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High-performance current and speed control are required to obtain smooth output torque, current tracking, and speed tracking in permanent-magnet synchronous motor (PMSM) drives. The motor speed and stator current control rely on multiple nonlinear motor parameters, which play a crucial role in shaping the performance of PMSM. Moreover, tuning the speed and current controller parameters using the conventional control technique depends on these PMSM parameters, also variation of these parameters will have a decisive influence on the dynamic performance of PMSM. To enhance the robustness of vector control and tracking methodology against PMSM parameter uncertainties and load disturbances, a novel artificial intelligence (AI)-based advanced speed and current control technique for PMSM is proposed in this article. Subsequently, the methodology for designing and training the suggested Dense Neural Network (DNN) controllers are elicited. The proposed controllers can handle the inevitable fluctuation and non-linearity in motor parameters at different load points and drive conditions. The proposed DNN scheme is validated in terms of settling time, dynamic responsiveness, tolerance to parameter fluctuations, and overall robustness. A comparative analysis is conducted against adaptive proportional-integral (API) control applied to the same PMSM within the OPAL-RT real-time simulator (RTS). The viability of the proposed control scheme is substantiated through simulation, Software-In-the-Loop (SIL) and Hardware-In-the-Loop (HIL) testing with an RTS and an automotive-grade controller board across diverse conditions.

Integration of the impacts of time and space harmonics simultaneously during the design stages of inverter-fed induction machines (IMs) is crucial for the accurate calculation of electromagnetic (EM) torque and ripples. Traditional magnetic field-based models offer an analytical approach for determining the EM torque in IMs by calculating inductances. However, by assuming infinite core permeabilities, these models neglect the impact of the core magnetomotive force (MMF) drops due to the difficulties in accurately calculating these drops and the impracticality of isolating the contribution from each phase, which is essential for inductances calculations. These factors contribute to deficiencies in this modeling approach, which become more noticeable when the combined impacts of time and space harmonics on MMF drop calculations are also disregarded. Therefore, this paper introduces a novel magnetic-field-based model to predict the torque and torque ripples of inverter-fed induction motors by addressing the above limitations. This involves modifying the turns and winding function, and calculating the core MMF drops based on the timely variation of non-sinusoidal core flux densities, considering their major and minor flux-density loop effects. Consequently, the associated energy is used to calculate the net available energy, thereby enhancing torque calculations. Compared to the experimental results obtained from an 11kW prototyped induction motor, the proposed model exhibits notable enhancements, achieving average accuracies of 96.4% for average torque and 94.51% for torque ripples, in contrast to the respective traditional model accuracies, 81.1% and 45.1%.

Electric vehicles (EVs) are propelled by electric traction drive systems (ETDSs), which consist of various components including an electric motor, power electronic converter, and gear box. During manufacturing, end-of-line testing is the ultimate step for ensuring the quality and performance of electric motors in electric vehicle (EV) traction drive systems (ETDSs). The outcome of end-of-line testing of electric motors is significantly influenced by the tolerances of their structural parameters, such as stator inner and outer diameters, magnet dimensions, air gaps, and other geometric parameters. The existing literature provides insights into parametric sensitivity, offering guidance for enhancing the reliability of end-of-line testing. In this manuscript, the importance of end-of-line testing and the role of manufacturing tolerances of e-motor structural parameters in the manufacturing process of ETDSs are discussed. The impact of tolerances of e-motor structural parameters on the test results, such as torque, efficiency, back EMF, and e-NV (noise and vibration), is investigated. Finally, key challenges and research gaps in this area are identified, and recommendations for future research to mitigate the drawbacks of end-of-line testing are provided.