Deep Learning-Based Reduced Order Models for Electric Motors

Full vehicle system modeling is used in applications such as electric vehicles and energy systems and plays a pivotal role in understanding system behavior, system degradation, and maximizing system utilization. The behavior of these systems is dictated by multi-physics complex interactions that are well suited for finite-element simulations, but modeling system behavior and system response is computationally intensive and requires high-performance computing resources. Additionally, such models cannot be deployed to hardware (HIL/PIL) to predict real-time system response. Another alternative is reduced order modeling using curve fitting and system identification, which makes subsystem models computationally feasible. However, in many critical systems, this approach is not preferred as these surrogate models are less accurate and do not represent the full spectrum of component behavior. With deep learning, we can now rely on data to develop small footprint, detailed models of components without approaching the problem from first principles. In this presentation, walk through the development of a deep learning– based reduced order model (ROM) for a permanent magnet synchronous motor (PMSM), a popular component for electric vehicles and future green transportation.