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 and Giuseppe Leonardo Cascella
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Elia Brescia: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy
Donatello Costantino: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy
Paolo Roberto Massenio: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy
Vito Giuseppe Monopoli: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy
Francesco Cupertino: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy
Giuseppe Leonardo Cascella: Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, Italy

Energies, 2021, vol. 14, issue 7, 1-26

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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