JMAG Users Conference 2026 in India

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

However, dependence on rare-earth materials introduces critical challenges, including supply-chain instability, environmental burden, and long-term cost volatility.

This paper examines the emerging class of magnet-free motor technologies—specifically
– Switched Reluctance Motors (SRM),
– Synchronous Reluctance Motors (SynRM),
– Induction Motors (IM), and
– Electrically Excited Synchronous Motors (EESM)
as scalable alternatives capable of eliminating rare-earth reliance without compromising EV performance.

Advances in wide-bandgap power electronics, high-bandwidth control algorithms, optimized electromagnetic topologies, and improved thermal management have mitigated traditional limitations of magnet-free architectures, enabling competitive torque density, efficiency, and NVH characteristics.

Through comparative analysis and application-level evaluation, this work demonstrates that next-generation magnet-free motors can meet or exceed the performance requirements of modern EV powertrains while oƯering superior sustainability, manufacturability, and cost resilience.

The findings position magnet-free motor technology as a transformative pathway toward environmentally responsible, geopolitically secure, and economically scalable EV production.