JMAG Users Conference 2026 in Germany

The YASA topology was first introduced to the academic community in 2007 through Dr Woolmer’s DPhil research. Since then, the technology has advanced significantly, both in its underlying engineering principles and in its successful industrialisation. Most notably, YASA machines are now widely deployed in high-performance hybrid and electric vehicles, enabling new powertrain architectures, reducing vehicle mass, and improving overall system efficiency.
This presentation will explore the key technical challenges associated with the topology, the breakthroughs required for its commercialisation, and the role simulation and optimisation play in making the motor fit for purpose for future vehicles.

Dual-rotor electric machines offer high torque density and efficiency but require advanced electromagnetic simulation due to their unique topology. Oppositely skewed windings, two interacting air gaps and large conductor cross-sections introduce three-dimensional effects that cannot be captured accurately by conventional 2D finite element analysis.

This presentation introduces the simulation workflow used at DeepDrive for the development of automotive dual-rotor electric drives. The focus is on accurate prediction of torque ripple, current displacement effects and permanent magnet demagnetization using transient 3D FE simulation. The limitations of conventional 2D approaches and the resulting computational effort are discussed.

To enable fast design iterations, the 3D simulation results are combined with surrogate models for efficient multi-parameter optimization. The methodology is demonstrated using examples from in-wheel motors and extended to range extender generators and central drive applications.

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.

In high volume serial production optimization of the assembly process can increase the line capacity and reduce the energy which is needed for the assembly processes. Induction heating is an efficient way to heat up stator housing to reach the necessary expansion of the inner diameter to be able to fit in the stator core. With a multiphyics optimization approach it is possible to reduce the energy consumption of the assembly process and ensure that the stator housing has a uniform temperature distribution across the whole part, to avoid any manufacturing error during the stator insertion.

In serial production of an electrical machine understanding the assembly processes can help to avoid errors during the manufacturing which could lead increased production time or even scrap materials. For rotor shaft assembly process high frequency induction heating is used to heat up the rotor. One of the critical parameters of the process is to achieve homogenic temperature distribution across the rotor, so the expansion of the shaft hole diameter is equal over the axial direction. With JMAG it is possible to simulate the joule loss which is generated by the induction coil and map the loss density into a 3D Thermal FEA simulation. In this presentation measured and simulated temperatures of the rotor core is compared.

Battery systems are increasingly exposed to fast electrical switching events such as contactor operations, fault handling, and dynamic reconfiguration. Conventional interconnect design is primarily based on steady-state DC assumptions and therefore neglects transient electromagnetic effects that dominate current distribution during commutation.
This work presents a systematic investigation of battery cell interconnect topologies under transient excitation using 3D finiteelement simulations in JMAG. High-resolution models are employed to capture transient current density distributions, eddy current losses, and voltage response characteristics during imposed current ramps. The study demonstrates that current paths during switching differfundamentally from DC-based assumptions, leading to geometry-dependent effects such as current crowding and delayed current penetration.
Based on the simulation results, key design parameters are identified and guidelines for commutation-robust interconnect layoutsare derived. The presented workflow highlights how JMAG enables transient-aware design of battery interconnects and supports efficient evaluation of complex geometries under realistic operating conditions.

In the competitive market of automotive industry having an electrical machine which hit the performance requirements on the A Sample level can be a significant advantage to beat the competitors on the time to market war. For this a state of the art design workflow has to be established which consider multiphyics multidomain phenomes with the capability of utilizing an HPC environment for massive parallel computing. With JMAG multiphyisics solver which also capable of utilizing hundreds of core during simulation only minimum trade off have to be made between simulation time and accuracy In this presentation a workflow which include detailed loss simulation of an electrical machine and running a 3D thermal FEA simulation which using detailed HTC maps from a 3^rd party CFD solver will be presented.

Accurate prediction of electric machine performance requires simulation models that can capture the complex interactions between electromagnetic behaviour, losses, and operating conditions. This presentation introduces a practical methodology for correlating automotive grade IPM machines finite element models with experimental measurements to achieve high-fidelity performance prediction. The approach covers the validation and calibration of electromagnetic parameters, magnetic models, torque prediction, and loss estimation techniques within the simulation environment. A structured correlation process between test measurements and FEA results will be presented, including segregation of machine loss components. The methodology demonstrates how properly calibrated models can improve the accuracy of performance prediction, efficiency mapping, and overall confidence in finite element modelling based electric machine development.