Optimization Design Of Motor Research Articles

Optimal Design of Surface-Mounted Permanent Magnet Motor With Multi-Level Magnet Combination Using Analytical Method

Optimal Design of Surface-Mounted Permanent Magnet Motor With Multi-Level Magnet Combination Using Analytical Method

Optimal Design and Analysis of High-Speed Permanent Magnet Synchronous Motor Based on Genetic Algorithm and TOPSIS Method

Optimal Design and Analysis of High-Speed Permanent Magnet Synchronous Motor Based on Genetic Algorithm and TOPSIS Method

Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation

Motor design involves multiple complex parameters, and traditional methods rely on experience and experimentation, which are inefficient and difficult to optimize. With the rapid development of emerging industries such as electric vehicles and intelligent manufacturing, the performance requirements for motors are constantly increasing. Optimizing design under multi-objective and multi-constraint conditions has become a key challenge. To address this, this paper proposes a motor design performance optimization algorithm based on Graph Neural Networks (GNN) representation and adaptive weighting. GNN, as a deep learning model capable of handling complex structured data, can model the multi-parameter relationships in motor design and automatically extract key features through its feature propagation mechanism, thus overcoming the difficulty of capturing parameter dependencies with traditional methods. At the same time, Mixed-Integer Linear Programming (MILP) provides a powerful global optimization tool that can find the global optimal solution when dealing with complex decision variables and constraints, overcoming the shortcomings of traditional optimization algorithms in terms of global convergence. Moreover, the adaptive weighting mechanism allows the optimization algorithm to dynamically adjust the weights according to the influence of parameters on motor performance, ensuring the accuracy and adaptability of optimization results in different scenarios. Through the organic combination of these three methods, this paper aims to solve the problems of low efficiency, poor global convergence, and inability to dynamically adjust the importance of design parameters in traditional motor design optimization. By introducing advanced machine learning models and optimization algorithms, this work constructs an efficient motor design performance optimization framework.

NVH multiobjective and robust optimization of electric motor design, including power losses

As part of electric motor design, compromises between NVH considerations, electrotechnical design and performance targets must be made. Electromagnetic excitation within the airgap results in structure-borne and airborne noise transmission to the vehicle. High-frequency tones are characteristic of this noise. Efficient computation methods exist to estimate these vibration and noise levels. From the early design stage, NVH performance can be diagnosed and optimized. The focus on NVH can no longer be separated from efficiency optimization. Simulation workflows and optimization loops, developed for NVH-related targets, are updated to consider copper and iron losses. The modification of the geometrical design must be addressed to reduce both dynamic excitation and energy loss, or at least offer compromises. Electric motor design optimization requires the handling of multiple objectives and constraints from multiple physics. In addition, design robustness must be ensured as part of the industrial process: manufacturing tolerances have to be considered to define a robust optimized design.

Robust Design Optimization for a Hairpin Winding Permanent Magnet Motor Considering Permanent Magnet Inconsistency and Manufacturing Uncertainty

Robust Design Optimization for a Hairpin Winding Permanent Magnet Motor Considering Permanent Magnet Inconsistency and Manufacturing Uncertainty

Optimal Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicle Applications Using a Machine Learning-Based Surrogate Model

This paper presents an innovative design for an interior permanent magnet synchronous motor (IPMSM), targeting enhanced performance for electric vehicle (EV) applications. The proposed motor features a double V-shaped rotor structure with irregular ferrite magnets embedded in the slots between the permanent magnets. This design significantly enhances torque performance. Furthermore, a machine learning-based surrogate model is developed by integrating fine and coarse mesh data. Optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this surrogate model effectively reduces computational time compared to traditional finite element analysis (FEA).

Optimal design of a BLDC motor using African vulture optimization algorithm

BLDC motors are extensively favoured in robots, electric vehicles, and many industrial applications due to their superior torque, efficiency, and speed control features as compared to traditional motors. In this paper, collective intelligence-based optimization approaches are used to improve the cost-effectiveness, volume, and total loss of a BLDC motor. The geometric dimensions of the motor are considered the optimization parameters. The study employs several optimization algorithms, such as the Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Cuckoo Search Algorithm (COA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the African Vulture Optimization algorithm (AVOA), used for the optimal BLDC motor design. The investigation can provide data on the convergence rates and optimal design parameters of a BLDC motor through optimization approaches. The standard deviation has also been calculated for each optimization technique to assess the stability and consistency of finding the optimum solution. The paper also includes a sensitivity analysis of the AVOA regulating parameter to obtain the best solutions. In comparison to GA, PSO, COA, GWO, and WOA, the AVOA technique achieves faster convergence. Additionally, the ANOVA test is employed as a statistical test to validate the efficacy of the AVOA. According to the results, AVOA outperforms all other optimization algorithms for the design of the BLDC motor.

Optimal Design of a Novel Consequent-Pole Interior Permanent Magnet Motor with Flared-Structured Rotor

Interior permanent magnet motors are widely used in applications requiring high power density and high efficiency due to their high torque-generating capabilities. Recently, given the price fluctuations and unstable supply of rare earth permanent magnets, alternative configurations with reduced use of permanent magnets are being sought. Among the various candidates related to this, the consequent-pole type rotor structure can halve the number of permanent magnets used compared with conventional structures. However, in a no-load analysis, the waveform of the back electromotive force becomes asymmetric, generating a harmonic component. As a result, there is a disadvantage that the torque ripple increases. To overcome these shortcomings, we propose a novel rotor structure that applies a consequent-pole structure to an embedded permanent-magnet motor structure, wherein a number of permanent magnets are arranged in a flared structure to constitute a single polarity. In the proposed flared-structured magnet arrangement, it is possible to adjust the angle of the permanent magnet and the polar angle to mitigate the asymmetry of the back-EMF waveform. The proposed structure was optimized with a genetic algorithm and a prototype of the optimal model was constructed and experimentally evaluated to verify its validity. Finally, the performance improvement and validity of the proposed structure were verified by comparing the analysis results of the optimal model with the experimental results.

퍼스널 모빌리티용 탈희토류 영구자석 동기모터 최적 설계

퍼스널 모빌리티용 탈희토류 영구자석 동기모터 최적 설계

Determination of performance characteristics using FEA-analytical for outer rotor BLDC motor

This paper presents an analytical approach to determine the performance characteristics of an outer rotor Brushless DC (BLDC) motor using finite element analysis (FEA). An accurate performance characteristic is a crucial deliverable during the early motor design process in order to minimize the cost of change while delivering a feasible design to the application. There are several popular methods to determine motor performance, including template-based software as well as FEA. However, these two methods have limitations in terms of geometry and time. Therefore, this paper proposes that the limitations can be addressed by using a combination of FEA and mathematical equations. First, the optimum motor design is determined using an FEA analytical approach. Second, the torque constant <em>K</em><sub>T</sub>, is derived from the FEA results and followed by performance curve generation using the mathematical equation of speed, current, and output power in terms of torque function. Finally, the outcome results are verified against the motor requirements, including an assessment of maximum speed, stall current, and maximum output power. In conclusion, this research introduces a methodology for determining full curve motor performance prior to prototype fabrication. The leverage benefit could be gained through robust design and low-cost development.

Crowding-based multi-objective artificial gorilla troops optimizer for brushless direct current motor design optimization

PurposeThis paper aims to present a novel multi-objective version of the Gorilla Troops optimizer (GTO), based on crowding distance, to achieve the optimal design of a brushless direct current motor.Design/methodology/approachIn the proposed algorithm, the crowding distance technique was integrated into the GTO to perform the leader selection and also for the external archive refinement from extra non-dominated solutions. Furthermore, with a view to improving the diversity of non-dominated solutions in the external archive, mutation operator was used. For constrained problems, an efficient strategy was adopted. The proposed algorithm is referred to as CD-MOGTO.FindingsTo validate the effectiveness of the proposed approach, it was initially tested on three constrained multi-objective problems; thereafter, it was applied to optimize the design variables of brushless direct current motor to concurrently fulfill six inequality constraints, maximize efficiency and minimize total mass.Originality/valueThe results revealed the high potential of the proposed algorithm over different recognized algorithms in solving constrained multi-objective issues and the brushless direct current motors.

Optimal Design of Asynchronous Motor based on Finite Element Simulation

Aiming at motors’ deficiencies such as short life, low efficiency, and high cost in China, this paper optimizes the design of asynchronous motors, so as to improve its performance and efficiency, prolong its life, and reduce its cost. Simulating an asynchronous motor by Maxwell, a finite element simulation software, this paper analyzes the distribution diagrams of waveform and magnetic field including its voltage, current, torque, and back electromotive force to verify the design reliability. Moreover, the parametric simulation of the motor is implemented through the function of Maxwell parameter solver, and the electromagnetic performance under different initial design parameters is simulated. Then, a sensitivity analysis algorithm is adopted to analyze the sensitivity of the motors’ initial design parameters. Meanwhile, this paper establishes the platform of motor optimization design, through which the optimization design of motors with different specifications can be fulfilled.

Accurate and Efficient Surrogate Model-Assisted Optimal Design of Flux Reversal Permanent Magnet Arc Motor

An accurate and efficient surrogate model plays a significant role in the optimal design of electrical motors. This paper presents an accurate and efficient surrogate modeling method for a flux reversal permanent magnet arc motor (FR-PMAM) based on machine learning algorithm. Specifically, the motor topology and the optimized design of the FR-PMAM are firstly illustrated. Then, the PM magnetic motive force-permeance model is developed to select structural parameters of FR-PMAM. After that, a finite-element analysis (FEA) model of FR-PMAM is calculated to obtain sample data. A machine learning algorithm random forest is introduced into the field of motor optimal design for the first time to evaluate the contribution of each parameter to the design objectives. Based on the sample data, an efficient machine learning algorithm CatBoost is innovatively employed to establish the accurate and efficient surrogate model of the FR-PMAM that can capture the complex function relationship between output objectives and input parameters. Subsequently, an improved genetic algorithm with the elitist reservation strategy is adopted to obtain the optimal combination of the structural parameters of FR-PMAM. Finally, a prototype of the FR-PMAM is manufactured. The FEA and experimental results validate the effectiveness of the investigated surrogate modeling method.

Sizing, Modeling, and Performance Comparison of Squirrel-Cage Induction and Wound-Field Flux Switching Motors

In this study, the analytical design and electromagnetic performance comparison of a squirrel-cage induction motor (SCIM) and a wound-field flux switching motor (WFFSM) for high-speed brushless industrial motor drives is undertaken for the first time. The study uses analytical sizing techniques and finite element analysis (FEA) to model and predict the performance of both motors at a 7.5 kW output power. This study includes detailed equations and algorithms for sizing and modeling of both types of motors, as well as performance calculations that aid in motor selection, design optimization, and system integration. The main findings show that the SCIM has superior torque performance for starting and overload conditions, while the WFFSM offers advantages in power factor, efficiency over a wide operating range, and potential for higher peak power output. To this end, the WFFSM is capable of high-speed and high-efficiency operation while the SCIM is suitable for applications requiring variable speed operation. The validation study shows good agreement between analytical and FEA calculations for both motors. The results provide insights into the design and performance characteristics of both motors, enabling researchers to explore innovative approaches for improving their efficiency, reliability, and overall performance.

An optimized stator and rotor design of squirrel cage induction motor for EMU train

This paper aims to objectively calculate the 480-kW squirrel cage induction motor (SCIM) design for the electric multiple unit (EMU) trains. This is done by optimizing the stator slot and rotor slot design to get efficiency and power factor targets. The stator slot design is achieved by limiting the width and height of the stator slot pitch according to the specified range. A depth-to-width ratio is used according to the range to optimize the design of the rotor bar slot. The design process of the induction motor consists of three steps: it determines the specification design target, calculates the specified parameters of the induction motor, and simulates the design to obtain the most optimal motor design using ANSYS Maxwell. The simulation performance values obtained an efficiency of 92.547 % and a power factor of 0.915. This value is obtained from the optimization of the rotor slot and has met the minimum requirements of efficiency and power factor in designing a SCIM. The design proposed in this paper can be developed and applied in the Indonesian domestic railway manufacturing industry.

Optimizing the Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicles with a Hybrid ABC-SVM Algorithm

This paper presents a comprehensive investigation of the optimal design of an interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs), utilizing the hybrid artificial bee colony algorithm–support vector machine (HAS) algorithm. The performance of the drive motor is a crucial determinant of the overall vehicle performance, particularly in EVs that rely solely on a motor for propulsion. In this context, interior permanent magnet synchronous motors (IPMSMs) offer a compelling choice due to their high torque density, wide speed range, superior efficiency, and robustness. However, accurate analysis of the nonlinear characteristics of IPMSMs necessitates finite element analysis, which can be time-consuming. Therefore, research into methods for deriving an optimal model with minimal computation is of significant importance. The HAS is a powerful multimodal optimization technique that is capable of exploring several optimal solutions. It enhances the navigation capability by combining the artificial bee colony algorithm (ABC) with the kernel support vector machine (KSVM). Specifically, the algorithm improves the search ability by optimizing the movement of bees in each region generated by the KSVM. Furthermore, hybridization with the Nelder–Mead method ensures accurate and quick convergence at pointers discovered in the ABC. To demonstrate the effectiveness of the proposed algorithm, this study compared its performance with a conventional algorithm in two mathematical test functions, verifying its remarkable performance. Finally, the HAS algorithm was applied to the optimal design of the IPMSM for EVs. Overall, this paper provides a thorough investigation of the application of the HAS algorithm to the design of IPMSMs for electric vehicles, and its application is expected to benefit from the combination of machine-learning techniques with various other optimization algorithms.

Optimal Design and Cogging Torque Minimization of a Permanent Magnet Motor for an Electric Vehicle

Optimal Design and Cogging Torque Minimization of a Permanent Magnet Motor for an Electric Vehicle

Optimal design of brushless DC motor for electromobility propulsion applications using Taguchi method

Abstract This paper presents the optimal design of the permanent magnet brushless DC (BLDC) motor for electromobility propulsion applications. Two BLDC machines were analyzed: (a) – exterior rotor machine, and (b) – interior rotor machines The optimization of both motors structures was executed by the Taguchi method. The device structure was described by three design variables. The functional parameter (efficiency) and the economic parameter (total mass of the permanent magnets) were included in the objective function. The BLDC motors have been modelled using a finite element method (FEM). Finally, the functional parameters for both motor constructions were compared. The selected results of the calculations were presented and discussed.

Torque Component Redistribution and Enhancement for Hybrid Permanent Magnet Motor With Permanent Magnet Offset Placement

In this article, a hybrid permanent magnet (PM) motor with offset placement design is proposed. In the proposed design method, the center axes of rare-earth PM and nonrare-earth PM are shifted artfully to improve motor performances, including output torque and antidemagnetization capability. The relationship among the PM offset placement, current angle, and electromagnetic performances is established. In order to reduce the current angle to achieve maximum output torque, the PM shift angle is preliminarily designed. To further improve motor performances, a multiobjective optimization approach is subsequently carried out for optimal motor design. In addition, to demonstrate the advantages of the PM offset placement, a symmetric placement PM motor is included to be compared with the proposed offset-placement hybrid PM motor based on electromagnetic characteristics. Finally, a prototype motor is built and tested. Based on simulation and experimental results, it is proven that the PM offset design can improve motor performances while it provides a new research path for hybrid PM motors.

A Computationally Efficient High-Fidelity Multi-Physics Design Optimization of Traction Motors for Drive Cycle Loss Minimization

Continuous improvement in performance of interior permanent magnet (IPM) machines is critical for electric vehicle traction applications. However, due to the cross-coupling and saturation effects, a significant amount of time-consuming finite element analysis (FEA) simulations are required to accurately estimate machine performance. Moreover, iterative design optimization will take significantly longer. In this article, an improved rapid performance estimation technique utilizing surrogate models is developed and coupled with a design optimization algorithm. The proposed framework has significantly less computational cost than alternative surrogate-based approaches, and efficiently employs drive cycle loss minimization for a multi-physics, multi-objective traction motor design optimization. Simulation and experimental results suggest the proposed optimization framework yields optimal designs more efficiently than existing methods while maintaining accuracy.