Program

Physics-based modeling and system-level optimization are foundational to Tesla’s engineering approach. From EV powertrains to Optimus actuators, designs begin with physics and integrate electromagnetic, thermal, structural, and other system considerations into a unified optimization framework.

For Optimus, these methods extend from individual actuators to the robot as a complete system. Mission and task requirements inform the actuation strategy and hardware architecture, with motors, inverters, and gearing optimized together to deliver the required performance efficiently.

This systems-level approach enables Optimus to be designed around what the robot needs to accomplish, rather than around individual components. The goal is a highly capable, efficient, and scalable actuation system that minimizes unnecessary hardware and complexity.

Tesla, Inc.

Optimus engineering, Humanoid Robot

Dr. Konstantinos Laskaris

Recent advances in reinforcement learning and imitation learning are accelerating the deployment of robots that operate in close proximity to humans. Toyota has been developing a variety of robotic systems, including the basketball robot CUE and ELEY, a mobile manipulator capable of learning and reproducing human tasks through demonstration. To enable safe and agile operation in human environments, these robots require actuators that simultaneously achieve high backdrivability for compliant physical interaction and high instantaneous torque for human-like dynamic motion.
This presentation introduces our motor development efforts aimed at meeting these challenging requirements. Focusing on Halbach-array outer-rotor motors, we discuss design approaches for balancing competing objectives such as torque output, rotor inertia, cogging torque, and resistance to demagnetization. Practical design examples, prototype evaluation results, and lessons learned through electromagnetic analysis are presented. In addition, future directions for motor technologies supporting next-generation human-centric robots are discussed.

TOYOTA MOTOR CORPORATION

R-Frontier Div., Frontier Research Center

Dr. Yoshihiro Okumatsu

Motor requirements for next-generation robots and mobility systems are first identified. A concentrated-winding SPMSM is then optimized for minimum weight under multiple combinations of continuous- and peak-torque requirements, considering electromagnetic, thermal, and demagnetization characteristics. The relationship between the torque requirements and the minimum achievable motor weight, together with the governing constraints, is clarified and summarized as a motor weight characteristic map for machine and motion design.

Kawasaki Heavy Industries, Ltd.

System Technology Development Center, Corporate Technology Division

Mr. Yoshiyasu Shibayama

Engineers hope to leverage induction heating analyses of motor cores as an effective method for initially evaluating heating conditions and coil designs. However, conventional simulations can result in discrepancies between analysis and measured results. That is why practical use depends on higher analysis accuracy. This case study examines the modeling process to determine the potential of improving analysis accuracy by comparing magnetic field and temperature results against actual measurements. Our presentation also lays out a future analysis approach founded in the knowledge gained from this case study.

AISIN DIGITAL ENGINEERING Corporation

CAE Engineering Department 2

Mr. Daiki Aso

The NTN Corporation is developing induction coils with innovative shapes to lower costs and reduce CO2 emissions by increasing the efficiency of power-to-heat conversion for induction hardening of shafts.
Currently, designers examine the coil shape using analyses. However, the complex designs and high labor intensity make automating the coil design process an essential goal. This case study runs a parametric optimization of three-dimensional induction coil shapes.
The design specifications require a large-scale analysis that runs numerous steps using a full model. Initial estimates predicted several months to run this simulation. Our approach took advantage of subcycling, parallel processing, surrogate models, and other such techniques to reduce the computational load to a practical level.
This presentation will also provide a look at the measurement results taken from testing a prototype of the optimal coil shape.

NTN Corporation

Production Engineering Development Department, Production Engineering Headquarters

Mr. Yu Uehara

We introduce an example of shortening the development period for drone motors by leveraging the thermal analysis functionality of JMAG. Conventionally, CFD analysis from the early design phase has issue, as the trial-and-error process required significant time. We established a design process in which rapid parameter studies using JMAG thermal analysis are employed for early-stage evaluation and structural screening, followed by CFD for final verification. In the presentation, we compare and evaluate the JMAG thermal analysis results against CFD data to discuss the effectiveness and analysis accuracy of this process.

NIDEC CORPORATION

Research Department 1, Nidec Product Technology R&D Center

Dr. Kentaro Hirose

In recent years, the practical adoption of next-generation power devices using SiC and GaN technologies has accelerated the increase of switching frequencies in power converters. This trend is expected to improve converter efficiency and reduce the size.
On the other hand, transformer design for power converters faces issues such as the impact of parasitic capacitance on EMI characteristics and design constraints due to miniaturization. Considering these factors during the design process often requires significant engineering effort.
This presentation introduces a design exploration case study that utilizes optimization techniques incorporating transformer parasitic capacitance, together with coupled electromagnetic, thermal, and structural analyses, to achieve an optimized transformer design.

TOYOTA MOTOR CORPORATION

Digital Verification Dept.

Mr. Yohsuke Kinoshita

As electronic devices continue to operate at higher frequencies, the need to predict inductor frequency characteristics during the design phase is increasing. This presentation introduces an evaluation of broadband impedance characteristics of an inductor in the MHz frequency range using JMAG's full-wave analysis capabilities. The applicability of the proposed approach is discussed with reference to selected measurement results.

TAIYO YUDEN CO., LTD.

Material Research & Development Department

Mr. Naoya Terauchi

Stationary-type magnetic refrigeration systems for hydrogen liquefaction are attractive because they operate without moving components. However, the requirement to energize superconducting coils with alternating current results in AC losses that significantly affect the thermal load and overall system efficiency. This presentation reports the evaluation of AC losses in superconducting coils for a magnetic refrigerator using JMAG. The validity of the analysis methodology was verified through comparison with experimental coil results, and a conceptual study of coil models for a future-scale system is also presented.

Railway Technical Research Institute

Advanced Superconductivity Technology Division Electromagnetic Systems

Dr. Yoshiki Miyazaki

Modern electric-vehicle traction motors must simultaneously deliver high torque, high efficiency, low noise, and robust performance under variable operating and manufacturing conditions. This work presents a data-driven simulation workflow developed with JMAG for high-confidence electric-drive design. The workflow combines efficient 2D finite-element analysis for large design studies with targeted 3D electromagnetic analysis to capture critical effects such as flux linkage, leakage, current prediction, resolver behavior, and sensor misalignment. JMAG-derived torque, flux-linkage, loss, and current maps are integrated with GM design and optimization tools to support points-of-interest evaluation and FEA-in-the-loop optimization across the motor operating region.
The approach also addresses manufacturing variability through design of experiments, sensitivity analysis, Monte Carlo sampling, and analysis of variance. These methods enable the evaluation of realistic distributions for key geometric and material tolerances, identify the dominant contributors to performance and energy-loss variation, and support more robust design decisions than nominal or peak-to-peak assessments alone. The resulting workflow provides accurate torque and current predictions, demonstrates strong simulation-to-test correlation, and enables scalable exploration of multidomain trade-offs early in development. Overall, the results establish JMAG as an efficient and validated platform for simulation-driven traction-motor development, connecting detailed electromagnetic physics, optimization, and manufacturing robustness in a unified process.
Keywords: traction motor; electric drive; finite-element analysis; JMAG; design optimization; Monte Carlo analysis; design of experiments; manufacturing variation; motor maps

General motors

Global Virtual Electrification

Dr. Jay Jihyun Kim

N-T characteristics are a critical design benchmark in motor development. However, the design process typically relies on engineers in charge of designing the magnetic circuit to not only run the magnetic field analyses but also organize and post-process the analysis results to obtain the N-T characteristics. To simplify this process, we configured an environment using JMAG scripts that automates N-T characteristic calculations. Our environment takes advantage of JMAG-Express rather than JMAG-Designer as well to enable even those new to JMAG to specify the condition settings and run evaluations. The framework launches the JMAG scripts to automatically calculate the N-T characteristics when JMAG-Express loads the analysis results. One single workflow from running evaluations to calculating the N-T characteristics minimizes the workload and makes calculations previously limited to engineers responsible for the magnetic design accessible to other designers and engineers. This case study provides an overview of the calculation environment, the configuration process, and specific use cases.

DENSO CORPORATION

Mr. Tamon Yamada

When reciprocating machines, such as reciprocating compressors, are driven by induction motors, the load torque varies periodically with the crank angle, resulting in periodic fluctuations in the motor current and rotational speed. Since excessive current pulsations can cause motor overheating, accurate evaluation of these pulsations during the design stage is essential. In addition, vibration behavior requires careful consideration compared with operation under conventional constant-load conditions.
This paper investigates the electrical and vibration characteristics of an induction motor subjected to pulsating load torque using electromagnetic field analysis and structural analysis. The analytical results obtained under pulsating load torque conditions are compared with those obtained under constant load torque conditions. Furthermore, the calculated electrical characteristics under pulsating load torque conditions are compared with experimental measurements to validate the accuracy of the proposed analysis.

TMEIC Corporation

Design Section 3 Rotating Machinery Department 2 Rotating Machinery Systems Division

Mr. Takumi Kikuta

In this study, an analytical method was developed to predict the vibration noise of motor when vehicle is running. Time harmonics of the current, spatial harmonics of the motor, and variation of electromagnetic excitation force with speed were considered. The spatial mode of the electromagnetic excitation force was shown, and the relationship between the magnetic flux density and the electromagnetic excitation force was shown by the Maxwell stress equation. A method to reflect the velocity effect and spatial mode of the electromagnetic excitation force as the frequency dependence and phase of the structural analysis was shown. The reduction of 6th order of electromagnetic excitation force and the reduction of acoustic power by the reduction of the 5th order of the current were confirmed. This study is expected to contribute to the model-based development of vehicles by raising the hierarchy of prediction from the component level to the system level.

Honda R&D Co., Ltd.

Electric Module Development Department, Drive System Development Division, Automobile R&D Center

Mr. Yuya Watanabe

［TBA］

AISIN CORPORATION

Motor Engineering Department Product Development center Power Train Product Division

Dr. Tsuyoshi Miyaji

In recent years, advancements in electromagnetic field analysis software have led to a known good agreement between analytical and experimental results regarding the torque generated by permanent magnet synchronous motors. Therefore, it has become standard practice to use electromagnetic field analysis software in the design of permanent magnet synchronous motors.
On the other hand, along with the increasing demand for higher output in permanent magnet synchronous motors, there is also a growing demand for higher rotational speeds. However, as permanent magnet synchronous motors operate at higher speeds, there is a challenge in that the discrepancy between analytical and experimental results regarding torque becomes larger. Therefore, this presentation will report on examination of error factors and compensation methods.

Institute of Science Tokyo

Professor, Department of Electrical and Electronic Engineering, School of Engineering

Prof. Masatsugu Takemoto

The conventional method to analyze the operation of machines that leverage electromagnetic force uses JMAG to obtain the electromagnetic force of parts in specific orientations for import into RecurDyn multi-body dynamic analysis software. However, this approach cannot precisely capture variations in the electromagnetic force as the orientation of the parts changes, lowering the analysis accuracy. Our solution to maintain accuracy configures a coupled analysis that shares the electromagnetic force and part orientation between JMAG and RecurDyn through small time steps. This case study outlines that analysis method and demonstrates how the analysis improves accuracy by comparing the analysis and measured results.

MITSUBISHI ELECTRIC CORPORATION

Material Analysis & Technology Sect. Manufacturing Systems Planning Dept. FUKUYAMA WORKS

Ms. Ami Kuwada

Further improvement in motor efficiency is required to realize a decarbonized society. PM motors are increasingly adopted in a wide range of fields, including industrial applications, because of their compact size and high efficiency. Toyo Denki Seizo began selling industrial PM motors in 2000 and has improved these products over the years through experimental evaluation and analysis of motor performance.

To meet the demand for higher motor efficiency, identifying sources of losses in each motor component is important for effective loss reduction. Stray load loss can be determined by subtracting the experimentally determined values of constant losses, which is the sum of the no-load iron loss and mechanical loss, and DC copper loss from the total loss. However, the stray load loss contains several loss components originating from different sources, making it difficult to determine the contribution of each loss component from motor test results alone. In contrast, magnetic field analysis is effective for analyzing the contribution of each loss component to stray load loss because each loss component can be calculated individually. The contributions of these loss components to stray load loss can be evaluated by comparing motor test results with magnetic field analysis results. Furthermore, by using a magnetic field analysis model validated against motor test results, we can evaluate losses during the design stage. This approach is expected to reduce the number of prototypes and shorten the development period.

In this presentation, magnetic field analysis with individual modeling of each winding strand is applied to a 160 kW-class industrial PM motor to calculate the losses generated in each component. The contribution of each loss component to stray load loss is evaluated through magnetic field analyses under sinusoidal-current conditions and further evaluated under PWM inverter-drive conditions to assess the effects of carrier-frequency and harmonic components. By comparing magnetic field analysis results with motor test results, the validity of the model is examined. Finally, the potential for further loss reduction is demonstrated through magnetic field analysis with an idealized strand arrangement.

TOYO DENKI SEIZO K.K.

Next generation motor development group, Platform development department, Business development center

Mr. Ryosuke Sata

Magnetic geared motors employ a magnetic gear based on the magnetic field modulation principle, enabling low-speed, high-torque operation without a mechanical gearbox. Their high torque density, reliability, low noise, and oil-free operation make them attractive for mobility applications such as automotive, marine, and railway propulsion systems.

This presentation introduces the development of a 6,000 Nm-class magnetically geared motor using JMAG. Electromagnetic analyses were conducted to evaluate back-EMF, torque characteristics, and losses, including the in-plane eddy current losses in the pole-piece rotor. Through appropriate modeling techniques, a simulation-based design process capable of accurately predicting motor performance prior to prototyping was established.

Experimental results obtained from the prototype showed good agreement with simulation predictions in terms of back-EMF, torque, losses, and temperature rise. The presentation highlights both the analysis methodology and the experimental validation, demonstrating that simulation can provide performance evaluations equivalent to actual machine testing.

Mitsubishi Heavy Industries, Ltd.

Research & Innovation Center Power Electronics Research Department

Mr. Tomoya Mifune

Electrified powertrains and e-machines present increasingly demanding structural dynamics and NVH challenges, driven by high electromagnetic force harmonics and stringent noise targets. Addressing these issues requires close integration between electromagnetic and structural-dynamic simulation domains, which are traditionally handled by separate tools with limited data compatibility.
To meet this need, we combine JMAG for electromagnetic pre-processing with EXCITE M (AVL), our structural dynamics and NVH simulation tool. EXCITE M’s electromechanical coupling relies on a magnetic field computation based circuit model that represents the machine through multi-dimensional look-up tables of flux linkage, stator tooth forces and torque, parameterized by rotor position and d-q currents, and radial and azimuthal rotor displacement in the case of eccentricity. To connect these domains efficiently, we developed a dedicated interface that generates and transfers the required look-up table data directly from JMAG's magneto-static FE simulations into EXCITE M, eliminating manual data handling and post-processing.
This presentation introduces the coupling methodology, the interface architecture, and demonstrates the resulting workflow on a representative e-machine application, including distinct effects such as dynamic eccentricity, transient effects, and PWM.

AVL List GmbH

AST – Advanced Simulation Technologies

Mr. Michael Schrottner

The use of generative AI is expanding beyond support for tasks such as writing and information retrieval toward AI agents that can operate software and execute tasks autonomously. By equipping these agents with software-specific knowledge and operating procedures in the form of “Skills,” their application to specialized fields such as engineering design and analysis is also expected to grow.

In this presentation, JSOL will provide an overview of AI agents and JMAG Skills, including their underlying technical mechanisms, while Nissan Motor Co., Ltd. will present two examples of their application to actual engineering work. The first case involves improving the efficiency of analysis setup by having an AI agent perform repetitive tasks such as naming, material assignment, insulation condition setup, and mesh configuration. The second case involves using JMAG Skills to develop a function that exports analysis settings stored in a JMAG project to a text-based format and reimports them after editing.

Through these case studies, we will discuss the benefits and challenges of using AI agents for improving operational efficiency and developing new functions, as well as their potential application to a broader range of CAE tasks.

Nissan Motor Co., Ltd.

Powertrain and EV Component System Technology Department

Mr. Yuta Koga

This presentation introduces an approach to building an Agentic AI system for magnetic simulation, with JMAG and large language models (LLMs) serving as complementary foundations. JMAG provides physics-based modeling and numerical simulation, while LLMs support language understanding, task planning, and engineering reasoning. Reusable skills capture domain knowledge and engineering procedures, providing a structured basis for translating engineering intent into executable tasks. The proposed framework supports integration with workflows across the product lifecycle—from customer requirements analysis and product design to product testing and support for customers’ end-use applications.
This approach aims to streamline simulation operations while broadening engineers’ perspectives on design and development. It supports engineers in exploring alternative design concepts, developing new technical approaches, and assessing their feasibility through simulation.

JingCi Material Science Co., Ltd

R&D

Dr. Zhijun Zuo

Recently, demand for Synchronous Reluctance Motors (SynRMs) has been growing to achieve high efficiency and mitigate rare-earth price volatility and supply risks. Although parameter and topology optimization are widely applied to flux barrier shapes governing SynRM performance, the impact of manufacturing variations is often neglected. Conventional optimization considering variations requires repeated Finite Element Analysis (FEA) via Monte Carlo simulations, incurring prohibitive computational costs.
To overcome this challenge, we propose an offline optimization workflow that combines a Gaussian Process Regression surrogate model with Polynomial Chaos Expansion. Using SynRM design as a case study, this presentation reports an optimization that accounts for manufacturing variations while reducing computation time to less than 1/10 of the conventional Monte Carlo method.

Fuji Electric Co., Ltd.

Digital Engineering Department, Digital Innovation Laboratory, Corporate R&D Headquarters

Mr. Hayato Naojima

In recent years, optimization has become important in induction motor design to improve efficiency and satisfy various design requirements. However, FEA-based optimization requires significant computation time, and the computation time increases rapidly as the number of design variables increases. In addition, nonlinear characteristics caused by magnetic saturation and leakage flux can reduce the prediction accuracy of surrogate models and make optimization results less reliable. This presentation presents an approach to accelerate the optimal design of induction motors by developing a surrogate model trained using historical design assets. It also reports evaluation results demonstrating improved surrogate model accuracy and applicability to optimization through reducing prediction errors caused by nonlinear characteristics.

Toshiba Corporation

Motor and Drive System Technology Group, Electrical/Mechanical Systems and Power Electronics R&D Dept., Infrastructure Systems R&D Center

Ms. Kako Yamahata

In recent years, inverters for xEVs have been required to achieve even higher efficiency. Three-level inverters are attracting attention as a technology that contributes to improved system efficiency by reducing current harmonics through multilevel output voltage, thereby suppressing motor losses. However, the increased complexity of circuit topologies and control algorithms poses challenges for performance verification and design parameter optimization during the early stages of development.
This presentation introduces a case study on the use of JMAG-RT in three-level inverter development, focusing on the construction of a system-level simulation environment that integrates motor and inverter models.

Renesas Electronics Corporation

UX Group / Systems and Solutions Team

Mr. Yuichi Takagi

［TBA］

DAIHATSU MOTOR CO., LTD.

Powertrain Development Div.

Mr. Katsuyuki Kanenaka

In electric motor development, with the growing risk of rare earth supply shortages, design risks related to the impact of demagnetization on performance are increasing. Meanwhile, conventional design processes are limited to “verifying the performance impact of switching to alternative magnets,” and optimizing designs using these alternatives requires significant effort.
This presentation will introduce a case study demonstrating how to perform optimization calculations for magnet shapes based on the characteristics of each magnet grade by utilizing JMAG’s optimization and thermal analysis functions.
In addition, we defined how to use optimization functions during the concept and detail design phases as part of our efforts to standardize our motor design process. We will also present a case study on the development of an optimization design process.

Nabtesco Corporation

Electric & Electronics Engineering Department Technology and R&D Dividion

Mr. Rihito Ogura

In aerospace propulsion, every added kilogram compromises aircraft performance. Aerospace electric motors must be designed to meet aircraft requirements while accommodating manufacturing variations. As a result, machine mass is often dictated by the worst-case design, with margins set according to a realistic worst-case stack-up of tolerances.
This presentation explores how manufacturing tolerances influence key machine parameters, how their effects can be quantified through FEA, and how tolerance-driven variability can be incorporated directly into the design process. Which tolerances matter most? How much performance variation do they create? And can design choices make a machine inherently more robust?
Using examples from Evolito motors, this study demonstrates the use of JMAG to simulate manufacturing variability within FEA. The results show how robust design optimisation can reduce unnecessary design margins, enabling lighter machines without compromising safety or performance.

Evolito

Motor Performance

Ms. Natalia Narozanska

Dimensional variations of parts, assembly variance, and other manufacturing tolerances affect motor performance. Quantitatively evaluating these effects during the design stage can identify causes and potential countermeasures before production to help reduce rework during development. This case study configured an analysis model for a motor that accounts for foreseeable variations to aggregate and analyze data under numerous manufacturing tolerance conditions. We also extracted the characteristic modes of variation caused by manufacturing tolerances from the data obtained through analyses and examined whether it is possible to identify the relationship between each mode of variation and the variations in motor performance. This presentation explains our analysis method and findings.

Daikin Industries, Ltd.

Motor Technologies Group, Technology and Innovation Center

Mr. Koji Miyazato

We are engaged in the research and development of high-temperature superconducting induction motors featuring squirrel-cage rotor winding made of high-temperature superconductors. To date, we have demonstrated outstanding characteristics, including a peak efficiency exceeding 99.5% and rotational stability during rapid acceleration and deceleration. Furthermore, we are advancing multiple application development projects based on these fundamental research findings through industry-academia collaborations; for instance, our high-temperature superconducting submerged pump for liquid hydrogen has reached a practical level of viability.
　To facilitate the practical implementation of high-temperature superconducting induction motors, precise analysis and design technologies must be established. The characteristics of high-temperature superconductors are defined by a constitutive equation relating electric field and current density; therefore, it is essential to establish an analysis method that accurately incorporates this nonlinear equation.
　This presentation introduces electromagnetic field analysis results for a practical high-temperature superconducting induction motor, obtained using JMAG®-Designer while accounting for nonlinear electric field vs. current density characteristics. It also compares these analysis results with experimental data and discusses their quantitative accuracy. Finally, based on these findings, the presentation offers an outlook on the potential and future of high-temperature superconducting induction motors.

Kyoto University

Program-specific Professor, Department of Electrical, Electronic, and Digital Science and Engineering, Graduate School of Engineering

Dr. Taketsune Nakamura

In superconducting coils, accurate evaluation of transient phenomena such as current decay, heat generation, and temperature rise after a quench is important for coil protection and the design of operating conditions. In this study, the quench transient behavior of a superconducting coil was numerically analyzed using JMAG. An analysis model was developed to evaluate the temporal changes in coil current, dissipation of electromagnetic energy, heat generation, and temperature. The validity of the numerical approach was also examined through comparison with experimental results. In addition, as an extended study, a system including a magnetically coupled secondary coil was analyzed to investigate the influence of energy transfer on the transient response during a quench.

Sumitomo Heavy Industries, Ltd.

Advanced Technology Section /Technology Research Laboratories

Mr. Yuta Ebara

Axial flux motors offer high design flexibility due to their three-dimensional structure, making the application of optimization analysis challenging. This presentation introduces case studies of electromagnetic and optimization analyses for axial flux motor design. In addition, a material selection study using a newly available feature that treats materials as discrete variables is presented, and the potential of applying optimization analysis to material selection is discussed. Furthermore, examples of optimization studies that consider not only motor performance but also factors such as cost as objective functions are introduced.

Resonac Corporation

Magnetic Materials Development Team Powder Metal R&D Department Mobility R&D Center, Mobility Business Headquarters

Mr. Kohei Aiba

Conventional, parametric design approaches limit the solution space to predefined geometric parameters. Topology optimization expands this space through the free distribution of material within a design domain, enabling more capable designs. Since this requires significantly more designs to be calculated, a larger number of licenses (PSL vs standard) is needed to reach an optimal result within a reasonable optimization timeframe.

In the application scenario "Air Pocket Optimization," the optimal distribution of air pockets within the rotor is investigated with the magnet in a fixed position. The model setup, objective functions, and boundary conditions are presented, along with an evaluation based on a reduced-order model using version 24.2.

Finally, the topology-optimized design is compared with the parametric reference design based on key KPIs. The conclusion and outlook focus on the combined optimization of air pockets and magnet position.

Daimler Truck AG

TT/XTA – Innovation for eDrive Systems

Mr. Daniel Romanowski

IHI strives to capitalize on Model-Based Development (MBD) across multiple technical areas to optimize product designs while considering system-wide tradeoffs. However, Finite Element Analysis (FEA) and other high-fidelity simulations are essential for adequate accuracy in some technological domains. This computational load becomes a bottleneck to running optimizations using one-dimensional models at the conceptual design stage.
This presentation proposes an adaptive optimization for motor design in electrified systems. The process adopts surrogate models trained online through FEA results in JMAG to adaptively switch a portion of the FEA run to surrogate models. These adaptive optimizations not only run faster but also sustain the design solution quality. We will look at some real-world examples and the effectiveness of adaptive optimizations during this presentation.

IHI Corporation

Corporate Research and Development Division

Dr. Ryusho Nakazawa

High power density is increasingly required for traction motors. To achieve this, it is important to consider not only electromagnetic performance but also thermal and structural characteristics during the design process. However, conventional design approaches require electromagnetic, structural, and thermal analyses to be performed independently with repeated feedback among them, resulting in increased design effort. This presentation introduces a case study on improving design efficiency for automotive traction motors through multi-physics optimization that integrates electromagnetic, thermal, and structural finite element analyses.

DENSO CORPORATION

Corporate R&D Div.

Mr. Tsuyoshi Goda

The propeller motors require high torque density for drone applications. To meet the demanded torque requirement, sintered magnets using NeFeB with Dy rare-earth material. Although NeFeB-based sintered magnet can generate high magnetic energy, the segment magnet might not be easily attached on the rotor surface. It is especially challenge for high rotor poles design among small propeller motors. This presentation proposes a new outer-rotor motor design using the high-pole cylindrical magnet structure. In order to increase the magnetic energy for bonded magnet, an anisotropic magnet power is selected to formulate this cylindrical magnet. More importantly, a sinusoidal injection molding die is designed to generate the high-pole cylindrical magnet with sinusoidal magnet flux distribution. It is concluded that the comparable torque density can be achieved based on the proposed cylindrical magnet with high rotor poles and sinusoidal flux distribution. All the simulation results are obtained by JMAG FEA tool. Two propeller motor prototypes with different magnet materials are tested for the experimental verification.

Prolific Technology Inc.

Motor Research Division

Dr. Shih-Chin Yang

Iron loss properties of materials alone cannot precisely predict losses in DC-biased reactors because the DC magnetization distorts the hysteresis loop. This case study uses a play model to estimate the DC-biased B-H characteristics of our MH20D pure iron powder core material as well as run hysteresis loss analyses. These analysis results that closely match measurements prove the effectiveness of our approach for loss prediction in DC-biased reactors. This case study also took advantage of this analysis method to evaluate and optimize reactor designs capitalizing on the unique characteristics of pure iron powder magnetic cores. Our presentation will show some examples of the material selections and reactor designs used for these analyses as well.

Kobe Steel, Ltd.

Electric and Magnetic Control Research Section, Applied Physics Research Laboratory, Technical Development Group

Mr. Tetsuya Ogawa

［TBA］

MITSUBISHI ELECTRIC CORPORATION

Motor Systems Technology Dept., COMPONENT DEVELOPMENT CENTER

Mr. Ryota Koizumi

As the demand for motor design increases in line with the acceleration of electrification, there is a growing shortage of designers and a demand for shorter development times. To address these issues, we are working on the development of a 'Motor Design Agent' using our proprietary generative AI technology.
In this presentation, we will present an automated motor design and development process with JMAG-Designer and AI Agents. The AI Agent works with JMAG-Designer to automatically perform tasks from idea generation, CAD model creation, analysis condition setting, design optimization and result evaluation.
The Motor Design AI Agent is not a tool that derives a single solution, but rather an agent that helps design engineers be more creative. This technology enables faster and longer-term verification than humans and increases the possibility of discovering innovative shapes. In addition, the complete automation of the design process is expected to significantly reduce design development time.

MotorAI Inc.

Dr. Yuki Shimizu

As the cost of electronic components such as memory and storage continues to rise, minimizing hardware investment while maximizing the utilization of PSL licenses has become a key challenge.
By comparing computational performance against hardware cost, we analyze cost-effectiveness and identify the optimal system configuration for each use case based on real benchmark data

ARD Corporation

Solutions Engineering Department

Mr. Takuya Ichikawa

★★

ニュートンワークス株式会社

★★

★★ 氏

In recent years, product development has increasingly required system-level optimization across multiple engineering domains, including electronics, controls, mechanics, and thermal management. In motor development as well, design studies that consider various physical phenomena beyond electromagnetic analysis are becoming increasingly important.
This presentation will introduce the Cadence vision and its broad product portfolio, along with the key features and latest capabilities of the thermal-fluid simulation software scFLOW. Through examples of integration with JMAG, we will demonstrate an approach that expands design exploration beyond electromagnetic analysis to a broader multiphysics perspective. We will also discuss new possibilities for simulationdriven development in engineering environments that are evolving from motor design to system-level design.

Cadence

System Design and Analysis

Mr. Naoki Tsunezawa

As the cost of on-premises hardware continues to rise, interest in the cloud as an alternative platform for simulation workloads is growing.
In this session, we will introduce practical ways to use the cloud for JMAG simulations, helping reduce simulation wait times and allowing engineers to devote more time to design and validation.
In the first half, AMD will explain how to select the right Amazon EC2 instances powered by AMD EPYC™ processors for different simulation scales and use cases, based on factors such as clock speed, core count, and memory capacity.
In the second half, AWS will introduce the benefits of scalable cloud resources, parallel execution of multiple simulation cases, and purpose-built HPC instances. Attendees will learn how running JMAG on AWS can provide faster performance and greater flexibility.

Advanced Micro Devices, Inc., / Amazon Web Services Japan G.K.

MegaDataCenter / Auto MFG

Mr. Yuki Sakamoto / Mr. 舛重 国規

★★

Qingdao Yunlu Advanced Materials Technology Co., Ltd.

★★

★★ 氏

Enhancing motor performance requires advances in materials and designs that fully utilize their properties. We aim to identify customer challenges from a motor-design perspective and propose optimal material solutions. This presentation will introduce higher-torque solutions using orientation-controlled magnets and Permendur, as well as higher-speed solutions using amorphous alloys. Using JMAG analysis examples, we will demonstrate how the characteristics of these materials affect motor performance and present our materials-driven approach to solving customer challenges.

Proterial, Ltd.

Marketing & Sales Division, Motor Solutions Dept.

Mr. Kyohei Aimuta

This presentation introduces a material-oriented approach to motor design, focusing on ultrathin high-flux electrical steel (ES) for high-performance drone and robotic motor applications. The effects of material properties and lamination thickness on core loss and motor performance, particularly at high electrical frequencies, are discussed. JMAG finite-element analysis is used to evaluate motor characteristics such as torque, efficiency, and iron loss, and to investigate the relationship between application requirements, electrical-steel selection, and motor design.
Practical examples using ultrathin high-flux ES for high-frequency motor applications will be presented to demonstrate its potential for improving motor performance. In addition, a conceptual workflow integrating material data, analytical models, JMAG simulation, and data-driven methods will be introduced as a possible approach for future AI-assisted motor design. The presentation aims to show how advanced electrical-steel development and electromagnetic simulation can work together to support the development of high-performance motors for emerging applications.

China Steel Corporation (TAIWAN)

Inspection & Electromagnetic Technology Section (T71), Green Energy & System Integration Research & Development Department

Dr. Guan-Ming Chen

The use of simulation is expanding from single-physics analysis to multiphysics, where multiple physical domains are combined, and further toward AI-driven design and analysis.
In this presentation, we will introduce the world of multiphysics extending from JMAG, the future possibilities of design and analysis enabled by AI, and a new simulation environment connected through Simcenter X.

Siemens K.K.

Presales Division

Mr. Shuichi Matsumoto