This year marks 40 years since JMAG was born. Over the past 40 years, computer simulation technology has made great leaps forward. In the early days, it took a full day to solve even a 1,000-element problem, and the accuracy was not very good. Nevertheless, expectations for the new technology were high, and enthusiasm for its use grew with each passing year, and the development of simulation technology has been vigorously pursued in response.
Nowadays, problems with hundreds of thousands of elements can be solved in a few minutes, and the number of elements is no longer a concern. Multiphysics has also become commonly performed. These advancements have greatly improved accuracy and reliability, and have helped to establish simulation technology as an indispensable CAE technology in the product development process, rather than an analytical technique used by a small group of people.
This increase in speed and accuracy extends the scope of simulation beyond the physical space of the product to the design space. It is now possible not only to predict product performance, but also to present design alternatives that satisfy requirements, and even to make quantitative predictions about design quality. This is the beginning of the automation of the design process.
This wave of automation demands further improvements and leaps in simulation speed and accuracy.
Can simulation technology meet expectations this time around?
We would like to discuss the challenges and possibilities.

Electromagnetic field computation for present-day demanding high-end applications such as traction drives for electric vehicles (EV) is the major building block of a complex multiphysics optimization landscape. Given feasible but limited computational resources, such as high-performance computing clusters, there is a master tradeoff between simulation accuracy and computational efficiency. By carefully combining different analysis and computational methods, the related Pareto tradeoff curve might be shifted towards higher performance. That is, rather than going for more simulation accuracy or more computational efficiency, our strategy is to go for more simulation accuracy and more computational efficiency at the same time. This requires orchestrating finite element analysis (FEA), network methods, and machine learning (ML).
With this talk, we offer a glimpse into the journey at Bosch. We set the stage with a feasibility study of a high torque density yokeless and segmented armature axial flux machine for high-speed EV drives [1]. These are proposed as replacements for traditional radial flux machines in the axle traction drive of battery EVs. The focus of the study is on torque density and the ability of such machines to operate at high speeds. Different slot-pole combinations and magnet segmentations are compared, based on several factors: their torque capability, material usage, torque density, and losses of each component (magnet, copper, and iron losses) at maximum speed.
It turns out that losses in the magnets and stator coils are both computationally demanding and a limiting factor for the considered designs. This motivates a proof-of-concept investigation in 2-D, for current diffusion in rectangular conductors, where the diffusion process is influenced by the presence of non-linear ferromagnetic material [2]. Conventional methods like FEA in time domain might turn cumbersome. High-frequency harmonics yield a small penetration depth of the currents, which needs to be resolved by the finite element discretization. In addition, the time discretization not only needs to capture the relevant harmonic contents, but also the diffusion time scale. An alternative approach orchestrates FEA with partial element equivalent circuit (PEEC) methods. The core idea is decoupling the FEA of non-linear ferromagnetic domains from the diffusion analysis of linear conducting domains, which can be treated efficiently by PEEC in the frequency domain. This novel method is applicable to complex multi-conductor configurations, even in the presence of non-linear ferromagnetic material.
Traditionally, FEA is used in the design phase of electrical machines to numerically optimize their performance, which involves computing features such as torque, losses, and flux linkages at each operating point. These intermediate features are used in turn to calculate several downstream key performance indicators (KPIs, e.g., maximum torque on limit curve). However, such computations can be quite heavy and time-consuming. Another orchestration opportunity for improving the computational efficiency and flexibility of simulations used in the design of electrical machines is hybrid modeling, which combines physics-based with data-driven models. We propose a new hybrid model [3] that combines ML with physics-based post-processing. A deep neural network (DNN) is trained on a large volume of stored FEA data in a supervised manner. The DNN maps design parameters (geometry, electrical, material) to intermediate features (torque, losses, flux linkages). During the design phase, FEA is replaced by the DNN, whose response to the design parameters is fed into a physics-based post-processing system to estimate characteristic maps and KPIs.
The talk concludes by a brief assessment of the presented orchestration methods and a birds-eye view on the simulation landscape at Bosch.

References
[1] Ma, Yiwen, et al. “Feasibility Study of High Torque Density Yokeless and Segmented Armature Axial Flux Machine for High Speed EV Drives.” IEMDC The 14th IEEE International Electric Machines and Drives Conference, 2023.
[2] Morisco, David Philipp, et al. “Extended Modelling Approach of Hairpin Winding Eddy Current Losses in High Power Density Traction Machines.” ICEM The 2020 International Conference on Electrical Machines, 2020.
[3] Parekh, Vivek, et al . “Performance Analysis of Electrical Machines Using a Hybrid Data and Physics Driven Model.” IEEE Transactions on Energy Conversion (2023).

It is possible to efficiently develop motor if we can evaluate not only magnetic design but also thermal and vibration characteristic at early phase of motor design. As high frequency component of electric current such as PWM frequency causes temperature raise and vibration, it is important to consider frequency component of electric current.
In this paper, we calculate electric current by magnetic field analysis considering PWM frequency and compare the calculated result with measured data. We also report the case of evaluating magnetic field, thermal, and vibration simultaneously the function of JMAG-Express V22.2.

With Electric vehicle foot print increased day by day, sustainable powertrain architecture becomes the need of the hour. Sustainable powertrain encompasses scalable and durable design choices in terms of manufacturing, cost, performance. This study represents different optimization techniques implemented using JMAG to achieve the optimal motor architecture for a Last mile connectivity application.

The application of electric motors in electric vehicles differs significantly from their use in other industrial applications. They are typically required to exhibit superior power density and operate at higher performance levels in terms of parameters such as speed and torque, leading to more stringent challenges in motor design. Motor design must consider of various wider range of operating conditions and more efficient utilization of the motor’s physical space, demanding precise control over internal dimensions. This necessitates effective and precise methods for evaluating the rational utilization of dimensions.
VAST Power utilizes JMAG Express Classic as the starting point for motor design projects. Through parameterized motor characteristics estimation, it allows for the rapid determination of fundamental motor dimensions such as outer diameter and stack height etc.
Following JMAG Express Classic, JMAG Express is used for detailed analysis, confirming magnetic flux density in critical dimensions and assessing motor operational characteristics, including Back Electromotive Force (BEMF), starting torque, rated torque, torque ripple, and efficiency mapping etc.
The basic analysis flow created in JMAG Express can be transferred to JMAG Designer efficiently, where it can be adapted to company-specific drawings and variables, enabling integrated project analysis with multiple studies.
Building on the integrated project analysis with multiple studies, it becomes possible to conduct optimization analysis by assigning weights to different critical parameters, ensuring simultaneous fulfillment of customer operational requirements and cost considerations.

Wireless power transfer (WPT) that charging various electric devices without cables have attracted much attention in recent years. WPT changes the magnetic properties depending on the misalignment between transmitting and receiving coils. Therefore, it is necessary to consider both magnetic and electric circuit properties to design high performance.
In this presentation, we propose a hybrid method of topology optimization applying to the magnetic core and parameter optimization applying to the winding coils and electric circuit parameters. We designed WPT using this method and confirmed the performance.

The automotive industry is undergoing a once-in-a-century transformation, and electrification is accelerated at a rapid pace. The requirements for electric vehicle motors are diverse, and it is extremely difficult to find a structure that satisfies all the requirements. One method that has been attracting attention as a solution to this problem is topology optimization, which is expected to obtain new shapes that are difficult for engineers to conceive of.
The on/off method using NGnet, which has been included in JMAG for some time, can easily generate global shapes, but it is difficult to obtain minute shapes, and the flexibility of shape expression is limited.
We conducted a basic study of SynRM optimization by the on/off method using a Gabor filter, which is newly included in JMAG. We clarified the differences in the optimized shapes depending on the condition settings and conducted optimization also considering structural feasibility.

Motors for drive mobility are required to be compact and lightweight.
Axial gap structures are attracting attention for low speed, high torque specifications. However, in order to investigate this axial gap structure, three-dimensional electromagnetic field analysis is required, and the problem is that the analysis takes a lot of time.
In this presentation, I will introduce the results of examining the electrical characteristics of the axial gap structure using parallel computing and the design support tool implemented in JMAG.

This presentation describes an adjustable field magnetization PM motor with a 3D magnetic path and asymmetric magnet arrangement, which aims to achieve both high-torque operation and high-efficiency operation in the high-speed and low-torque regions. The characteristics of the adjustable field magnetization and the optimum operating parameters assuming the adjustable field magnetization are confirmed by 3D-FEA.A prototype motor based on the proposed method was fabricated, and the motor characteristics and the effect of adjustable field magnetization were verified by load tests.

The main components of a motor are the iron core, coils, magnets, and secondary conductors, and reduction of their losses has been considered.
Losses also occur in other parts of the motor. That is, plates to hold the iron core and brackets to hold the bearings. To improve the efficiency of motors, it is important not only to reduce losses in the main components, but also to reduce losses in other components. However, to calculate the loss of motor peripheral components, the analysis model becomes large and the computational load increases.
In this presentation, the loss of the motor structure is calculated using a large-scale analysis with massive parallel processing, and the loss of the peripheral components was calculated to clarify the components whose loss should be considered in the design and compared with the experiment.

The biggest advantage of introducing electromagnetic field analysis is the reduction of development lead time, including prototyping and evaluation tests. We have also introduced electromagnetic field analysis for several years and have been working on the construction of analysis technology.
In addition to magnetic characteristics, the evaluation items of motor design include thermal characteristics and vibration characteristics, and in order to eliminate prototype production and evaluation tests, it is important to evaluate these by coupled analysis. Therefore, this time, we have built a new analysis environment for our products by combining the electromagnetic field analysis know-how we have cultivated at our company with the thermal analysis function installed in JMAG-Express, and we would like to introduce the process.

Issues in output density and heat generation are two of the ever-present topics in motors.
This presentation introduces a case study of a motor that has been miniaturized by implementing optimization calculations that account for actual cooling conditions and JMAG heat generation analysis.

This research, which is a part of the realization of a 30kN-class thrust liquid rocket engine, focus on necessary a cooling structure, flow rate, and supply pressure to avoid melting due to heat generation of a small, high output, high rotation axial gap motor applied to a liquid propellant supply single-shaft electric pump.
We discussed rotor radial vibration and heat generation in consideration of electromagnetism for the 90kW class radial gap PM motors at the 2019 and 2020 JMAG Users Conferences.
Regarding the heat generation, we analyzed and evaluated JMAG-RT as the design information by linking it to the electric pump model and cooling model built with Siemens AMESIM for the purpose of grasping the cooling effect with accuracy. In this presentation, we will present a case study of a thermal equivalent circuit model including a cooling system, to realize a 200kW class high-speed axial gap motor that will not be damaged. By comprehensively evaluating the results of JMAG’s thermal equivalent circuit model together with the CFD results, this example enables us to understand the risk of motor electromagnetic element burnout and thermal demagnetization.

Computer-Aided Engineering (CAE) and physics simulations have had a profound effect of the development of products and have played a key role in improving product performance, durability, and sustainability. Product development teams now regularly simulate behavior such as fluid flow, acoustics, stress and deformation, electromagnetics, and thermal effects, and simulate multiple complex physics (multiphysics) as well. Teams are even able to leverage virtual manufacturing simulations to select the best manufacturing methods that will be appropriate for a given product. The use of CAE is therefore accelerating a Shift to Zero prototyping, while providing confidence that the products as designed can actually be made; will meet all product requirements such as performance and durability; and address all business requirements such as cost, quality, and sustainability.
The automotive industry has been a leading adopter of computer-aided engineering. However, the move to electrification introduces new challenges such as for noise, vibration & harshness (NVH) performance compared to conventional ICE vehicles. In the absence of internal combustion engine noise, the sound and vibration characteristics of an ePowertrain become much more relevant and important. An ePowertrain generates higher frequency vibrations and so the vehicle’s sound and vibration propagation performances have to be assessed for this broader frequency range, even though the low-frequency and transient events which may produce objectionable disturbances to vehicle occupants also need to be considered.
In this context, the role of CAE especially coupling different physics simulations solutions from Hexagon and JMAG electromagnetics simulations become important. This presentation will explore an example NVH analysis workflow that provides insight into the system response to electrical excitations calculated in JMAG Designer. Through this workflow, engineers can assess the high frequency ePowertrain response using Romax Spectrum, then pass this information to Actran to predict how that noise and vibration propagates through the vehicle. The ePowertrain can also be integrated with other vehicle subsystems in Adams Car to allow engineers to assess the impact of low frequency excitations on drive comfort. Confidence in the results of such workflows in turn will allow automotive companies to make progress on their Shift to Zero objectives.

We are continuing to develop JMAG for design automation of electrical devices. In this session, how JMAG contributes to design automation is presented based on the new features of V23.
The design automation is a process of exploring a large design space and then narrowing it down by increasing the level of detail in the model. JMAG-Express supports multi-disciplinary evaluation not only in the early stages of design, but also throughout the entire design process. We have also developed functions for gradual optimization to narrow down the design space, and optimization can be performed even on models with high fidelity.
Pre-and post-processor performance are being improved for large models that are intended for virtual prototyping.
Component design must be consistent not only with the equipment itself, but also with the system, and JMAG-Express is the base for outputting device characteristics to the control, NVH, and system levels.

Due to the inherently quiet nature of electric vehicles, carefully minimizing all sources of NVH is critical to delivering excellent customer experiences. eMotor NVH is a particularly challenging field since the smallest electromagnetic forcing harmonics can create unpleasant noises and therefore must be minimized. This presentation discusses the development of a detailed 3D eMotor model with full eccentricity facilitating the prediction and minimization of eMotor NVH.

Ultra-High rotation speed of drive motor is one of the key issues to realize downsizing of EV units.
On the other hand, the expansion of the upper frequency limit of the 1-st order rotor rotation due to higher RPM may cause an increase in eccentricity by the rotor shaft bending mode and an imbalance of magnetic attractive force by the eccentric locus.
The latter is a source of new excitation input and may cause serious deterioration of motor NV performance by exciting the structural resonance system of the motor unit.
In this paper, we have investigated the effect of the dynamic eccentricity of the rotor shaft center axis on the electromagnetic excitation force under unbalanced excitation inputs using Multiphysics simulation of a simple motor unit FE model.
And it clarified the motor NV issue and the mechanism of its occurrence in the Ultra-High speed range.

A few years ago, we introduced JMAG in the design process of developing electromagnetic pumps to accurately capture the experience and design values accumulated over the years as well as to improve operation efficiency, and increase reliability.
Looking back on how we first started using JMAG and the projects we worked on, we describe the challenges we faced in introducing JMAG into the design process showing examples of the differences observed between analysis results and actual tests.

In recent years, in medical blood analyzers, there has been rapid progress in technology that enables highly accurate prediction of diseases at low cost using a small amount of sample with less damage on patients. The device includes a reaction process using magnetic particles with blood components and a magnetic collection process. In the magnet collection process, magnetic particles are in flowed liquid and a permanent magnet is brought closer to outside of capillary to continuously adsorb the particles to the inner wall of the capillary at a high recovery rate. In this study, we used JMAG subroutine to create an equation of motion that consider fluid drag, magnetic force, gravity, and friction force. We report the results of simulation.

The JMAG software provides advanced features for the accurate calculation of iron losses. For hysteresis losses, the Play Model has been integrated, allowing the hysteresis behaviour to be calculated in the time-domain, based on a mathematical description that is derived from measured hysteresis loops. Further, eddy current loss modules are included where the skin effect at higher frequencies, that leads to a local redistribution of flux density and currents withing the lamination, is taken into account. This is implemented by coupling the main 2D or 3D FE model with an additional 1-dimensional model at every node of the mesh in the depth of the lamination.
However, regarding excess losses, an equivalent module has not been developed within the JMAG software, leaving the total iron loss calculation somewhat incomplete.
In the presentation, a comparison will be made between the JMAG loss calculation modules and the more traditional statistical loss models as developed by Bertotti. The comparison will be based on different wave form measurements, using an Epstein frame, for a number of iron core materials for a range of frequencies and polarisations. This allows the predictions of elementary material models in JMAG to be compared with the outcome of the statistical model, and pinpoint the validity limits of the statistical model. Further, the approach will indicate the importance of excess losses for the investigated materials.

Our company has a variety of product lines, and many require high-speed rotating machines. We have developed magnetic bearing technology as one of the core technologies to realize high-speed rotating machines such as these. Magnetic bearings can rotate at high speeds while holding the rotor without any mechanical contact. Other advantages include zero mechanical loss and no need for lubricating oil. Analysis design technology, such as magnetic field analysis, are very important in the development of magnetic bearings. This time we investigated a method to run analysis at an even higher accuracy, and evaluated the validity of this method with actual measurements. This material presents the results of that investigation. In addition, by using this analysis with the even higher accuracy, we also evaluated the performance of the magnetic bearings. Their performance was increased by making changes to the geometry, the results of which we will also present.

Cogging torque is increased by residual stress. It is necessary for cogging torque analysis with high accuracy to consider magnetic properties such as hysteresis and residual stress. This presentation introduces an example of improving cogging torque analysis accuracy using material modeling function of JMAG.

In the process of magnet extrusion molding, the extruded material is anisotropic and oriented to concentrate the resulting magnetic flux in a desired direction during the magnetization stage. However, the resulting surface magnetic flux density after molding is weak and sufficient magnetization is not achieved. Since magnetic field orientation varies as the piece is extruded, its magnetization state changes over time. Such a phenomenon needs to be accounted for and can be treated by the multi-step magnetization function. In this presentation, we will report on the design optimization of an extrusion molding system including the effect of motion with the multi-step magnetization function.

In recent years, high accuracy has been demanded in electromagnetic field analysis used for motor design, and as one measure, it is useful to reflect changes in magnetic properties that occur during motor manufacturing in the analytical model of the finite element method. The stator core is often manufactured by integrating cores that are divided at the core back. It is important to reflect the effect of the degradation of nonlinear magnetic properties caused by the split in the electromagnetic field analysis. In this study, the nonlinear magnetic properties of a stator core with a split in the coreback section were modeled by comparing the results with the results of actual measurements using evaluation samples. The application of the model to motor design was also discussed.

H3X doesn’t maximize power density just for its own sake. H3X delivers integrated motor drives that either reduce the operating costs of a vehicle or enable new capabilities, be it longer range, more passengers, or higher payload capacity.
This presentation will challenge traditional machine design assumptions, and discuss the veicle-level impacts of machine design choices. Examples include challenging the perceived benefits of high tip speed designs, how sometimes a heavier motor can result in a more power-dense system, and how to optimize the machine efficiency map shape to minimize energy consumption throughout a typical flight profile using variable pitch propeller systems.

The accuracy of the efficiency simulation has significant impact on the electromagnetic development of the electrical machine (EM), especially in the application of the electrical vehicle which requires extremely high economic performance in the typical driving cycles. A validated model based on the combination of the JMAG-Designer and analytical post-processing tool was presented in this document, with the separated simulation results of copper DC losses, copper AC losses, iron losses, mechanical losses and PWM-harmonic losses, in which the copper DC losses and iron losses were calculated by JMAG-Designer. In general, the comparison of EM total losses and efficiency maps shows good fit between measurements and simulations.

Given the results of an extensive integrated mapping of the motor and of the inverter as function of multiple operating conditions the effect of the combined drive unit optimization is shown.
First part will be focused on how valuable results in terms of losses can be found integrating both inverter and motor since the early stage of simulations, second part will be focused on the beneficial effect of a full EDU optimization depending on the specific driving mode compared to classic approaches.

Accurate prediction of the temperature of motor parts when the vehicle is in drive cycle is necessary to improve efficiency in motor development and reduce costs.
A magnetic field and thermal analysis was run taking into account temperature dependence by coupling JMAG’s motor model and Amesim’s vehicle system model to establish a method for predicting the temperature of motor parts while the vehicle is in drive cycle.

Up until now, the performances of the developed control software were evaluated by performing tests on the actual machine. However, controlling the environmental conditions of the test can be very costly. Moreover, parameter tuning of highly nonlinear control systems, such as inverter overmodulation or torque control using voltage phase adjustment, incurs even more man-hours. On top of that, it is expected that the trend of increasing speed and torque will also contribute to the increase of man-hours.
To reduce the man-hours required for the performance evaluations and control design, our model-based development includes control development software that feature automatic code generation tools, as well as a JMAG-RT model that faithfully and accurately simulate the motor behavior.
In this paper, we confirm the validity of our model by comparing it to the experimental data and then conclude on the usefulness of the model-based development for control design. Moreover, as the computational cost of model-based development is becoming an increasingly important matter in the field, we introduce some examples of computation time reduction.

The study covers coupled optimization of active electromagnetic components (such as magnets, copper, and lamination) and passive electric components (e.g., insulation paper, winding coating) in electric machines. The insulation system of the electric machine acts as an EMC transfer path that influences electromagnetic compatibility and safety as it can lead to increased parasitic capacitive coupling and sequentially, early failures of bearings due to bearing currents or electric discharges. The study proposes a multi-physics optimization approach that simultaneously targets performance, safety, and EMC objectives, integrating electromagnetic and electrical reliability considerations. The optimization considers electric machine geometries and their impact on parasitic capacitance, addressing EMC issues and enhancing the electric reliability of the e-machine.

The electrification of aircraft is playing a significant role in reducing the aviation industry’s environmental footprint. The industry presents specific challenges to design engineers due to the design constraints. Developing an electric propulsion system that meets the desired efficiency and power-to-weight ratio requires efficient tools and workflows to cover all design aspects and facilitate interdisciplinary collaboration.
In an agile environment, quick response from the design engineering team to volatile requirements is crucial. Since the electromagnetic design is usually laying the foundation for other disciplines to build upon, a fully parameterized model stripped down to a low as reasonably possible simulation time is used to explore the available design space for new sets of requirements and provide appropriate design variants on short notice. Assessing relevant key figures on the way, engineers directly have the data on hand required to make informed decisions, thus fostering agile electrical machine development for time-critical project demands.

Product performance varies in characteristics due to various manufacturing error factors. To suppress design rework due to performance variation, it is important to optimize design considering variation from the upstream design stage. In general, evaluation of variation requires evaluation of a huge amount of error factor levels, and the computational cost becomes a bottleneck. In this presentation, we will report a case study in which HPC was used to speed up calculations and optimization calculations were performed for motors, considering variations.

The induction hardening process for steels is broadly divided into induction heating processing and cooling processing, and the solid-phase transformation (hereafter referred to as “phase transformation”) in these processes is used to obtain a hard metallic structure known as “martensite” on surfaces.
In the induction heating analysis, the temperature distribution, stress distribution, and deformation, etc., of an object to be heated can be obtained with high fidelity using JMAG which can couple magnetic analysis and thermal analysis. However, the current version of JMAG does not account for phase transformation in the analysis. Therefore, it is not possible to analyze the stress distribution and deformation associated with the phase transformation to martensite in the cooling processing, in addition to the phase transformation to austenite in the induction heating processing. We therefore tried a coupled analysis between JMAG and the structural analysis software Simufact, which can analyze the phase transformation of the steel. In this presentation, we introduce our initiatives in induction hardening analysis through this coupling between JMAG and Simufact.

With increasing demand for electrification, there is a growing need for induction heating that is both clean and capable of rapid increases in temperature in the motor manufacturing process. Uniform heating is an important requirement in terms of the quality of the shrink fitting process for motor stators and housing by using induction heating. This requires selecting the optimum frequency, power, and heating time.
We used optimization calculations using JMAG to search for process conditions with the objective of uniform heating. In this presentation, we will additionally introduce our initiatives in using the results of the optimization calculations to determine the required specifications for high-frequency power supply.

Heat treatment using induction heating is attracting attention from the perspective of reducing CO2 emissions. In this method, the workpiece is held mechanically in most cases, so there is no need to consider electromagnetic force acting on the workpiece. However, when aiming for more efficient induction heating, there will be cases where it is difficult to completely grasp the workpiece. For example, if the workpiece is supported only by frictional force, if the electromagnetic force exceeds the frictional force, the workpiece will start to move, which will greatly affect induction heating. Therefore, when developing equipment and methods, it is important to understand in advance the magnitude and direction of the electromagnetic force generated in the workpiece. This time, I performed a three-coupled analysis of magnetic field, heat, and structure using JMAG, and attempted to analyze the electromagnetic force acting on a workpiece during induction heating, and will report on the results.

Recently, with the development of electromagnetic field analysis software, it is known that analysis and experimental results for the torque generated by permanent magnet synchronous motors are in good agreement. Therefore, electromagnetic field analysis software is commonly used in the design of permanent magnet synchronous motors. On the other hand, the permanent magnet synchronous motors are increasingly operated under high-speed rotation with field weakening control, because as the demand for higher output is increasing for the permanent magnet synchronous motors. However, when the permanent magnet synchronous motors are increased in speed with field weakening control, the error between the analytical and experimental results of torque becomes large. This presentation reports on the investigation of the error factors.

Design and process optimization in the engineering field means finding the configuration that provides the best performance for a given set of conditions. Optimization methodologies are numerous and strongly related to the nature of the product or process under consideration.
Topological and parametric optimization are two of the most widely implemented optimization approaches; the former is a specific optimization method restricted to a particular set of problems (shape optimization), while the latter is more general and applicable to various engineering (and non-engineering) problems.
Parametric optimization is particularly efficient when coupled with design process automation, which allows the evaluation of configuration performance based on input parameters. In effect, the optimization algorithm can drive the logic of the process and change the design configuration to achieve the objectives.
Historically, optimization algorithms began with the pursuit of a single objective. The industry’s need for continuous improvement has led to deal with multiple, often conflicting, aspects of the design simultaneously, so optimization strategies have focused on multi-objective approaches aimed at determining the best designs in terms of more than one unique performance output.
Clearly, there is a strong relationship between the optimization strategy and the problem under consideration, and no single algorithm can fit all cases. In general, a good optimization strategy cannot ignore a proper analysis of the problem, which always starts with gathering information about the solution space.
The goal is to provide an overview of what parametric optimization means in engineering applications, analyze the main elements of the optimization problem, introduce the logic behind some of the most commonly used algorithms, and finally reflect on best practices when approaching optimization.

Motors are used in a wide range of industries due to their convenience.
Permanent magnet synchronous motors (PMSM), in particular, are widely used as traction motors for EVs due to their high power density and fast dynamic response.
However, when the motor rotates at high speeds, the drive current stops flowing and the speed cannot be increased, requiring weak flux control to raise the speed above the rated voltage.
This seminar will introduce the workflow for adapting an optimized lookup table for magnetic field weakening torque control of traction IPMSMs. The seminar will also introduce the key points to use with JMAG-RT.
How to quickly and efficiently tune control parameters and improve the performance of motor control systems is an important challenge. MathWorks offers a solution for model-based automatic tuning of control systems.

Two themes will be presented on Optimus, which supports optimal design.
The first is the introduction of a solution that uses Optimus as a platform and JMAG-Ansys to carry out electromagnetic field and structural analysis. The performance evaluation of EV motors requires consideration of multiple phenomena such as electromagnetic, thermal, vibration and noise, and connecting the gold-standard tools in each of these fields will promote development efficiency.
The second theme will show how Optimus can be used to integrate surrogate AI models with Optimus, including fast non-parametric optimisation.

“Power Electronics Trainer” is a set of circuit, motor, and control simulation models that can support both beginners and advanced users of power electronics. The control simulation model also models sensor gains in the circuit and peripherals such as ADC, QEP, and PWM in the microcontroller, allowing for a smooth transition from simulation to experimentation through automatic code generation.
In motor development, a model based on the general voltage equation is sufficient for beginners, but simulation using the FEA model, which is used in plant design, is essential for the practical level.
In this presentation, we will introduce an example of using JMAG-RT as a simulation model for Power Electronics Trainer. SynRM and PMSM designed with JMAG will be used for the actual machine. Since the transition from simulation to experiment can be done smoothly, it is possible to immediately check the differences between the experimental results and the FEA model, which is also useful for evaluation.

Simba is a modeling and simulation software for motor drive systems. With an intuitive and sleek interface, it helps engineers analyze and design complex systems. Simba’s unprecedented speed, accuracy, and simplicity allow engineers to minimize the simulation setup time and maximize the time for solving actual engineering problems.
Recently Simba has released two new major features; Spice-level detailed nonlinear switching device and JMAG-RT link. By combining these two features, detailed electromagnetic phenomena in machines such as the magnetic saturation, the spatial harmonics and iron losses can be easily considered in the inverter simulation while capturing voltage overshoot, electromagnetic interference, and other transient interactions.
In this presentation, first, we will review Simba’s profile and basic features. Then JMAG-RT link in Simba will be discussed while taking a traction motor model for EVs as an example (80kW 8poles 48 slots). In this part, the spatial harmonics analysis as well as the loss analysis from both the motor and the inverter are demonstrated. Subsequently, the advantage of using Simba’s Spice-level detailed nonlinear switch devices in the inverter modeling together with JMAG-RT models is explored further.

Amorphous and nanocrystalline are two of the latest soft magnetic materials known to mankind. Due to its better loss performance, amorphous materials can be made into motors to greatly increase the driving range of electric vehicles. Due to its higher magnetic permeability, nanocrystalline materials can be used as automotive OBC common mode inductors or electric drive filter magnetic rings to achieve smaller volumes with the same impedance. Qingdao Yunlu Advanced Materials Technology Co., Ltd.’s amorphous ribbon market share ranks first in the world and nanocrystalline ribbon market share ranks first in China. We hope to use the most advanced magnetic materials to better support the development of electric vehicles.

Actual motor evaluation requires measurement of multiple signals such as current, heat, and vibration with multiple measuring instruments, which is time-consuming and troublesome.
Our product enables integrated measurement of current, temperature, acceleration, etc., as well as calculations such as differential-integral and dq conversion with a single unit, thereby reducing the time and effort required for data synchronization and post-processing, and facilitating comparison with analysis values.
In this presentation, we will introduce motor evaluation using our measuring instruments.

Vibration of motor-applied products has become a problem due to the increase in motor rotation speed.
Using the system analysis tool SimulationX as a platform, we will introduce system analysis and countermeasure examples using JMAG-RT that take into account motor excitation force and vibration of mechanisms and structural factors.

HEXAGON provides structural, acoustic, and gear analysis software (MSC Nastran, Actran, Romax, etc.) that is highly relevant to JMAG.
If HEXAGON software is used in a different department from the motor design department, data needed for collaboration may be unclear. In this session, we will introduce a design approach that allows both motor and structural designers to work together to improve NV performance.