Online Inductance Identification Using PWM Current Ripple for Position Sensorless Drive of High-Speed Surface-Mounted Permanent Magnet Synchronous Machines

Zhang, Jindong; Peng, Fei; Huang, Yunkai; Yao, Yu; Zhu, Zichong

doi:10.1109/tie.2021.3130327

Cited by 24 publications

(7 citation statements)

References 28 publications

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“…The RLS method with a forgetting factor is widely adopted in the research on online motor parameter identification for its simplicity [32]. The typical system in the RLS can be expressed as:…”

Section: Inductance Identification Accuracy Analysis With Position Errormentioning

confidence: 99%

Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information

Xing,

Zhang,

Zhuang

et al. 2024

WEVJ

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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.

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Section: Inductance Identification Accuracy Analysis With Position Errormentioning

confidence: 99%

Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information

Xing,

Zhang,

Zhuang

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…In [13] the parameter observability in an α-β-frame is proven before an estimation process with a recursive least squares (RLS) method is presented. Very recently in [14] an estimation of inductance is used to obtain the estimated position. The estimated methods do not consider the presence of noise.…”

Section: B Inductance Estimationmentioning

confidence: 99%

Using a Bivariate Polynomial in an EKF for State and Inductance Estimations in the Presence of Saturation Effects to Adaptively Control a PMSM

Zwerger

Mercorelli

2022

IEEE Access

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This paper takes into consideration a combined extended Kalman filter (CEKF) by using a bivariate polynomial for the estimation of L d and L q in saturation conditions. In the context of the Kalman filter (KF), L d and L q are modelled as nonlinear augmented states to control a permanent magnetic synchronous machine (PMSM). Once L d and L q are estimated, continuous monitoring of the machine saturation conditions is achieved to ensure the desired torque even under saturation conditions. The proposed adaptive control method based on maximum torque per ampere (MTPA) consists of an adaptive feedforward and PI controller. A discussion in light of the measured results using Hardware-in-the-loop is also included.INDEX TERMS PMSM, extended Kalman filter, parameter estimation, bivariate polynomial.

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“…Due to high efficiency, high power density, and stable rotor structural strength [1,2], ultra-high-speed surface-mounted permanent magnet synchronous motors (UHS-SPMSMs) are being widely used to drive fuel cell air compressors [3][4][5], which in turn provide pressurized air to fuel cell stacks to ensure the power output demand and energy conversion efficiency of hydrogen fuel cell vehicles. UHS-SPMSMs and centrifugal compressors are integrated as the overall compressor system, whose rational speed is typically capable of 100 kr/min and above.…”

Section: Introductionmentioning

confidence: 99%

A Reliable and Efficient I-f Startup Method of Sensorless Ultra-High-Speed SPMSM for Fuel Cell Air Compressors

Xing,

Xu,

Zhang

et al. 2024

Actuators

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Extended back electromotive force (EEMF)-based position sensorless field-oriented control (FOC) is widely utilized for ultra-high-speed surface-mounted permanent magnet synchronous motors (UHS-SPMSMs) driven fuel cell air compressors in medium-high speed applications. Unfortunately, the estimated position is imprecise due to too small EEMF under low speed operation. Hence, current-to-frequency (I-f) control is more suitable for startup. Conventional I-f methods rarely achieve the tradeoff between startup acceleration and load capacity, and the transition to sensorless FOC is mostly realized in the constant-speed stage, which is unacceptable for UHS-SPMSM considering the critical requirement of startup time. In this article, a new closed-loop I-f control approach is proposed to achieve fast and efficient startup. The frequency of reference current vector is corrected automatically based on the active power and the real-time motor torque, which contributes to damping effect for startup reliability. Moreover, an amplitude compensator of reference current vector is designed based on the reactive power, ensuring the maximum torque per ampere operation and higher efficiency. Furthermore, the speed PI controller is enhanced by variable bandwidth design for smoother sensorless transition. These theoretical advantages are validated through experiments with a 550 V, 35 kW UHS-SPMSM. The experimental results demonstrated the enhanced startup performance compared with conventional I-f control.

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Online Inductance Identification Using PWM Current Ripple for Position Sensorless Drive of High-Speed Surface-Mounted Permanent Magnet Synchronous Machines

Cited by 24 publications

References 28 publications

Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information

Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information

Using a Bivariate Polynomial in an EKF for State and Inductance Estimations in the Presence of Saturation Effects to Adaptively Control a PMSM

A Reliable and Efficient I-f Startup Method of Sensorless Ultra-High-Speed SPMSM for Fuel Cell Air Compressors

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