Large language models (LLMs) exhibit cultural bias from over-represented viewpoints in training data, yet cultural alignment remains a challenge due to limited cultural knowledge and a lack of exploration into effective learning approaches.We introduce a cost-efficient and cognitively grounded method: fine-tuning LLMs on native speakers' word-association norms, leveraging cognitive psychology findings that such associations capture cultural knowledge.Using word association datasets from native speakers in the US (English) and China (Mandarin), we train Llama-3.1-8B and Qwen-2.5-7Bvia supervised fine-tuning and preference optimization.We evaluate models' cultural alignment through a two-tier evaluation framework that spans low-level lexical associations and high-level cultural value alignment using the World Values Survey.Results show significant improvements in lexical alignment (16-20% English, 43-165% Mandarin on Precision@5) and high-level cultural value shifts.On a subset of 50 questions where US and Chinese respondents diverge most, finetuned Qwen nearly doubles its response alignment with Chinese values (13 → 25).Remarkably, our trained 7-8B models match or exceed vanilla 70B baselines, demonstrating that a few million of culture-grounded associations achieve value alignment without expensive retraining.Our work highlights both the promise and the need for future research grounded in human cognition in improving cultural alignment in AI models.

Modern code review is a ubiquitous software quality assurance process aimed at identifying and resolving potential issues (e.g., functional, evolvability) within newly written code. Despite its effectiveness, the process demands large amounts of effort from the human reviewers involved. To help alleviate this workload, researchers have trained various deep learning-based language models to imitate human reviewers in providing natural language code reviews for submitted code. Formally, this automation task is known as code review comment generation. Prior work has demonstrated improvements in code review comment generation by leveraging machine learning techniques and neural models, such as transfer learning and the transformer architecture. However, the quality of the model-generated reviews remains sub-optimal due to the quality of the open-source code review data used in model training. This is in part due to the data obtained from open-source projects where code reviews are conducted in a public forum, and reviewers possess varying levels of software development experience, potentially affecting the quality of their feedback. To accommodate this variation, we propose a suite of experience-aware training methods that utilise the reviewers’ past authoring and reviewing experiences as signals for review quality. Specifically, we propose experience-aware loss functions (ELF), which use the reviewers’ authoring and reviewing ownership of a project as weights in the model’s loss function. Through this method, experienced reviewers’ code reviews yield larger influence over the model’s behaviour. Compared to the SOTA model, ELF was able to generate higher quality reviews in terms of accuracy (e.g., +29% applicable comments), informativeness (e.g., +56% suggestions), and issue types discussed (e.g., +129% functional issues identified). The key contribution of this work is the demonstration of how traditional software engineering concepts such as reviewer experience can be integrated into the design of AI-based automated code review models.

As the impact of large language models increases, understanding the moral values they reflect becomes ever more important.Assessing the nature of moral values as understood by these models via direct prompting is challenging due to potential leakage of human norms into model training data, and their sensitivity to prompt formulation.Instead, we propose to use word associations, which have been shown to reflect moral reasoning in humans, as lowlevel underlying representations to obtain a more robust picture of LLMs' moral reasoning.We study moral differences in associations from western English-speaking communities and LLMs trained predominantly on English data.First, we create a large dataset of LLMgenerated word associations, resembling an existing data set of human word associations.Next, we propose a novel method to propagate moral values based on seed words derived from Moral Foundation Theory through the human and LLM-generated association graphs.Finally, we compare the resulting moral conceptualizations, highlighting detailed but systematic differences between moral values emerging from English speakers and LLM associations. 1

Code review is an important practice in software development, yet it is time-consuming and requires substantial effort. While open-source datasets have been used to train neural models for automating code review tasks, including review comment generation, these datasets contain a significant amount of noisy comments (e.g., vague or non-actionable feedback) that persist despite cleaning methods using heuristics and machine learning approaches. Such remaining noise may lead models to generate low-quality review comments, yet removing them requires a complex semantic understanding of both code changes and natural language comments. In this paper, we investigate the impact of such noise on review comment generation and propose a novel approach using large language models (LLMs) to further clean these datasets. Based on an empirical study on a large-scale code review dataset, our LLM-based approach achieves $66-85 \%$ precision in detecting valid comments. Using the predicted valid comments to fine-tune the state-of-the-art code review models (cleaned models) can generate review comments that are $13.0 \%-12.4 \%$ more similar to valid human-written comments than the original models. We also find that the cleaned models can generate more informative and relevant comments than the original models. Our findings underscore the critical impact of dataset quality on the performance of review comment generation. We advocate for further research into cleaning training data to enhance the practical utility and quality of automated code review.

Code review is a crucial practice in software development. As code review nowadays is lightweight, various issues can be identified, and sometimes, they can be trivial. Research has investigated automated approaches to classify review comments to gauge the effectiveness of code reviews. However, previous studies have primarily relied on supervised machine learning, which requires extensive manual annotation to train the models effectively. To address this limitation, we explore the potential of using Large Language Models (LLMs) to classify code review comments. We assess the performance of LLMs to classify 17 categories of code review comments. Our results show that LLMs can classify code review comments, outperforming the state-of-the-art approach using a trained deep learning model. In particular, LLMs achieve better accuracy in classifying the five most useful categories, which the state-of-the-art approach struggles with due to low training examples. Rather than relying solely on a specific small training data distribution, our results show that LLMs provide balanced performance across high-and low-frequency categories. These results suggest that the LLMs could offer a scalable solution for code review analytics to improve the effectiveness of the code review process.

Series-end winding motor drives (SWMDs) are more well known for the characteristics of high dc-link voltage utilization and controllable zero-sequence loop. Besides, SWMDs also have the potential ability to reduce the leg-current stress (LCS) by rearranging the winding-connection sequence (WCS), especially in multiphase SWMDs (MP-SWMDs). Yet, this characteristic is not widely noticed in the existing works. To this end, this article presents the analysis of WCS in MP-SWMDs for LCS reduction, which provides the possibility to improve the load capability and reliability compared with the existing WCS. Based on the phasor diagrams of the leg- and phase-currents, the principle of the WCS in MP-SWMDs for LCS reduction is revealed, which is discussed separately for the symmetrical and asymmetrical (multiple three-phase) phase-windings. By rearranging the WCS and reversing part of windings in MP-SWMDs, optimal WCSs for LCS reduction are derived accordingly. Subsequently, the corresponding modulation schemes based on the relative potential are developed. Moreover, the linear modulation ranges of different WCSs in MP-SWMDs are also investigated. With the verification of the experimental results, the current amplitude of middle legs in MP-SWMDs under the optimal WCS for LCS reduction can be reduced significantly compared with the star-connected winding counterparts. Simultaneously, the linear modulation range is also reduced, which indicates that MP-SWMDs under the optimal WCS for LCS reduction are ideal for low- or medium-speed motor drives with heavy load.

Series Elastic Actuator-based exoskeleton can offer precise torque control and transparency when interacting with human wearers. Accurate control of SEA-produced torques ensures the wearer’s voluntary motion and supports the implementation of multiple assistive paradigms. In this paper, a novel variable transmission series elastic actuator (VTSEA) is developed to meet torque-speed requirements in different exoskeleton-assisted locomotion modes, such as running, walking, sit-to-stand, and stand-to-sit. The VTSEA features a SEA-coupled variable transmission ratio adjusting mechanism and works between three discrete levels of transmission ratio depending on the user’s initiative. The proposed prototype can also improve transparency in human-robot interaction. Also, an accurate torque controller with inertial compensation is developed for the VTSEA via the singular perturbation theory, and its stability is proved. The feasibility of the proposed VTSEA prototype and the precise output torque performance of VTSEA are verified by experiments.

Ground segmentation on the 3D point cloud is fundamental to many applications, such as SLAM and object segmentation. As it is usually a preprocessing module of these applications, high efficiency and accuracy are the basic requirements for guaranteeing the whole system’s performance. To this end, we avoid ground fitting and region division on the 3D point cloud. We propose a pixel-wise image-based method named DipG-Seg, which projects the 3D point cloud onto two cylindrical images, horizontal range-and z-images, then segments based on them. To realize fast and accurate ground segmentation, we first introduce innovative designs for image-based features. Specifically, we improve the slope feature with consideration of the LiDAR model and propose combining features with different sizes of receptive fields for better recognition of the ground. Then, based on these features, we devise a pre-segmenting pattern for pixel-wise classification. For fine segmentation, we devise a hierarchical refinement framework integrating a nonlinear filter and majority-vote kernel-based convolution, which is demonstrated to enhance the accuracy by over 7% on the basis of pre-segmenting. Comprehensive experiments were conducted on a real-world platform, SemanticKITTI, and nuScenes datasets. The results have demonstrated that our method can achieve an accuracy of 94.41% and a speed of 127 Hz on 64-beams LiDAR, outperforming the state-of-the-art methods and guaranteeing competitive robustness. Our method will be available at: https://github.com/EEPT-LAB/DipG-Seg.

Power factor correction (PFC) converters with a diode bridge are extensively used in different AC-DC applications. However, the diode bridge has to employ four diodes to complete the AC to DC at the expense of high conduction losses. Thus, many bridgeless PFC converters have been proposed with dual converter cells to minimize the number of conducted diodes for better efficiency. Unfortunately, these dual-converter cell-based bridgeless converters need almost double components. To solve this issue, this paper proposes a true bridgeless buck-type PFC converter, which annihilates the diode bridge completely with only fewer component counts. The proposed converter uses buck and buck-boost cells to obtain the novel bridgeless topology, which features a simple structure and control to achieve high PF and low total harmonics of input current (THDi). Simulations are given to validate the feasibility of the proposed topology and the control method. Comparisons with the conventional buck PFC converter are also given to confirm the better performances of the proposed topology.

Fault tolerance control to multiple motors has been studied under incomplete inverter topologies. An improved modulation scheme for driving multiple permanent magnet synchronous motors (PMSMs) by leg-missing inverter is proposed in this article. In the traditional scheme, the modulator is utilized in separate intervals for controlling one motor, while zero vectors are applied to other ones at the same time. This scheme cannot work well under high speed and heavy load conditions. To this end, an improved global modulator is designed to promote voltage utilization and operating region of the system on the premise of switching state coordination of the shared leg of the inverter. Various voltage vector (VV) distributions are discussed and detailed. The proposed strategy can realize a decent modulation effect without a complicated algorithm structure. Moreover, the effectiveness of the proposed modulation scheme is verified via a simulation test.

Electric vehicles (EVs) can act as virtual energy routers (VERs) in the grid, giving them the flexibility to change the direction of energy flow. Therefore, a day-ahead scheduling for radial microgrids deploying EV-based VERs is proposed. In the day-ahead scheduling, the charging/discharging operation, state of charge (SOC), and available time of EV-based VERs are involved in the social utility maximization problem. With the laxity model of EV-based VERs and the forecasted reference demand, the supply and demand in the microgrid are optimized to minimize generation costs and maximize consumer utility in a whole day. Binary variables, which indicate the charging and discharging choices of the EV-based VERs, exist in the day-ahead scheduling. Therefore, mixed integer nonlinear programming (MINLP) is adopted to solve the optimization problem. The simulation cases are then provided to verify the effectiveness of the suggested day-ahead scheduling approach.

Axial-flux permanent magnet (AFPM) machines are welcomed widely in many applications thanks to the high torque density and compactness. In the AFPM machine, the concentrated winding and distributed winding have different characteristics. In order to increase the winding factor and reduce the winding ends, a novel concentric winding AFPM machine is proposed in the paper. The stator of the proposed machine is divided into three circles from the inner side to the outer side. Then, the three-phase windings are arranged on the three circles, respectively. The phase angle difference is achieved through the mechanical offset between the teeth of three circles. Thus, the winding factor can be kept as 1 with a few winding ends by adjusting the pole-slot combination. In addition, the staggered teeth between the three circles can also reduce the cogging torque. In the optimization, the back EMFs of the three phases are nearly balanced. 3D finite element analysis also shows that the machine has a strong overload capability.

The application of wireless power transfer (WPT) in electric vehicles (EVs) has brought great convenience, safety, and flexibility to EV owners. Traditional EV charging converters can only support wired charging or wireless charging with low integration. In order to realize both wired and wireless charging functions in the same system, excessive power switches will be utilized, resulting in a redundant structure and low power density. To solve this problem, this paper proposes a novel multi-functional converter for EV charging. Using one set of power switches, the proposed converter can output DC voltage for wired charging or high-frequency AC voltage for wireless charging. Circuit topology and control method are discussed and analyzed. Finally, simulations in MATLAB/SIMULINK are conducted to verify the effectiveness of the proposed multi-functional converter.

Precision in multi-torque-speed characteristics, disturbance resistance, transparency, and back-drivability are expected in exoskeleton actuators' design and control. The introduction of series elastic actuators (SEA) for lower-extremity exoskeletons has recently attracted more attention owing to their special merits compared to traditional actuators in high torque control accuracy, favorable output compliance, and unique shock tolerance. In this work, a SEA with a variable transmission ratio for the hip exoskeleton is developed for different working conditions requiring variable torque-speed characteristics. A crank-slide mechanism that actively changes the torque transmission path based on different tasks to meet the torque-speed requirements for the low-extremity exoskeleton. The high-precision torque delivery is the main concern in SEA control. Also, the crank-slider mechanism added in SEA will bring a new control difficulty with the frequent switching of the transmission ratio which may cause severe mechanical vibration and unsafe factors to users. Thus, a novel torque control architecture, based on disturbance observer integrated with a three-mass modeling method for VTSEA is proposed. Simulations are carried out to validate that the developed controller can restrain disturbance and provide accurate assistive torque tracking effectively. This work serves as a fundamental for employing and developing hip exoskeleton for locomotion assistance.

In recent years, transverse flux permanent magnet motors (TFPMMs) have had a promising application in electric propulsion systems due to their modularity, simple winding distribution, and high reliability. However, the low motor space utilization, low average output torque, and complex manufacturing and assembly constraints have limited the further development of TFPMMs. Therefore, this paper proposes a novel double-stator transverse flux reversal permanent magnet machine (DS-TFRPMM). This new motor has no permanent magnets on the rotor and can achieve higher operating speeds. In addition, the permanent magnets are on the outer stator to improve motor space utilization. Firstly, this paper describes the structure and operating principle of the DS-TFRPMM. A 3D model of the DS-TFRPMM is then constructed, and a 3D-finite element analysis (FEA) is carried out to obtain the magnetic field distribution and the critical performance data. The simulation results show that the proposed motor has the average output torque of 11.70Nm, the torque density of 10.9Nm/L, and the 1.19Nm peak-peak value of cogging torque. Finally, the paper optimizes some critical parameters of the DS-TFRPMM and provides the dimensions of the vital motor components for optimum output performance.

BACKGROUND: Being among the most common malignancies worldwide, hepatocellular carcinoma (HCC) accounting for the third cause of cancer mortality. The regulation of cell death is the most crucial step in tumor progression and has become a crucial target for nearly all therapeutic options. Cuproptosis, a copper-induced cell death, was recently reported in Science. However, its primary function in carcinogenesis is still unclear. METHODS: Cuproptosis-related lncRNAs significantly associated with overall survival (OS) were screened by stepwise univariate Cox regression. The signature of cuproptosis-related lncRNAs for HCC prognosis was constructed by the LASSO algorithm and multivariate Cox regression. Further Kaplan-Meier analysis, proportional hazards model, and ROC analysis were performed. Functional annotation was performed using gene set enrichment analysis (GSEA). The relationship between prognostic cuproptosis-related lncRNAs and HCC prognosis was further explored by GEPIA( http://gepia.cancer-pku.cn/ ) online analysis tool. Finally, we used the ESTIMATE and XCELL algorithms to estimate stromal and immune cells in tumor tissue and cast each sample to infer the underlying mechanism of cuproptosis-related lncRNAs in the tumor immune microenvironment (TIME) of HCC patients. RESULTS: Four cuproptosis-related lncRNAs were used to construct a prognostic lncRNA signature, which was an independent factor in predicting OS in HCC patients. Kaplan-Meier curves showed significant differences in survival rates between risk subgroups (p = 0.002). At the same time, we found that the expression levels of most immune checkpoint genes increased with increasing risk scores. Tumorigenesis and immunological-related pathways were primarily enhanced in the high-risk group, as determined by GSEA. The results of drug sensitivity analysis showed that compared with patients in the high-risk group, the IC50 values of erlotinib and lapatinib were lower in patients in the low-risk group, while the opposite was true for sunitinib, paclitaxel, gemcitabine, and imatinib. We also found that elevated AL133243.2 expression was significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and reduced immune cell infiltration, suggesting that HCC patients with low AL133243.2 expression in tumor tissues may have a better response to immunotherapy. CONCLUSION: Collectively, the cuproptosis-associated lncRNA signature can serve as an independent predictor to guide individual treatment strategies. Furthermore, AL133243.2 is a promising marker for predicting immunotherapy response in HCC patients. This data may facilitate further exploration of more effective immunotherapy strategies for HCC.

Dual-motor synchronization driving system is widely applied in electrified transportation tools and other industrial fields. This paper proposes a novel dual-motor synchronization drive method combining classical cross-coupled and electronic virtual line shafting (EVLS) control. The improved cross-coupled structure adopting adaptive gain feeds back speed synchronization error to modify speed reference. A virtual shaft is designed to resist disturbance and enhance the robustness of system. Besides, model predictive torque control (MPTC) is employed to further improve the dynamic response. Finally, a simulation model is established to verify the effectiveness of proposed dual-motor control strategy. The test results indicate that the system has fast dynamic response and good synchronization precision.

This paper provides a preliminary study of promoting the direct torque control (DTC) to series-end winding permanent magnet synchronous motor (SW-PMSM) drives. The DTC schemes of the conventional PMSM drives cannot be directly applied to the SW-PMSM drives since they have different drive topologies. With one more leg added to the inverter, the SW-PMSM drive has a different voltage vector distribution, which leads to a different switching table for the hysteresis controllers of the flux and the torque. In addition, the zero-sequence subspace also exists in the SW-PMSM drive, and it might generate the undesired zero-sequence current. Based on the above issues, the voltage vector distribution of the SW-PMSM drive is studied in this paper, and the voltage vectors without zero-sequence components are selected as the candidates to prevent generating zero-sequence current. Next, the switching table for the hysteresis controllers is re-derived according to the candidate voltage vectors, and the DTC for the SW-PMSM drive is obtained. Subsequently, the DTC of the multi-phase SW-PMSM drive is also investigated in the same manner. Finally, the effectiveness of the proposed DTC schemes for the SW-PMSM drives is verified.

Axial flux permanent magnet (AFPM) machines are welcomed in industries due to the compactness. In order to improve the fault-tolerant capability and decrease phase voltage level, a dual three-phase dual stator AFPM machine with mechanical offset is proposed. In this paper, a theoretical analysis is conducted for the harmonics of the proposed machine. The mechanical offset mainly affects the amplitudes of harmonics. Then, a 3D finite element analysis is performed to verify the theoretical analysis. The orders of main air-gap flux density harmonics are related to the number of rotor pole pairs and teeth, but the harmonics have little influence in the back electromotive force. As for the axial force density, the orders of main harmonic components are the two times number of stator pole pairs, rotor pole pairs, and teeth. The results are consistent with the theoretical analysis and in-depth discussion.

Humans use countless basic, shared facts about the world to efficiently navigate in their environment.This commonsense knowledge is rarely communicated explicitly, however, understanding how commonsense knowledge is represented in different paradigms is important for both deeper understanding of human cognition and for augmenting automatic reasoning systems.This paper presents an in-depth comparison of two large-scale resources of general knowledge: ConceptNet, an engineered relational database, and SWOW a knowledge graph derived from crowd-sourced word associations.We examine the structure, overlap and differences between the two graphs, as well as the extent to which they encode situational commonsense knowledge.We finally show empirically that both resources improve downstream task performance on commonsense reasoning benchmarks over text-only baselines, suggesting that large-scale word association data, which have been obtained for several languages through crowd-sourcing, can be a valuable complement to curated knowledge graphs. 1

Transportation electrification has attracted much attention in modern society. Among all electrified transportation tools, electric vehicle (EV) is absolutely the one that has great potential to compete with and further take the place of traditional fossil fuel vehicles. This article is to outline and investigate advanced electric machines and their control strategies for EV applications. The key is not only to reveal new design ideas, topologies, structures, methodologies, control strategies, pros and cons, and foresight for advanced electric machines but also to fully integrate these ideas into practical EV applications. This critical review will clarify the development trends of electric machines and their controls.

This paper is to present an unmanned aerial vehicle (UAV)-oriented wireless power transfer (WPT) system which accommodates with an energy-efficient receiver. Actually, the key is to use a buck converter to greatly reduce the current through the receiving coil and the rectifying diodes. As a result, the total power loss on the receiver is effectively reduced. First, to realize the proposed design, the whole system is modeled to analyze its system-level performance. Then, the loss on the receiver is modeled to instruct the parameter design of the buck converter, namely the duty ratio and switching frequency. After that, an experimental prototype is developed to validate the effectiveness of the proposed system. Results show that the proposed WPT system can achieve a dc-to-load efficiency of 82%, which is 9% higher than the case without the buck. Moreover, when the charging current is 6 A, the total loss on the receiver is reduced up to 14.4 W by properly configuring the buck converter.

Researchers have recently attached more attention on permanent magnet brushless AC motors for electrified propulsion. Considering the further improvement of torque density, this paper proposes a novel pulse width modulation (PWM) magnet array and quantitatively analyzes the magnetic field with different magnet configurations. It is a good potential for reducing the magnet volume. Based on a slotless motor design which eliminates the influence of additional harmonic sources except for that from magnets, investigations on motor performance with proposed magnet array are made. Also, the improvement of torque density is discussed and verified.

This paper presents a new selectable regional charging wireless power transfer (SRC-WPT) system, which can charge multiple receivers simultaneously. Meanwhile, they are put on the charging area of the system. This SRC-WPT system employs a transmitter array for WPT, which rows are controlled by relays. Furthermore, the transmitters in each row have different frequency characteristics, which can be used as the natural switches. Thus, this SRC-WPT system can selectively activate the charging region for the receivers through the different frequencies and relays. Besides, when compared with the existing multi-to-multi WPT designs, the proposed control method of the SRC-WPT system is much more simple and reliable. Finally, a model is built to verify the performances of the proposed system and the corresponding control method.

Outer loop PI controllers have been widely applied before the inner model predictive torque control (MPTC) to determine the torque reference, which could increase the number of tuning parameters for the total control scheme. In this paper, to simplify the structure of total control scheme and improve the performance of MPTC in dual three-phase permanent-magnet synchronous motor (DTP-PMSM), the conventional PI controller is replaced by a nonlinear function to determine the output torque reference, which has only one adjustable parameter. Then, the additional offset is necessary to compensate the speed residual error. Furthermore, a torque load observer is designed to observe the torque and determine the value of offset. Finally, the simulation is given, which demonstrates the effectiveness of the proposed method.

In this paper, a novel kind of wireless motors, namely, the LCC-compensated wireless switched reluctance motor, is proposed and implemented. The definite advantages are that there is no power converter at the motor side and particularly no switched-capacitor array at the transmitter side to realize the multiple-frequency operation. The key is to develop an LCC compensation involving an inductor and two capacitors (the so-called LCC) for a multiple-frequency wireless power transfer system. As a result, only one transmitter is needed to targetedly feed three receivers, which directly energize the three phase windings of the motor. Particularly, there is no need to control switched capacitors to change the resonant frequency of the transmitter. Meanwhile, the burst firing control method is employed to realize the speed control. Both calculation and experimental results are presented to validate the feasibility of the proposed system. In the system prototype, the transmission distance can reach up to 150 mm and the transmission efficiency can be achieved up to 80%.

This paper is to present an effective wireless power transfer (WPT) system to recharge the battery of electric unmanned aerial vehicle (e-UAV). The proposed system can generate an approximately even magnetic field which allows the e-UAV to get enough energy at different points above the charging pad. Also, based on the design, the proposed system is capable to transmit enough power at a high efficiency. Both simulation and experimentation are carried out to prove the validity of the proposed WPT system for e-UAV charging.

This paper presents a quantitative comparison of three topologies of double-sided long-stator type permanent magnet linear machines (PMLMs) as possible candidates for the ropeless elevator propulsion system. First, the parameters of each PMLM topology are designed using the same criteria. Then the finite element method (FEM) is employed to evaluate the performance of each topology. Specifically, the translator mass, propulsion forces, detent forces, and no-load EMFs are analyzed and compared. The quantitative comparison results show that the Halbach array PMLM configuration is preferable for the ropeless elevator application because of its small detent force as well as low total mass.

The proposed fault-tolerant flux-reversal doubly-salient (FT-FRDS) magnetless motor drive consists of armature winding for driving and DC-field winding for field excitation. The purpose of this paper is to investigate two remedial strategies for fault-tolerant operations of the proposed motor drive under short-circuit faults. First, short-circuit phase can be disabled and the short-circuit fault can then be regarded as the open-circuit fault. By reconstructing the healthy armature phases, the reduced torque can be remedied and this is known as the fault-tolerant brushless AC (FT-BLAC) operations. Second, short-circuit fault can also be remedied based on the DC-field regulation alone, and this is known as the fault-tolerant DC-field (FT-DC) operation. These two remedial operations are compared and verified by the finite-element-method (FEM).

In this paper, a new hybrid-excited flux-switching (HEFS) machine is proposed with the outer-rotor configuration, which possesses the distinct feature of fault-tolerant operation. Comparing with the conventional permanent-magnet (PM) machine, it combines merits of flux control, high mechanical integrity, and low-cost. Furthermore, its fault-tolerant feature ensures its continuous operation in the event of winding faults. Hence, a new 12/10-pole HEFS machine is designed and implemented in this paper. By using time-stepping finite element method, open circuit (OC) fault and short circuit (SC) faults on the armature winding are investigated in the proposed machine for the fault-tolerant operation. The phase-current reconfiguration and flux control are applied for the remediation of the OC fault, while the SC faults is remedied by the phase-current reconfiguration merely. Both approaches demonstrate their good performances for the fault-tolerant operation.

Electric vehicles (EVs) are regarded as one of the most effective tools to reduce the oil demands and gas emissions. And they are welcome in the near future for general road transportation. When EVs are connected to the power grid for charging and/or discharging, they become gridable EVs (GEVs). These GEVs will bring a great impact to our society and thus human life. This paper investigates and discusses the opportunities and challenges of GEVs connecting with the grid, namely, the vehicle-to-home (V2H), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G) technologies. The key is to provide the methodologies, approaches, and foresights for the emerging technologies of V2H, V2V, and V2G.

This paper presents a dual-memory permanent magnet brushless machine for automotive integrated starter-generator (ISG) application. The key is that the proposed machine adopts two kinds of PM materials, namely NdFeB and AlNiCo for hybrid excitations. Due to the non-linear characteristic of demagnetization curve, AlNiCo can be regulated to operate at different magnetization levels via a magnetizing winding. With this distinct merit, AlNiCo can provide the assistance for online tuning the air-gap flux density. Firstly, the configuration of proposed machine is presented. Secondly, the finite element method (FEM) is applied for the field calculation and performance verification. Finally, both simulation and experimental results confirm that the proposed machine is very suitable for the ISG application.

This paper presents a comparison of two chaoized PWM algorithms for motor drives in the electric vehicle (EV), which are the chaotic sinusoidal pulse width modulation (SPWM) and the chaotic space-vector pulse width modulation (SVPWM). The SPWM scheme can be chaoized by three modulation methods, including the chaotically amplitude-modulated frequency modulation (CAFM), the chaotically position-modulated position modulation (CPPM), and the hybrid chaotic frequency modulation (HCFM), while the chaotic SVPWM can be fulfilled by the chaotically frequency-modulated frequency modulation (CFFM) and the CAFM methods. The performance indexes used in the comparative analysis are the electromagnetic interference (EMI) and the mechanical resonance (MR). The chaotic PWM algorithm is designed and implemented to increase the electromagnetic compatibility (EMC) and the mechanical performance for EV motor drives, and the aforementioned performance indexes are compared for the practical applicability.