Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information
Sensorless control of permanent magnet synchronous motors is preferable in some applications due to cost and mounting space concerns. The performance of most existing position estimation methods greatly depends on the accuracy of the motor inductance. As the estimated position should not be involved...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/15/1/35 |
| _version_ | 1797339277946781696 |
|---|---|
| author | Jilei Xing Junzhi Zhang Xingming Zhuang Yao Xu |
| author_facet | Jilei Xing Junzhi Zhang Xingming Zhuang Yao Xu |
| author_sort | Jilei Xing |
| collection | DOAJ |
| description | Sensorless control of permanent magnet synchronous motors is preferable in some applications due to cost and mounting space concerns. The performance of most existing position estimation methods greatly depends on the accuracy of the motor inductance. As the estimated position should not be involved in the parameter identification process in a sensorless control system, an online inductance identification method independent of the rotor position information is developed in this paper. The proposed method utilizes the recursive least square algorithm and the particle swarm optimization algorithm to realize real-time identification of the inductance along the direct axis and the quadrature axis, respectively, based on the deduced parametric equations without position information. The proposed method is efficient enough to be implemented within 0.2 ms and does not introduce any additional signal injection. A test bench is built to validate the characteristics of the method, and the experimental results show that the identified inductance can converge to the actual value rapidly and is robust to changes in the initial values and stator current. With the proposed method, accurate estimation of the rotor position and speed can be obtained using traditional model-based position estimators, and the stability of the sensorless control system can be improved significantly. |
| first_indexed | 2024-03-08T09:44:36Z |
| format | Article |
| id | doaj.art-5ccd67af2af94553a96909341b921206 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-08T09:44:36Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-5ccd67af2af94553a96909341b9212062024-01-29T14:26:36ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-01-011513510.3390/wevj15010035Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position InformationJilei Xing0Junzhi Zhang1Xingming Zhuang2Yao Xu3School of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaSchool of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaBIT HuaChuang Electric Vehicle Technology Co., Ltd., Beijing 100081, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSensorless control of permanent magnet synchronous motors is preferable in some applications due to cost and mounting space concerns. The performance of most existing position estimation methods greatly depends on the accuracy of the motor inductance. As the estimated position should not be involved in the parameter identification process in a sensorless control system, an online inductance identification method independent of the rotor position information is developed in this paper. The proposed method utilizes the recursive least square algorithm and the particle swarm optimization algorithm to realize real-time identification of the inductance along the direct axis and the quadrature axis, respectively, based on the deduced parametric equations without position information. The proposed method is efficient enough to be implemented within 0.2 ms and does not introduce any additional signal injection. A test bench is built to validate the characteristics of the method, and the experimental results show that the identified inductance can converge to the actual value rapidly and is robust to changes in the initial values and stator current. With the proposed method, accurate estimation of the rotor position and speed can be obtained using traditional model-based position estimators, and the stability of the sensorless control system can be improved significantly.https://www.mdpi.com/2032-6653/15/1/35permanent magnet synchronous motorsonline inductance identificationsensorless controlrecursive least square algorithmparticle swarm optimization |
| spellingShingle | Jilei Xing Junzhi Zhang Xingming Zhuang Yao Xu Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information World Electric Vehicle Journal permanent magnet synchronous motors online inductance identification sensorless control recursive least square algorithm particle swarm optimization |
| title | Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information |
| title_full | Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information |
| title_fullStr | Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information |
| title_full_unstemmed | Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information |
| title_short | Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information |
| title_sort | online inductance identification of permanent magnet synchronous motors independent of rotor position information |
| topic | permanent magnet synchronous motors online inductance identification sensorless control recursive least square algorithm particle swarm optimization |
| url | https://www.mdpi.com/2032-6653/15/1/35 |
| work_keys_str_mv | AT jileixing onlineinductanceidentificationofpermanentmagnetsynchronousmotorsindependentofrotorpositioninformation AT junzhizhang onlineinductanceidentificationofpermanentmagnetsynchronousmotorsindependentofrotorpositioninformation AT xingmingzhuang onlineinductanceidentificationofpermanentmagnetsynchronousmotorsindependentofrotorpositioninformation AT yaoxu onlineinductanceidentificationofpermanentmagnetsynchronousmotorsindependentofrotorpositioninformation |