Relationships Between Vehicle Speed, Ride Quality, and Road Roughness

2008 ◽  
pp. 89-89-22 ◽  
Author(s):  
WDO Paterson ◽  
T Watanatada
Keyword(s):  
Ride Quality ◽  
Vehicle Speed ◽  
Road Roughness

Energy Harvesting, Ride Comfort, and Road Handling of Regenerative Vehicle Suspensions

2011 ◽  
Author(s):  
Lei Zuo ◽  
Pei-Sheng Zhang
Keyword(s):  
Energy Harvesting ◽  
Ride Comfort ◽  
Average Power ◽  
Great Influence ◽  
Vehicle Body ◽  
Ride Quality ◽  
Vehicle Speed ◽  
Road Roughness ◽  
Tire Stiffness ◽  
Suspension Stiffness

This paper presents a comprehensive assessment of the power that is available for harvesting in the vehicle suspension system and the tradeoff among energy harvesting, ride comfort, and road handing with analysis, simulations and experiments. The excitation from road irregularity is modeled as a stationary random process with road roughness suggested in the ISO standard. The concept of system H2 norm is used to obtain mean value of power generation and the root mean square values of vehicle body acceleration (ride quality) and dynamic tire-ground contact force (road handling). For a quarter car model, analytical solution of the mean power is obtained. The influence of road roughness, vehicle speed, suspension stiffness, shock absorber damping, tire stiffness, wheel and chasses masses to the vehicle performances and harvestable power are studied. Experiments are carried out to verify the theoretical analysis. The results suggest that road roughness, tire stiffness, and vehicle driving speed have great influence to the harvesting power potential, where the suspension stiffness, absorber damping, vehicle masses are insensitive. At 60mph on good and average roads 100–400 watts average power is available in the suspensions of a middle-size vehicle.

scholarly journals Road Roughness Estimation Based on the Vehicle Frequency Response Function

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 89
Author(s):  
Qingxia Zhang ◽  
Jilin Hou ◽  
Zhongdong Duan ◽  
Łukasz Jankowski ◽  
Xiaoyang Hu
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Gaussian White Noise ◽  
Estimation Method ◽  
Time Shift ◽  
Ride Quality ◽  
The Road ◽  
Road Roughness ◽  
Measured Response

Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness.

scholarly journals A new approach to assess the influence of road roughness on driver speed behavior based on driving simulator tests

2016 ◽  
Vol 11 (2) ◽  
pp. 144-152 ◽  
Author(s):  
Mariano Pernetti ◽  
Mauro D’Apuzzo Mauro D’Apuzzo ◽  
Francesco Galante
Keyword(s):  
Driving Simulator ◽  
Experimental Studies ◽  
Vertical Vibration ◽  
The Other ◽  
Added Value ◽  
Published Data ◽  
Vehicle Speed ◽  
Safety Level ◽  
The Road ◽  
Road Roughness

Vehicle speed is one of main parameters describing driver behavior and it is of paramount importance as it affects the travel safety level. Speed is, in turn, affected by several factors among which in-vehicle vibration may play a significant role. Most of speed reducing traffic calming countermeasures adopted nowadays rely on vertical vibration level perceived by drivers that is based on the dynamic interaction between the vehicle and the road roughness. On the other hand, this latter has to be carefully monitored and controlled as it is a key parameter in pavement managements systems since it influences riding comfort, pavement damage and Vehicle Operating Costs. There is therefore the need to analyse the trade-off between safety requirements and maintenance issues related to road roughness level. In this connection, experimental studies aimed at evaluating the potential of using road roughness in mitigating drivers’ speed in a controlled environment may provide added value in dealing with this issue. In this paper a new research methodology making use of a dynamic driver simulator operating at the TEST Laboratory in Naples is presented in order to investigate the relationship between the driver speed behavior on one hand, and the road roughness level, road alignment and environment, vehicle characteristics on the other. Following an initial calibration phase, preliminary results seem fairly promising since they comply with the published data derived from scientific literature.

Predicting Truck Load Variation Using Q-Truck Model

2014 ◽  
Vol 534 ◽  
pp. 105-110
Author(s):  
Rosnawati Buhari ◽  
Mohd Ezree Abdullah ◽  
Munzilah Md Rohani
Keyword(s):  
Asphalt Concrete ◽  
Radial Strain ◽  
Suspension System ◽  
Vehicle Speed ◽  
Pavement Distress ◽  
Traffic Loads ◽  
Road Roughness ◽  
Heavy Truck ◽  
Truck Load ◽  
Pavement Damage

The study of heavy vehicle forces on pavement is important for both vehicle and pavement. Indeed it was identified several factors such as environment, materials and design consideration affects pavement damage over time with traffic loads playing a key role in deterioration. Therefore, this paper presents dynamically varying tire pavement interaction load, thus enable to assess the strain response of pavements influenced by road roughness, truck suspension system, variation of axle loading and vehicle speed. A 100m pavement with good evenness was simulated to check the sensitivity of the dynamic loads and heavy truck vertical motions to the roughness. The most important performance indicators that are required in pavement distress evaluation are radial strain at the bottom of the asphalt concrete and vertical strain at the subgrade surface was predicted using peak influence function approach. The results show that truck speed is the most important variables that interact with truck suspension system and thus effect of loading time are extremely important when calculating the critical.

Adaptive Amplifier for a Test Vehicle Moving Over Bridges: Theoretical Study

2020 ◽  
pp. 2150042
Author(s):  
Y. B. Yang ◽  
Z. L. Wang ◽  
K. Shi ◽  
H. Xu ◽  
J. P. Yang
Keyword(s):  
Dynamic Responses ◽  
Vehicle Speed ◽  
Test Vehicle ◽  
Vehicle Performance ◽  
Single Degree Of Freedom ◽  
Closed Form Solutions ◽  
Numerical Studies ◽  
Element Simulation ◽  
Road Roughness ◽  
Overall Performance

A vibration amplifier is first proposed for adding to a test vehicle to enhance its capability to detect frequencies of the bridge under scanning. The test vehicle adopted is of single-axle and modeled as a single degree-of-freedom (DOF) system, which was proved to be successful in previous studies. The amplifier is also modeled as a single-DOF system, and the bridge as a simple beam of the Bernoulli–Euler type. To unveil the mechanism involved, closed-form solutions were first derived for the dynamic responses of each component, together with the transmissibility from the vehicle to amplifier. Also presented is a conceptual design for the amplifier. The approximations adopted in the theory were verified to be acceptable by the finite element simulation without such approximations. Since road roughness can never be avoided in practice and the test vehicle has to be towed by a tractor in the field test, both road roughness and the tractor are included in the numerical studies. For the general case, when the amplifier is not tuned to the vehicle frequency, the bridge frequencies can better be identified from the amplifier than vehicle response, and the tractor is helpful in enhancing the overall performance of the amplifier. Besides, the amplifier can be adaptively adjusted to target and detect the bridge frequency of concern. For the special case when the amplifier is tuned to the vehicle frequency, the amplifier can improve the vehicle performance by serving as a tuned mass damper, as conventionally known. This case is of limited use since it does not allow us to target the bridge frequencies. Both bridge damping and vehicle speed are also assessed with their effects addressed.

Study on the Dynamic Wheel Load of Multi-Axle Vehicle Based on Distribute Loading Weight

2010 ◽  
Vol 159 ◽  
pp. 35-40
Author(s):  
Zhong Hong Dong
Keyword(s):  
Dynamic Load ◽  
Influence Factors ◽  
Vehicle Speed ◽  
Tire Pressure ◽  
Wheel Load ◽  
The Road ◽  
Road Roughness ◽  
Tire Stiffness ◽  
On The Road

To study the dynamic wheel load on the road, a dynamic multi-axle vehicle mode has been developed, which is based on distribute loading weight and treats tire stiffness as the function of tire pressure and wheel load. Taking a tractor-semitrailer as representative, the influence factors and the influence law of the dynamic load were studied. It is found that the load coefficient increases with the increase of road roughness, vehicle speed and tire pressure, yet it decreases with the increase of axle load. Combining the influences of road roughness, vehicle speed, axle load and tire pressure, the dynamic load coefficient is 1.14 for the level A road, 1.19 for the level B road, 1.27 for the level C road, and 1.36 for the level D road.

Manoeuvring speed of a 6×6 autonomous vehicle using a database obtained from multi-body dynamic simulation

2009 ◽  
Vol 223 (8) ◽  
pp. 979-985 ◽  
Author(s):  
S-L Cho ◽  
K-C Yi ◽  
J-H Lee ◽  
W-S Yoo
Keyword(s):  
Dynamic Simulation ◽  
Autonomous Vehicle ◽  
Sensor Data ◽  
Vehicle Speed ◽  
The Road ◽  
Driving Speed ◽  
Road Roughness ◽  
Motion Speed ◽  
Vertical Accelerations ◽  
Vehicle Dynamic Simulation

For an autonomous vehicle that travels off-road, the driving speed is limited by the driving circumstances. To decide on a stable manoeuvring speed, the driving system should consider road conditions such as the height of an obstacle and road roughness. In general, an autonomous vehicle has many sensors to preview road conditions, and the information gathered by these sensors can be used to find the proper path for the vehicle to avoid unavoidable obstacles. However, sensor data are insufficient for determining the optimal vehicle speed, which could be obtained from the dynamic response of the vehicle. This paper suggests an algorithm that can determine the optimal vehicle speed running over irregular rough terrains such as when travelling off-road. In the determination of the manoeuvring speed, the vehicle dynamic simulation is employed to decide whether the vehicle response is within or beyond the prescribed limits. To determine the manoeuvring speed in real time, the dynamic simulation should be finished much more quickly than the real motion speed of the vehicle. In this paper, the equation of motion of the vehicle is derived in terms of the chassis local coordinates to reduce the simulation time. The velocity transformation technique, which combines the generality of Cartesian coordinates and the efficiency of relative coordinates, was combined with a symbolic computation to enhance further the computational efficiency. First the developed algorithm calculates the level of the previewed road roughness to determine the manoeuvring speed. Then, the maximum stable speed is judged against the database, which already has stored the maximum vertical accelerations as a function of the road roughness and vehicle speed.

scholarly journals Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory

2020 ◽  
Vol 1 ◽  
Author(s):  
Meshkat Botshekan ◽  
Jacob Roxon ◽  
Athikom Wanichkul ◽  
Theemathas Chirananthavat ◽  
Joy Chamoun ◽  
...  
Keyword(s):  
Inverse Analysis ◽  
Random Vibration ◽  
Statistical Physics ◽  
Mechanistic Model ◽  
Ride Quality ◽  
Vertical Acceleration ◽  
Fluctuation Dissipation ◽  
Power Spectral ◽  
Road Roughness ◽  
L2 Norm

Abstract We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mechanistic model of roughness-induced pavement–vehicle interaction, the inverse analysis employs an L2 norm regularization to estimate ride quality metrics, such as the widely used International Roughness Index, from the acceleration PSD. Evoking the fluctuation–dissipation theorem of statistical physics, the inverse framework estimates the half-car dynamic vehicle properties and related excess fuel consumption. The method is validated against (a) laser-measured road roughness data for both inner city and highway road conditions and (b) road roughness data for the state of California. We also show that the phone position in the vehicle only marginally affects road roughness predictions, an important condition for crowdsourced capabilities of the proposed approach.

scholarly journals Simultaneous Identification of Bridge Structural Damage and Moving Loads Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies

2021 ◽  
Vol 14 (1) ◽  
pp. 119
Author(s):  
Solmaz Pourzeynali ◽  
Xinqun Zhu ◽  
Ali Ghari Zadeh ◽  
Maria Rashidi ◽  
Bijan Samali
Keyword(s):  
Explicit Form ◽  
Structural Damage ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Condition Assessment ◽  
Vehicle Speed ◽  
Moving Loads ◽  
Road Roughness ◽  
Bridge Structures ◽  
Simultaneous Identification

Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion.

Energy Harvesting, Ride Comfort, and Road Handling of Regenerative Vehicle Suspensions

2013 ◽  
Vol 135 (1) ◽  
Author(s):  
Lei Zuo ◽  
Pei-Sheng Zhang
Keyword(s):  
Energy Harvesting ◽  
Ride Comfort ◽  
Average Power ◽  
Great Influence ◽  
Vehicle Body ◽  
Ride Quality ◽  
Road Roughness ◽  
Tire Stiffness ◽  
Suspension Stiffness ◽  
The Mean

This paper presents a comprehensive assessment of the power that is available for harvesting in the vehicle suspension system and the tradeoff among energy harvesting, ride comfort, and road handing with analysis, simulations, and experiments. The excitation from road irregularity is modeled as a stationary random process with road roughness suggested in the ISO standard. The concept of system H2 norm is used to obtain the mean value of power generation and the root mean square values of vehicle body acceleration (ride quality) and dynamic tire-ground contact force (road handling). For a quarter car model, an analytical solution of the mean power is obtained. The influence of road roughness, vehicle speed, suspension stiffness, shock absorber damping, tire stiffness, and the wheel and chasses masses to the vehicle performances and harvestable power are studied. Experiments are carried out to verify the theoretical analysis. The results suggest that road roughness, tire stiffness, and vehicle driving speed have great influence on the harvesting power potential, where the suspension stiffness, absorber damping, and vehicle masses are insensitive. At 60 mph on good and average roads, 100–400 W average power is available in the suspensions of a middle-sized vehicle.

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