Parallelizable Complex Neural Dynamics Models for PMSM Temperature Estimation with Hardware Acceleration

TL;DR

This paper introduces a parallelizable, hardware-efficient neural dynamics model for PMSM temperature estimation, combining physics-informed approaches with GPU acceleration to improve accuracy and real-time performance.

Contribution

It proposes a novel physics-informed neural model with linear decoupling and reparameterization, optimized for parallel hardware acceleration, enhancing thermal estimation of electric motors.

Findings

Superior estimation accuracy demonstrated

Effective GPU-based parallel acceleration achieved

Validated on real electric motor data

Abstract

Accurate and efficient thermal dynamics models of permanent magnet synchronous motors are vital to efficient thermal management strategies. Physics-informed methods combine model-based and data-driven methods, offering greater flexibility than model-based methods and superior explainability compared to data-driven methods. Nonetheless, there are still challenges in balancing real-time performance, estimation accuracy, and explainability. This paper presents a hardware-efficient complex neural dynamics model achieved through the linear decoupling, diagonalization, and reparameterization of the state-space model, introducing a novel paradigm for the physics-informed method that offers high explainability and accuracy in electric motor temperature estimation tasks. We validate this physics-informed method on an NVIDIA A800 GPU using the JAX machine learning framework, parallel prefix sum algorithm, and Compute Unified Device Architecture (CUDA) platform. We demonstrate its superior estimation accuracy and parallelizable hardware acceleration capabilities through experimental evaluation on a real electric motor.