Search Results (35)

This paper compares two types of interior permanent magnet synchronous motors (IPMSMs) to determine the most effective arrangement for electric vehicle (EV) applications. The comparison is based on torque ripple, power, efficiency, and mechanical objectives. The study introduces a novel technique that optimizes notching parameters in a selected motor topology by inserting a ship-shaped notch into the bridge area between double U-shaped layers. In addition, this study presents two comprehensive approaches of robust combinatorial optimization that are used in machines for the first time. In the first approach, modeling is performed to identify important variables using Pearson Correlation and the mathematical model of the Anisotropic Kriging model from the Surrogate model. Then, in the second approach, the proposed algorithm, Multi-Objective Genetics Algorithm (MOGA), and Surrogate Quadratic Programming (SQP) are combined and implemented on the Anisotropic Kriging model to choose a robust model with minimum error. The algorithm is then verified with FEM results and compared with other conventional optimization algorithms, such as the Genetics Algorithm (GA) and the Particle Swarm Optimization algorithm (PSO). The motor characteristics are analyzed using the Finite Element Method (FEM) and global map analysis to optimize the performance of the IPMSM for EV applications. A comparative study shows that the enhanced PMSM developed through the optimization process demonstrates superior performance indices for EVs. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

This paper presents a simulation framework for evaluating power flow, energy efficiency, thermal behavior, and energy consumption in electric vehicles (EVs) under standardized driving conditions. A detailed Simulink model is developed, integrating a lithium-ion battery, inverter, permanent magnet synchronous motor (PMSM), gearbox, and a field-oriented control strategy with PI-based speed and current regulation. The framework is applied to four standard driving cycles—UDDS, HWFET, WLTP, and NEDC—to assess system performance under varied load conditions. The UDDS cycle imposes the highest thermal loads, with temperature rises of 76.5 °C (motor) and 52.0 °C (inverter). The HWFET cycle yields the highest energy efficiency, with PMSM efficiency reaching 92% and minimal SOC depletion (15%) due to its steady-speed profile. The WLTP cycle shows wide power fluctuations (−30–19.3 kW), and a motor temperature rise of 73.6 °C. The NEDC results indicate a thermal increase of 75.1 °C. Model results show good agreement with published benchmarks, with deviations generally below 5%, validating the framework’s accuracy. These findings underscore the importance of cycle-sensitive analysis in optimizing energy use and thermal management in EV powertrain design. Full article

Permanent magnet synchronous motors for electric vehicles (EVs) prioritize high power density and lightweight design, leading to elevated thermal flux density. Consequently, cooling methods and heat conduction in stator windings become critical. This paper proposes a compound cooling structure combining direct oil spray cooling on stator windings and housing oil channel cooling (referred to as the winding–housing composite oil cooling system) for permanent synchronous motors in EVs. A systematic design methodology for oil jet nozzles and housing oil channels is investigated, determining the average convective heat transfer coefficient on end-winding surfaces and the heat dissipation factor of the oil channels. Finite element analysis (FEA) was employed to simulate the thermal field of a 48-slot 8-pole oil-cooled motor, with further analysis on the effects of oil temperature and flow rate on motor temperature. Based on these findings, an optimized oil-cooled structure is proposed, demonstrating enhanced thermal management efficiency. The results provide valuable references for the design of cooling systems in oil-cooled motors for EV applications. Full article

The proliferation of electric vehicle (EV) racing competitions, such as Formula electric vehicle (FEV) competitions, has intensified the quest for high-performance electric propulsion systems. High-speed permanent magnet synchronous motors (PMSMs) for FEVs necessitate an optimized control strategy that adeptly manages the complex interplay between electromagnetic torque production and minimal power loss, ensuring peak operational efficiency and performance stability across the full speed range. This paper delves into the optimization of high-speed PMSM, pivotal for its application in FEVs. It begins with a thorough overview of the FEV motor’s basic principles, followed by the derivation of a detailed mathematical model that lays the groundwork for subsequent analyses. Utilizing MATLAB/Simulink, a simulation model of the motor drive system was constructed. The proposed strategy synergizes the principles of maximum torque per ampere (MTPA) with the flux weakening control technique instead of conventional zero direct axis current (ZDAC), aiming to push the boundaries of motor performance while navigating the inherent limitations of high-speed operation. Covariance matrix adaptation evolution strategy (CMA-ES) was deployed to determine the optimal d-q axis current ratio achieving maximum operating torque without overdesign problems. The implementation of the optimized control strategy was rigorously tested on the simulation model, with subsequent validation conducted on a real test bench setup. The outcomes of the proposed technique reveal that the tailored control strategy significantly elevates motor torque performance by almost 22%, marking a pivotal advancement in the domain of high-speed PMSM. Full article

With the growing global focus on environmental protection and carbon emissions, electric vehicles (EVs) are becoming increasingly popular. Permanent magnet synchronous motors (PMSMs) have emerged as a core component of the drive system due to their high-power density and compact design. The rotor temperature of PMSMs significantly affects their operating efficiency, management strategies, and lifespan. However, real-time monitoring and acquisition of rotor temperature are challenging due to cost and space limitations. Therefore, this study proposes a hybrid model named RIME-XGBoost, which integrates the RIME optimization algorithm with XGBoost, for the precise modeling and prediction of PMSM rotor temperature. RIME-XGBoost utilizes easily monitored dynamic parameters such as motor speed, torque, and currents and voltages in the d-q coordinate system as input features. It simultaneously optimizes three hyperparameters (number of trees, tree depth, and learning rate) to achieve high learning efficiency and good generalization performance. The experimental results show that, on both medium-scale datasets and small-sample datasets in high-temperature ranges, RIME-XGBoost outperforms existing methods such as SMA-RF, SO-BiGRU, and EO-SVR in terms of RMSE, MBE, R-squared, and Runtime. RIME-XGBoost effectively enhances the prediction accuracy and computational efficiency of rotor temperature. This study provides a new technical solution for temperature management in EVs and offers valuable insights for research in related fields. Full article

Fuel cells (FCs) offer several operational advantages when integrated as a power source in electric vehicles (EVs). Since the voltage of these cells is typically low, usually less than 1 V, the power conversion system requires a DC–DC converter capable of providing a high voltage conversion ratio to match the input voltage of the motor propulsion system, which can exceed 400 V and reach up to 800 V. The modular DC–DC boost converter proposed in this paper is designed to achieve a high voltage step-up ratio for the input FC voltages through the use of isolated series-connecting boosting submodules connected. The power electronic topology employed in the submodules (SMs) is designed to provide a flexible output voltage while maintaining a continuous input current from the fuel cells with minimal current ripple to improve the FC’s performance. The proposed step-up modular converter provides several benefits including scalability, better controllability, and improved reliability, especially in the presence of partial faults. Computer simulations using MATLAB/SIMULINK^® software (R2024a) have been used to study the feasibility of the proposed converter when connected to a permanent magnet synchronous motor (PMSM). Also, experimental results using a 1 kW prototype composed of four SMs have been obtained to validate the performance of the proposed converter. Full article

The transportation sector is experiencing a profound shift, driven by the urgent need to reduce greenhouse gas (GHG) emissions from internal combustion engine vehicles (ICEVs). As electric vehicle (EV) adoption accelerates, the sustainability of the materials used in their production, particularly in electric motors, is becoming a critical focus. This paper examines the sustainability of both traditional and emerging materials used in EV traction motors, with an emphasis on permanent magnet synchronous motors (PMSMs), which remain the dominant technology in the industry. Key challenges include the environmental and supply-chain concerns associated with rare earth elements (REEs) used in permanent magnets, as well as the sustainability of copper windings. Automakers are exploring alternatives such as REE-free permanent magnets, soft magnetic composites (SMCs) for reduced losses in the core, and carbon nanotube (CNT) windings for superior electrical, thermal, and mechanical properties. The topic of materials for EV traction motors is discussed in the literature; however, the focus on environmental, social, and economic sustainability is often lacking. This paper fills the gap by connecting the technological aspects with sustainability considerations, offering insights into the future configuration of EV motors. Full article

The fuel economy of electric vehicles (EVs) is an important factor in determining the competitiveness of EVs. Since the fuel economy is affected by the efficiency of an e-powertrain composed of a motor and inverter, it is necessary to select a high-efficiency topology for the e-powertrain. In this paper, a novel topology of e-powertrains to improve the fuel economy of EVs is proposed. The proposed topology aims to improve the system efficiency by integrating open-end winding (OEW) and winding changeover (WC). The proposed OEW-PMSM with WC enables to drive a permanent magnet synchronous motor (PMSM) in four different modes. Each mode can increase inverter efficiency and motor efficiency by changing motor parameters and maximum modulation index. In this paper, the system efficiency of the proposed topology was evaluated using electromagnetic finite element analysis and a loss model of power semiconductors. In addition, the vehicle simulations were performed to evaluate the fuel economy of the proposed topology, thereby proving the superiority of the proposed topology compared with the conventional PMSM. Full article

This study proposed the hybrid energy storage paradigm (HESP) equipped with front-wheel permanent magnet synchronous motors (PMSMs) for battery electric vehicles (BEVs). In this case, all four wheels are driven by a single motor using mechanical coupling to distribute the motor’s power to each wheel evenly. The HESP is a combination of several supercapacitors (SCs) and an NMC-lithium battery equipped with an advanced artificial neural network (ANN) that will enhance the regenerative braking system (RBS) efficiency of energy storage during braking. The three-phase inverter switching algorithm ensures efficient regenerative braking and fine adjustment of the brake force distribution. Under the RBS, the HESP with the ANN first transfers braking energy to the SC and, when the safety standard is reached, the SC transfers it to the battery. The RBS control maintains an even distribution of braking force at all distances to ensure stability during braking. The results show that a traditional BEV can drive 245.46 km (35 cycles), while an EV with an RBS-only battery can drive 282.56 km (40 cycles). An EV with HESP-RBS can drive 338.78 km (48 cycles), which is an increase of 93.32 km (13 cycles). The HESP-RBS increased the regenerative efficiency by 38.01% when compared to a traditional BEV. Full article

This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications. Full article

To reduce pollution and energy consumption, particularly in the automotive industry, energy saving is the main concern, and hence, Electric vehicles (EVs) are getting significantly more attention than vehicles with internal combustion engines (IC engines). Electric motors used in Electric Vehicles (EVs) must have high efficiency for maximum utilization of the energy from the batteries or fuel cells. Also, these motors must be compact, lightweight, less expensive and very easily recycled. Further, to obtain better dynamic performance, various motor control strategies are used to control the speed of the motor. And to have increased reliability, sensorless speed control techniques that offer sufficiently high performance are used. The sensorless speed control techniques are largely divided into three groups: state observer methods, indirect measurement methods and saliency-based methods. Generally, the state observer uses back emf or flux linkage to estimate the speed of the motor. Since the back emf is directly proportional to the rotor speed, at low-speed back emf based method will give poor performance. The current-based Model Reference Adaptive System (MRAS) model is also popular for estimating low speed; however, assessments deteriorate during high performance applications such as EV. This paper presents an artificial neural network (ANN)-deployed sensorless speed control of permanent magnet synchronous motor (PMSM) drive used in EVs. In this paper, the estimation of speed using the current-based MRAS model is discussed and compared with the proposed ANN-based controller, which shows significant improvement in the performance of EV motor drives. The MATLAB simulation and experimental results are presented to validate the proposed algorithm. Full article

To avoid damaging the motor and its surrounding equipment, detecting Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) applied in electric vehicles is a crucial task. In this paper, the detection of ITSC faults in stator winding for PMSMs is carried out by means of the Extended KALMAN Filter (EKF) algorithm combined with the Fuzzy Logic Estimator (FLE). To estimate the motor parameters, including the rotor position and speed, the EKF algorithm uses the measured stator currents and voltages beside the stator resistance, which is calculated in advance using fuzzy logic and fed to the EKF. The change behaviors of the estimated parameters were then used to detect short circuit faults in the PMSM. Using Matlab/Simulink, the proposed FL-EKF algorithm was implemented and tested on a faulty PMSM controlled by Field Oriented Control (FOC). The observation of a perfect estimation of the stator resistance through the simulation helps to precisely detect the failure, and that demonstrates the sensitivity and robustness of the proposed approach. Full article

One of the widespread electric motors for electric vehicles (EVs) is permanent magnet synchronous machine (PMSM) drives. It is because of the power density and high energy of the PMSM with moderate assembly cost. The widely adopted PMSM as the motor of choice for EVs, together with variety of applications urges stringent monitoring of temperature to ignore high temperatures. Temperature monitoring of the PMSM is highly complex to accomplish because of complex measurement device for internal components of the PMSM. Temperature values beyond a certain range might result in additional maintenance costs together with major operational problems in PMSM. The latest developments in artificial intelligence (AI) and deep learning (DL) methods pave a way for accurate temperature prediction in PMSM drivers. With this motivation, this article introduces an enhanced remora optimization algorithm with stacked bidirectional long short-term memory (EROA-SBiLSTM) approach for temperature prediction of the PMSM drives. The presented EROA-SBiLSTM technique mainly focuses on effectual temperature prediction using DL and hyperparameter tuning schemes. To accomplish this, the EROA-SBiLSTM technique applies Pearson correlation coefficient analysis for observing the correlation among various features, and the p-value is utilized for determining the relevant level. Next, the SBiLSTM model is used to predict the level of temperature that exists in the PMSM drivers. Finally, the EROA based hyperparameter tuning process is carried out to adjust the SBiLSTM parameters optimally. The experimental outcome of the EROA-SBiLSTM technique is tested using electric motor temperature dataset from the Kaggle dataset. The comprehensive study specifies the betterment of the EROA-SBiLSTM technique. Full article

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field. Full article