SVM PMSM Drive With Low Resolution Hall-Effect Sensors

Lidozzi, Alessandro; Solero, Luca; Crescimbini, Fabio; Napoli, A. Di

doi:10.1109/tpel.2006.886603

Cited by 89 publications

(25 citation statements)

References 8 publications

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“…Then a triplen harmonic back-EMF as shown in (2) can be derived by the measured voltage with the aid of a Yconnected resistor network as in Fig. 1 between the central point "s" of this Y-connected resistor network and the machine winding neutral point "n" as Clearly, on the condition of third harmonic much bigger than other triplen harmonics, the voltage u sn could just represent third-harmonic back-EMF irrespective of the operational mode.…”

Section: Detection Of Third Harmonic Back-emfmentioning

confidence: 99%

Influence of Stator Current and Machine Saliency on Sensorless Control Performance Based on Third-Harmonic Back-EMF

Liu

Zhu

2014

7th IET International Conference on Power Electronics, Machines and Drives (PEMD 2014)

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This paper investigates the influence of stator current and machine saliency on rotor position estimation of permanent magnet synchronous machine (PMSM) based on third harmonic back-EMF which is contained in windings irrespective of the operational mode and parameters. Due to machine saliency and saturation of air gap flux-linkage, the amplitude and phase angle of third harmonic back-EMF will be affected by the stator current. Several experimental analyses are carried out based on a dSPACE platform with a laboratory PMSM validate that the machine speed and q-axis current will not affect the phase angle of the third harmonic back-EMF, whereas there will exhibit rotor position estimation error by applying negative d-axis current, and with large positive d-axis current, the third harmonic back-EMF will be seriously distorted due to critical air gap saturation.

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Section: Detection Of Third Harmonic Back-emfmentioning

confidence: 99%

Influence of Stator Current and Machine Saliency on Sensorless Control Performance Based on Third-Harmonic Back-EMF

Liu

Zhu

2014

7th IET International Conference on Power Electronics, Machines and Drives (PEMD 2014)

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“…An improved vector-tracking observer was proposed to eliminate the complexity of the system in [5]. A method based on back electromotive force (EMF) was proposed in [6]. Although this θ θ θ ω θ…”

Section: Introductionmentioning

confidence: 99%

Accurate and Efficient Torque Control of an Interior Permanent Magnet Synchronous Motor in Electric Vehicles Based on Hall-Effect Sensors

Lei

Zhang

Huang³

2017

Energies

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Abstract:In this paper, an effective method to achieve accurate and efficient torque control of an interior permanent magnet synchronous motor (IPMSM) in electric vehicles, based on low-resolution Hall-effect sensors, is proposed. The high-resolution rotor position is estimated by a proportional integral (PI) regulator using the deviation between actual output power and reference output power. This method can compensate for the Hall position sensor mounting error, and estimate rotor position continuously and accurately. The permanent magnetic flux linkage is also estimated based on a current PI controller. Other important parameters, such as the d-axis and q-axis inductances, stator resistance, and energy loss, are measured offline by experiments. The measured parameters are saved as lookup tables which cover the entire current operating range at different current levels. Based on these accurate parameters, a maximum torque per ampere (MTPA) control strategy, combined with the feedforward parameter iteration method, can be achieved for accurate and efficient torque control. The effectiveness of the proposed method is verified by both simulation and experimental results.

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“…While the SVM shows outstanding performances in the field of recognition systems for intelligent vehicles (IVs) and intelligent transportation systems (ITSs), it has become one of the most popular tools for pedestrian detection [2]- [8], vehicle detection [9], [10], driver monitoring [11]- [13], traffic sign recognition [14], [15], license plate recognition [16], traffic monitoring [17]- [21], and intelligent control [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Number Reduction: Survey and Experimental Evaluations

Jung

Kim

2014

IEEE Trans. Intell. Transport. Syst.

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Abstract-Although a support vector machine (SVM) is one of the most frequently used classifiers in the field of intelligent transportation systems and shows competitive performances in various problems, it has the disadvantage of requiring relatively large computations in the testing phase. To make up for this weakness, diverse methods have been researched to reduce the number of support vectors determining the computations in the testing phase. This paper is intended to help engineers using the SVM to easily apply support vector number reduction to their own particular problems by providing a state-of-the-art survey and quantitatively comparing three implementations belonging to postpruning, which exploits the result of a standard SVM. In particular, this paper confirms that the support vector number of a pedestrian classifier using a histogram-of-oriented-gradientbased feature and a radial-basis-function-kernel-based SVM can be reduced by more than 99.5% without any accuracy degradation using iterative preimage addition, which can be downloaded from the Internet.Index Terms-Reduced-set method, support vector machine (SVM), support vector number reduction (SVNR).

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SVM PMSM Drive With Low Resolution Hall-Effect Sensors

Cited by 89 publications

References 8 publications

Influence of Stator Current and Machine Saliency on Sensorless Control Performance Based on Third-Harmonic Back-EMF

Influence of Stator Current and Machine Saliency on Sensorless Control Performance Based on Third-Harmonic Back-EMF

Accurate and Efficient Torque Control of an Interior Permanent Magnet Synchronous Motor in Electric Vehicles Based on Hall-Effect Sensors

Support Vector Number Reduction: Survey and Experimental Evaluations

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