Vehicle Vibration Research Articles

Evaluation of Ride Comfort under Vortex-Induced Vibration of Long-Span Bridge

The increasing number of suspension bridges has led to designs favoring greater length and flexibility, resulting in a common problem of vortex-induced vibration. While vortex-induced vibration typically does not cause structural damage, it diminishes the bridge’s fatigue resistance and has a detrimental impact on ride comfort. Additionally, this study introduces a road–bridge–vehicle vibration model, proposing an evaluation method for assessing ride comfort during vortex-induced vibrations in long-span bridges. This method features simplified modeling and swift calculations, circumventing the need for intricate finite element modeling and iterative solving. Furthermore, it evaluates ride comfort for vehicles crossing a prototype long-span suspension bridge using the Overall Vibration Total Value (OVTV) and Motion Sickness Incidence (MSI) criteria. This study also analyzes the influence of various parameters on OVTV and MSI, including vehicle speed, road grade, vortex-induced vibration frequency, and amplitude. It establishes a reference limit for vortex-induced vibration amplitude based on OVTV and MSI values. Moreover, the study substantiates that, within the context of vortex-induced vibration, the MSI value is more suitable for evaluating driving comfort compared to the OVTV.

Bridge Frequency Identification Using Cross-Power Spectra of Vehicle Vibrations from Multiple Moving Speeds

In recent years, a rapid bridge health monitoring technology has been developed using an instrumental moving vehicle. Using recorded vehicle vibration data, bridge frequencies are identified for bridge health monitoring or finite element model updating. Target bridge frequencies with significant amplitudes in the vehicle’s vibration frequency spectra are expected to be found. However, in the coupled vehicle–bridge interaction (VBI) system, bridge vibration-relevant vehicle dynamics might not be noticeable. The bridge frequency would be difficult to identify because of the potential influence of road roughness. To resolve this difficulty, a novel bridge frequency identification method is proposed to mitigate the negative effects of road roughness. First, theoretical derivations are done to ascertain the VBI system dynamic characteristics considering road surface roughness. Our findings showed that the road roughness-relevant vehicle dynamics are closely related with the traveling speed, whereas the bridge frequency remains approximately constant. Theoretical investigations indicated that cross-power spectra between vehicle dynamics at multiple moving speeds are effective to mitigate the negative effects of road roughness. Presumably, it is feasible to identify the target bridge frequency from the cross-power spectra. Both the dynamic characteristics of the VBI system and the effectiveness of the proposed method for bridge frequency identification were examined using finite element simulations and laboratory experiments. Compared to existing methods, the proposed method is widely applicable to real-world situations and difficulties.

Applying a PID-SMC synthetic control algorithm to the active suspension system to ensure road holding and ride comfort.

Vehicle vibration has an essential effect on vehicle stability and smoothness. This article introduces a new solution to direct an active suspension system called the PID-SMC hybrid algorithm. In this work, a dynamic model is considered with external disturbances and parametric uncertainties. Besides, the design process of the hybrid controller is also clearly shown. Different from previous studies, this controller is built upon the synthesis of two component signals which are generated from two separate controllers. The error signals of the two-component controllers are derived from the results of the body displacement and acceleration measured by the sensors. A simulation process is done by the MATLAB software to evaluate the system's quality. Two cases are used for the simulation, including four scenarios examined for each case. Based on the results obtained from the simulation and calculation technique, the acceleration and displacement values of a vehicle body were greatly decreased once the PID-SMC method was used, compared with the rest of situations. In the first case, the maximum value of the acceleration is only 0.54 (m/s2), while the average value and the RMS value are 0.06 (m/s2) and 0.07 (m/s2), respectively. In the second case, the maximum value of vehicle body displacement is only 9.54 (mm), only 8.75%, compared to cars with only mechanical suspensions. Besides, the change in the dynamic load at the wheel is also relatively small. Therefore, the road holding and ride comfort of the automobile has been improved. In the near future, this algorithm will be combined with intelligent control algorithms to apply to many different types of random stimuli from the road surface.

Identification of asphalt pavement transverse cracking based on 2D reconstruction of vehicle vibration signal and edge detection algorithm

Transverse cracking is related to asphalt pavement structural performance. However, the method for transverse cracks identification by vehicle vibration signal has hardly been applied because of an extremely low recognition accuracy. The study aims to precisely identify the pavement transverse cracks by vehicle vibration response. A two-dimensional reconstruction method that transformed the vehicle vibration signal into an image was proposed. Five edge detection operators were used to generate the features of vibration reconstruction images. Besides, this study combined two basic characteristics, Euler number, and total target area. Eight classification models based on the above two features were trained by the RUSBoost integrated classifier. The results showed that the classification model based on the Prewitt operator eight connected domain Euler number and the total target area performed excellently. The testing accuracy can reach 93.5%. The classification model presented in this study has an excellent effect on pavement transverse crack identification and feasibility.

Active torsional vibration suppression of hybrid electric vehicle drive system based on optimal harmonic current instruction calculation method

Torsional vibrations in the drive system of a hybrid electric vehicle powered by an engine are generated by cyclic torque fluctuations. These fluctuations stem from the combustion torque and the reciprocating inertial torque of the cylinder in the reciprocating piston engine. To address this issue, a real-time engine transient torque model that considers the thermodynamic characteristics of each component is established. A method was designed to identify the torsional vibration of a powertrain based on the engine torque, electric motor torque, and powertrain model. Building on this foundation, a method was proposed to calculate the optimal harmonic current instruction of the electric motor to mitigate torsional vibration in the Hybrid electric vehicles (HEV) powertrain. Furthermore, a proportional integral-resonance (PIR) controller was designed for the injection of an electric motor reference harmonic current. Finally, a method was proposed for suppressing the torsional vibration in HEV drive systems that incorporate active harmonic current injection. The simulation and test results indicate that the proposed method can effectively diminish the amplitudes of the drive system's 2nd, 4th, 6th, and 8th harmonic torque, ensuring stable operation of the powertrain.

An approach of on-board multistage health monitoring for heavy haul railway bridge using bogie acceleration

Recently on-board damage detection becomes a promising strategy in the field of bridge structural health monitoring. Due to the randomness of track irregularity and the weak vehicle vibration feedback from the bridge damages, it is difficult to effectively extract the damage characteristics from vehicle responses. This paper proposes an innovative multistage health monitoring approach with bogie accelerations for heavy haul railway bridge, which considers the influence of track irregularity and realizes the localization and quantification of bridge damage. Firstly, the bogie accelerations are preprocessed by the presented time-domain correlation analysis denoising method (TCADM) to suppress the unfavorable interference of track irregularity. Secondly, the Empirical Mode Decomposition (EMD) is exploited to decouple the effect of vibration coupling among bridge spans on bogie accelerations, and a confidence classification threshold related to Mahalanobis distance is defined by the Gaussian inverse cumulative distribution function to detect damage. Thirdly, the sliding window strategy is utilized to localize damaged region, realizing a trade-off between time cost and localization accuracy by the custom window length. Fourthly, the classification threshold and the k-means clustering analysis are combined to quantify the damage severity. The effectiveness and robustness of the proposed framework are appraised using blind tests, where one monitor operator simulates various damaged conditions by combining a 2D train-track-bridge coupling model with the Monte Carlo Sampling Method, and the other estimates the bridge health state using the provided bogie dataset without any prior knowledge. The results indicate that the proposed framework performs well in detecting, localizing and quantifying damage.

Analysis of Acoustic Flow Field Characteristics of Key Components of High-Speed Train Bogies

In this paper, we aim to address the challenge of airflow interference during fault detection in high-speed train bogies by introducing a flow field and investigating the characteristics of the sound field distribution of critical components under its influence. This approach overcomes the limitation observed in previous studies that ignored the impact of the flow field. Furthermore, we evaluate and compare various layouts for inter-track acoustic sensor arrays. The study results demonstrate that the axle box and gearbox are primarily affected by frequency bands above 200 Hz, while the motor is mainly influenced by frequencies above 150 Hz. Moreover, the motor is most significantly impacted by vehicle speed, followed by the gear box and finally the shaft box. When altering the vibration frequency of the sound source, there is a decreasing trend in minimum sound pressure level with increasing frequency. Additionally, the gradient of the sound pressure level change at three locations (motor, gear box, and axle box) exceeds that of both vehicle speed variation and vibration intensity of the sound source. By adjusting the vibration intensity of the sound source, similar gradients are observed for both maximum and minimum values of sound pressure level at these three locations. Therefore, vibration intensity has a consistent effect on changes in sound pressure level at these three places but only influences its magnitude. The aforementioned methods can be employed for the analysis of wind noise and structural noise, thereby offering novel insights for future advancements in noise reduction processing.

The Dynamic Train–Track Interaction on a Bridge and in a Tunnel Compared with the Simultaneous Vehicle, Track and Ground Vibration Measurements on a Surface Line

The vehicle–track interaction generates forces and consequently vibrations in the environment. The interaction has been analysed by the simultaneous measurements of vehicle, track and ground vibrations during test runs with varied train speeds. The special effects of the passage over a bridge and through a tunnel are studied and compared with the measurements on a conventional ballasted surface line. The maximum amplitudes, narrow band and one-third octave band spectra are presented for the axle-box accelerations and for the track, bridge and ground vibrations. The different frequencies and frequency bands are related to wheel out-of-roundness, track alignment errors, the sleeper passage and the wheelset–track resonance. An axle impulse component has been observed at the track, at the near-field soil and as a scattered version in the far field. Specific results can be found for the bridge track, where clearly speed-dependent bridge resonances occur due to the axle sequence of the train, and for the tunnel track where soft rail pads are responsible for a strong amplification around the wheelset–track resonance. On the other hand, the axle impulses are strongly reduced by the tunnel track, and the scattered axle impulse component is not as relevant as for the surface track. As a consequence, a strong mid-frequency amplitude reduction of the tunnel compared to the surface line has been measured for low and high train speeds by the Federal Institute of Material Research and Testing (BAM) and by other institutes.

Asphalt pavement surface repair area detection based on smartphone sensors

Asphalt pavement repair areas affect pavement performance and service levels. It is necessary to distinguish the repair areas from normal sections. Based on vehicle vibration signals, this study identified ten pavement repair areas and divided them into four cases by factors including length and form in conjunction with the driving approach. Additionally, time domain analysis, frequency analysis, and probability distribution analysis were used to form the characteristics of the repair cases as well as the normal sections. It was found that the maximum value, extreme deviation, standard deviation in the time domain, maximum amplitude in the frequency domain, and peak of the probability density curve would serve as judgment indexes. A framework for identifying the repair areas was also established based on the five indexes. By validation, the overall accuracy can reach 95.0%, demonstrating a strong generalization capability.

A study of the axle box ASD spectrum of high-speed rail vehicles based on field test measurements in China

The axle box ASD (Acceleration Spectral Density) spectrum serves as the main resource of high frequency vibration for rail vehicles, which is usually taken as a critical load spectrum for the evaluation of vibration fatigue of rail vehicle sub-assemblies. However, the continuous in-service failures occurring in a plenty of rail vehicles invariably suggested a low reliability for the ASD defined in IEC 61373 standard for some scenarios. This paper thus investigated on the characteristics of axle box ASD spectrum through measurements acquired from a number of high speed rail vehicles in China, so as to develop a more realistic vibration spectrum for the considered operation condition. Firstly, a brief review for typical fatigue failures of rail vehicles was conducted to understand fatigue failure modes caused by the high frequency vibration of wheel/rail system. Subsequently, the definition of axle box ASD spectra in IEC 61373 was analyzed and further compared with the ASD spectra of axle box obtained from measurements to demonstrate the reliability of standard vibration level. The results suggest that the ASD spectrum given in IEC 61373 underestimates the vibration level in high frequency ranges, especially the frequency band above 500 Hz. Finally, a modified ASD spectrum profile of axle box considering the characteristic frequency bands of wheel/rail system was proposed to better representation of vibration characteristics of axle box, especially for those arising from the wheel/track vibration modes. This is expected to better characterize the vibration characteristics of axle box for rail vehicles, and improve the estimation accuracy of fatigue lifetime for the components subjected to the axle box vibration.

Vibrations control of railway vehicles using decentralized proportional integral derivative controller with flow direction optimization algorithm

The reduction of vibration-induced discomfort in vehicles is an important goal in the field of transportation engineering. Several mathematical models with various controlling techniques, from classical to modern, have been employed to achieve better ride comfort. Still, no comprehensive solution has yet been found. Therefore, this paper proposes a 17-degree-of-freedom (minimum number of coordinates) dynamic model of a full-scale railway vehicle integrated with wheel-rail contact forces and an active suspension system. Two controllers, termed system and force tracking controllers, suppress the vehicle body's vibrations. Based on a multi-loop control structure, three optimally tuned Proportional Integral Derivative controllers evaluate the desired control forces and performs the system controller’s action. While the force-tracking controller generates the command voltage to track that forces. The parameters of controllers are tuned with a novel metaheuristic optimization algorithm known as the flow direction algorithm (FDA), and the results are compared with two other optimization techniques, i.e., particle swarm optimization and ant colony optimization. The simulated results show that the ride comfort of the vehicle is improved with FDA, as the root mean square values of the lateral, roll, and yaw accelerations are reduced by 42.01%, 33.12%, and 48.24%, respectively. Moreover, the simulated results of the proposed model are validated with the experimental results of accelerations. The simulated results show that the proposed system tuned with the metaheuristic algorithm outperforms with a significant reduction in vehicle vibrations.

Bridge frequency identification in city bus monitoring: A coherence-PPI algorithm

Recently, drive-by-bridge inspection methods have attracted substantial scholarly interest; however, their practical implementation has encountered significant challenges. In engineering practice, even simply extracting bridge frequencies from ordinary or commercial vehicles appears to be difficult; components related to factors such as road roughness often dominate vehicle vibration responses. This study proposes a novel coherence-PPI (Prominent Peak Identification) algorithm based on the Bayesian framework and applies it to city bus bridge monitoring to extract bridge frequencies. The fundamental idea is to recognize the bridge frequency as a common vibration component across various vehicle runs. The algorithm comprises the following three steps: First, coherences were computed for all vehicle runs to interpret the signal relationships. Second, a Bayesian framework was established to statistically determine the threshold that can maximize the occurrence of bridge frequency. Third, the prominent peaks (PPs) were selected based on the threshold, and their distribution was counted to identify the fundamental frequency of the bridge. In contrast to the previous studies that focused on avoiding differences (e.g., by trying to reduce variation, driving in the same lane, and using the same speed), this methodology encourages the introduction of variability in drive-by measurements to filter bridge frequencies, rendering it particularly compelling for practical engineering applications. The proposed methodology was validated through numerical studies, including the Monte Carlo method, and field tests using city buses. The results demonstrated that the method can effectively eliminate the effects of road roughness, environmental noise, and vehicle parameter variations and accurately identify the bridge frequency.

A deep‐learning framework for classifying the type, location, and severity of bridge damage using drive‐by measurements

AbstractThis paper proposes a new deep‐learning framework for drive‐by bridge condition monitoring. The proposed approach represents a bridge monitoring regime that enables the presence, type, location, and severity of bridge damage to be identified purely from measurements taken on a passing vehicle, without needing any pre‐measured training data. The computational framework adopts a numerical vehicle–bridge interaction (VBI) model, which is automatically calibrated using only the vehicle vibration measurements. The calibrated model is used to generate labeled training data, eliminating the practical difficulties associated with collecting training data for damaged bridge scenarios. The numerically simulated data are used to train a convolutional neural network that can then classify the actual damage characteristics of a bridge. The method is tested using a laboratory‐scale VBI model, and results show that the algorithm can accurately identify the presence of seized bearings and cracking in the bridge beams. The algorithm shows good accuracy when identifying the type and extent of damage. The location of bearing damage can generally be identified; however, the location of cracking is less accurate at lower damage levels. The proposed framework represents a significant improvement on existing techniques for indirect bridge monitoring.

Reducing vehicle-bridge coupled vibration of a new-type maglev vehicle with mid-mounted suspension on curve via forced guidance device

To reduce the vehicle-bridge coupled vibration of a new-type maglev vehicle with mid-mounted suspension (MVMS) on a curve and increase its curve-passing speed, the forced guidance device (FGD) for MVMS is designed and the effectiveness of the FGD is proved. Firstly, on the basis of the FGD for HSST (High Speed Surface Transport) maglev vehicle and the geometric relationship between the movable sliders and the cabin as MVMS passes through a curved track, the FGD scheme for MVMS was designed. Secondly, the dynamic models with 5-bogies MVMSs (with and without FGD), control system, 11 flexible bridges, and track irregularities were established. Finally, the vehicle-bridge coupled vibration response of MVMSs on the curved track was analysed. Results show that the accelerations of the bridges, levitation modules, and cabin can be reduced significantly by installing FGD, especially at higher speeds. Compared to the accelerations without FGD, the maximum lateral and vertical accelerations of each bridge midspan can be reduced respectively by 56% and 43% at the speed of 45 km/h, those of the levitation modules can be reduced by 48% and 90%, respectively, and those of the cabin can be reduced by 37% and 53%, respectively.

Design and Optimization of Hydropneumatic Suspension Simulation Test Bench with Electro-Hydraulic Proportional Control

Available hydropneumatic suspension simulation test benches have insufficient loading accuracy and limited functionality rendering them unsuitable for performance testing of heavy vehicles with this type of suspension. Therefore, a multi-functional compound simulation test bench was designed that used an electro-hydraulic proportional control technique. A mathematical model was established to describe the hydraulic loading system, and the transfer function of the system was derived. The gain and phase margins confirmed the stability of the system. A simulation model was established in the Simulink environment and step and sine signals of different frequencies were applied separately to analyze the dynamic characteristics of the system. The results showed that the system responded slowly and exhibited phase lag and signal distortion. The dynamic characteristics of the system were improved by incorporating an adaptive fuzzy PID controller. Simulation results showed that the response of the system to the step signal stabilized at the preset value within 0.3 s with no oscillation or overshoot. The improved system performed well in replicating the random vibrations of heavy vehicles operating on Class B and C roads. This confirmed that the system can satisfy the loading requirements of heavy vehicle hydropneumatic suspensions and can be used as a simulation test bench for such suspensions.

Loading rate effect on mixed-mode I + II fracture of V-notched Brazilian disc rock specimen under impact loads

Dynamic failure processes of rock masses containing numerous discontinuities including pores, cracks and notches have been the major issues in structural safeties of underground buildings subjected to impact loads, such as earthquake, blast and vehicle vibration. At present, most of the studies are focused on the dynamic fracture behaviors of rocks containing crack-like defects, while the studies on rocks containing notch-type defects are very rare, especially under mixed-mode loading conditions. This paper aims to study the effects of loading rate on mixed-mode I + II fracture characteristics of flattened V-notched Brazilian disc rock specimen under impact loads by means of an split Hopkinson pressure bar (SHPB) system combined with a high-speed camera. Dynamic fracture tests were performed on a total of 125 groups of V-notched rock specimens with different notch angles (0°, 30°, 45°, 60°, and 90°) and loading angles (0°, 30°, 45°, 60°, and 90°) at different air pressures (0.16 MPa, 0.18 MPa, 0.20 MPa, 0.22 MPa, and 0.24 MPa). Based on dimensional analysis, a numerical method was proposed to establish the relationship between mixed-mode notch stress intensity factor (NSIF) at the V-notch tip and external axial load. The dynamic notch fracture toughness values of flattened V-notched Brazilian disc rock specimens were calculated from the maximum values of dynamic force in the SHPB tests according to the proposed numerical method. The effects of loading rate, notch angle, and loading angle on the mixed-mode I + II notch fracture toughness value and crack initiation angle were quantitatively analyzed, which were verified using a modified maximum tangential stress (MTS) criterion by considering the loading rate effect. It is found that the loading rate has a significantly positive effect on the notch fracture toughness value, but has a negative effect on the crack initiation angle due to the inertial effect under dynamic impact loads. Generally, pure mode I fracture occurs for the loading angle of 0° or 90°, pure mode II fracture occurs for the loading angle varying from 35° to 45°, and mixed-mode fracture occurs for other loading angles. Good consistencies of the mixed-mode I + II notch fracture toughness values and crack initiation angles of the V-notched rock specimens are observed between the modified MTS criterion prediction and experimental results. This work may improve the understanding of the dynamic fracture behaviors of rock masses containing notch-type defects during tunnel excavation and underground construction process.

EFFECTS OF GENERAL ASYMMETRIES ON THE ROAD VEHICLE LATERAL VIBRATION

The mechanistic deterioration of pavements poses an eccentric kinematic excitation to the vehicle wheels, a situation that amplifies the lateral vibration of the vehicles. Additionally, asymmetric loading of these vehicles translating over the pavement potholes could shift the centre of mass away from the centre of geometry in the lateral direction. Most revised works of literature seem to have not investigated the effects of general asymmetries on lateral vibration separately. This study modelled the effects of combined general asymmetries on the lateral vibration of full car sprung mass. About 27 general symmetrical (control model) and asymmetrical 3D 18 DOF models were developed using SIMWISE 4D and investigated. Depending on the type of loading configuration, it was found that the lateral dynamic response of the sprung mass is seemingly defined by the nature of the kinematic excitation. The developed study models could be used in the analysis of vehicle accidents and the study findings could contribute to an understanding of full vehicle vibrations.

Modelling and Dynamic Analysis of Adaptive Neuro-Fuzzy Inference System-Based Intelligent Control Suspension System for Passenger Rail Vehicles Using Magnetorheological Damper for Improving Ride Index

The ride comfort and safety of passenger rail vehicles depend on the performance of the suspension system in attenuating vibrations induced by track irregularities. This paper investigates the effectiveness of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based semi-active controlled suspension system using a magnetorheological fluid damper in reducing nonlinear lateral vibrations of a passenger rail vehicle. A complete rail vehicle model is developed, including the carbody, front and rear bogies, and the passive suspension system’s nonlinear stiffness and damping characteristics are considered from experimental data. The passive suspension model is validated through experiments, and an ANFIS-based controller is incorporated with the secondary vertical suspension system to improve ride behavior. Three semi-active suspension strategies are considered under varying speeds and track irregularities, and their effectiveness is compared to the nonlinear passive suspension system in terms of rms acceleration, rms displacement, ride quality, and comfort. The results shows that the ANFIS-based semi-active suspension system with a magnetorheological fluid damper outperforms the passive suspension system and semi-active strategies in all tested conditions. There is a reduction in rms acceleration by approximately 11.11% to 23.64% and rms displacement by about 5.36% to 32.06%. Moreover, it significantly improves ride quality (9.20% to 31.02%) and comfort (9.96% to 31.50%). The rms acceleration and displacement are reduced, and the Sperling ride index and Percentage Reduction Index values demonstrate that the ANFIS-based semi-active suspension effectively minimizes the impact of rail irregularities and vibrations, resulting in a significant gain in ride quality and passenger comfort.

Research on Stability Control Technology of Hazardous Chemical Tank Vehicles Based on Electromagnetic Semi-Active Suspension

Liquid sloshing in the tank can seriously affect the stability of hazardous chemical tanker trucks during operation. To this end, this paper proposes a solution based on an electromagnetic semi-active suspension system to prevent chemical spills and ensure safe driving of hazardous chemical tank vehicles. A comprehensive investigation was conducted across four domains: theoretical research, simulation model establishment, co-simulation platform construction, and simulation data analysis. Three fuzzy controllers were used to suppress the vibration of the tank vehicles, and a simulation study of the stability control of the tank vehicles under electromagnetic semi-active suspension was carried out. The results show that the electromagnetic semi-active suspension can significantly reduce the vertical, pitch, and roll vibrations of the tank vehicles by 17.60%, 25.78%, and 27.86%, respectively. The research results of this paper are of great significance for improving the safety and stability of hazardous chemical tanker trucks.

Assessment of noise and vibration characteristics of Botryococcus braunii microalgae and hydrogen blends in internal combustion engines for highway vehicles

Extensive study has revealed biodiesel as a promising fuel obtained from a variety of sources, with its availability being a significant benefit. While biodiesel's performance and emission characteristics have been widely researched and proved suitable as a fuel, the most important aspects of noise and vibration characteristics in highway vehicles is remain unknown. In this study the effect of Botryococcus braunii microalgae on the four stoke diesel engine has been studied under the highway vehicle loading conditons. The biodiesel blends used in this study were B10% (10% of Botryococcus braunii biodiesel + 90% of pure diesel) and B20% (20% of Botryococcus braunii biodiesel + 80% of pure diesel). In addition to the biodiesel blends and neat diesel, the hydrogen was also supplied at the flow rate of 5 lpm. In order to avoid the backfire, flashback arrestor had been connected in the fuel inline manifold. The results obtained by using pure diesel were taken as reference and the biodiesel blend results were compared with them. A series of test conducted on the blends at five different loads of 0%, 25%, 50%, 75% and 100%. Sound pressure sensor and accelerometer were used to measure noise and vibration with respect to engine loads. Addition of hydrogen reduced the mass fraction in all blends, regardless of the fuel type. Hydrogen aids the combustion process thus there is an significant reduction in both highway vehicles engine noise and vibration has been noted irrespective of operating conditions. From the results, it is clear that the addition of hydrogen in both biodiesel and diesel showed a significant reduction in sound and acceleration. This finding supports the promising potential of microalgae blends as viable alternatives for highway vehicles.