Vehicle Vibration Research Articles

Calibration of Two Novel Segmentation Approaches by Synthetic Road Vehicle Vibrations

Calibration of Two Novel Segmentation Approaches by Synthetic Road Vehicle Vibrations

Model-Free Adaptive Control of an Active Half-Vehicle-Seat System Coupled with a Nonlinear Energy Sink Inerter (NESI)

In order to reduce vehicle vibration and improve vehicle ride comfort and handling stability, a nonlinear energy sink inerter (NESI) is designed by combing an inerter and nonlinear energy sink (NES) for use in the seat suspension and vehicle suspension for the half-vehicle-seat (HVS) system; furthermore, a model-free adaptive control (MFAC) method based on the genetic algorithm is proposed to enhance the dynamic performance of the passive HVS system. The dynamic model of the active HVS system coupled with NESI using the MFAC method is established; its dynamic responses under pavement random and shock excitations are acquired using the numerical method and the dynamic performance is evaluated by seven evaluation indicators. The efficacy of the MFAC method is demonstrated through comparative analysis with the original passive HVS system, the HVS system coupled with NESI, and the active HVS system coupled with NESI using the proportional integral derivative (PID) control method. In addition, the influence of the installed position of MFAC on the dynamic performance of the active HVS system coupled with NESI is examined. The results show that for the active HVS system coupled with NESI using the MFAC method, compared with the other three HVS systems, the root mean square (RMS) values of the vehicle body vertical acceleration, vehicle body pitch acceleration, seat vertical acceleration, and front and rear suspension dynamic travel under pavement random excitation are smaller, the corresponding peak amplitudes under pavement shock excitation reduce, and the vibration attenuation time shortens; the RMS values of the front and rear dynamic tire loading under pavement random excitation are slightly smaller, the corresponding peak amplitudes under pavement shock excitation increase, and the vibration attenuation time decreases, which reflects the best dynamic performance among the four HVS systems and shows the effectiveness of the MFAC method. Furthermore, the control effect of the MFAC method is the best when it acts both on the seat and vehicle suspensions.

Effect of vehicular vibrations on L-4 lumbar vertebrae – A finite element study

Lower Back Pain (LBP) is a global health issue, with increasing prevalence, partly attributed to vehicular vibrations experienced by motorcyclists. The L4 lumbar vertebra is responsible for greater mobility and flexibility of the body, but also is the most crucial body element affected by vehicular vibrations. Anthropometric properties, types of speed humps, and vehicle types are the critical variables that impact bone health during riding, need to be studied. To understand the potential zones of injury, computational simulation can be performed under the influence of vehicle vibrations while crossing different types of speed humps at varying speeds. In the present study, finite element method (FEM) is used to evaluate stress and deformation in the bone. The L4 cortical bone is modelled by considering the CT-Scan data and assumed to be homogeneous and isotropic material. Vibration data is collected using two vehicle types (Type I and Type II) on four different humps (Trapezoidal, Bitumen Semi-circular, Rubber Semi-circular, and Rumble strip). The bone's dynamic behavior is studied using FEM simulation, which involved static structural, modal and transient dynamic analyses. The findings from static analysis indicate that the most concentrated stress is located in the lower pedicle region and is an expected commonplace for injuries because of vibrations. In transient dynamic analysis, Type I vehicle showed a 25 % higher stress than Type II.

Comparative analysis of ride comfort evaluation indices of high-speed vehicles based on a vehicle-seat-human body coupled dynamics model

As high-speed vehicles become increasingly lightweight, the influence of passengers on the vehicle vibration system cannot be overlooked. In this paper, a 3D vehicle-seat-human body coupled dynamics model is established to comprehensively evaluate the ride comfort of high-speed vehicles. The model combines the passenger biomechanics with the carbody flexibility. This is often neglected in previous studies and only the carbody vibrations are considered. Applying the validated model, four types of ride comfort evaluation indices are calculated and compared at 250–400 km/h. Results show that the ride comfort of high-speed vehicles at different speeds is excellent when the carbody acceleration and Sperling indices are used. While the results are significantly different when the total equivalent weighted acceleration and annoyance rate indices based on human body vibrations are used. At full load and speed of 400 km/h, the passengers sitting near the windows at both ends of the carbody felt ‘slightly uncomfortable’ or ‘less comfortable’ with an annoyance rate of about 30%, which indicates that up to 30% of the passengers might find the vibrations intolerable. This study underscores the necessity of integrating human body vibrations and passenger subjective psychology into ride comfort assessments, which can provide theory references for the selection and optimization of evaluation indices for high-speed lightweight vehicles.

Semi-Active Suspension Control Strategy Based on Negative Stiffness Characteristics

This paper investigates the potential of negative stiffness suspensions for enhanced vehicle vibration isolation. By analyzing and improving traditional control algorithms, we propose and experimentally validate novel skyhook, groundhook, and hybrid control strategies for suspensions with negative stiffness characteristics. We establish pavement models, incorporate negative stiffness into suspension modeling, and develop a performance evaluation index. Our research identifies shortcomings of classical semi-active control algorithms and introduces a new band selector to combine improved control methods. Simulation results demonstrate that the proposed semi-active suspension control strategy based on negative stiffness effectively reduces body vibration and enhances vehicle ride performance.

A high-precision visual detection method for rail profile based on Scheimpflug condition

Rail profile is an important indicator of rail service status, which should be always kept in good condition. How to efficiently collect rail profiles with high precision is a critical issue for their condition-based maintenance. This paper focuses on the method for accurate visual detection of rail full profile (RFP), including optical modeling, visual calibration, and error compensation. The proposed optical unit, featuring double line structured-light sensors, introduces an innovative method under the Scheimpflug condition to avoid the limitation of depth of field in RFP measurement. Subsequently, double visual sensors are calibrated together using the common reference target to extract the internal and external parameters respectively. The dynamic errors resulting from the impact of vehicle vibrations are ultimately investigated to rectify the distorted profiles to the normal. Both laboratory and field tests are performed to verify the effectiveness of the proposed system in terms of precision and efficiency.

Harmonic Components Isolation in Vehicle Vibrations: Enhancing Quarter-Car Model Analysis with an Extended Kalman Filter Approach

<div>This article addresses the essential task of understanding vibrations produced by vehicles to enhance the design of authentic laboratory tests. The article focuses on two primary sources of vibrations: those arising from vehicle–road surface interaction, which is largely random, and those emanating from the drivetrain, characterized as a summation of harmonics with a time-varying fundamental frequency. The method involves the application of the extended Kalman filter (EKF) paired with robust nonlinear least-squares (NLS) initialization to isolate the harmonic components effectively. Through a comprehensive analysis involving mean-square-error (MSE) evaluation via Monte Carlo simulation, considering additive white Gaussian noise (AWGN) and a two-degrees-of-freedom quarter-car model’s simulation response to the road, the research demonstrates the EKF’s proficiency. The results indicate the EKF’s capability to accommodate AWGN with a signal-to-noise ratio (SNR) up to 0 dB and road-induced random background vibrations up to an SNR of −3 dB, maintaining an MSE order of approximately 10<sup>−3</sup>.</div>

Annoyance caused by simultaneous exposure to noise and seat vibrations inside of a mini excavator: multimodal interaction and the effects of changes in low-frequency content of the sound signal

Drivers of commercial vehicles are exposed to noise and vibrations during their occupation. The signal characteristics of these exposures can lead to different degrees of annoyance perception, even below the legal daily exposure limits. In addition, simultaneous exposure to sound and vibration may lead to potential interaction effects between the two stimuli, influencing the total annoyance perception. Therefore, a perception experiment was designed to ask the participants about the perceived total annoyance caused by simultaneous noise and seat vibrations in commercial vehicles, presenting modified sound and seat vibrations, recorded inside of a mini-excavator. Multimodal interaction effects of noise and vibrations were investigated by changing the intensity of both stimuli. Furthermore, the influence of low-frequency signal components of the sound signal with and without simultaneous seat vibrations was investigated by specific changes in the sound spectrum.

Time-delay feedback control on vertical vibration of maglev train under unsteady aerodynamic force

The stability and safety of maglev trains are significantly affected by unsteady aerodynamic forces during high-speed operation. In this paper, we employ a time-delay feedback control strategy to suppress the nonlinear vertical vibrations of maglev train under periodic unsteady aerodynamic force. Firstly, the amplitude-frequency response equation of the maglev vehicle is derived using the multiple scales method, and the stability of the steady-state solutions is determined. Then, the influences of velocity feedback gain coefficient and velocity time delay on the nonlinear vibrations of the maglev vehicle are investigated. The optimal value range of the time delay is identified. The results demonstrate that the time delay feedback control can effectively suppress the nonlinear vibration of maglev vehicles and enhance their operational stability under the premise of reasonable selection of time delay parameters. The theoretical research presented in this paper can serve as a theoretical reference for the design, improvement, and optimization of control systems for high-speed maglev trains.

Development of operational tire-suspension contact force analysis using frequency based substructuring

With increase of EVs, we need to improve road noise performance as background noise caused by engines are reduced. On the other hand, to predict road noise performance, it is necessary to consider both tire and vehicle vibration characteristics, and various prediction techniques have been reported. Frequency based substructuring method (FBS) can predict the road noise performance by combining vibration characteristics of components of a vehicle, and is effective for reducing the development cost because it can clarify the development goals of both OEM and suppliers. With this regard, FBS is very useful for implementing MBD in the development of vibration noise. In this paper, for the road noise up to 500 Hz, in which structure-borne noise is the main source, we calculate the coupled vibration characteristics consisting of two subsystems: a tire (including a rigid wheel) and a suspension, by calculating in the rolling state for each subsystem by simulation and combined them by FBS. To evaluate the method, we compared the results with the vibration characteristics of the entire system in the rolling state. In particular, we calculated the transfer function of a tire in a rolling state to take the effect of tire rolling into account .

New Roughness Index of Passenger Car Rotational Vibration

Current road roughness indexes are limited to reflecting the unwanted rotational vibration of vehicles. This study focused on the relationship between the international roughness index (IRI) and rotational vibration around the longitudinal axis (pitch) and horizontal axis (roll) of the vehicle body. Sinusoidal wave input of different wavelengths and phase shifts between the left and right wheel tracks were used as excitation of a full-car model. Marked pitch and roll vehicle vibration were observed at shorter wavelengths (<1 m) and lower speeds (<50 km/h) of harmonic excitation. The IRI was limited to predicting the pitch and roll vibration of the full-car model at lower wavelengths, below 1 m. A simple alternative solution based on the IRI model was proposed as a robust, sensitive tool to detect unwanted rotational vibration of a vehicle. The proposal was based on the IRI approach using an alternative IRI model speed of 30 km/h, instead of the standard 80 km/h. The IRI for 30 km/h was several times more sensitive to pitch and roll vibration in the wavelength band up to 1.5 m than the IRI for 80 km/h. The new approach is recommended as an additional roughness index for monitoring roads with an allowable speed of up to 60 km/h.

Nonlinear Modeling and Analysis of Vehicle Vibrations Crossing Over a Speed Bump

Nonlinear Modeling and Analysis of Vehicle Vibrations Crossing Over a Speed Bump

Energy management optimization of a gravitational energy harvester powering wireless sensor nodes for freight trains monitoring

Energy harvesting is a promising solution for the realization of self-powered wireless sensor nodes (WSNs), minimizing battery waste and environmental impact. The harvesting devices studied in this paper are gravitational vibration-based energy harvesters (GVEHs), converting the ultra-low-frequency ambient vibrations of structures or vehicles into electric power. The main efficiency losses are related to the AC/DC rectification and battery storage processes. Experimental tests confirm the optimized layout of negative voltage converter (NVC) using MOSFETs and a schottky diode with a 1000 μF smoothing capacitor, achieving power rectification efficiency of 65%. The rectified and smoothed power is stored in a Li-Ion 3.6 V 40 mAh coin cell and supplied to the load, consisting of a 3.3 V micro-controller unit, temperature sensor and sub-GHz wireless communication module. A nano power boost charger with buck converter manages power between the battery and the load. Experimental battery charge tests are performed for charging power evaluation at different external excitation amplitudes and frequencies. WSN average power consumption is analyzed with master–slave communication tests at different signal strengths and relative distances between the nodes. Finally, a duty cycle between active and sleep phase is defined to guarantee continuous activity of the WSN and net positive charge to the battery.

Experimental and Simulation Study on Flow-Induced Vibration of Underwater Vehicle

At high speeds, flow-induced vibration noise is the main component of underwater vehicle noise. The turbulent fluctuating pressure is the main excitation source of this noise. It can cause vibration of the underwater vehicle’s shell and eventually radiate noise outward. Therefore, by reducing the turbulent pressure fluctuation or controlling the vibration of the underwater vehicle’s shell, the radiation noise of the underwater vehicle can be effectively reduced. This study designs a cone–column–sphere composite structure. Firstly, the effect of fluid–structure coupling on pulsating pressure is studied. Next, a machine learning method is used to predict the turbulent pressure fluctuations and the fluid-induced vibration response of the structure at different speeds. The results were compared with experimental and numerical simulation results. The results show that the deformation of the structure will affect the flow field distribution and pulsating pressure of the cylindrical section. The machine learning method based on the BP (back propagation) neural network model can quickly predict the pulsating pressure and vibration response of the cone–cylinder–sphere composite structure under different Reynolds numbers. Compared with the experimental results, the error of the machine learning prediction results is less than 7%. The research method proposed in this paper provides a new solution for the rapid prediction and control of hydrodynamic vibration noise of underwater vehicles.

Multimodal Driver Condition Monitoring System Operating in the Far-Infrared Spectrum

Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in driver behavior. Analyzing driver states using visible spectrum imaging is particularly challenging under low-light conditions, such as at night. Additionally, relying on a single behavioral indicator often fails to provide a comprehensive assessment of the driver’s condition. To address these challenges, we propose a system that operates exclusively in the far-infrared spectrum, enabling the detection of critical features such as yawning, head drooping, and head pose estimation regardless of the lighting scenario. It integrates a channel fusion module to assess the driver’s state more accurately and is underpinned by our custom-developed and annotated datasets, along with a modified deep neural network designed for facial feature detection in the thermal spectrum. Furthermore, we introduce two fusion modules for synthesizing detection events into a coherent assessment of the driver’s state: one based on a simple state machine and another that combines a modality encoder with a large language model. This latter approach allows for the generation of responses to queries beyond the system’s explicit training. Experimental evaluations demonstrate the system’s high accuracy in detecting and responding to signs of driver fatigue and distraction.

Prediction of rail wear band evolution under excitation by periodic track irregularities and its influence on vehicle dynamic performance

Periodic track irregularities (PTIs) are an important and sensitive source of vehicle vibration and have attracted considerable attention from railway researchers. However, previous studies have mainly focused on the irregularities themselves, with limited investigation of how the rail wear band (RWB) generated by PTIs excitation affects vehicle responses. To assess this, a three-dimensional rail wear prediction model is developed in this study to simulate the evolution of the RWB under PTIs conditions and then the effect of RWB excitation on the vehicle dynamic performance is investigated. The results show that the spatial trace of the rail contact points generated by the PTIs also exhibits clear periodic characteristics. Accordingly, the distribution pattern of the RWB also reflects these periodic features. The excitation of the periodic rail wear band (PRWB) has a negative effect on the vehicle running stability, especially when the vehicle is traveling at speeds that induce coupling resonance, which exacerbates the consequences. As the wear level of the PRWB increases, there is also an escalation in the vibration of the carbody. Furthermore, the amplitude of the change in the wheelset balance position resulting from variations in rail profile is found to serve as an effective descriptor for characterizing the wear intensity of PRWB, and the vibration amplitude of the carbody exhibits a clear linear correlation with PRWB excitation. The research results are helpful for understanding the relationship between PRWB excitation and vehicle vibration behavior and serve as a theoretical basis for track maintenance and profile optimization.

Learning Position From Vehicle Vibration Using an Inertial Measurement Unit

Learning Position From Vehicle Vibration Using an Inertial Measurement Unit

Experimental and numerical analyses on the modal behaviour of an aluminium alloy sheet

Abstract The study of modal behaviour for vehicle body panels represents a field of interest for automotive engineering, having a major influence on automotive comfort, with respect to vehicle noise, vibration and harshness, but also concerning the durability of vehicle body. The investigation of natural frequencies and vibration mode shapes is crucial in the design phase, because essential information can be obtained concerning the behaviour of the components in dynamic conditions. The current paper focuses on the investigation of vibration modes and frequency response function on an aluminium alloy sheet. The aim of the paper is to perform the study of modal behaviour both experimentally and theoretically, through measurements using the impact hammer method and respectively through finite element analysis. The CAD model of the aluminium alloy sheet has been developed in Catia V5 environment in view of elaborating the finite element model, which has been used for investigation of modal behaviour, taking into account the case of free vibrations. Through the modal finite element analysis performed in Ansys Workbench, the natural frequencies of the aluminium alloy sheet, as well as the corresponding mode shapes have been obtained. Experiments have been performed on an aluminium alloy sheet, mounted on an elastic support with low natural frequency. The impact hammer method has been used for obtaining the natural frequencies, through frequency analysis of accelerations measured with two piezoelectric accelerometers mounted on the aluminium alloy sheet. The frequency response function has been computed from the response acceleration measured on the aluminium alloy sheet and the impact force measured using the force transducer of the impact hammer. Virtual instruments have been developed in LabVIEW environment for data acquisition, time domain and frequency analysis, as well as for determining the frequency response function using the measured signals. The natural frequencies obtained from the finite element analyses have been compared to the results of measurements and very good similarities have been ascertained. The experimental investigation of modal behaviour contributed to the validation of the finite element model, but further research is required to investigate the possibilities of improving the modal behaviour of the aluminium alloy sheet.

Analysis of human body vibration response on high-speed trains under crosswinds

The increase in the speed of high-speed trains and the widespread use of lightweight vehicle body technology have made vehicle systems more sensitive to aerodynamic excitation. However, few studies have focused on the worsening of passenger ride comfort, as opposed to just safety, when trains pass along windy railway lines. This study first analyzes the connection between flow structure and aerodynamic load, using bidimensional empirical mode decomposition (BEMD). Then, it examines the effect of aerodynamic loads on the vibration characteristics of vehicles and human bodies, at different speeds under crosswinds, by combining a vehicle-track-seat-human body coupling model. Finally, the effects of crosswinds on vehicle and human body vibration are analyzed, including differences in vibration characteristics of the human head when in different positions. The BEMD results indicate that the flow field around the vehicle is dominated by low-frequency oscillation. These low-frequency components show close alignment with the aerodynamic loads’ main frequency, and the high-speed trains’ natural frequency, which may lead to vehicle resonance shaking, and to derailment. Additionally, it is found that lateral vibration comfort for human legs and thighs is worst in both the head and middle cars, while vertical vibration comfort for the human head is worst in the head car. When running at three speeds under crosswinds, fluctuations in aerodynamic loads and track irregularities are major causes of discomfort for humans, particularly affecting vertical vibration comfort. In crosswinds, vertical vibration of the human head is significantly greater than lateral vibration, indicating a loss of comfort, primarily in the vertical direction, when trains are excited by crosswinds. It is observed that lateral vibration comfort is worst at the front end of the head car, but best at their rear end. Vertical vibration comfort is best in the middle section of the head car.

Stochastic optimization of levitation control system for maglev vehicles subjected to random guideway irregularity

The dynamic performance of maglev vehicle-bridge coupled systems subjected to random guideway irregularity, especially the levitation stability of the running vehicle subsystem, is a critical issue affecting the safe operation and the comfort riding of maglev railways. This study is devoted to stochastic optimization of the levitation control system considering the coupled vibration of maglev vehicle and bridge under the random guideway irregularity. To alleviate the computational burden in the stochastic optimization process, the explicit expressions of the critical responses of the maglev vehicle-bridge coupled systems and the sensitivities of critical responses with respect to the feedback gains of the proportional-integral-derivative (PID) levitation controller are respectively constructed in terms of the guideway irregularity by virtue of the dimension-reduced formulation capability of the explicit time-domain method (ETDM), enabling stochastic dynamic analysis and stochastic sensitivity analysis to be executed at high efficiency. The stochastic optimization problem is formulated as the minimization of the standard deviation of the air gap variation with the constraint on the standard deviation of the current variation, and the optimal values of the PID gains are searched by the gradient-based method of moving asymptotes (MMA), in which the statistical moments and moment sensitivities of the critical responses required in the optimization process are obtained by ETDM. A numerical example with a 2-degree-of-freedom maglev vehicle moving on a multi-span simply-supported bridge is used to demonstrate the efficacy of ETDM for the stochastic analysis of maglev vehicle-bridge coupled systems under guideway irregularity. An engineering example involving a 3-car maglev train traversing a multi-span simply-supported bridge is further presented to show the effectiveness of the ETDM-based approach for the stochastic optimization of large-scale engineering problems. The levitation stability of the maglev train under the action of random guideway irregularity is considerably improved with the use of optimal feedback gains.