Multiobjective design optimization and analysis of magnetic flux distribution for slotless permanent magnet brushless DC motor using evolutionary algorithms

Chakkarapani, Kamal; Thangavelu, Thyagarajan; Dharmalingam, K.; Thandavarayan, Pragadheeshwaran

doi:10.1016/j.jmmm.2019.01.029

Cited by 43 publications

(24 citation statements)

References 19 publications

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“…The mentioned procedure decreases the accuracy of the sensitivity analysis. In this article, the weighted sum approach 15 is used to implement the MO sensitivity analysis. The combination of the weighted sum approach and MO Taguchi optimization method 35 is applied to identify essential parameters on objectives.…”

Section: Sensitivity Analysis Based On Doementioning

confidence: 99%

“…In these approaches, the MO function inverts to single objective (SO) function then a SO optimization algorithm is used for the optimization. The drawbacks of DM are (a) to obtain the Pareto front, there needs to solve a problem many times, and (b) the mapping of MO problem to SO leads to a decrease in the accuracy of the results 15,16 . The second type of MO optimization algorithm is a direct optimization procedure.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Mahmouditabar

Vahedi

Mosavi

et al. 2020

Int Trans Electr Energ Syst

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Summary The flux switching permanent magnet (FSPM) motor is relatively a new topology of the permanent magnet (PM) motors, which both PM and armature winding are placed at the stator. This feature leads to more robust design, better heat dissipation, and a proper option for a wide range of industrial applications. The critical part of the development of FSPM motor is the design optimization of the structure to improve the electromagnetic performance of the motor. In this article, first to reduce the computation time and required memory of the optimization procedure, the multiobjective sensitivity analysis based on design of experiment is performed to specify the most effective parameters on the objectives. Then, the initial samples data of the optimization procedure is obtained by 2D finite element method (FEM) model of the FSPM motor, which is validated by the prototype of the motor. Furthermore, based on FEM results the multilayers perceptron artificial neural network for the approximation of relation between design variables and objectives is implemented. Finally, using the nondominated sorting genetic algorithm‐II, the optimization procedure of the FSPM motor is done. The accuracy of the presented optimization procedure is validated by a comparison of the initial prototype and final design of the motor.

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Section: Sensitivity Analysis Based On Doementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Mahmouditabar

Vahedi

Mosavi

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…The performance of MOOAs is presented as in Reference 8. The performances of MOOAs are compared by developing true Pareto front which act as a reference point as in Reference 9. From the reference point, different metrics are compared to analyze the performance of various MOOAs as in References 10‐12.…”

Section: Introductionmentioning

confidence: 99%

Thermal analysis of brushless DC motor using multiobjective optimization

Chakkarapani

Thangavelu

Dharmalingam

2020

Int Trans Electr Energ Syst

Self Cite

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In this article, the design of brushless DC (BLDC) motor is performed using multiobjective optimization algorithm (MOOA) by satisfying multiple objectives. Initially, sensitivity analysis is carried out to find the most influencing parameters that affect the performance of BLDC motor. MOOAs such as Pareto envelope-based selection algorithm (PESA), Pareto archived evolution strategy (PAES) and nondominated sorting genetic algorithm-II (NSGA-II) is employed in the optimal design of the BLDC motor. The proposed MOOA have three objectives namely: output torque maximization, volume minimization, and minimization of total losses. MOOAs are analyzed using performance metrics and qualitative comparison is provided to select the best algorithm. Later, finite element method (FEM) is used to investigate the transient and thermal characterization on the BLDC motor designed using NSGA-II. The thermal results thus obtained using NSGA-II for the above motor under different operating conditions is also compared with the existing single objective optimization algorithm. From the comparisons, it is observed that NSGA-II algorithm significantly outperforms the existing single objective optimization algorithm. Finally, the usefulness of the designed machine based on NSGA-II is compared with the results obtained from simulation and hardware analysis.

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“…Liu et al (2018) investigated that the spindle energy consumption was dependent on a specific motor and was useful in analyzing spindle motor efficiency for energy reduction. Chakkarapani et al (2019) proposed a comprehensive framework of design optimization of motor parameters for maximizing output torque and minimizing total volume and total power losses to obtain the optimum geometric specifications of a motor.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of lathe spindle system for optimum energy efficiency

et al. 2020

Journal of Cleaner Production

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Spindle system is the major mechanical component in machine tools, and its performance is responsible for a significant portion of the total consumed energy of machine tools. Conventional design optimization of spindle system is partially focused on parameter optimization of spindle motor or transmission system, contributing to an increase of the motor efficiency. Given that concurrent interactions among them is complex, very little efforts has been done to conduct integration optimization for optimum energy efficiency. To this end, a new approach of spindle system design is presented with consideration of the above two aspects adequately, to achieve the maximum energy and material efficiency. Firstly, the energy characteristic of spindle system is explicitly modeled on the basis of energy flow analysis. Then, a multi-objective optimization model for parameter optimization of spindle motor and transmission system is developed to take the both maximum energy efficiency and minimum volume as objectives, which is subjecting to a set of constraints with related to the cutting parameters boundary, processing requirements and shifting power losses. Finally, a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is presented to solve the developed optimization model. The performance of the proposed design approach of lathe spindle system is demonstrated through different working conditions. The experimental results indicate that the design of energy and material efficient machine tools can be achieved.

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Multiobjective design optimization and analysis of magnetic flux distribution for slotless permanent magnet brushless DC motor using evolutionary algorithms

Cited by 43 publications

References 19 publications

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Thermal analysis of brushless DC motor using multiobjective optimization

Design optimization of lathe spindle system for optimum energy efficiency

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