LAPSE:2023.26955
Published Article
LAPSE:2023.26955
Predictive Trajectory Control with Online MTPA Calculation and Minimization of the Inner Torque Ripple for Permanent-Magnet Synchronous Machines
Simon Decker, Matthias Brodatzki, Benjamin Bachowsky, Benedikt Schmitz-Rode, Andreas Liske, Michael Braun, Marc Hiller
April 3, 2023
Abstract
This paper presents an extended predictive trajectory control scheme combined with an inner torque ripple minimization considering the current-, flux-linkage-, and voltage-planes of permanent magnet synchronous machines. The extension of a fundamental machine model with flux-linkage harmonics allows the calculation of the inner torque ripple and enables its minimization. For this, the control is divided in two cases: (1) The dynamic operation or large signal behavior which uses the maximal torque gradient for the trajectory strategy during each control period for fastest dynamic operation, and (2) The stationary operation or small signal behavior, utilizing a real time capable polynomial approximation of the rotor position dependent torque hyperbolas (iso-torque curves) of permanent magnet synchronous machines for the ideal torque to current reference values. Since dynamic and steady-state operation is covered, torque to current look-up tables, such as maximum torque per ampere (MTPA)/maximum torque per volt/voltage (MTPV) look-up tables, are not required anymore. The introduced, new control approach is implemented in Matlab/Simulink based on finite element analysis and measured data. Furthermore, test-bench implementations based on measurement data are presented to show the real-time capability and precision.
Keywords
online maximum torque per current, optimal control, permanent-magnet synchronous machine, predictive control, torque ripple minimization, trajectory control
Suggested Citation
Decker S, Brodatzki M, Bachowsky B, Schmitz-Rode B, Liske A, Braun M, Hiller M. Predictive Trajectory Control with Online MTPA Calculation and Minimization of the Inner Torque Ripple for Permanent-Magnet Synchronous Machines. (2023). LAPSE:2023.26955
Author Affiliations
Decker S: Institute of Electrical Engineering (ETI), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany [ORCID]
Brodatzki M: Institute of Electrical Engineering (ETI), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany [ORCID]
Bachowsky B: Institute of Electrical Engineering (ETI), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
Schmitz-Rode B: Institute of Electrical Engineering (ETI), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
Liske A: Institute of Electrical Engineering (ETI), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
Braun M: Institute of Electrical Engineering (ETI), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
Hiller M: Institute of Electrical Engineering (ETI), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
Journal Name
Energies
Volume
13
Issue
20
Article Number
E5327
Year
2020
Publication Date
2020-10-13
ISSN
1996-1073
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Original Submission
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PII: en13205327, Publication Type: Journal Article
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