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

Song, Tao; Zhang, Zhenyang; Liu, Huijuan; Hu, Wen-Luan

doi:10.1049/iet-epa.2019.0069

Cited by 46 publications

(38 citation statements)

References 31 publications

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“…The optimal design for a PMSM can be created by combining design methods such as analytical models [1,2], magnetic equivalent circuit (MEC) models [3,4], and finite element analysis (FEA) [5][6][7][8] with an optimal design algorithm. Although these design methods have their advantages, the FEA optimization method combined with the optimal design algorithm has the highest accuracy [5]. signals.…”

Section: Introductionmentioning

confidence: 99%

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

You

2020

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

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

You

2020

Applied Sciences

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“…In our previous study [18], the electromagnetic performance of a driving motor was optimised by combination of the modern optimisation algorithm and finite‐element model (FEM). The optimisation method is only setting the temperature of the winding and the permanent magnets (PMs) to a fixed value, and the effect of PMs and winding temperature change on the electromagnetic performance of the motor are ignored.…”

Section: Introductionmentioning

confidence: 99%

Multi‐physics and multi‐objective optimisation design of interior permanent magnet synchronous motor for electric vehicles

Song

Liu

Zhang

et al. 2020

IET Electric Power Applications

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The electrical machine design method plays a decisive role in electric vehicles (EVs). However, only a few designs consider the coupling of multiple fields simultaneously. This study proposes an improved multi‐physics and multi‐objective optimisation design approach for designing a 75 kW interior permanent‐magnet synchronous machine dedicated to EVs. Five optimal objectives including power density, temperature rise, price, torque ripple, and cogging torque are selected, and the first three objectives are optimised based on the sensitivity analysis of design parameters, where an improved thermal resistance network combining with finite‐element analysis model is developed. Meanwhile, the torque ripple and cogging torque are involved in subsequent design optimisation. The temperature rise and structural strength are recalculated by using commercial finite‐element model software, respectively, for validating the accuracy of optimisation design. Finally, a prototype motor is manufactured; both simulation and experimental results verify the feasibility and validity of the proposed optimisation design method.

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“…Optimal design is a method of finding the values of design variables to obtain an optimal solution within a range of constraints. The optimal design for PMSMs is created by combining design methods such as the analytical model, magnetic equivalent circuits (MEC) model, and finite element analysis (FEA) with optimal design algorithms [1][2][3][4][5][6][7][8]. First of all, there are studies on optimal design using the analytical model [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…K-means clustering algorithm was utilized to obtain the best solution out of the eight clusters. Finally, some research on optimization combined with FEA have been published [5][6][7]. The work in [5] performed multi-objective shape optimization of a PMSM based on FEA and particle swarm optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling

You

2019

Energies

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To obtain accurate optimal design results in electric machines, the finite element analysis (FEA) technique should be used; however, it is time-consuming. In addition, when the design of experiments (DOE) is conducted in the optimal design process, mechanical design, analysis, and post process must be performed for each design point, which requires a significant amount of design cost and time. This study proposes an automated DOE procedure through linkage between an FEA program and optimal design program to perform DOE easily and accurately. Parametric modeling was developed for the FEA model for automation, the files required for automation were generated using the macro function, and the interface between the FEA and optimal design program was established. Shape optimization was performed on permanent magnet synchronous motors (PMSMs) for small electric vehicles to maximize torque while maintaining efficiency, torque ripple, and total harmonic distortion of the back EMF using the built-in automation program. Fifty FEAs were performed for the experimental points selected by optimal Latin hypercube design and their results were analyzed by screening. Eleven metamodels were created for each output variable using the DOE results and root mean squared error tests were conducted to evaluate the predictive performance of the metamodels. The optimization design based on metamodels was conducted using the hybrid metaheuristic algorithm to determine the global optimum. The optimum design results showed that the average torque was improved by 2.5% in comparison to the initial model, while satisfying all constraints. Finally, the optimal design results were verified by FEA. Consequently, it was found that the proposed optimal design method can be useful for improving the performance of PMSM as well as reducing design cost and time.

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

Cited by 46 publications

References 31 publications

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

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

Multi‐physics and multi‐objective optimisation design of interior permanent magnet synchronous motor for electric vehicles

Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling

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