Comparative Performance Assessment of Predictive Torque Control Strategy for Motor Drive Applications

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)
118 Downloads (Pure)

Abstract

Recently the majority of research in the field of predictive torque control (PTC) focuses on torque ripple and computational burden reduction. The main disadvantage of the traditional PTC strategy is the high computational time requirement, and it restricts the utilization of the PTC in automotive application. In this paper, a new PTC is used based on the modification of conventional PTC that reduces the computational time. At the same time, the modified PTC proposed in this paper reduces semiconductor losses and thermal stress. Moreover, a comparative analysis between conventional PTC and modified PTC has been conducted in terms of driving, electrical and thermal performance. It is found from a simulation study in MATLAB/Simulink® that the modified PTC reduces the switching loss by up to 20%, while the efficiency is increased by more than 5% compared to a conventional PTC. Finally, a reduction in the heatsink temperature response is also noticed during this assessment.

Original languageEnglish
Title of host publication48th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Xplore
Pages1-6
Number of pages6
ISBN (Electronic)9781665480253
DOIs
Publication statusPublished - 9 Dec 2022

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2022-October

Bibliographical note

Funding Information:
This work was supported by the Horizon 2020 Programme of the European Commission under grant agreement no. 824311 (ACHILES project). Authors acknowledge Flanders Make for the support to our research group.

Funding Information:
This work was supported by the Horizon 2020 Programme of the European Commission under grant agreement no. 824311 (ACHILES project). Authors acknowledge Flanders Make for the support to our research group

Publisher Copyright:
© 2022 IEEE.

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

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