Speed and current sensor fault-tolerant induction motor drive for electric vehicles based on virtual sensors

Aguilera, Facundo; Barrera, Pablo M. de la; Angelo, Cristian H. De

doi:10.1007/s00202-022-01541-4

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…Currently, electric vehicles (EVs) are gaining increasing popularity in the automotive landscape. These vehicles rely heavily on precise and accurate current measurements for essential functions such as battery management, motor control, autonomous driving, and charging systems [11][12][13][14][15]. Fluxgate current sensors, known for their high precision, wide bandwidth (including DC), and galvanic isolation, are particularly favored in this context [16][17][18].These sensors have become a focal point in research aimed at enhancing performance.…”

Section: Introductionmentioning

confidence: 99%

Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks

Li,

Ren,

Luo

et al. 2024

Sensors

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This study introduces a novel fluxgate current sensor with a compact, ring-shaped configuration that exhibits improved performance through the integration of magnetization residence times and neural networks. The sensor distinguishes itself with a unique magnetization profile, denoted as M waves, which emerge from the interaction between the target signal and ambient magnetic interference, effectively enhancing interference suppression. These M waves highlight the non-linear coupling between the magnetic field and magnetization residence times. Detection of these residence times is accomplished using full-wave rectification circuits and a Schmitt trigger, with a digital output provided by timing sequence detection. A dual-layer feedforward neural network deciphers the target signal, exploiting this non-linear relationship. The sensor achieves a linearity error of 0.054% within a measurement range of 15 A. When juxtaposed with conventional sensors utilizing the residence-time difference strategy, our sensor reduces linearity error by more than 40-fold and extends the effective measurement range by 150%. Furthermore, it demonstrates a significant decrease in ambient magnetic interference.

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Section: Introductionmentioning

confidence: 99%

Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks

Li,

Ren,

Luo

et al. 2024

Sensors

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“…FDI techniques for decoupling disturbances are presented in [9,13,[19][20][21]. On the other hand, several FDI schemes with robustness against modeling errors have been proposed [22][23][24][25]. However, fewer FDI schemes have the capability of working in the presence of disturbances and modeling errors simultaneously unless some restrictive assumptions are made concerning the model uncertainties, [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Structured Residuals Design Techniques for actuator and sensor Fault Detection and Isolation in a Permanent Magnet DC Motor

Antić

Rosić

Djurović

et al. 2023

Preprint

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The reliability of actuators and sensors is an important topic in many electric motor drives. A comparison of structured residual synthesis methods for actuator and sensor fault detection and isolation (FDI) in a permanent magnet (PM) DC motor, applying two different approaches to the design of primary residuals, is presented in the paper. The first method involves a standard approach and is based on system transfer matrix synthesis. The second, more convenient approach involves the synthesis of primary residuals based on the analysis of subsystems that describe the observed system. Both methods are applicable for linear and stationary systems but were successfully applied to a laboratory system that shows nonlinear and non-stationary properties and is affected by a constant disturbance, thanks to the proposed technique of residual translation with the previous design of internal residuals. A real-time experiment performed on a laboratory setup, which consists of a DC motor, an amplifier designed in the form of a linear electronic circuit, and a Compact RIO 9075 real-time processor, is used to illustrate the drawbacks and advantages of the analyzed approaches.

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“…However, it is important to note that even with these improvements, the number of adaptive observers remains unchanged at three. Speed and current sensor fault-tolerant induction motor drive is presented in [29]. This method uses three full-order observers, the first one under healthy sensor operation, the second one under speed sensor fault operation, and the last one under current sensor fault operation.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Current Sensors Fault Detection and Tolerant Control Strategy for Three-Phase Permanent Magnet Synchronous Motor Drives

Azzoug,

Pusca,

Sahraoui

et al. 2023

Machines

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This paper presents the experimental validation of a Fault-Tolerant Control (FTC) system for Permanent Magnet Synchronous Motor (PMSM) drives, specifically focusing on current sensors. The FTC system is designed to detect and diagnose both single and multiple faults in the current sensors and to reconfigure the control loop to ensure uninterrupted operation in the presence of such faults. Several crucial aspects are addressed in the proposed approach, including fault detection, isolation of faulty sensors, and reconfiguration of the control system through accurate current estimation. To achieve this, a novel adaptation of the Luenberger observer is proposed and employed for estimating the stator currents. The effectiveness of the fault-tolerant control strategy is demonstrated through experimental tests conducted on a 7.2 kW PMSM utilizing a field-oriented vectorial strategy implemented in a dSpace 1104 platform.

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Speed and current sensor fault-tolerant induction motor drive for electric vehicles based on virtual sensors

Cited by 11 publications

References 37 publications

Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks

Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks

Comparison of Structured Residuals Design Techniques for actuator and sensor Fault Detection and Isolation in a Permanent Magnet DC Motor

Experimental Validation of Current Sensors Fault Detection and Tolerant Control Strategy for Three-Phase Permanent Magnet Synchronous Motor Drives

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