Multi-objective design optimization of 8/14 switched reluctance motor

Smaka, Senad; Konjicija, Samim; Masic, Semsudin; Cosovic, Mirsad

doi:10.1109/iemdc.2013.6556138

Cited by 13 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, torque density (torque per lamination volume or torque per motor weight) is often used as an optimisation objective in motor design [12,16]. However, increasing the torque density unilaterally may make the thermal load of the motor too high, which means that the motor may have the risk of burning.…”

Section: β1 Efficiency and Torque Density In Relation To Thermal Energymentioning

confidence: 99%

“…Therefore, four optimisation objectives mentioned are essential. The weighted average method [8, 16] and Pareto‐optimal solutions [5] are usually used to solve the problem of optimal value selection. Among them, the multi‐objective optimisation problem can be transformed into a single‐objective problem by the weighted average method.…”

Section: Multi‐objective Optimisationmentioning

confidence: 99%

“…Among them, the multi‐objective optimisation problem can be transformed into a single‐objective problem by the weighted average method. The weighted average method can be divided into the multiplication/division form and the addition form, wherein the addition form must need to find the standard value of each optimisation objective and normalise it [16]. Therefore, the weighted average method of multiplication/division form is used to obtain the final optimisation objective function.…”

Section: Multi‐objective Optimisationmentioning

confidence: 99%

“…Nowadays, there is a lot of research on multi-objective design optimisation of motor. In general, there are three methods for multi-objective design optimisation of motor: linear analytical model [6][7][8][9], magnetic equivalent circuits (MEC) model [10][11][12][13][14], and finite element analysis (FEA) model [15][16][17][18][19][20][21], all of them can be combined with modern optimisation algorithms such as non-dominated sorting GA-II, artificial bee colony algorithm, modified particle swarm optimisation (MPSO) etc [22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi‐objective optimisation design and performance comparison of permanent magnet synchronous motor for EVs based on FEA

Song

Zhang

Liu

et al. 2019

IET Electric Power Applications

View full text Add to dashboard Cite

The requirements of high efficiency, power density, and low price for the motor of electric vehicles (EVs) make the design of the driving motor become a process of multi-objective optimisation. For purpose of the permanent magnet synchronous motor (PMSM) used for EVs has the higher efficiency, wider range of speed regulation with flux-weakening and better cost superiority, a multi-objective optimisation design approach based on finite element analysis (FEA) and modified particle swarm optimisation (MPSO) algorithm which takes efficiency, flux-weakening rate, and price as optimisation objectives is proposed in this study. Five PMSMs with different rotor topologies (V-shape, U-shape, double V-shape, delta-shape, and double tangential-shape) are optimised by the proposed optimisation method and their performance characteristics, including flux-weakening ability, efficiency, price, and anti-demagnetisation ability, are compared. The results suggest that double V-shape rotor topology has the wider constant power range and double-layer PMs topology has stronger anti-demagnetisation ability and wider high efficiency interval, whereas single-layer topology has lower cost price. Furthermore, a PMSM prototype with V-shape PMs is manufactured, so that the feasibility of multi-objective optimisation design approach and accuracy of FEA are verified by prototype experiments.

show abstract

Section: β1 Efficiency and Torque Density In Relation To Thermal Energymentioning

confidence: 99%

Section: Multi‐objective Optimisationmentioning

confidence: 99%

Section: Multi‐objective Optimisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi‐objective optimisation design and performance comparison of permanent magnet synchronous motor for EVs based on FEA

Song

Zhang

Liu

et al. 2019

IET Electric Power Applications

View full text Add to dashboard Cite

show abstract

“…Stochastic evolutionary methods, such as genetic algorithm (GA) and particle swarm optimization (PSO) [17], are favorable in machine design optimization because they can search a high dimension of the design space in a computationally efficient manner [13]. The stochastic evolutionary methods have been coupled with an FEA solver to optimize the designs of SRMs [14]- [15]. However, due to the use of the computationally costly FEA solver, the overall computational costs of the combined approaches are intensive, especially for multiobjective optimization problems.…”

mentioning

confidence: 99%

Multiobjective Optimization of Switched Reluctance Motors Based on Design of Experiments and Particle Swarm Optimization

2015

IEEE Trans. Energy Convers.

194

109

View full text Add to dashboard Cite

Abstract-This paper proposes a comprehensive framework for multiobjective design optimization of switched reluctance motors (SRMs) based on a combination of the design of experiments and particle swarm optimization (PSO) approaches. First, the definitive screening design was employed to perform sensitivity analyses to identify significant design variables without bias of interaction effects between design variables. Next, optimal third-order response surface (RS) models were constructed based on the Audze-Eglais Latin hypercube design using the selected significant design variables. The constructed optimal RS models consist of only significant regression terms, which were selected by using PSO. Then, a PSObased multiobjective optimization coupled with the constructed RS models, instead of the finite-element analysis, was performed to generate the Pareto front with a significantly reduced computational cost. A sample SRM design with multiple optimization objectives, i.e., maximizing torque per active mass, maximizing efficiency, and minimizing torque ripple, was conducted to verify the effectiveness of the proposed optimal design framework.Index Terms-Design of experiments (DoE), multiobjective optimization, particle swarm optimization (PSO), response surface (RS), sensitivity analysis, switched reluctance motor (SRM).

show abstract