LAPSE:2023.30534
Published Article
LAPSE:2023.30534
A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models
Elia Brescia, Donatello Costantino, Paolo Roberto Massenio, Vito Giuseppe Monopoli, Francesco Cupertino, Giuseppe Leonardo Cascella
April 14, 2023
Permanent magnet machines with segmented stator cores are affected by additional harmonic components of the cogging torque which cannot be minimized by conventional methods adopted for one-piece stator machines. In this study, a novel approach is proposed to minimize the cogging torque of such machines. This approach is based on the design of multiple independent shapes of the tooth tips through a topological optimization. Theoretical studies define a design formula that allows to choose the number of independent shapes to be designed, based on the number of stator core segments. Moreover, a computationally-efficient heuristic approach based on genetic algorithms and artificial neural network-based surrogate models solves the topological optimization and finds the optimal tooth tips shapes. Simulation studies with the finite element method validates the design formula and the effectiveness of the proposed method in suppressing the additional harmonic components. Moreover, a comparison with a conventional heuristic approach based on a genetic algorithm directly coupled to finite element analysis assesses the superiority of the proposed approach. Finally, a sensitivity analysis on assembling and manufacturing tolerances proves the robustness of the proposed design method.
Keywords
artificial neural networks, cogging torque, finite element analysis, Genetic Algorithm, manufacturing tolerance, modular stator, permanent magnet machines, segmented stator, software design, surrogate models, tolerance analysis, topological optimization
Suggested Citation
Brescia E, Costantino D, Massenio PR, Monopoli VG, Cupertino F, Cascella GL. A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models. (2023). LAPSE:2023.30534
Author Affiliations
Brescia E: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy [ORCID]
Costantino D: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy
Massenio PR: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy
Monopoli VG: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy [ORCID]
Cupertino F: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy
Cascella GL: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy [ORCID]
Journal Name
Energies
Volume
14
Issue
7
First Page
1880
Year
2021
Publication Date
2021-03-29
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14071880, Publication Type: Journal Article
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LAPSE:2023.30534
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doi:10.3390/en14071880
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Apr 14, 2023
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