A novel metaheuristic approach for simultaneous loss minimization and torque ripple reduction of DTC- IM driven EV

Abstract

The efficiency and torque ripple of an electric vehicle (EV) determine its performance and driving range. An optimum reference flux increases efficiency and decreases torque ripple and harmonics. This strategy used in the current literature is based on either a lookup table or a search control approach. However, these methods have convergence issues at optimal values, require large memory spaces, have higher computational complexity, and are difficult to implement. In the recent literature, efforts have been made to improve either the efficiency or the ripple, whereas in this paper, a multi-objective dynamic reference flux selection algorithm based on teamwork optimization is used to improve the efficiency and ripples simultaneously for a wide range of operating scenarios. The proposed dynamic reference flux selection algorithm is evaluated numerically and compared using standard drive cycles, and the amount of energy a vehicle uses during different drive cycles is compared. The results obtained justify the effectiveness and feasibility of the proposed algorithm over a wide range of driving conditions.