Optimization Techniques for Cogging Torque Reduction and Thermal Characterization in Brushless DC Motor

This paper presents soft computing-based optimization techniques for the cogging torque reduction and thermal characterization by finite element analysis in a permanent magnet brushless DC motor (BLDC). Stator and rotor structure of BLDC motor are optimized to reduce the cogging torque, noise, and vibration by using the design parameters namely: length of magnet, length of air gap and opening in the stator slot which are selected by performing variance-based sensitivity analysis. The proposed method is suitable in the preliminary design phase of the motor to determine the optimal structure to improve the efficiency. The comparison of results obtained using firefly algorithm , ant colony optimization algorithm and Bat algorithm indicate that Firefly-based optimization algorithm is capable of giving improved design parameter output. Cogging torque is created due to the interaction of magnets in the rotor and the stator slot of the motor. Thorough thermal analysis is also conceded out to investigate the thermal characteristics at dissimilar portions of the motor namely: stator core, stator slot, rotor core and permanent magnet at different operating environments in the continuous operation mode. Thermal investigation is required for the various high speed e-vehicle applications. The usefulness of the designed machine simulation is compared with the results obtained from hardware analysis. The outcomes attained from software simulation studies are validated through experimental hardware setup.