Multi-physics based Design Optimization of 100 kW-Class Interior Permanent Magnet Synchronous Motor for Electric Vehicle = 전기자동차용 100 kW급 매입형 영구자석 동기전동기의 다중물리 해석 기반 최적 설계

다국어 초록 (Multilingual Abstract)

This study proposes a multi-physics design framework for high-performance interior permanent magnet synchronous motors (IPMSMs) used in electric vehicles (EVs), integrating five physical domains: electromagnetics, structural mechanics, vibration mechanics, thermodynamics, and fluid mechanics. Single-physics approaches cannot sufficiently capture the multi-domain interactions that occur under real driving conditions; therefore, an integrated analysis is essential for designing high-power, high-efficiency EV traction motors. Each physics model was constructed using the Finite Element Method and validated through comparison with experimental data and published references. In particular, this study is significant in that it presents the first EV motor design framework that performs multi-physics optimization considering all five physics simultaneously, and establishes a practical optimization methodology by replacing high-cost multi-physics simulations with Kriging surrogate models, thereby overcoming the computational limitations of full-scale FEM-based optimization.
To reflect the sustained-load characteristics of EV driving environments, the target operating condition for optimization was defined as the continuous operating point (108 kW, 207 Nm, 5000 r/min). This continuous operating point was selected based on the load-distribution characteristics (cluster densities) observed across representative driving cycles, and all subsequent multi-physics analyses and optimization processes were performed under this condition.
Using the defined continuous operating point, multi-physics analysis was performed for an 8-pole, 48-slot IPMSM. The electromagnetic analysis incorporated non-linear magnetization and loss components to compute torque and loss characteristics. The structural analysis evaluated rotor stress under combined centrifugal force, electromagnetic force, and thermal expansion. The vibration analysis assessed resonances and displacements caused by air-gap flux harmonics. The thermal analysis predicted temperature distributions by incorporating winding, core, and magnet heat sources together with cooling-path boundary conditions. The fluid analysis computed flow fields and pressure drops in the cooling channel to evaluate actual cooling performance.
To reduce the high computational cost of multi-physics analysis, a Kriging surrogate model was constructed based on 6,790 simulation data points and combined with NSGA-II to perform multi-objective optimization for six performance indicators required at the continuous operating point: torque ripple, electromagnetic losses, structural safety factor, maximum vibration displacement, maximum insulation temperature, and cooling-path pressure drop.
The final optimized design achieved the following performance at the continuous operating point: 11.36% torque ripple, 1.028 kW electromagnetic losses, 1.207 structural safety factor, 0.133 µm maximum vibration displacement, 86.14°C maximum insulation temperature, and 1,717 Pa cooling-channel pressure drop. These results demonstrate that the proposed design meets the electromagnetic, thermal, and mechanical performance requirements in a balanced manner under continuous-operation conditions.
The integrated multi-physics design framework proposed in this study can be extended to high-fidelity loss modeling, advanced cooling-topology optimization, coupled electromagnetic–structural–control analysis, and digital-twin-based state prediction, and is expected to serve as a foundational technology for next-generation high-power EV traction motor development.

국문 초록 (Abstract)

본 논문은 전기자동차용 매입형 영구자석 동기전동기(IPMSM)의 고성능 설계 를 위해 전자기, 구조, 진동, 열, 유동의 다섯 가지 물리를 통합한 멀티피직스 설계 프레임워크를 제안한다. 단일 물리 기반 접근은 실제 운전 조건에서 발생하는 다물 리 상호작용을 충분히 반영하기 어렵기 때문에, 고출력 · 고효율이 요구되는 전기차 구동 모터 설계에는 통합적 분석이 필수적이다. 본 연구에서는 각 물리 모델을 Finite Element Method 기반으로 구축하고, 실험 및 참고문헌과의 비교를 통해 모델의 타당 성을 확보하였다. 특히 본 연구는 전자기–구조–진동–열–유동의 다섯 가지 physics 를 모두 포함한 EV 모터 멀티피직스 최적 설계를 최초로 제시하였으며, 계산비용이 큰 멀티피직스 해석을 surrogate 모델(Kriging) 기반으로 효과적으로 대체하여 실질 적 최적 설계에 적용 가능한 방법론을 구현하였다는 점에서 중요한 의의를 갖는다.
전기차 운전 환경의 지속 부하 특성을 반영하기 위해, 본 연구는 최적 설계의 목 표 운전점을 연속 운전점(continuous operating point: 108 kW, 207 Nm, 5000 r/min) 으로 정의하였다. 이 연속 운전점은 다양한 주행 사이클에서 관찰되는 부하 분포 (cluster densities)에 기반하여 선정된 것이며, 이후 모든 멀티피직스 해석 및 최적화 과정의 기준 조건으로 사용되었다.
정의된 연속 운전점을 기준으로 8극–48슬롯 IPMSM을 대상으로 멀티피직스 해 석을 수행하였다. 전자기 해석은 비선형 자화 특성과 손실 요소를 포함하여 토크 및 손실 특성을 산출하고, 구조 해석은 원심력 · 전자기력 · 열팽창이 결합된 회전자 응력을 분석하였다. 진동 해석은 공극 자속 고조파에 의한 가진력을 고려하여 공진 및 변위를 평가하였으며, 열 해석은 권선 · 코어 · 자석 발열과 냉각 경로를 반영하여 온도 분포를 예측하였다. 유동 해석은 냉각 채널의 속도장 및 압력강하를 계산하여 실제 냉각 성능을 평가하였다.
멀티피직스 해석의 높은 계산비용을 줄이기 위해 총 6,790개의 시뮬레이션 데이 터를 기반으로 Kriging 대리모델을 구축하고 NSGA-II와 결합하여, 연속 운전점을 만족하기 위한 여섯 가지 성능지표(토크리플, 전자기 손실, 구조 안전계수, 진동 변 위, 절연체 최고온도, 압력강하)에 대한 다목적 최적설계를 수행하였다.
최종 최적 설계는 연속 운전점 조건에서 다음의 성능을 달성하였다: 토크리플 11.36%, 전자기 손실 1.028 kW, 구조 안전계수 1.207, 최대 진동 변위 0.133 µm, 절연체 최고온도 86.14°C, 냉각채널 압력강하 1,717 Pa. 이는 제안된 설계가 연속 운전점에서 요구되는 전자기 · 열 · 기계적 성능을 균형 있게 충족함을 보여준다.
본 연구에서 제안된 멀티피직스 기반 통합 설계 기법은 향후 고정밀 손실 모델 링, 냉각 토폴로지 개선, 전자기–구조–제어 연성 설계, 디지털 트윈 기반 상태예측 등으로 확장될 수 있으며, 차세대 고출력 전기자동차 모터 개발의 핵심 기반 기술로 활용될 수 있을 것이다.

목차 (Table of Contents)

• Abstract i
• Contents iii
• List of Tables vii
• List of Figures ix
• Nomenclature xiii
• Chapter 1 INTRODUCTION 1
• 1.1 Research Background: Increasing EV Demand for Carbon Reduction and High-power Motor Development 1
• 1.1.1 Electric Vehicles as an Alternative Fuel-based Means of Transportation for Net Zero 1
• 1.1.2 Improving Driving Range of Electric Vehicles through High- power and High-efficiency Motors 4
• 1.1.3 Achieving High Power Density, Lightweight Design, and High Efficiency through Advanced Motor Design 5
• 1.2 Research Motivation: Necessity of Multi-physics Analysis in Motor Design 6
• 1.2.1 Need for Motor Performance Prediction Models for Optimal Motor Design 6
• 1.2.2 Need for Performance Prediction Models Considering Multi- physics 7
• 1.3 Goal and Significance of This Study 9
• 1.3.1 Goal of the Study 9
• 1.3.2 Significance of the Study 10
• 1.4 Thesis Structure 10
• Chapter 2 THEORETICAL BACKGROUND ON MULTI-PHYSICS AND VALIDATION OF EACH PHYSIC’S MODELING 13
• 2.1 Electromagnetics 13
• 2.1.1 Governing Equation: Maxwell’s Equation 14
• 2.1.2 Application to Motor 15
• 2.2 Structural Mechanics 15
• 2.2.1 Governing Equation: Force Balance Equation 15
• 2.2.2 Application to Motor 17
• 2.3 Vibration Mechanics 17
• 2.3.1 Governing Equation: Equation of Motion 17
• 2.3.2 Application to Motor 19
• 2.4 Thermodynamics 20
• 2.4.1 Governing Equation: Heat Balance Equation 20
• 2.4.2 Application to Motor 21
• 2.5 Fluid Mechanics 22
• 2.5.1 Governing Equation: Continuity & Navier–Stokes Equation 22
• 2.5.2 Application to Motor 25
• 2.6 Each Physics Finite Element Method Validation 26
• 2.6.1 Electromagnetics: Magnetic Field Homogeneity of Solenoid Magnet 26
• 2.6.2 Structural Mechanics: High Temperature Superconducting (HTS) Magnet Structure Test Equipment 30
• 2.6.3 Vibration Mechanics: Vibration of MRI Induced by Gradient Coil 32
• 2.6.4 Thermodynamics: Bitter Magnet Motor Temperature Prediction 35
• 2.6.5 Fluid Mechanics: Drag Force in Cylinder 37
• Chapter 3 MULTI-PHYSICS ANALYSIS MODELING OF INTERIOR PERMANENT MAGNET SYNCHRONOUS MOTOR FOR ELECTRIC VEHICLE 40
• 3.1 Multi-physics Analysis Modeling of Motor 40
• 3.1.1 Electromagnetic Modeling 41
• 3.1.2 Structural Mechanics Modeling 43
• 3.1.3 Vibration Mechanics Modeling 45
• 3.1.4 Thermodynamics Modeling 47
• 3.1.5 Fluid Mechanics Modeling 51
• 3.1.6 Material Properties for Analysis 53
• 3.2 Multi-physics Analysis Model for Design Optimization 57
• 3.2.1 Overall Sketch of Analysis Model 57
• 3.2.2 Parameters 59
• Chapter 4 MULTI-PHYSICS DESIGN OPTIMIZATION OF INTERIOR PERMANENT MAGNET SYNCHRONOUS MOTOR FOR ELECTRIC VEHICLE 62
• 4.1 Parameter Boundaries for Optimization 62
• 4.2 Optimization Algorithm (NSGAⅡ Optimization with Surrogate Model) 64
• 4.2.1 Multi Objective Function Optimization Algorithm: NSGAⅡ 64
• 4.2.2 Optimization Design Flow Chart 65
• 4.2.3 Surrogate Model of Multi-physics Analysis 65
• 4.2.4 Validation of the Surrogate Model 67
• 4.3 Optimization Result 69
• 4.3.1 Electromagnetics 71
• 4.3.2 Structural Mechanics 73
• 4.3.3 Vibration Mechanics 74
• 4.3.4 Thermodynamics 76
• 4.3.5 Fluid Mechanics 77
• Chapter 5 DISCUSSION 78
• 5.1 Challenges and Constraints 78
• 5.2 Advancement Potential 79
• Chapter 6 CONCLUSION 81
• Appendix A APPENDICES 83
• A.1 Uniform Current Density Solenoidal Coil Field Error Coefficients 83
• Bibliography 86
• Abstract (In Korean) 95