Advanced e-Motor Design with JMAG | Stuttgart

DeepDrive, a young high-tech company based in Munich, has developed an innovative electric motor technology based on a dual-rotor radial flux design. With the target to become a key automotive supplier, DeepDrive focuses on reducing development times, cutting prototype costs, and minimizing time-to-market. A critical element in this process is the application of advanced simulation techniques. The motor technology is based on a novel counter-skewed wave winding, offering high torsional stiffness, excellent electromagnetic properties, and efficient thermal management. Due to the motor’s advanced and highly specific geometry, precise 3D simulations are essential to achieve optimal performance. For this, DeepDrive utilizes JMAG software to ensure accuracy in modeling and design.

In this presentation, the simulation strategies and optimization approaches employed in the development process will be detailed. Furthermore, simulation results will be validated against empirical measurement data, showcasing the effectiveness of the applied methods in advancing this cutting-edge motor technology.

The axial flux machine (AFM) proves to be a quite competitive alternative to a radial flux machine (RFM), especially if the axial length of the available space is limited and high torque and power density are required at the same time. A computational efficient workflow for electromagnetic design, including efficiency map calculation, for AFMs is proposed and implemented by using a 2.5D multi-layer approach. With the proposed electromagnetic design workflow in combination with a PSL-2D JMAG license and an in-house optimization algorithm, a multi-objective optimization of the AFM for traction drive requirements has been conducted. Optimization results and a comparison between 2.5D multi-layer approach and 3D-FE round up the presentation, highlighting the quality of the proposed method.

This presentation demonstrates a methodology on how to convert a single-case-setup advanced JMAG FEA-model to a high-fidelity system model ready to be used in commercially available system modelling tools such as Matlab/Simulink. The target application include advanced traction machines for road vehicles, where the typical usage is very dynamic in the speed-torque domain, and typically at least two quadrants of operation is expected in system modelling. Advanced FEA-modelling includes the analysis of higher-level physical phenomena such as slot harmonics and eddy currents in most parts of the machine. Axial Flux Machines with Soft Magnetic Composites is one example covered in this demo. In order to accurately capture the dynamics of several independent non-linear loss factors within the machine, the entire speed-torque operating region should be analysed through FEM. A full 3D FEM is typically associated with long simulations times even for single cases. This presentation outlines framework on how to automate pre-processing of a JMAG-model to enable accelerated analysis through parallelization and solve multiple FEM-cases within a reasonable timeframe. A comprehensive post-processing framework is also presented, where multi-dimensional, non-linear regression is automatically applied on a batch of FEM-cases with the goal of compiling a high-fidelity system model of the machine performance. The post-processing is done via a stand-alone graphical interface and offers analysis of individual FEM-cases, visualization of the machine performance over the entire operating region, and powerful tools to perform maximum-efficiency mapping including advanced losses.

IAV is engaged in electro-magnetical design from high-performance powertrain drives all-embracingly down to small-scale actuators. One key topology for these auxiliary applications is the claw pole motor, which is based on radial, tangential and axial flux paths. Compared to the more familiar radial flux AC machines, the design optimization and FEA simulation of such a system implies some noteworthy differences, which are especially the three-dimensional geometry, the voltage control method as well as the high influence of power loss on performance. All these challenges demand a clear focus on the optimization strategy and require an FEA tool being capable of fast, accurate and robust simulations. The presentation will demonstrate IAV’s approach for high fidelity and yet low effort optimization runs based on design of experiments and machine learning on the example of the rarely examined small-scale claw pole motor.

The drive towards zero prototypes requires CAE solutions that offer accurate predictions, support variability analysis, and enable rapid what-if studies. This is particularly beneficial in electric powertrain design, where noise and vibration are critical. The need to balance noise performance with durability, efficiency, and weight adds complexity. Electric machine NVH (eNVH) design faces challenges due to traditional silos between electrical and mechanical teams, limiting collaboration and optimization. This presentation will illustrate how to evaluate drivetrain NVH performance by adjusting electric machine parameters using the advanced interface with JMAG-Designer in Romax Spectrum and Romax Evolve 2024.1, fostering closer collaboration, informed design decisions, and fewer prototypes.

Coming soon

(1) Live demo: Multi-faceted Evaluation using JMAG-Express.

(2) Live demo: External use of a surrogate model.

(3) Live demo: MPP 1,024 calculation on JSOL’s HPC

Since the noise emitted by an electrical machine due to high frequency pure tones has an important influence on the Noise Vibration Harshness (NVH) behaviour of a vehicle, it is necessary to determine possible fault patterns.
Static and dynamic eccentricities due to misalignment are common fault patterns.
For this purpose, electromagnetic simulations using the Maxwell Stress Tensor (MST) to determine radial forces are executed.
These forces are analyzed and compared regarding different skewing configuration and skewing angle in 2D and 3D finite-element (FE) simulations.
The 3D FE simulations show that axial forces occur at the transition points between the magnetic segments of a linear step skewed rotor.