This study proposes an innovative stacking ensemble learning framework for classifying faults in induction motors utilized in Electric Vehicles (EVs). Employing a comprehensive dataset comprising motor data, such as speed, torque, current, and voltage, the analysis encompasses six distinct conditions: normal operating mode, over-voltage fault, under-voltage fault, overloading fault, phase-to-phase fault, and phase-to-ground fault. The proposed model integrates Gradient Boosting (GB), K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) algorithms in a synergistic manner. The findings reveal that the RF–GB–DT–XGBoost combination achieves a remarkable accuracy of 98.53%, significantly surpassing other methods reported in the literature. Performance is evaluated through metrics including accuracy, precision, sensitivity, and F1-score, with results analyzed in comparison to practical applications and existing studies. Validated with real-world data, this study demonstrates that the proposed model offers a groundbreaking solution for predictive maintenance systems in the EV industry, exhibiting high generalization capacity despite complex operating conditions. This approach holds transformative potential for both academic research and industrial applications. The dataset used in this study was generated using a MATLAB 2018/Simulink-based Variable Frequency Drive (VFD) model that emulates real-world EV operating conditions rather than relying solely on laboratory data. This ensures that the developed model accurately reflects practical electric vehicle environments.

Sensorless speed motor drives are essential for developing control techniques, reducing cost, streamlining the system, and enhancing reliability. This study explores the area of sensorless speed control for Permanent Magnet Synchronous Motors (PMSMs) by proposing a hybrid control technique. This technique integrates the model reference adaptive system (MRAS) and pseudo-sliding mode control with an Approach Reaching Law (ARL). The MRAS is implemented as a robust sensorless observation method that efficiently handles uncertainties, adapts to dynamic conditions, and aims to achieve dependable performance by having the controlled system mimic a reference model. On the other hand, the pseudo-sliding mode control entails using a continuous approximation (CA) methodology to successfully resolve the chattering problem often seen in conventional sliding mode control approaches. This approach of control allocation enables more seamless control signals, hence improving the longevity of the system and minimizing unwanted oscillations. The ARL component relies on the Exponential Reaching Law (ERL), which enables fast and precise convergence to the intended sliding surface. The exponential characteristics of the ERL lead to faster reaction times and enhanced dynamic performance, guaranteeing the timely attainment of the sliding surface while being robust against changes in parameters and external disturbances. To assess the efficacy of the proposed sensorless hybrid control approach, uncertainties and disturbances were simulated, including PMSM parameters variation, load torque application, and speed level changes. The effectiveness of the suggested approach is compared to control strategies for PMSM, such as the classical ERL-SMC (Type 1) and the pseudo-sliding mode ERL-SMC (Type 2) schemes. The simulations were conducted exclusively using MATLAB/Simulink.

The Permanent Magnet Synchronous Motor (PMSM) stands as a pivotal component in various applications, yet it remains susceptible to an array of faults within both its rotor and stator, there arises an imperative to swiftly and intelligently address these issues. In this study, a novel approach was undertaken wherein a PMSM design was conceptualized within the Ansys Maxwell program, followed by the deliberate introduction of a fault at the rotor's magnetic level. Specifically, three distinct fault scenarios were delineated based on the number of broken magnets (BM), namely 2, 3, and 4, localized within specific rotor areas. Notably, the magnetic flux density was selected as the focal parameter for this investigation. To effectively detect and diagnose faults stemming from BM, an innovative Convolutional Neural Network (CNN) architecture was devised. Leveraging images of the PMSM design captured during operational phases at various time intervals, the CNN exhibited remarkable efficacy in discerning and categorizing fault instances. Upon analysis of the derived outcomes, it becomes evident that the CNN exhibited unparalleled accuracy in fault detection, achieving a remarkable 100% success rate when juxtaposed with alternative methodologies such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), which yielded accuracy rates of 97%. El motor síncrono de imanes permanentes (PMSM) es un componente fundamental en diversas aplicaciones.aplicaciones, pero sigue siendo susceptible a una serie de fallos tanto en el rotor como en el estator.estator, surge la necesidad imperiosa de abordar estos problemas de forma rápida e inteligente. En esteestudio, se adoptó un enfoque novedoso en el que se conceptualizó un diseño de PMSM en el programael programa Ansys Maxwell, seguido de la introducción deliberada de un fallo en el nivel magnético del rotor.nivel magnético del rotor. Específicamente, se delinearon tres escenarios de falla distintos basados en elnúmero de imanes rotos (BM), a saber, 2, 3 y 4, localizados en zonas específicas del rotor.En particular, se seleccionó la densidad de flujo magnético como parámetro central de esta investigación.Para detectar y diagnosticar eficazmente los fallos derivados de los BM, se utilizó una innovadora arquitectura de red neuronal convolucional (CNN).Convolucional (CNN). Aprovechando imágenes del diseño del PMSMdurante las fases operativas en varios intervalos de tiempo, la CNN mostró una notable eficacia a la hora de discernir y diagnosticar fallos derivados del BM.la CNN mostró una notable eficacia a la hora de discernir y clasificar los casos de fallo. Tras analizar los resultadosTras el análisis de los resultados obtenidos, se hace evidente que la CNN exhibió una precisión sin precedentes en la detección de fallos, alcanzando una notable tasa de éxito del 100%.en la detección de fallos, alcanzando una notable tasa de éxito del 100% cuando se yuxtapone conalternativas como las máquinas de vectores soporte (SVM) y los vecinos más cercanos (KNN),que arrojaron índices de precisión del 97%. O motor síncrono de ímã permanente (PMSM) é um componente essencial em várias aplicações.em várias aplicações, mas continua suscetível a uma série de falhas tanto no rotor quanto no estator.Por isso, é imperativo resolver esses problemas de forma rápida e inteligente. Nesteestudo, foi adotada uma nova abordagem em que um projeto de PMSM foi conceituado no programaprograma Ansys Maxwell, seguido pela introdução deliberada de uma falha no nível magnético do rotor.nível magnético do rotor. Especificamente, três cenários de falha distintos foram delineados com base nonúmero de ímãs quebrados (BM), a saber, 2, 3 e 4, localizados em áreas específicas do rotor.Notavelmente, a densidade do fluxo magnético foi selecionada como o parâmetro focal para essa investigação.Para detectar e diagnosticar com eficácia as falhas decorrentes de BM, uma arquitetura inovadora de rede neural convolucional(CNN) inovadora foi desenvolvida. Aproveitando as imagens do projeto do PMSMPMSM capturadas durante as fases operacionais em vários intervalos de tempo, a CNN demonstrou umanotável eficácia no discernimento e na categorização de instâncias de falhas. Após a análise dos resultados derivadosresultados derivados, fica evidente que a CNN apresentou uma precisão sem igual nadetecção de falhas, alcançando uma notável taxa de sucesso de 100% quando comparada commetodologias alternativas, como Support Vector Machines (SVM) e K-Nearest Neighbors (KNN),que produziram taxas de precisão de 97%.

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This paper explores the implementation of a Sliding Mode Predictive Control (SMPC) strategy using a Model Reference Adaptive System (MRAS) for a Permanent Magnet Synchronous Motor (PMSM). The study aims to achieve accurate speed tracking and reduced torque ripple in PMSMs using SMPC and MRAS. The model predictive control depends on estimating the prediction currents to obtain the voltage vectors to control the inverter pulses. Further, by using the cost function the selection of the best voltage conductor is obtained. The disturbance in the PI controller affects the speed efficiency, so a sliding mode is added to reduce the speed disturbance and get a better convergence. Therefore, the speed of the machine extracted from the sensor remains unacceptable, and to enhance the results, MRAS is used to estimate the new speed. Simulation results indicate that the proposed control approach effectively improves the performance of the PMSM in terms of both speed regulation and torque ripple reduction. The findings of this study offer significant insights into the potential of SMPC with MRAS as a control strategy for PMSMs in various industrial applications.

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency, compact structure, and excellent dynamic performance. However, achieving accurate speed control with high robustness under load disturbances and parameter uncertainties remains a significant challenge. Conventional proportional–integral (PI) controllers often suffer from overshoot, slow dynamic response, and sensitivity to nonlinear operating conditions. To address these limitations, this paper proposes an intelligent control strategy that combines third-order sliding mode control (TOSMC) with the Golden Jackal Optimization (GJO) algorithm for optimal PMSM speed regulation. The proposed TOSMC-GJO approach aims to enhance the operational performance, robustness, and reliability of PMSM drives. The control structure consists of an optimized outer-loop speed controller and an inner-loop predictive current controller to improve current quality and eliminate the need for conventional PI tuning. The controller parameters are optimized using a fitness function designed to minimize tracking error, overshoot, settling time, torque ripples, and total harmonic distortion (THD). Simulation results under variable speed and load torque conditions demonstrate that the proposed TOSMC-GJO controller achieves superior performance compared with PI control and TOSMC optimized using Grey Wolf Optimization (GWO). The proposed strategy eliminates speed overshoot and reduces the response time to 0.0052 s, compared with 0.0056 s for TOSMC-GWO and 0.011 s for PI control. In addition, the THD of stator currents is reduced to 6.12%, improving current quality and reducing harmonic distortion. The proposed controller also provides smoother torque response, better disturbance rejection capability, and improved waveform symmetry. These results confirm that integrating high-order nonlinear control with metaheuristic optimization significantly improves the dynamic performance, operational reliability, and robustness of PMSM drive systems under demanding operating conditions.