to reduce speed, torque, and current fluctuations under various load and speed conditions. In this paper, both simulation and experimental work are used to investigate the various dynamic characteristics of the SRM in Evs. From the simulation findings, the proposed ASSC controller exhibits less overshoot (1.05%), settling time (0.02s) and risetime (0.01s) than PID, intelligent and hybrid control approaches. The proposed ASSC method combines numerical data and real-time rules knowledge with ANN to predict error responses in the SR motor. Also, it is significantly reducing the speed, torque, current and flux ripples of the SRM from low to high speed with different load conditions. Further, to verify the simulation results experimental work is conducted and similar results are measured under different load and speed conditions. From experimentation, efficiency maps are developed for PID, intelligent, hybrid, and ASSCs across the entire operating range. The maps show that the controller’s maximum efficiency is, respectively, 85%, 88%, 91%, and 95%. The supervisory controller is 10% more efficient than PID, intelligent, and hybrid controllers in terms of various dynamic characteristics of SRM. From the observations, both experimental and simulation results corroborated that the recommended ASSC improves the SRM efficiency.

Keywords

• Adaptive supervisory self-learning control technique
• SRM
• controller efficiency maps
• model in-loop (MIL) simulation
• hardware in-loop (HIL) simulation