Explainable Neural Dynamics Models for Electric Motor Temperature Estimation

Accurate temperature estimation of permanent magnet synchronous motors serves as the fundamental basis for designing effective thermal management strategies. Model-based estimation methods exhibit superior real-time performance, but the intricate modeling process requires substantial expert knowledge and lacks versatility. Conversely, data-driven estimation methods, while offering flexibility, often lack physical implications in terms of system dynamics. This paper proposed a structured linear neural dynamics model for motor temperature estimation. This model is data-driven, with prior knowledge integrated into its structure, which preserves flexibility while guaranteeing system stability through the Perron-Frobenius theorem. Additionally, this paper achieves the decoupling of control input from state transitions and the embedded deployment of this model. The method is validated with a real dataset. The lightweight feature is demonstrated by the implementation of an STM32 Microcontroller with 1.808 KB and 27 mW. The paper is accompanied by the open source data and code at GitHub: https://github.com/ms140429/Explainable-Neural-Dynamics-Model