Dynamics of vehicle–pavement system based on a viscoelastic Euler–Bernoulli beam model

Snehasagar, G.; Krishnanunni, C.G.; Rao, B. N.

doi:10.1080/10298436.2018.1562189

Cited by 17 publications

(5 citation statements)

References 23 publications

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“…The dynamics of vehicle–road interaction have received great attention from researchers over the past decade [ 4 , 5 , 6 , 7 ]. In traditional road dynamics, vehicles are considered moving loads excited by the roughness of the road surface, with little consideration for the influence of road vibrations [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The study examined the effects of vehicle speed, road roughness parameters, and wheel–road contact stiffness on dynamic wheel–road forces and embankment response. Snehasagar [ 6 ] studied the effect of a viscoelastic road-surface model on the dynamics of the vehicle–road coupling system. The Galerkin method and the Runge–Kutta method were used to discretize the differential equations generated by the system’s motion dynamics equation.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Li,

Xu,

Wang

et al. 2024

Sensors

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Electric vehicles with hub motors have integrated the motor into the wheel, which increase the unsprung mass of the vehicle, and intensifies the vibration of the underspring components. The motor excitation during driving also intensifies the wheel vibration. The coupling effect between the two makes the performance of electric vehicles deteriorate. The article employed a disc-type permanent-magnet motor as the hub motor, taking into consideration the increase in sprung mass caused by the hub motor and the adverse effects of vertical vibration from motor excitation. Based on random road-surface excitation, and considering the secondary excitation caused by wheel motor drive and vehicle-road coupling, a coupled-dynamics model of a semi-active-suspension vehicle-road system for vertical vehicle motion is investigated under multiple excitations. Using body acceleration, suspension deflection, and dynamic tire load as evaluation indicators, a BP neural network PID controller based on the sparrow search algorithm optimization is proposed for the semi-active-suspension system. Compared with PID control and particle swarm optimization (PSO-BPNN-PID), the research findings indicate that the optimized semi-active suspension significantly improves the ride comfort of hub-motor electric vehicles, and meets the requirements for control performance under different vehicle driving conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Li,

Xu,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Whether an asphalt pavement model can precisely describe the behavior of a pavement depends mostly on the rheological model of the asphalt layer. Kelvin, Maxwell, Venderpoel, and Burgers models are the classical models used to simulate asphalt concrete materials [1,21]. Other effective or modified models have also been developed [22].…”

Section: Introductionmentioning

confidence: 99%

Viscoelastic dynamic response of asphalt pavement with imperfect interface bonding base on transfer matrix

et al. 2022

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The interface bonding condition between asphalt pavement layers is always imperfect because of the differing material properties of different layers and the limitation of construction techniques. Therefore, designing asphalt pavement by considering the interface to be completely continuous is not precise. The viscoelastic character of the asphalt layer contributes significantly to the performance of asphalt pavement. To explore the influence of the imperfect interface on the viscoelastic dynamic response of asphalt pavement, the Hankel–Laplace transformation was applied to convert the partial differential equations to ordinary differential equations and a transfer matrix was introduced to describe the behavior of the imperfect interface. Based on the boundary condition and integral inverse transformation technique, an analytical solution for the displacement, stress, and strain of the pavement can be obtained. By considering the variation in the interface condition and the viscoelastic characteristics of the asphalt layer, the dynamic response of the pavement was calculated based on a practical example. The results show that the interface condition between the asphalt layer and the base course has a marked influence on the viscoelastic dynamic response of the pavement and that enhancing the strength of this interface can reduce the deflection of the pavement. The series elastic component in the modified Burgers model is another significant factor that affects the calculation results of the pavement dynamic response.

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“…In most of the research analyzing the interaction between the vehicle and the road, the vehicle model is generally simplified, and the road is abstracted as a beam model to establish the vehicle–road dynamics equation [ 8 , 9 , 10 , 11 , 12 ]. The research results of Zhang et al show that the influence of vehicle–road coupling vibration on the propagation of pavement cracks is very obvious [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Reliability of a Core Control Unit of Highway Electromechanical Equipment Based on Virtual Sensor Data

Lin

Luo

et al. 2022

Sensors

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The printed circuit board (PCB) is the core control unit of electromechanical equipment. In order to determine the influence of the coupling vibration caused by vehicle–road interaction on the PCB reliability of roadside electromechanical equipment, first, the dynamic load of the vehicle tire is solved by establishing the dynamic model of a vehicle road. Then, the acceleration response data generated by road vibration are obtained by solving the road finite element model. Finally, the power density spectrum of the acceleration response is taken as input excitation, and the deformation response of the PCB under vehicle–road coupling vibration is analyzed. The experimental results show that when the vehicle is driving close to the roadside, the vibration caused by vehicle–road coupling will lead to a large deformation of the PCB, and the deformation value reaches 0.170 mm, which can cause structural damage to the PCB. This shows that the vehicle–road coupling vibration can affect the reliability of the roadside electromechanical equipment; thus, the optimal design of the PCB layout is created. After optimization, the first-order modal frequency of the PCB is increase by 5.4%, which reduces the risk of the components breaking away from the PCB substrate.

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Dynamics of vehicle–pavement system based on a viscoelastic Euler–Bernoulli beam model

Cited by 17 publications

References 23 publications

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Viscoelastic dynamic response of asphalt pavement with imperfect interface bonding base on transfer matrix

Research on the Reliability of a Core Control Unit of Highway Electromechanical Equipment Based on Virtual Sensor Data

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