Pulse width modulation (PWM) techniques are widely used to control the switching of semiconductors in power converters. This paper presents a comprehensive overview of PWM techniques for two-level voltage source inverters and provides a comparative analysis of commonly employed PWM techniques, including sinusoidal PWM, zero-sequence injection PWM, third-harmonic injection PWM, space vector modulation, and optimized pulse pattern with selective harmonic mitigation. Besides providing a detailed literature review, this study includes multiple experimental results to evaluate the performance of these PWM techniques across different key metrics, such as output power quality, DC-link voltage utilization, dynamic response, and complexity. Given the growing trend of electrification in the transportation sector, particular emphasis is placed on motor drive applications. Furthermore, this paper discusses the impact of digital controllers, wide-bandgap semiconductors, and model predictive control on PWM techniques while addressing the challenges and future trends within this field.

Integrating asynchronous pulse width modulation (PWM) and synchronous optimal PWM (SOPWM) in a hybrid PWM scheme has proven to be an effective modulation strategy for ac motor drives operating over a wide speed range. However, hybrid PWM is rarely used in permanent magnet synchronous motor (PMSM) drives in traction applications due to the noise sensitivity issues associated with SOPWM and the complex PWM transition schemes. To overcome these drawbacks, this article introduces an enhanced hybrid PWM technique suitable for integration with the well-established field-oriented control (FOC) strategy. The proposed technique relies on an innovative robust SOPWM and simple smooth PWM transition schemes to improve the robustness of hybrid PWM against noise in the control loop introduced by the measured currents and rotor position. Experimental results are provided to validate the effectiveness of the proposed hybrid PWM technique used in the closed-loop control of a PMSM drive.

Permanent-magnet synchronous motors (PMSMs) inherently exhibit torque oscillations due to their magnetic properties, impacting performance and causing increased noise and vibrations. While pulse-width modulation techniques commonly minimize current harmonics, they often overlook torque fluctuations arising from the interaction between current and back-EMF harmonics in PMSMs. Therefore, achieving both minimal torque harmonic content and reduced switching frequency is essential to obtain a smooth torque and low switching losses. This paper introduces a novel low-frequency torque harmonic minimization optimal pulse pattern (OPP-THM) method, designed to directly minimize torque harmonics in PMSMs, rather than solely focusing on current harmonics. By considering back-EMF harmonics in the optimization of switching angles, the proposed approach effectively suppresses low-order torque harmonics without compromising the contribution of back-EMF harmonics to average torque, thereby enhancing the PMSM performance. The effectiveness of the proposed method is demonstrated through validation on a 4kW experimental PMSM drive employing field-oriented control under steady-state and transient conditions.

The adoption of synchronous optimal pulse-width modulation (SOPWM) in two-level voltage-source inverters (2L-VSIs) offers low switching-to-fundamental-frequency ratio (SFR) operation while maintaining reduced current total harmonic distortion (THD). Despite these advantages, the performance of SOPWM is highly sensitive to signal noise in the modulation index and reference voltage angle. To prevent degradation, conventional PI controllers are conservatively tuned with slow dynamic response, which limits overall system performance. Finite control set model predictive control (FCS-MPC) integrated with SOPWM offers a promising solution, combining the fast dynamic response of FCS-MPC with the optimal steady-state performance of SOPWM. Nevertheless, the intricate tuning of weighting factors in FCS-MPC presents a significant challenge, particularly in balancing between enhanced harmonic performance and fast dynamic response. This paper introduces a simplified FCS-MPC approach that eliminates the need for complex weighting factor tuning while retaining the excellent dynamic performance of FCS-MPC and ensuring the low current THD achieved by SOPWM under steady-state conditions. The efficacy of the proposed method is validated through MATLAB/Simulink (R2023b) simulations and experimental results.

Bearing faults are a critical concern in electrical machines, particularly permanent magnet synchronous motors (PMSMs), commonly used in electric vehicles. Early and accurate classification of bearing fault severity is essential for predictive maintenance, as it enhances cost-effectiveness, ensures safety, and extends product life. Although vibration-based monitoring offers rich diagnostic information, it remains costly and requires excess modifications. In contrast, current-based non-invasive techniques offer advantages in cost and integration but face challenges with accuracy due to operational complexities. This study presents six distinct artificial neural networks (ANNs)-based cascaded classification schemes for bearing fault severity classification. Discrete wavelet transform (DWT) with Symlet (Sym) is used for multi-resolution feature extraction in currents, combined with motor speed data to generate multi-channel features. These features are fed into an ANN-based level I algorithm using various fusion techniques, offering a more interpretable algorithmic framework. One approach employs a multi-input ANN for level I, integrated with an ANN-based level II for refined severity classification. This two-level cascaded approach achieves an accuracy over 99% on the Paderborn University dataset in various operational scenarios. The model is trained and analyzed using MATLAB. The proposed cascaded algorithms outperform single-stage models, and enhanced signal preprocessing improves accuracy and noise resilience. Additionally, the proposed risk-based performance indicator offers insights into maintenance strategies, while an optimum algorithm selection framework identifies an algorithm by considering a trade-off between computational complexity and accuracy.