PARAMETER IDENTIFICATION APPROACH USING IMPROVED TEACHING AND LEARNING BASED OPTIMIZATION FOR HUB MOTOR CONSIDERING TEMPERATURE RISE

作     者：Li, Yong Wang, Juan Zhang, Taohua Hu, Han Wu, Hao 

作者机构：Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Beijing Inst Space Launch Technol Beijing 100076 Peoples R China 

出 版 物：《METROLOGY AND MEASUREMENT SYSTEMS》 (Metrol. Meas. Sys.)

年 卷 期：2023年第30卷第1期

页      面：99-115页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0804[工学-仪器科学与技术] 

基　　金：National Natural Science Foundation of China China Postdoctoral Science Foundation [2019M660105, 2020T130360] Jiangsu Province Postdoctoral Science Foundation [2021K443C] 

主　　题：parameters identification teaching-learning-based optimization hub motor temperature rise 

摘      要：Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance,d -axis inductance, d-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, d-axis inductance, d-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified.