Search Results (38)

This study investigates the electromagnetic and NVH characteristics of an outer-rotor surface-mounted permanent magnet synchronous generator (SPMSG) for wave energy applications, focusing on the effect of rotor eccentricity. To reflect potential fault due to manufacturing or assembly defects, a 0.5 mm rotor eccentricity was introduced in finite element method (FEM) simulations. The torque ripple waveform was analyzed using fast Fourier transform (FFT) to identify dominant harmonic components that generate unbalanced electromagnetic forces and induce structural vibration. These harmonic components were further examined under variable marine operating conditions to evaluate their impact on acoustic radiation and vibration responses. Based on the simulation and analysis results, a design-stage methodology is proposed for predicting vibration and noise by targeting critical harmonic excitations, providing practical insights for marine generator design and improving long-term operational reliability in wave energy systems. Full article

This study presents an analysis of the electromagnetic noise and vibration in a surface-mounted permanent magnet synchronous machine (SPMSM), focusing on their excitation sources. To investigate this, the excitation sources were identified through an analytical approach, and their effects on electromagnetic noise and vibration were evaluated using a finite element method (FEM)-based analysis approach. Additionally, an equivalent curved-beam model based on three-dimensional shell theory was applied to determine the deflection forces on the stator yoke, accounting for the tooth-modulation effect. The stator’s natural frequencies were derived through the characteristic equation in free vibration analysis. Modal analysis was performed to validate the analytically derived natural frequencies and to investigate stator deformation under the tooth-modulation effect across various vibration modes. Furthermore, noise, vibration, and harshness (NVH) analysis via FEM reveals that major harmonic components align closely with the natural frequencies, identifying them as primary sources of elevated vibrations. A comparative study between 8-pole–9-slot and 8-pole–12-slot SPMSMs highlights the impact of force variations on the stator teeth in relation to vibration and noise characteristics, with FEM verification. The proposed method provides a valuable tool for early-stage motor design, enabling the rapid identification of resonance operating points that may induce severe vibrations. This facilitates proactive mitigation strategies to enhance motor performance and reliability. Full article

Free stroke, which means the intermittent no-load operation state of dampers, can cause an abnormal noise and unavoidably lead to the deterioration of vehicle NVH performance. In electric vehicles, the noise is particularly intolerable because there are no engine sounds to mask it. Focusing on this, the mechanism of the free-stroke phenomenon is analyzed. A method, which involves parametric models and numerical simulation, is proposed to prevent free-stroke phenomena during the damper design phase. This paper proposes a free-stroke mechanism based on a fluid–structure interaction (FSI) numerical method, combined with experiments, which intends to provide a design reference with guaranteed performance for dampers. Initially, according to parametric cavitation models and by applying numerical methods, simulations for the proposed FSI model are calculated. By analyzing the simulation results, strain variation characteristics near the bottom of the damper valves are revealed, which establish the relationships between strain change, cavitation and the free-stroke phenomena. Meanwhile, the specific position and distribution of free-stroke failure are clearly located by running diverse loading speeds. Finally, all the theoretical analysis results are verified using damper noise tests and indicator bench tests. Full article

Evaluating noise, vibration, and harshness (NVH) performance is crucial in vehicle development. However, NVH evaluation is often subjective and challenging to achieve through numerical simulation, and typically prototypes are required. Dynamic driving simulators are emerging as a viable solution for assessing NVH performance in the early development phase before physical prototypes are available. However, most current simulators can reproduce vibrations only in a single direction or within a limited frequency range. This paper presents a comprehensive design optimization approach to enhance the dynamic response of a full-spectrum driving simulator, addressing these limitations. Specifically, in complex driving simulators, vibration crosstalk is a critical and common issue, which usually leads to an inaccurate dynamic response of the system, compromising the realism of the driving experience. Vibration crosstalk manifests as undesired vibration components in directions other than the main excitation direction due to structural coupling. To limit the system crosstalk, a flexible multibody dynamics model of the driving simulator has been developed, validated, and employed for a global sensitivity analysis. From this analysis, it turns out that the bushings located below the seat play a crucial role in the crosstalk characteristics of the system and can be effectively optimized to obtain the desired performances. Bushings’ stiffness and locations have been used as design variables in a multiobjective optimization with the aims of increasing the direct transmissibility of the actuators’ excitation and, at the same time, reducing the crosstalk contributions. A surrogate model approach is employed for reducing the computational cost of the process. The results show substantial crosstalk reduction, up to 57%. The proposed method can be effectively applied to improve the dynamic response of driving simulators allowing for their extensive use in the assessment of vehicles’ NVH performances. Full article

In order to confront the challenge of efficiently evaluating interior wind noise levels in passenger vehicles during the early stages of shape design, this paper proposes a methodology for predicting interior wind noise. The methodology integrates vehicle shape features with a whale optimization Xception model (WOA-Xception). A nonlinear mapping model is constructed between the vehicle shape features and the wind noise level at the driver’s right ear. This model is constructed using key exterior parameters, which are extracted from wind tunnel test data under typical operating conditions. The exterior parameters include the front windshield, A-pillar, and roof. The key hyperparameters of the Xception model are adaptively optimized using the whale optimization algorithm to improve the prediction accuracy and generalization ability of the model. The prediction results on the test set demonstrate that the WOA-Xception model attains mean absolute percentage error (MAPE) values of 9.78% and 9.46% and root mean square error (RMSE) values of 3.73 and 4.06, respectively, for sedan and Sports Utility Vehicle (SUV) samples, with prediction trends that align with the measured data. A comparative analysis with traditional Xception, WOA-LSTM, and Long Short-Term Memory (LSTM) models further validates the advantages of this model in terms of accuracy and stability, and it still maintains good generalization ability on an independent validation set (mean absolute percentage error of 9.45% and 9.68%, root mean square error of 3.77 and 4.15, respectively). The research findings provide an efficient and feasible technical approach for the rapid assessment of in-vehicle wind noise performance and offer a theoretical basis and engineering references for noise, vibration, and harshness (NVH) optimization design during the early shape phase of vehicle development. Full article

The electrification of automobiles makes low-frequency road noise the main factor affecting the performance of automobile NVH (Noise, Vibration and Harshness). High-precision and high-efficiency road noise prediction results are the basis for NVH performance improvement and optimization. However, using the traditional TPA (transfer path analysis) method and CAE (Computer-Aided Engineering) method to analyze the road noise problem has the problems of complex transfer path, difficult acquisition of modeling parameters, long duration and high cost. Therefore, based on the road noise hierarchy constructed according to the road noise transmission path, the LSTM (Long Short-Term Memory) network is introduced to establish a data-driven prediction model, which effectively avoids the defects of the TPA method and CAE in analyzing road noise problems. Based on the LSTM prediction model, the AFW (adaptive feature weight) method is introduced to improve the model’s attention to the key features in the input data and finally improve the accuracy and robustness of the road noise prediction model. The results show that the accuracy (RMSE = 1.74 (dB)) and generalization ability (MAE = 2.60 (dB), $R^2$ = 0.924) of the AFW-LSTM model are better than other models. Full article

This paper evaluated the acoustic characteristics of electric vehicles (EVs) using both psychoacoustic and soundscape methodologies by analyzing three key psychoacoustic parameters: loudness, roughness, and sharpness. Through correlation analysis between perceived values and objective parameters, we identified specific sound sources requiring improvement, including vehicle body acoustics, wheel noise, and acceleration-related sounds. The relationship between comfort perception and acoustic parameters showed varying correlations: loudness (0.0411), roughness (2.3452), and sharpness (0.9821). Notably, the overall correlation coefficient of 0.5 suggests that psychoacoustic parameters alone cannot fully explain human comfort perception in EVs. The analysis of sound propagation revealed elevated vibration levels specifically in the driver’s seat area compared to other vehicle regions, identifying key targets for improvement. The research identified significant acoustic events at three key frequencies (50 Hz, 250 Hz, and 450 Hz), requiring in-depth analysis to determine their sources and understand their effects on the vehicle’s NVH characteristics. The study successfully validated its results by demonstrating that a combined approach using both psychoacoustic and soundscape parameters provides a more comprehensive understanding of passenger acoustic perception. This integrated methodology effectively identified specific areas needing acoustic refinement, including: frame vibration noise during rough road operation; tire-generated noise; and acceleration-related sound emissions. Full article

This study presents a generalized and systematic approach to modeling complex dynamic systems using Frequency-Based Substructuring (FBS). The aim is to develop an efficient method for system identification and subsystem decomposition, enabling the creation of reduced-order models for non-linear dynamic systems that are modular and reconfigurable. The methodology combines receptance (Frequency Response Function, FRF) properties from individual subsystems to predict the overall system’s response. This technique extends existing methods by Jetmundsen and D.D. Klerk and adapts them to subsystems with full degrees of freedom (DoFs), making it suitable for flexible and distributed structures. To demonstrate its effectiveness, the method is applied to vehicle noise and vibration analysis, where subsystems are initially treated as rigid bodies, but are later adapted to flexible characteristics. The results show that this hybrid approach accurately predicts system responses, offering significant advantages for NVH target setting when subsystem FRF matrices are sourced either from testing or numerical simulations. This methodology enhances the capability to model complex dynamic systems with improved precision and reduced computational cost. A comparison with traditional modeling techniques confirms the validity of the approach. Full article

Torque ripple and radial electromagnetic (EM) vibration can lead to motor vibration and noise, which are crucial to the motor’s NVH (Noise, Vibration, and Harshness) performance. Researchers focus on two main aspects: motor body design and control strategy, employing various methods to optimize the motor and reduce torque ripple and radial EM vibration. Rotor notching and segmented rotor skewing are frequently used techniques. However, determining the optimal notch and skew strategy has been an ongoing challenge for researchers. In this paper, an 8-pole, 36-slot ISG motor is optimized using a combination of Q-axis and magnetic bridge notching (QMC notch) as well as segmented rotor skewing to reduce torque ripple and radial EM vibration. Three skewing strategies—step skew (SS), V-shape skew (VS), and zigzag skew (ZS)—along with four segmentation cases are thoroughly considered. The results show that the QMC notch significantly reduces torque ripple, while skewing designs greatly diminish radial EM vibrations. However, at 14 $f_e$, the EM vibration frequency is close to the motor’s third-order natural frequency, leading to mixed results in vibration reduction using skewing techniques. After a comprehensive analysis of all skewing strategies, four-segment VS and ZS are recommended as the optimal approaches. Full article

In electrified vehicles, the masking noise behavior of internal combustion engines is absent, making the tonal excitation of the electric machine particularly noticeable in vehicle acoustics, which is perceived as disturbing by consumers. Due to manufacturing tolerances, the tonal NVH characteristics of the electric machine are significantly influenced at wide frequency ranges. This paper presents a systematic exploration of the influence of static eccentricity as one manufacturing tolerance on the NVH behavior of Permanent Magnet Synchronous Machines (PMSMs). The study utilizes a novel test bench setup enabling isolated variations in static eccentricity of up to 0.2 mm in one PMSM. Comparative analysis of acceleration signals reveals significant variations in the dominance of excitation orders with different eccentricity states, impacting critical operating points and dominant frequency rages of the electric machine. Despite experimentation, no linear correlation is observed between increased eccentricity and changes in acceleration behavior. Manufacturing eccentricity and deviations in rotor magnetization are discussed as potential contributors to the observed effects. The findings emphasize static eccentricity as a critical parameter in NVH optimization, particularly in electrified powertrains. However, the results indicate that further investigations are needed to explore the influence of eccentricities and magnetization deviations on NVH behavior comprehensively. Full article

The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry. Full article

Interior permanent magnet (IPM) motors in traction applications often employ discrete rotor skewing constructions to reduce torsional excitations and back-EMF harmonics. Although skewing is very effective in reducing cogging torque, the impact on torque ripple is not well understood and can vary significantly over the operating envelope of a motor. Skewing also leads to the creation of a non-zero axial force that may compromise the bearing life if not considered. This paper introduces a holistic methodology for analyzing the effect of skewing, aiming to minimize torsional excitations, axial forces and back-EMF harmonics whilst mitigating the impact on performance and costs. Firstly, analytical models are employed for calculating cogging torque, torque ripple and axial forces. Then, 2D and 3D finite element analysis are used to incorporate the influence of non-linear material behavior. A detailed structural model of the powertrain is employed to calculate the radiated noise and identify key areas allowing a motor designer to reduce noise, vibration and harshness (NVH). A meticulous selection process for the skewing angle, the number of skew stacks and the orientation of skew stacks is developed, giving particular attention to the effect of the selected pattern on NVH in both forward and reverse rotating directions. Full article

Brake squeal—an audible high-frequency noise phenomenon in the range between 1 kHz and 15 kHz resulting from self-excited vibrations—is one of the main cost drivers while developing brake systems. Increasing damping is often a crucial factor in the context of self-excited vibrations. Countermeasures applied for preventing brake squeal have been investigated particularly for disk brakes in the past. However, in recent years, drum brakes have once again become more important, partly because of the issue of particle emissions. Concerning noise problems, drum brakes have a decisive advantage compared to disk brake systems in that the outer drum surface is freely accessible for applying damping devices. This paper focuses on the fundamental proving and evaluation of passive damping measures on a simplex drum brake system. To obtain a detailed understanding of the influence of additional damping on the squealing behavior of drum brakes, extensive experimental investigations are performed on a brake with an intentionally introduced high squealing tendency in the initial configuration. This made it possible to investigate the influence of different types of damping measures on their effectiveness. Techniques from the field of big data analysis and machine learning are tested to detect squeal in measured time series data. These techniques were remarkably reliable and made it possible to detect squeal efficiently even in data that was not generated on a traditional costly NVH brake dynamometer. To investigate whether the simulation method usually used for the simulation of brake squeal is applicable to depicting the influence of additional damping in drum brakes, a complex eigenvalue analysis was performed with Abaqus, and the results were compared with those from the experiments. Full article

The driving comfort and safety of the automotive powertrain are significantly related to the performance, lifetime, and functionality of the lubricant. The presented study focuses on investigating the performance loss of the lubricant due to water contamination resulting from environmental influences and iron particles originating from the wear of different machine elements. The main purpose is to determine critical factors that contribute to the degradation of the lubricant, and increase the tendency to NVH behavior, leading to adverse comfort losses to the respective user. Therefore, this performance loss is evaluated by test rig-based analysis of the friction behavior of wet clutches. Due to physical adsorption, a significant impact of water and iron contamination on the degradation of the lubricant is found, while the influence of the energy input is secondary. Full article

This paper presents a novel approach to address noise, vibration, and harshness (NVH) issues in electrically assisted bicycles (e-bikes) caused by the drive unit. By investigating and optimising the structural dynamics during early product development, NVH can decisively be improved and valuable resources can be saved, emphasising its significance for enhancing riding performance. The paper offers a comprehensive analysis of the e-bike drive unit’s mechanical interactions among relevant components, culminating—to the best of our knowledge—in the development of the first high-fidelity model of an entire e-bike drive unit. The proposed model uses the principles of elastic multi body dynamics (eMBD) to elucidate the structural dynamics in dynamic-transient calculations. Comparing power spectra between measured and simulated motion variables validates the chosen model assumptions. The measurements of physical samples utilise accelerometers, contactless laser Doppler vibrometry (LDV) and various test arrangements, which are replicated in simulations and provide accessibility to measure vibrations onto rotating shafts and stationary structures. In summary, this integrated system-level approach can serve as a viable starting point for comprehending and managing the NVH behaviour of e-bikes. Full article