– Advanced eMotor Design Training with JMAG (JMAG workshop)

• Multiphysics Optimization Approach using JMAG
• Impacts of Manufacturing Effects using JMAG
• Integrated Motor Drive Simulation

　2025年6月4日 (水)

– Advanced eMotor Design Conference (JMAG Users Conference)
　2025年6月5日 (木)

Electric machines sit at the critical interface of power and motion. As prime movers, they enable propulsion systems that are not only more environmentally responsible but also more cost-effective over their lifecycle. While short-term fluctuations in EV adoption may make headlines, the long-term trajectory of electrified mobility is clear and it is inevitable.

The business case for battery-electric propulsion is improving year over year—not just for cars, but across virtually all modes of transport. The transition away from combustion for most use cases is no longer a question of if, but when? And from a motor design perspective, the most exciting opportunities lie ahead.

This keynote will explore advanced motor technologies can come to life enabled by new design tools and methods. With them we can now bring to life new machine topologies, with new design features and details, made of new materials and guided by new controls to create cleaner, less costly, and more capable mobility solutions across land, air, and sea.

Rapid eMotor 3D Virtual Prototype Development to Speed Up Innovation Loop | Jacob Krizan, Ford Motor Company

Innovation speed is critical to gaining a competitive edge in any emerging field. During this discussion, will explore the keys for an eMotor designer to efficiently use detailed 3D models to compute critical metrics. This enables rapid iteration through new technology and detailed innovative design ideas without requiring physical prototype iteration thereby greatly speeding up the innovation loop.

It is said that the introduction of machine learning-based surrogate models can significantly enhance the power of DDD. However, these surrogate models require extensive training using simulation results, which makes the speed of generating FEA results increasingly important—even for 2D analyses.

We have been continuously improving JMAG’s performance to meet the growing demands of DDD.

Additionally, the question of how surrogate models should be used in DDD remains an important topic of investigation in our JMAG development efforts.

The latest advancements will be presented in the upcoming presentation.

In this session, we introduce a systematic approach to estimate the true cost of simulation, testing and the value unlocked at each step of the motor development process. Our goal is to work closely with clients and partners to apply this Simulonomics framework and demonstrate the ROI of adopting the JMAG Design Factory. This session sets the stage for a panel discussion on how leading teams are scaling simulation to make motor design faster, leaner, and more profitable.