Backstepping design for fault detection and FTC of an induction motor drives-based EVs

Roubache, Toufik; Chaouch, Souad; Said, M. S. Nait

doi:10.7305/automatika.2017.02.1733

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…Studies on fault or anomaly detection for vehicle powertrains have been carried out by various approaches. They can be classified by rule-based methods [6][7][8][9][10][11][12], mathematical 2 of 21 model-based methods [13][14][15][16][17][18][19][20][21][22][23][24][25][26], and data-driven methods that use signal processing or machine learning [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

“…Pan proposed an internal short circuit fault detection method for lithium-ion battery cells using two current sensors with a symmetrical loop circuit topology [11]. In the case of using mathematical models, the method of using a linear mathematical model of a powertrain component, the method of estimating sensor values using state observers such as Extended Kalman Filters (EKF), Luenberger Observers [15,17,19], and the method based on structural analysis of the target system [18], etc., were proposed. Tabbache proposed a fault-tolerant control strategy for a speed sensor of an EV induction motor using the maximum likelihood voting technique [15].…”

Section: Introductionmentioning

confidence: 99%

“…Tabbache proposed a fault-tolerant control strategy for a speed sensor of an EV induction motor using the maximum likelihood voting technique [15]. Roubache conducted fault detection for an EV induction motor using EKF and back-stepping control [17]. Meyer proposed an inter-turn short circuit fault detection and fault degree identification method using moving horizon observer for the Toyota Prius traction motor [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

Jeong

Cho

et al. 2021

Applied Sciences

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Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

Jeong

Cho

et al. 2021

Applied Sciences

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show abstract

“…Afterwards, several systems for ITSC effect compensation were developed based on the abovementioned paper. In Reference [33], an approach which deals with backstepping control was proposed, and it was then improved using additional schemes, such as the extended Kalman filter (EKF) [34] or the SMO [35] to estimate fault-related signals. Another technique is based on sliding mode control (SMC) [36,37] with an additional estimation unit-EKF [38].…”

Section: Introductionmentioning

confidence: 99%

Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives

Dybkowski

Bednarz

2019

Energies

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This paper deals with fault-tolerant control (FTC) of an induction motor (IM) drive. An inter-turn short circuit (ITSC) of the stator windings was taken into consideration, which is one of the most common internal faults of induction machines. The sensitivity of the classic, well-known voltage and current models to the stator winding faults was analyzed. It has been shown that these classical state variable estimators are sensitive to induction motor parameter changes during stator winding failure, which results in unstable operation of the direct field-oriented control (DFOC) drive. From a safety-critical applications point of view, it is vital to guarantee stable operation of the drive even during faults of the machine. Therefore, a new FTC system has been proposed, which consists of new modified rotor flux estimators, robust to stator winding faults. A detailed description of the proposed system is presented herein, as well as the results of simulation and experimental tests. Simulation analyses were performed using MATLAB/Simulink software. Experimental tests were carried out on the experimental test bench with a dSpace DS1103 card. The proposed solution could be applied as an alternative rotor flux estimation technique for the modern FTC drive.

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“…Several works have been presented such us [2,20]. However, the nonlinearity of the IM model makes the estimator's synthesis difficult.…”

Section: Introductionmentioning

confidence: 99%

Actuator Fault Estimation Based on LPV Unknown Input Observer for Induction Machine

Amrane¹,

Larabi²,

Aïtouche³

2017

STUD INFORM CONTROL

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This machine is not easy to control due to the nonlinearity of its dynamic model. Moreover, its state variables are not all measurable and it is under the influence of parametric variations. Therefore, a good control is necessary to guaranty the stability of its proper functioning. In the literature, most of the works deal with steady state vector control [8-10, 21, 25], Direct Torque Control (DTC) [19], Sliding Mode Control (SMC) [13,27]. In addition, nonlinear control applied to induction machine has been developed such in [14].The advantages of such approaches are their robustness to disturbances and their simplicity of implementation. The major disadvantage of the set-up is that all controls rely on the reliability of the measurement provided by sensors, Abstract: This paper presents an unknown inputs observer (UIO) applied to an induction machine (IM) in order to estimate the actuator faults and the state variables. Knowing that the machine used is highly nonlinear, a model based on LPV (linear parameter varying) system of the machine is used. Indeed, using the induction machine based on LPV model, we develop the structure of an observer where the actuator faults are the unknown inputs. The conditions for convergence are based on the Lyapunov theory that will ensure the stability. Based on the LMI (Linear Matrix Inequalities), the gains of the UIO subject to actuator faults are confirmed and then ensure the efficiency of our approach. The obtained results through simulations demonstrate the effectiveness of the proposed approach.

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Backstepping design for fault detection and FTC of an induction motor drives-based EVs

Cited by 6 publications

References 27 publications

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives

Actuator Fault Estimation Based on LPV Unknown Input Observer for Induction Machine

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