Design of sliding mode controller with improved reaching law through self-learning strategy to mitigate the torque ripple in BLDC motor for electric vehicles

• Published in: Computers & electrical engineering Vol. 118; p. 109438
• Main Authors: N, Prabhu, Thirumalaivasan, R., Ashok, Bragadeshwaran
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2024
• Subjects: BLDC motor; Efficiency map; Electric vehicles; HIL simulation; MIL; Sliding mode controller; Torque ripple; MIL; Efficiency map; Torque ripple; Electric vehicles; BLDC motor; HIL simulation; Sliding mode controller
• ISSN: 0045-7906
• DOI: 10.1016/j.compeleceng.2024.109438

•Sliding mode controller is designed for BLDC motor torque ripple control in EV.•Effectiveness of the controllers is assessed through various time domain analysis.•Effectiveness of the controllers is assessed through motor performance analysis.•Controller efficiency maps are generated in HIL simulation for designed controllers.•Sliding mode controller outperforms other controllers for EV application. Brushless Direct Current (BLDC) motors and their evolution in electric vehicles (EVs) garner attention for their streamlined operation and control methods, enhancing driving range while reducing energy consumption. The primary objective is integrating sliding mode control to enhance BLDC motor capabilities, efficiently regulating speed and torque. The proposed sliding mode (SM) controller mitigates torque ripple and improves dynamic performance amidst fluctuating loads. Speed stabilization and torque control involve comparing reference and predicted speeds, with error feedback integrated into the SM controller. Integrating real-time rules and numerical data with an ANN (Artificial Neural Network)-Fuzzy self-learning algorithm empowers the SM controller to anticipate and mitigate error rates in the BLDC motor, efficaciously modulating motor torque with minimal ripple. Extensive MATLAB/Simulink model-in-loop (MIL) and hardware-in-loop (HIL) simulations demonstrate SM controller superiority over conventional PID, Fuzzy, and PID-Fuzzy controllers, with a rise time of 0.01 s, steady-state error of 0.001 %, settling time of 0.02 s, and peak overshoot of 0.066 %. Moreover, the experiment results show an impressive 29.41 % reduction in torque ripple and the development of efficiency maps to attain the maximum efficiency of 96.12 %. This initiative substantiates that the proposed SM controller to enhance BLDC motor efficiency and EV driving range across real-time conditions.