Artificial intelligent-based modified direct torque control strategy: enhancing the dynamic torque response of permanent magnet electric traction

Abstract

Electric traction drive prefers direct torque control due to its simplicity and easy implementation on permanent magnet machines (PMMs). Variable switching frequency, as well as more torque and flux ripples, are the key challenges of conventional direct torque control. A modified switching table-based direct torque control has been widely adapted for controlling the PMM drives. Artificial intelligent-based switching table substitutes the switching table and hysteresis comparator provides a significant reduction in current harmonic distortion, torque, and flux ripple, which shows a greater advantage in speed control for smart electric vehicles. In this chapter, artificial intelligence-based multisector direct torque control is analyzed for a suitable voltage vector selection to minimize torque and stator flux ripple. To demonstrate, a comparison of the intended switching tables shows the virtues of each switching table on the performance of the multisector direct torque control strategy. This premises on the theory of keeping the divergence between the commanded torque and the calculated torque as small as possible and does not provide information on the conduction time mode of three-phase switching. It adapts changes in the three phase-current waveform to keep electromagnetic torque consistent, eliminating the commutation torque ripple that would have occurred with conventional direct torque control (CDTC). Simulation results are taken in MATLAB®/Simulink®, and it is observed that the PMM ripples are reduced, particularly at high rotational speeds.