Inter-turn fault (ITF) is a common fault for permanent magnet synchronous machine (PMSM). If the ITF is not detected in time, its propagation may result in the secondary damage, even the damage to the whole electrical machine. Recently, model predictive control (MPC) has been widely studied for the PMSM drive system because of its simple structure and excellent dynamic performance. However, the previous ITF diagnosis methods are mainly focused on vector control and direct toque control systems, and few researches are specifically focused on ITF diagnosis for the model-predictive-controlled-PMSM. Hence, to fill this gap and improve the reliability of the model-predictive-controlled-PMSM, this paper first proposes a ITF diagnosis method for the model-predictive-controlled-PMSM, taking into account the characteristics of the MPC system. In this method, wavelet transform is applied to extract the fault feature from the cost function existing in the MPC system, and the ITF is diagnosed by monitoring the normalized energy-related feature vector calculated from the wavelet transform coefficients. Additionally, to further show the performance of the proposed method, the popular diagnosis methods based on the stator current and zero sequence voltage component are studied for the comparative analysis. Both the simulation and experimental results verify the effectiveness of the proposed fault diagnosis method.

In conventional model predictive torque control (MPTC), only one voltage vector (VV) is applied during a whole control period, thus causing a large torque ripple. To improve the steady-state performance, some two-vector-based control schemes have been proposed. However, the selection of the optimal VV pair is complex as well as has a large computational burden, and the improvement of performance is still limited by the direction and amplitude of the output VV. This article proposes a low-complexity three-vector-based MPTC for SPMSM drives, which can precisely determine the appropriate active voltage vectors (AVVs) with the predicted torque error. Then, a modified switching table is developed to directly select the AVVs, thus greatly reducing the complexity and computational burden of the algorithm. To obtain a better steady-state performance, a duty cycle calculation method based on torque and stator flux difference parameters is newly proposed to achieve the deadbeat control of torque and stator flux. And then, the experimental comparisons with the double-vector-based MPTC are conducted. The results show that the proposed MPTC can effectively reduce the steady-state torque ripple while maintaining a good dynamic performance as well as almost a fixed switching frequency for all speed ranges.

Fault diagnosis is currently considered a crucial method to enhance the reliability of permanent magnet synchronous motor (PMSM) drives. In this article, an open-circuit fault (OCF) diagnosis method of a voltage source inverter-fed PMSM with model predictive control is proposed. The developed approach mainly consists of fault detection and localization. Besides, the normalized cost function variation is designed as a fault detection indicator according to the impact of OCFs on current signatures. It can not only monitor drive system conditions, but also discriminate fault categories. Afterward, the polarities of normalized αβ -current average values and phase angles of residual current vectors are utilized to locate the exact faulty switch in each fault type. Consequently, a total of 21 possible combinations of faulty switches can be correctly detected and located. Finally, the effectiveness in fault diagnoses and the robustness against operating point variations are verified by the experimental results.

Emerging topics such asenvironmental protection and energy utilization have pushed research and development of electric vehicles and hybrid electricvehicles (EVs/HEVs). In the last few decades, numerous technologies have been developed for EV/HEVimportance. In this article, key research topics in the area of EVs/HEVs, namely electric machines, electrochemical energy sources, wireless charging infrastructure, and latest EV/HEV models are covered. This paper aims to consolidate the key emerging technologies in this field and provide the readers a blueprint to begin their own journeys.

This paper proposes a novel outer-rotor flux-switching permanent-magnet (OR-FSPM) machine with specific wedge-shaped magnets for in-wheel light-weight traction applications. First, the geometric topology is introduced. Then, the combination principle of stator slots and rotor poles for OR-FSPM machines is investigated. Furthermore, to demonstrate the relationship between performance specifications (e.g., torque and speed) and key design parameters and dimensions (e.g., rotor outer diameter and stack length) of OR-FSPM machines at preliminary design stage, an analytical torque-sizing equation is proposed and verified by two-dimensional (2-D) finite-element analysis (FEA). Moreover, optimizations of key dimensions are conducted on an initially designed proof-of-principle three-phase 12-stator-slot/22-rotor-pole prototyped machine. Then, based on 2-D-FEA, a comprehensive comparison between a pair of OR-FSPM machines with rectangular- and wedge-shaped magnets and a surface-mounted permanent-magnet (SPM) machine is performed. The results indicate that the proposed OR-FSPM machine with wedge-shaped magnets exhibits better flux-weakening capability, higher efficiency, and wider speed range than the counterparts, especially for torque capability, where the proposed wedge-shaped magnets-based one could produce 40% and 61.5% more torque than the rectangular-shaped magnets-based machine and SPM machine, respectively, with the same rated current density (5 A/mm ² ). Finally, the predicted performance of the proposed OR-FSPM machine is verified by experiments on a prototyped machine.

The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.