Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm

Son, Ji-Chang; Ahn, Jong-Min; Lim, Jaewon; Lim, Dong-Kuk

doi:10.1109/access.2021.3054920

Cited by 17 publications

(7 citation statements)

References 47 publications

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“…The design variables are shown in Fig. 10 and are selected as the pole arc to pole pitch ratio (alpha) and the angle of the magnet (m θ ), as the torque ripple varies according to the shape of the magnet placement [31], [32].…”

Section: Design Optimization Of the Ipmsm For Hev Application Using I...mentioning

confidence: 99%

Performance Enhancement of the IPMSM for HEV Applications Using Grain-Oriented Electrical Steel and Design Optimization

et al. 2022

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This paper proposes a method for enhancing the performance of an interior permanent magnet synchronous motor (IPMSM) using grain-oriented electrical steel (GO) and design optimization. As the GO has superior magnetic characteristics in the rolling direction, the GO is applied to the stator teeth to increase the torque and reduce the iron loss of the IPMSM. However, such an approach leads to higher saturation on the core, resulting in worse pulsation characteristics. To handle with pulsation problem on the IPMSM, design optimization is conducted. In this paper, the interpolation multi-objective robust optimization algorithm (IMROA) was proposed. The IMROA can reduce the calculation time by interpolating the objective region and utilizing a stepwise sampling strategy. Moreover, the IMROA considers the robustness of the found solution and can derive a robust global solution that has robustness on the manufacturing tolerance and deformation during the operation and prevents unexpected results on the IPMSM.

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Section: Design Optimization Of the Ipmsm For Hev Application Using I...mentioning

confidence: 99%

Performance Enhancement of the IPMSM for HEV Applications Using Grain-Oriented Electrical Steel and Design Optimization

et al. 2022

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“…Therefore, it is essential to select the appropriate number of samples. For this paper, appropriate initial samples were selected by referring to the [25].…”

Section: Surrogate Model Creationmentioning

confidence: 99%

“…The more kriging grid, the higher accuracy of the solution; however, the reason why the grid cannot be infinitely finely divided is because all the values of all grid points must be compared, which can take a long time. The proper kriging grid setting is required considering the accuracy of the solution and interpretation time [25]. Therefore, in this paper, the kriging grid is not finely divided from the beginning of algorithm, it is divided later in the algorithm to reduce the interpretation time and increase the accuracy of the solution.…”

Section: Subdivided Kriging Gridmentioning

confidence: 99%

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

et al. 2021

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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.

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“…This algorithm was applied to minimize the cogging torque of an axial flux permanent magnet machine (AFPMSM), and three local solutions were obtained [16,17]. Moreover, Yoo et al [18] and Son et al [19] suggested and developed a computationally efficient algorithm that was applied for the multi-modal optimization of an interior permanent magnet synchronous motor (IPMSM) and a permanent magnet assisted synchronous reluctance motor (PMa-SynRM). Previous studies have focused on heuristic algorithms to address the multi-modal problem by finding multiple optima.…”

Section: Introductionmentioning

confidence: 99%

Multi-Fidelity Model-Based Size Optimization of Electric Machines

Ahn

Min

2022

IEEE Access

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The multi-modal problem and high computational cost represent challenges in the optimization of electric machines owing to their highly nonlinear electromagnetic response. To overcome these challenges, this paper proposes a multi-fidelity model-based sequential optimization method in which both low-and highfidelity models are employed in two phases. In phase 1, the reluctance network (RN) is adopted as the lowfidelity model and mainly contributes to alleviating the abovementioned challenges. To overcome the low accuracy of the RN, the optimal design is obtained using a finite element model (FEM) in phase 2. The multistart strategy and gradient-based algorithm are utilized instead of a heuristic algorithm in all phases to avoid excessive calculations. This multi-fidelity model concept presents a novelty compared to previous research that focused only on algorithm development. The effectiveness of the proposed method is validated with two examples, consisting of the TEAM workshop problem 25 and the torque ripple minimization of an interior permanent magnet synchronous motor. The optimal designs and computational time resulting from the proposed method are compared with those of the conventional method, where only a FEM is used during optimization. The results show that the proposed method is remarkable in finding superior optimal designs, while ignoring unimportant local optima. Additionally, it can save up to 90% of the computational time required by the conventional method.INDEX TERMS Computational efficiency, electric machine, finite element model, multi-fidelity model, multi-modal problem, reluctance network.

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Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm

Cited by 17 publications

References 47 publications

Performance Enhancement of the IPMSM for HEV Applications Using Grain-Oriented Electrical Steel and Design Optimization

Performance Enhancement of the IPMSM for HEV Applications Using Grain-Oriented Electrical Steel and Design Optimization

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

Multi-Fidelity Model-Based Size Optimization of Electric Machines

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