Speed and rotor flux estimation of induction machines using a two-stage extended Kalman filter

Hilairet, Mickaël; Auger, François; Berthelot, Éric

doi:10.1016/j.automatica.2009.04.005

Cited by 80 publications

(42 citation statements)

References 28 publications

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“…Hsieh and Chen generalized Friedland's filter and proposed an optimal solution for a two-stage Kalman estimator by applying a two-stage U-V transformation [20,21]. To reduce the number of arithmetic operations in speed and rotor flux estimation of induction machine, Hilairet proposed modified optimal two-stage Kalman estimator derived from Hsieh and Chen algorithm [22]. Considering the nonlinear characteristics of the lithium-ion battery model, we propose in this paper a robust state estimation including EKF, which is suited for nonlinear systems.…”

Section: Robust State Estimationmentioning

confidence: 99%

Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

et al. 2012

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A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.

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Section: Robust State Estimationmentioning

confidence: 99%

Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

et al. 2012

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“…With the field-oriented method, the dynamic behavior of the induction motor is rather similar to that of a separately excited DC motor [1]. The possibility to reduce the number of sensors involved in the control of electric motors has been a subject of systematic research during the last years [2][3][4][5][6]. As a result, state estimation-based control has become an active research area in the field of electric machines and power electronics.…”

Section: Introductionmentioning

confidence: 99%

Differential flatness theory and Extended Kalman Filtering for sensorless control of induction motors

Rigatos¹,

Siano²

2012

Stochastic Systems Theory and its Applications (SSS)

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Abstract-Diffenential flatness theory and Extended Kalman Filtering (EKF) are used for implementing a sensorless control scheme for induction motor drives. It is shown that the induction motor is a differentially flat system, since all state variables of the circuits describing the motor's dynamics can be expressed as functions of the flat output and its derivatives. The flat output consists of the angle of the rotor and of the the angle of the magnetic field. Flatness-based control for the complete (sixthorder) induction motor model is analyzed while the Extended Kalman Filter is proposed to estimate the state vector of the nonlinear electric motor using a limited number of sensors. The efficiency of the above mentioned Extended Kalman Filter-based control scheme, is tested through simulation experiments.

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“…Generally, the temporal equation of the estimated parameters θ[k] is unknown and therefore leads to a dynamical model as follows [83]:…”

Section: B Energy Storage Systemsmentioning

confidence: 99%

“…Furthermore, alternative approaches can be used to enhance parallel execution such as interleaved EKF [84] or multi-stage EKF [83], [85] (see Fig. 6).…”

Section: B Energy Storage Systemsmentioning

confidence: 99%

Industrial Applications of the Kalman Filter: A Review

Auger

Hilairet

Guerrero

et al. 2013

IEEE Trans. Ind. Electron.

487

188

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Abstract-The Kalman filter has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 70s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing and so on. This paper provides a brief overview of the industrial applications and implementation issues of the Kalman filter in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.

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Speed and rotor flux estimation of induction machines using a two-stage extended Kalman filter

Cited by 80 publications

References 28 publications

Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

Differential flatness theory and Extended Kalman Filtering for sensorless control of induction motors

Industrial Applications of the Kalman Filter: A Review

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