Hybrid Thermal Modeling with LPTN-Informed Neural Network for Multi-Node Temperature Estimation in PMSM

Citation Author(s):
Zirui
Liu
Huazhong University of Science and Technology
Wubin
Kong
Huazhong University of Science and Technology
Xinggang
Fan
Huazhong University of Science and Technology
Zimin
Li
Huazhong University of Science and Technology
Kai
Peng
Huazhong University of Science and Technology
Ronghai
Qu
Huazhong University of Science and Technology
Submitted by:
Zirui Liu
Last updated:
Mon, 01/22/2024 - 01:32
DOI:
10.21227/sbwe-k671
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Abstract 

To achieve improved multi-node temperature estimation with limited training data in Permanent Magnet Synchronous Motors (PMSMs), a novel approach of a Lumped-Parameter Thermal Network (LPTN)-informed neural network is proposed in this paper. Firstly, the parameter and model uncertainties of third or higher-order LPTNs with global parameter identification for temperature estimation are systematically stated based on numerical analysis. Then, a two-step parameter identification strategy for a third-order LPTN with simplified thermal transfer paths is proposed to resolve parameter uncertainty. This strategy uses only air-gap structure information to make all parameters converge to their unique solutions without the need for additional geometrical parameters or material features. In response to model uncertainty, an LPTN-informed Long Short-Term Memory (LSTM) framework is designed to compensate for model unaccounted errors and extend temperature estimation nodes that the highly abstract low-order LPTN does not consider.Experimental temperature estimation results validate the effectiveness of the proposed LPTN-informed LSTM framework under a limited 23.8 hours of training data.

Instructions: 

This repository contains the training data of a LPTN-informed LSTM for multi-node temperature estimation in PMSMs.

Funding Agency: 
the National Natural Science Foundation of China
Grant Number: 
52377050