Search Results (108)

Electric motors play a decisive role in electric vehicles by converting electrical energy into mechanical motion across various drivetrain components. However, failures in these motors can interrupt the motor function, with approximately 40% of these failures stemming from bearing issues. Key contributors to bearing degradation include shaft voltage, bearing current, and electric discharge material spalling current, especially in motors powered by inverters or variable frequency drives. This review explores the tribological and vibrational aspects of bearing currents, analyzing their mechanisms and influence on electric motor performance. It addresses the challenges faced by electric vehicles, such as high-speed operation, elevated temperatures, electrical conductivity, and energy efficiency. This study investigates the origins of bearing currents, damage linked to shaft voltage and electric discharge material spalling current, and the effects of lubricant properties on bearing functionality. Moreover, it covers various methods for measuring shaft voltage and bearing current, as well as strategies to alleviate the adverse impacts of bearing currents. This comprehensive analysis aims to shed light on the detrimental effects of bearing currents on the performance and lifespan of electric motors in electric vehicles, emphasizing the importance of tribological considerations for reliable operation and durability. The aim of this study is to address the engineering problem of bearing failure in inverter-fed EV motors by integrating electrical, tribological, and lubrication perspectives. The novelty lies in proposing a conceptual link between lubricant breakdown and damage morphology to guide mitigation strategies. The study tasks include literature review, analysis of bearing current mechanisms and diagnostics, and identification of technological trends. The findings provide insights into lubricant properties and diagnostic approaches that can support industrial solutions. Full article

The study of vibration characteristics and suppression methods in integrated electric drive systems of electric vehicles is of critical importance. To investigate these characteristics, both current harmonics within the motor and nonlinear factors within the drivetrain were considered. A 17-degree-of-freedom nonlinear torsional–planar dynamic model was developed, with electromagnetic torque and output speed as coupling terms. The model’s accuracy was experimentally validated, and the system’s dynamic responses were analyzed under different working conditions. To mitigate vibrations caused by torque ripple, a coordinated control strategy was proposed, combining a quasi-proportional multi-resonant (QPMR) controller and a full-frequency harmonic controller (FFHC). The results demonstrate that the proposed strategy effectively suppresses multi-order current harmonics in the driving motor, reduces torque ripple by 45.1%, and enhances transmission stability. In addition, the proposed electromechanical coupling model provides valuable guidance for the analysis of integrated electric drive systems. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. 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

The transportation sector in South Asia largely depends on internal combustion engine (ICE) vehicles, which are responsible for a large share of greenhouse gas (GHG) emissions, air pollution, and the increase in fuel prices. Although hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fully electric vehicles (EVs) constitute promising alternatives, the rate of their implementation is low due to factors such as the high initial investment, the absence of the required infrastructure, and the reliance on fossil fuel-based electricity. This study is the first of its kind to examine Bangladesh’s drivetrain options in a comprehensive way, with in-depth real-world emission testing and economic analysis as the main tools of investigation into the environmental and economic feasibility of different technologies used in the vehicles available in Bangladesh, including lifecycle costs and infrastructure constraints. The study findings have shown that hybrid and plug-in hybrid vehicles are the best options, since they have moderate emissions and cost efficiency, respectively. Fully electric vehicles, however, face two main challenges: the overall lack of charging infrastructure and the overall high purchase prices. Among the evaluated technologies, PHEVs exhibited the lowest environmental and economic burden. The Toyota Prius PHEV emitted 98% less NOx compared to the diesel-powered Pajero Sport and maintained the lowest per-kilometer cost at BDT 6.39. In contrast, diesel SUVs emitted 178 ppm NOx and cost 22.62 BDT/km, reinforcing the transitional advantage of plug-in hybrid technology in Bangladesh’s context. Full article

The aim of this article is to assess the sale structure impact of selected, popular brands of passenger vehicles on total CO₂ emissions in the context of the energy transition in the transport sector. A detailed analysis was conducted of the projected sales of gasoline-, diesel-, hybrid-, as well as electric-powered vehicles over the years 2021–2028. Based on the available empirical data, a mathematical model was developed to estimate emissions over the entire life cycle of vehicles, taking into account the unit carbon footprint of each type of drivetrain and the expected number of vehicles sold. The results indicate a gradual decline in total CO₂ emissions during the analyzed period, mainly due to the increasing share of alternative drivetrains. Despite the growth in electric vehicle sales, their impact on emission reductions remains limited due to the long lifespan of conventional vehicle fleets. The article concludes with a proposal to expand the LCA model to include regional, energy, and recycling components, which could help in formulating more effective climate policies. Full article

In electric vehicles (EVs), bearings in traction motors are increasingly prone to electrical damage under operational currents and voltages, leading to accelerated wear and reduced lifespan. This study examines the extent of bearing damage under low-speed, electrically charged conditions to understand wear behavior at boundary lubrication better. Bearings were driven at low speed by a motor, with inner and outer rings connected to a pulsed power supply’s positive and negative terminals, simulating real-world shaft voltage conditions. The applied electrical parameters included voltages from 5 V to 240 V and frequencies of 10 kHz, leading to voltages at the bearing peaking between 0.1 and 12 V measured by an oscilloscope and multimeter. The tested bearings were disassembled, and scanning electron microscopy (SEM) was used to assess the damage associated with varying electrical stresses. The results revealed distinct wear patterns and degradation effects when the shaft current and peak voltage reached 2.5 A and 12 V, emphasizing the critical need for protective strategies. Future work will focus on evaluating the impact of higher rotational speeds and controlled power supply conditions to analyze the effects of increased power supply settings and compare outcomes to low-speed scenarios. Full article

The widespread adoption of electric vehicles (EVs) has introduced new challenges in drivetrain lubrication, particularly concerning electrical corrosion, frictional wear, and hydrogen embrittlement. While polyalphaolefin (PAO)-based lubricants are commonly used, they struggle under high-speed and high-torque conditions. In contrast, polyphenyl ether (PPE)-based lubricants offer superior wear resistance and effectively suppress hydrogen generation, making them promising for EV applications. This study examines the effects of current direction and magnitude on tribofilm formation and frictional behavior in a PPE-lubricated environment. The results show that PPE exhibits unique tribofilm adhesion characteristics influenced by electrical conditions, unlike PAO. Surface analysis reveals that the tribofilm mainly consists of amorphous carbon, and friction under an electrical bias induces PPE oxidation, with oxidation products forming more readily at the positive electrode. Tribofilm formation correlated with increased friction and wear, particularly under currents of 10 mA or higher. Although PPE is more sensitive to electrical influences than PAO, it exhibits excellent wear resistance and maintains a low coefficient of friction even under electrification. This suggests that PPE could be suitable for lubrication in electrical environments and may serve as a promising lubricant for EV drive systems and similar applications. Full article

Electric machines (EMs) of electrified vehicle drivetrains can be tested on drivetrain test benches at an early stage of development. In order to protect the EMs from premature damage or failure during testing, monitoring their thermal condition is important. Due to the package requirements of compact and powerful EMs with high-speed requirements and high-power densities, the heat build-up inside the motor during operation is particularly high. For this reason, fluid cooling with heat exchangers is increasingly being used in EMs. The EMs analysed in this work are water-cooled by a cooling jacket. This influences the heat flow inside the machine through heat transfer mechanisms, making it difficult to detect damage to the EMs. This paper presents a novel method for non-destructive and non-contact thermal condition monitoring of water-cooled EMs on drivetrain test benches using thermography. In an experimental setup, infrared images of an intact water-cooled EM are taken. A bearing of the EM’s rotor is then damaged synthetically, and the experiment is repeated. The infrared images are then processed and analysed using appropriate software. The analysis of the infrared images shows that the heat propagation of the motor with bearing damage differs significantly from the heat propagation of the motor without bearing damage. This means that thermography opens up another method of condition monitoring for water-cooled EMs. The results of the investigation serve as a basis for future condition monitoring of water-cooled EMs on powertrain test benches using artificial intelligence (AI). Full article

Electrification and modularity are emerging as key trends in off-road vehicle development, prompting the need for innovative solutions in steering and modular coupling. This study presents an electromechanical connection and steering joint, conceived to replace traditional hydraulic systems and offer enhanced steering precision, modular adaptability, and system efficiency. By eliminating hydraulic components, the design reduces fluid leakage risks, lowers maintenance requirements, and improves energy integration with the vehicle’s electric drivetrain. The joint enables independent module articulation, including steering and controlled tilting, to optimize vehicle stability across diverse terrains. A prototype was built and tested under real-world conditions, assessing functional reliability, ease of integration, and operational performance. The findings demonstrate that electromechanical steering substantially boosts system flexibility compared to conventional hydraulic setups. Full article

In this paper, a 96 V, 2.5 kW, 36-slot, and 32-pole brushless direct-current (BLDC) motor is designed, analyzed, and tested in the laboratory and on the prototype vehicle to provide the required output performance for an electric vehicle (EV) according to the rated operating conditions. Applications for in-wheel electric drivetrains have the potential to deliver high efficiency and high torque. Consequently, in-wheel motor topology is proposed for small EVs, and the sizing equations, including primary, stator, and rotor dimensions, are developed step by step for the preliminary design. Then, a multi-goal function is introduced to obtain optimum motor design. This motor has an outer-rotor-type construction. In addition, a concentrated winding arrangement is used, which ensures low-end winding and thus low copper loss. Then, multiphysics using the coupled electromagnetic–thermal analysis is carried out. Elective analysis using the finite element method, a motor prototype, and experimental studies verifies the design effectively. Full article

Finding an architectural solution that satisfies electric motorcycles’ performance and riding range is challenging due to the electric powertrain constraints, such as low battery energy density. This paper analyzes the gearbox and powertrain efficiency for battery electric motorcycles (BEMs) based on the drivetrain architecture and under various vehicle use scenarios. The impact on the consumption of a constant-speed gearbox is investigated, focusing mainly on load-independent losses. The analysis is performed using an analytic approach and experimental data. Two different gearbox and powertrain solutions are characterized through test bench experiments, running them under free-driving conditions and no-load tests. A comparison of these powertrain solutions in terms of consumption for the same motorcycle model under different speed profiles (WMTC, LA-4) is conducted. The results show the energy losses and provide information for defining drivetrain components and architectures. Full article

Nowadays, electromobility has a significant role in transportation; different electrically driven vehicles are spreading continuously. Due to this form of drivetrain, fire safety hazards have also changed when compared to those of conventional vehicles. Lately, electric vehicle fires have become more common; thus, we have chosen to investigate the negative impacts of these fires on humans and the environment, in addition to the toxic properties of the resulting combustion products. In our research work, we conducted a full-scale fire experiment on an electric passenger car. Fire extinguishing was executed with fire-fighting foam, and its efficiency was examined. After extinguishing the fire, we took samples from the combustion gases and soil. Samples were subjected to laboratory investigations. Our results and experiences are presented in this article. Full article

The tribological performance of nanolubricants in electric drivetrains has gained attention due to the rapid growth of electric vehicles. Nanomaterials, especially those with high thermal conductivity and low electrical conductivity, are favored as lubricant additives for use in electrical conditions. Low-viscosity lubricants, known for their good thermal conductivity, are increasingly being considered for electric powertrains. Combining appropriate nanomaterials with lubricants can optimize nanolubricants for electric drivetrains, with stability, tribocorrosion, and electro-viscosity being key factors. Traditional tribometers, when modified to apply external electrical power, allow testing of nanolubricants under electrical conditions, providing insights into their behavior with positive and/or negative electrical charges. To achieve accurate and stable results, tribological test systems must be adapted, requiring well-isolated rigs for controlled data collection. This adaptation enables a better understanding of the interaction between nanomaterials and surfaces under lubrication. This paper reviews studies that use modified tribometers to analyze nanolubricant performance under mechanical and electrical conditions and explores the effects of electrical and thermal factors on lubricant properties, nanomaterials, and their mechanisms under triboelectric conditions. Full article

This study presents a method for determining the optimal gear ratio in electric trains by examining the effects of motor efficiency, wheel wear, and relative damage to the input and output shafts of the reduction gear. In electric trains, reduction gears and wheels are critical for converting the driving motor’s torque and determining the motor’s operational point, which in turn affects efficiency and durability. Over time, wheel wear from regular use and periodic profiling reduces the wheel radius, causing an effective increase in the gear ratio, which impacts the motor efficiency and load distribution across drivetrain components. This study models the dynamic behavior of the vehicle’s drivetrain system using MATLAB/Simulink and incorporates real-world data on wheel wear to address the problem. Through simulations with varying gear ratios, it analyzes changes in motor efficiency and uses Miner’s rule to assess the relative damage on the reduction gear’s input and output shafts. The results enable the identification of a gear ratio that balances motor efficiency and reduces cumulative fatigue damage, which is especially important for maintaining long-term drivetrain durability. This approach provides a systematic way to enhance the overall performance and lifespan of electric train systems by selecting a gear ratio that optimally aligns efficiency and durability. Full article