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Adaptive Inertia Observer-based Model-free Predictive Current Control for PMSM Driving System of Electrical Vehicle
Indexado
Scopus SCOPUS_ID:85182310293
DOI 10.1109/ICEMS59686.2023.10344806
Año 2023
Tipo

Citas Totales

Autores Afiliación Chile

Instituciones Chile

% Participación
Internacional

Autores
Afiliación Extranjera

Instituciones
Extranjeras


Abstract



To solve the problem of unsuitable inertia and weak robustness of the model predictive control (MPC) for the motor driving system on electrical vehicles (EVs), an adaptive inertia observer-based model-free predictive current control (MF-PCC) strategy is proposed in this paper, and applied to the permanent magnet synchronous motor (PMSM) driving system to adjust the system inertia online. The current load of the EV is converted to the mass and load inertia, and an adaptive inertia method is designed to realize the inertia match between system inertia and load inertia based on online estimated load torque. Control performances of this method are analyzed in principle by bode diagrams and zero-pole maps with different sampling periods and inertia ratios. The effectiveness and correctness of the proposed method are demonstrated according to the experimental results compared with MF-PCC with fixed inertia and conventional PCC strategy. The advantages of better dynamics and current quality with suitable robustness and stability are accomplished by adaptive inertia.

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Disciplinas de Investigación



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Scopus
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Publicaciones WoS (Ediciones: ISSHP, ISTP, AHCI, SSCI, SCI), Scopus, SciELO Chile.

Colaboración Institucional



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Autores - Afiliación



Ord. Autor Género Institución - País
1 Wei, Yao - Chinese Academy of Sciences - China
2 Ke, Dongliang - Chinese Academy of Sciences - China
3 Yu, Xinhong - Chinese Academy of Sciences - China
4 Wang, Fengxiang - Chinese Academy of Sciences - China
5 RODRIGUEZ-PEREZ, JOSE RAMON Hombre Universidad San Sebastián - Chile

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Financiamiento



Fuente
National Natural Science Foundation of China
Science and Technology Projects of Fujian Province
China Postdoctoral Science Foundation
Agencia Nacional de Investigación y Desarrollo

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Agradecimientos



Agradecimiento
This work was supported in part by the National Natural Science Funds of China under Grant 52277070, in part by the China Postdoctoral Science Foundation under Grant 2023M730598, in part by the Science and Technology Plan Project of Fujian Province under Grant 2021I0039, in part by the Science and Technology Plan Project of Fujian Province under Grant 2021T3064, and in part by the Science and Technology Plan Project of Fujian Province under Grant 2021T3035. J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208 and 1221293.

Muestra la fuente de financiamiento declarada en la publicación.