MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results

Mapelli, Ferdinando Luigi; Tarsitano, Davide; Cheli, Federico

doi:10.1016/j.epsr.2017.02.005

Cited by 23 publications

(26 citation statements)

References 19 publications

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“…Thus, different estimation methods are proposed [3][4][5][6][7][8] in order to overcome these uncertainties associated with the IM model. Considering the past speed-sensored studies [9][10][11][12][13][14][15] in the literature, rotor time constant estimation is performed by minimum order linear recursive least square algorithm in [9]. Shinohara et al [10] presented an instantaneous active and reactive power based estimator to simultaneously estimate R r and R s together with a rotor flux observer to obtain the rotor flux angle and magnitude.…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman filter based estimations for improving speed‐sensored control performance of induction motors

Yıldız¹,

Barut²,

Demir³

2020

IET Electric Power Applications

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In this study, an extended Kalman filter (EKF)‐based estimation algorithm is presented to improve the speed‐sensored control performance of induction motors (IMs). The proposed EKF‐based estimation algorithm is to simultaneously estimate the stator stationary axis components of stator currents and rotor fluxes, rotor angular speed, load torque including viscous friction term, rotor resistance and magnetising inductance in a single EKF algorithm without requiring any switching operation or a hybrid structure. In order to improve the speed‐sensored control performance, the measurement/output matrix of IM model is extended by the measured rotor speed in addition to stationary axis components of the measured stator currents. Therefore, the proposed EKF algorithm uses the speed and stator current errors between the measured and priori estimation values in order to calculate the posterior estimation ones. For performance evaluation, the eighth order (proposed) EKF algorithm is tested by simulations and real‐time experiments under challenging scenarios and compared with the developed sixth order EKF in real time. The obtained real‐time results also show that the eighth order (proposed) EKF algorithm provides additional and improved estimations with the increased but feasible execution time in terms of the sixth order EKF designed in this paper.

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Section: Introductionmentioning

confidence: 99%

Extended Kalman filter based estimations for improving speed‐sensored control performance of induction motors

Yıldız¹,

Barut²,

Demir³

2020

IET Electric Power Applications

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“…Ref. [7]'de ise reaktif güç ile birlikte motor momenti tabanlı modele uyarlamalı sistem (MUS) kullanılarak kestirilmektedir. Ancak alan zayıflama bölgesi sonuçları sunulmamaktadır.…”

Section: Introductionunclassified

Reduced Order Extended Kalman Filter based Parameter Estimations for speed-sensored Induction Motor Drive

Demir¹,

Barut²,

Yıldız³

2018

Pamukkale J Eng Sci

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Bu çalışmada, asenkron motorların alan zayıflama bölgesindeki yüksek başarımlı hız-algılayıcılı kontrolü için yeni bir indirgenmiş dereceli genişletilmiş Kalman filtresi tabanlı kestirici tasarlanarak benzetim ve gerçek-zamanlı deneylerle test edilmektedir. Önerilen indirgenmiş dereceli genişletilmiş Kalman filtresi ile vektör kontrol sistemi için gerekli olan rotor akısının stator duran eksen takımı bileşenleri kestirilmektedir. Ayrıca önerilen algoritma ile akı kestirimlerine ek olarak değeri çalışma koşulları ile değişen rotor direnci ve mıknatıslama endüktansı, sıfır hız ve alan zayıflama bölgesini de içeren geniş bir hız aralığında eş-zamanlı olarak kestirilmektedir. Önerilen kestirim algoritmasına ait benzetim sonuçları hız, yük momenti, rotor direnci ve mıknatıslama endüktansının zorlayıcı değişimleri altında oldukça tatmin edicidir. Bu nedenle önerilen kestirim algoritmasını kullanan doğrudan vektör kontrollü asenkron motor sürücüsünün başarımı da oldukça iyi olmaktadır. Ayrıca, elde edilen gerçek-zamanlı kestirim sonuçları da önerilen kestiricinin başarımını onaylamaktadır. In this study, for a high performance speed-sensored control in field weakening region of induction motors, a novel reduced order extended Kalman filter based estimator is designed and tested by experiments performed in simulation and real-time experiments. The proposed reduced order extended Kalman filter estimates the stator stationary axis components of rotor fluxes, which are required for vector control system. In addition to the flux estimations, rotor resistance and mutual inductance whose values vary according to operating conditions of induction motors are estimated simultaneously in a wide speed range including zero-speed and field-weakening. The simulation based estimation results associated with proposed estimation algorithm are quite satisfactory under challenging variations of rotor angular velocity, load torque, rotor resistance, and mutual inductance. Therefore, the performance of the direct vector controlled induction motor drive using the proposed estimation algorithm also becomes quite well. Moreover, the estimation results in real-time experiments also confirm the performances of the proposed estimator.

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“…The field-oriented control (FOC) algorithm needs accurate estimation of motor state variables in order to ensure full-torque performances and good efficiency of IM. For this purpose, a two-rotor resistance estimation method is presented in [229]. Reference [230] presents the motor condition monitoring for HEV and its review with focus on nine commonly used condition monitoring methods of IM.…”

Section: Induction Motormentioning

confidence: 99%

A comprehensive review on hybrid electric vehicles: architectures and components

2019

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The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuelefficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors' role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability. Keywords Hybrid electric vehicle Á Hybrid energy storage system Á Architecture Á Traction motors Á Bidirectional converter Abbreviations and symbols ABS Antilock braking system AC Alternating current ADTR Antidirectional-twin-rotary ADVISOR Advanced vehicle simulator ANN Artificial neural network ASCI Auto-sequential commutated mode singlephase inverter BEV Battery electric vehicle BLDC Brushless DC motor CD Charge depletion CDFIM Cascaded DFIM CF-qZSI Current-fed quasi-ZSI CMPPT Centralized MPPT CS Charge sustaining CSI Current source inverter CS-PMSM Compound-structure PMSM CVT Continuous variable transmission DC Direct current DFIM Doubly fed induction motor DMPPT Distributed MPPT DRM Double-rotor machines DTC Direct torque control e-CVT Electronic continuous variable transmission EM Electric motor EMS Energy management system EREV Extended range electric vehicle ESS Energy storage system EV Electric vehicle FC Fuel cell

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MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results

Cited by 23 publications

References 19 publications

Extended Kalman filter based estimations for improving speed‐sensored control performance of induction motors

Extended Kalman filter based estimations for improving speed‐sensored control performance of induction motors

Reduced Order Extended Kalman Filter based Parameter Estimations for speed-sensored Induction Motor Drive

A comprehensive review on hybrid electric vehicles: architectures and components

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