Parameter identification approach using improved teaching and learning based optimization for hub motor considering temperature rise

• Autorzy: Li Yong , Wang Yuan , Zhang Taohua , Hu Han , Wu Hao

Identyfikatory

DOI

10.24425/mms.2023.144396

• Języki publikacji: EN

Abstrakty

EN

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, 𝑞-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, 𝑞-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.

Słowa kluczowe

EN

parameters identification; teaching-learning-based optimization; hub motor; temperature rise

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2023
• Tom: Vol. 30, nr 1
• Strony: 99--115
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr., wzory

Twórcy

autor

Li Yong

• Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China

autor

Wang Yuan

• Beijing Institute of Space Launch Technology, Beijing 100076, China

autor

Zhang Taohua

• Beijing Institute of Space Launch Technology, Beijing 100076, China

autor

Hu Han

• Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China

autor

Wu Hao

• Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China

Bibliografia

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Uwagi

1. This work was supported by the National Natural Science Foundation of China (Grant No. 51705213), the China Postdoctoral Science Foundation (Grant No. 2019M660105 and Grant No. 2020T130360) and the Jiangsu Province Postdoctoral Science Foundation (Grant No. 2021K443C).

2. Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).