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Parallelizable Complex Neural Dynamics Models for PMSM Temperature Estimation With Hardware Acceleration
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DOI：10.1109/tec.2026.3685235.png)
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Abstract
En 中文
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, the parallel prefix-sum algorithm, and the Compute Unified Device Architecture (CUDA) platform. We demonstrate its superior estimation performance and parallelizable hardware acceleration capabilities through experimental evaluation on a real electric motor.
Keywords:
System thermal dynamics
state-space models
control-oriented modeling
physics-informed machine learning
parallel computing
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