TY - GEN
T1 - Design Optimisation of a High Power Density Electric Machine using Soft Magnetic Composites
AU - Roshandel, Emad
AU - Ertugrul, Nesimi
AU - Mahmoudi, Amin
AU - Kahourzade, Solmaz
PY - 2023/8/21
Y1 - 2023/8/21
N2 - Soft magnetic composites (SMCs) are considered in electrical machines applications due to their desirable magnetic properties, such as small eddy current losses. Their thermal isotropy feature is also desirable as it can allow the construction of SMC-based pole pieces to form the stator. In addition, the embedded concentrated winding structure allows to achieve higher power density electric machines. This paper presents an optimization study to offer a high-power density, low cogging torque, and high-efficiency electrical machine that are desirable in a wide range of applications. To achieve these aims, both 2-D finite element model (FEM) and 3-D FEM are developed for a benchmark machine. Then a sensitivity analysis is carried out about the arc and thickness of the permanent magnet (PM), and on the number of turns of the windings under a constant current density both under the no-load and full-load operation of the machine. The results obtained from the sensitivity analysis are used to predict the performance of the SMC-based electric machine in a large search space by means of a surrogate model. Then, a convex optimization problem is solved to find a high torque machine with the minimum cogging torque. The optimal design is validated using the 3-D and 2-D FE analysis (FEA) results performed previously. Finally, the optimal design performance parameters are obtained in the torque-speed envelope. These results are also compared with the performance characteristics of a conventional laminated machine to demonstrate the advantages of the use of SMC in motor design.
AB - Soft magnetic composites (SMCs) are considered in electrical machines applications due to their desirable magnetic properties, such as small eddy current losses. Their thermal isotropy feature is also desirable as it can allow the construction of SMC-based pole pieces to form the stator. In addition, the embedded concentrated winding structure allows to achieve higher power density electric machines. This paper presents an optimization study to offer a high-power density, low cogging torque, and high-efficiency electrical machine that are desirable in a wide range of applications. To achieve these aims, both 2-D finite element model (FEM) and 3-D FEM are developed for a benchmark machine. Then a sensitivity analysis is carried out about the arc and thickness of the permanent magnet (PM), and on the number of turns of the windings under a constant current density both under the no-load and full-load operation of the machine. The results obtained from the sensitivity analysis are used to predict the performance of the SMC-based electric machine in a large search space by means of a surrogate model. Then, a convex optimization problem is solved to find a high torque machine with the minimum cogging torque. The optimal design is validated using the 3-D and 2-D FE analysis (FEA) results performed previously. Finally, the optimal design performance parameters are obtained in the torque-speed envelope. These results are also compared with the performance characteristics of a conventional laminated machine to demonstrate the advantages of the use of SMC in motor design.
KW - Electric machine design
KW - electric machine optimization
KW - high power density electrical machines
KW - soft magnetic composites
UR - http://www.scopus.com/inward/record.url?scp=85170637313&partnerID=8YFLogxK
U2 - 10.1109/AUPEC58309.2022.10215683
DO - 10.1109/AUPEC58309.2022.10215683
M3 - Conference contribution
AN - SCOPUS:85170637313
T3 - 2022 32nd Australasian Universities Power Engineering Conference, AUPEC 2022
BT - 2022 32nd Australasian Universities Power Engineering Conference, AUPEC 2022
PB - Institute of Electrical and Electronics Engineers
T2 - 32nd Australasian Universities Power Engineering Conference, AUPEC 2022
Y2 - 26 September 2022 through 28 September 2022
ER -