scholarly journals Optimal Design of IPMSM for EV Using Subdivided Kriging Multi-Objective Optimization

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1490
Author(s):  
Jong-Min Ahn ◽  
Myung-Ki Baek ◽  
Sang-Hun Park ◽  
Dong-Kuk Lim
Keyword(s):  
Optimal Design ◽  
Objective Function ◽  
Pareto Front ◽  
Permanent Magnet Synchronous Motor ◽  
Kriging Model ◽  
Superior Performance ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Design Variables ◽  
Interior Permanent Magnet

In this paper, subdivided kriging multi-objective optimization (SKMOO) is proposed for the optimal design of interior permanent magnet synchronous motor (IPMSM). The SKMOO with surrogate kriging model can obtain a uniform and accurate pareto front set with a reduced computation cost compared to conventional algorithms which directly adds the solution in the objective function area. In other words, the proposed algorithm uses a kriging surrogate model, so it is possible to know which design variables have the value of the objective function on the blank space. Therefore, the solution can be added directly in the objective function area. In the SKMOO algorithm, a non-dominated sorting method is used to find the pareto front set and the fill blank method is applied to prevent premature convergence. In addition, the subdivided kriging grid is proposed to make a well-distributed and more precise pareto front set. Superior performance of the SKMOO is confirmed by compared conventional multi objective optimization (MOO) algorithms with test functions and are applied to the optimal design of IPMSM for electric vehicle.

Optimal shape design of multi-layered IPMSM using adaptive dynamic Taylor Kriging model

2018 ◽  
Vol 37 (2) ◽  
pp. 581-590
Author(s):  
Bin Xia ◽  
Junmo Yeon ◽  
Chang Seop Koh
Keyword(s):  
Permanent Magnet Synchronous Motor ◽  
Kriging Model ◽  
Torque Ripple ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Adaptive Dynamic ◽  
Multi Objective ◽  
Interior Permanent Magnet ◽  
And Performance

PurposeThis paper aims to propose a numerically efficient multi-objective optimization strategy, which can improve both the efficiency and performance during the optimization process. Design/methodology/approachThis paper discusses the multi-objective optimization algorithm by combining multi-objective differential evolution (MODE) algorithm with an adaptive dynamic Taylor Kriging (ADTK) model. FindingsThe proposed approach is validated through application to an analytic example and applied to a shape optimal design of a multi-layered interior permanent magnet synchronous motor for torque ripple reduction while maintaining the average torque. Originality/valueThe ADTK model selects its basis functions adaptively and dynamically so that it may have better accuracy than any other Kriging models. Through adaptive insertion of new sampling data, it guarantees minimum required sampling data for a desired fitting accuracy.

Updating Kriging Surrogate Models Based on the Hypervolume Indicator in Multi-Objective Optimization

2013 ◽  
Vol 135 (9) ◽  
Author(s):  
Koji Shimoyama ◽  
Koma Sato ◽  
Shinkyu Jeong ◽  
Shigeru Obayashi
Keyword(s):  
Objective Function ◽  
Objective Function Value ◽  
Kriging Model ◽  
Surrogate Models ◽  
Expected Improvement ◽  
Multi Objective Optimization ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Beam Design ◽  
Hypervolume Indicator

This paper presents a comparison of the criteria for updating the Kriging surrogate models in multi-objective optimization: expected improvement (EI), expected hypervolume improvement (EHVI), estimation (EST), and those in combination (EHVI + EST). EI has been conventionally used as the criterion considering the stochastic improvement of each objective function value individually, while EHVI has recently been proposed as the criterion considering the stochastic improvement of the front of nondominated solutions in multi-objective optimization. EST is the value of each objective function estimated nonstochastically by the Kriging model without considering its uncertainties. Numerical experiments were implemented in the welded beam design problem, and empirically showed that, in an unconstrained case, EHVI maintains a balance between accuracy, spread, and uniformity in nondominated solutions for Kriging-model-based multiobjective optimization. In addition, the present experiments suggested future investigation into techniques for handling constraints with uncertainties to enhance the capability of EHVI in constrained cases.

A method for the optimal design of low-density polymer foam core sandwiches using FEA and multiobjective optimization of design variables

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Çağrı Uzay ◽  
Durmuş Can Acer ◽  
Necdet Geren
Keyword(s):  
Optimal Design ◽  
Load Capacity ◽  
Low Density ◽  
Foam Core ◽  
Multi Objective Optimization ◽  
Polymer Foam ◽  
Multi Objective ◽  
The Core ◽  
Design Variables ◽  
Bending Load

Abstract In this study, a generative method was introduced to determine the optimal design of low-density polymer foam core sandwiches using finite element analysis (FEA) and multi-objective optimization of design variables without needing experiments. The method was also assessed. The sandwich structures were designed based on woven plain carbon fiber fabrics, PVC foam core, and polymer epoxy matrix. The design variables are the core density (40, 48, 60 kg/m3) and the core thickness (16, 20, 25 mm). The sandwich configurations were subjected to FEA under the three-point bending (TPB) loads. The force-reaction curves obtained from FEA were compared to experimental data available in the literature. Excellent agreement was achieved between the experimental and FEA simulated results at the linear elastic region of the curves. Thus, it allowed predicting the bending stiffness of the sandwiches via TPB analysis. Besides, a two-way analysis of variance (ANOVA) was conducted to determine the effects of parameters on sandwich mass and bending load capacity. Multi-objective optimization of design variables was also carried out according to the constructed mathematical models. The method provided in this study eases both designer’s and researcher’s work to obtain the optimal design variables without making costly experiments.

scholarly journals Multi-Objective Optimal Design of Permanent Magnet Synchronous Motor for Electric Vehicle Based on Deep Learning

2020 ◽  
Vol 10 (2) ◽  
pp. 482 ◽  
Author(s):  
Yong-min You
Keyword(s):  
Deep Learning ◽  
Optimal Design ◽  
Shape Optimization ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Kriging Model ◽  
Torque Ripple ◽  
Synchronous Motor ◽  
Objective Functions ◽  
Multi Objective

Recently, a large amount of research on deep learning has been conducted. Related studies have also begun to apply deep learning techniques to the field of electric machines, but such studies have been limited to the field of fault diagnosis. In this study, the shape optimization of a permanent magnet synchronous motor (PMSM) for electric vehicles (EVs) was conducted using a multi-layer perceptron (MLP), which is a type of deep learning model. The target specifications were determined by referring to Renault’s Twizy, which is a small EV. The average torque and total harmonic distortion of the back electromotive force were used for the multi-objective functions, and the efficiency and torque ripple were chosen as constraints. To satisfy the multi-objective functions and constraints, the angle between the V-shaped permanent magnets and the rib thickness of the rotor were selected as design variables. To improve the accuracy of the design, the design of experiments was conducted using finite element analysis, and a parametric study was conducted through analysis of means. To verify the effectiveness of the MLP, metamodels was generated using both the MLP and a conventional Kriging model, and the optimal design was determined using the hybrid metaheuristic algorithm. To verify the structural stability of the optimized model, mechanical stress analysis was conducted. Moreover, because this is an optimal design problem with multi-objective functions, the changes in the optimal design results were examined as a function of the changes in the weighting. The optimal design results showed that the MLP technique achieved better predictive performance than the conventional Kriging model and is useful for the shape optimization of PMSMs.

scholarly journals Improved Immune Algorithm Combined with Steepest Descent Method for Optimal Design of IPMSM for FCEV Traction Motor

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3904
Author(s):  
Ji-Chang Son ◽  
Myung-Ki Baek ◽  
Sang-Hun Park ◽  
Dong-Kuk Lim
Keyword(s):  
Optimal Design ◽  
Permanent Magnet Synchronous Motor ◽  
Torque Ripple ◽  
Descent Method ◽  
Immune Algorithm ◽  
Electric Machines ◽  
Steepest Descent Method ◽  
Superior Performance ◽  
Element Analysis ◽  
Traction Motor

In this paper, an improved immune algorithm (IIA) was proposed for the torque ripple reduction optimal design of an interior permanent magnet synchronous motor (IPMSM) for a fuel cell electric vehicle (FCEV) traction motor. When designing electric machines, both global and local solutions of optimal designs are required as design result should be compared in various aspects, including torque, torque ripple, and cogging torque. To lessen the computational burden of optimization using finite element analysis, the IIA proposes a method to efficiently adjust the generation of additional samples. The superior performance of the IIA was verified through the comparison of optimization results with conventional optimization methods in three mathematical test functions. The optimal design of an IPMSM using the IIA was conducted to verify the applicability in the design of practical electric machines.

Multiobjective optimization of hard coating parameters designing for damping of the bladed disk

2017 ◽  
Vol 232 (9) ◽  
pp. 1609-1619 ◽  
Author(s):  
Yugang Chen ◽  
Jingyu Zhai ◽  
Qingkai Han
Keyword(s):  
Pareto Front ◽  
Engineering Application ◽  
Front Surface ◽  
Model Material ◽  
Superior Performance ◽  
Hard Coating ◽  
Damping Capacity ◽  
Nsga Ii ◽  
Bladed Disk ◽  
Design Variables

In this paper, the damping capacity and the structural influence of the hard coating on the given bladed disk are optimized by the non-dominated sorting genetic algorithm (NSGA-II) coupled with the Kriging surrogate model. Material and geometric parameters of the hard coating are taken as the design variables, and the loss factors, frequency variations and weight gain are considered as the objective functions. Results of the bi-objective optimization are obtained as curved line of Pareto front, and results of the triple-objective optimization are obtained as Pareto front surface with an obvious frontier. The results can give guidance to the designer, which can help to achieve more superior performance of hard coating in engineering application.

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