Ride comfort optimization of a multi-axle heavy motorized wheel dump truck based on virtual and real prototype experiment integrated Kriging model

Gong, Bian; Guo, Xuexun; Hu, Sanbao; Xu, Lin

doi:10.1177/1687814015584257

Cited by 11 publications

(5 citation statements)

References 21 publications

(24 reference statements)

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“…Zhao et al [10] developed a linear cabin system model to identify the suspension parameters and validated the model by a bench test for the optimal design of a cabin suspension to improve ride comfort. Gong et al [11] proposed an optimization method based on a virtual and real prototype of an experimental integrated Kriging model for hydropneumatic suspension, showing a significant improvement in ride comfort. In another study, optimization was conducted with the design of the experiment (DOE) method on the model without considering the installation angle of suspension [3].…”

Section: Parameters Matching and Suspension Systems Optimizationmentioning

confidence: 99%

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Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Chen

Zheng

et al. 2020

IEEE Access

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The performance of a suspension system is affected by the behavior and posture of its components. However, published studies usually conduct this research using the based-equivalent model without considering the characteristic curves or postures. In this paper, an improved ride comfort model that considers three nonlinearities in suspensions is first developed, and this model is validated through experimental results and demonstrates good accuracy. Then, the dynamic response is presented to investigate the effects of multilevel suspension parameters and nonlinear factors on ride comfort, and it is concluded that the front chassis suspension is the most significant system for ride comfort. Next, a multivariable co-optimization method based on the improved model is proposed to obtain more accurate optimized results that are more suitable for automotive applications. Subsequently, a multiobjective genetic algorithm (MGA) is applied to obtain the Pareto solution set. Furthermore, comparing the RMS value before and after optimization shows an obvious reduction, with averages of 19.7%, 17.8%, and 12.0% for the weighted root mean square (RMS) of the driver seat acceleration and the RMS of the working spaces of the front chassis suspension and the rear chassis suspension, respectively. Finally, the results are also verified by experiments, indicating that the improved ride comfort model and the multivariable co-optimization method are feasible and practical.INDEX TERMS Ride comfort, nonlinearity of suspension components, multivariable co-optimization, bivariate analysis, commercial vehicle.

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Section: Parameters Matching and Suspension Systems Optimizationmentioning

confidence: 99%

“…The ride comfort model is developed through equations ( 10) and (11) and illustrated in Figure 15 in the Appendix. Then the experiments concerning the dynamic response are performed to verify the correctness of the improved model.…”

Section: Ride Comfort Model Verificationmentioning

confidence: 99%

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Chen

Zheng

et al. 2020

IEEE Access

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“…. , X 100 ]; (12) If the outer-loop MOPSO algorithm satisfies the convergence condition, jump to Step (13); otherwise, jump to Step (1). When one of the following two conditions is satisfied, it is determined as convergence: I.…”

Section: Multi-objective Optimization Algorithm Based On Dl-mopsomentioning

confidence: 99%

“…The suspension system of multi-axle trucks is an important component that affects the driving performance of vehicles. In previous studies, the optimization and control of truck suspensions mainly targeted road-friendliness, ride comfort, and steering stability [1][2][3], but not enough attention has been paid to an important performance index that affects the safety of the road traffic, namely, the dynamic load-sharing performance of the multi-axle groups. The dynamic load-sharing performance of the multi-axle groups refers to the ability to distribute the load evenly between the axles within the multi-axle groups during driving [4].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Geometric Parameters of Longitudinal-Connected Air Suspension Based on a Double-Loop Multi-Objective Particle Swarm Optimization Algorithm

et al. 2018

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Longitudinal-connected air suspension has been proven to have desirable dynamic load-sharing performances for multi-axle heavy vehicles. However, optimization approaches towards the improvement of comprehensive vehicle performance through the geometric design of longitudinal-connected air suspension have been considerably lacking. To address this, based on a 5-degrees-of-freedom nonlinear model of a three-axle semi-trailer with longitudinal air suspension, taking the changes of driving conditions (road roughness, speed, and load) into account, a height control strategy of the longitudinal-connected air suspension was proposed. Then, in view of the height of the air spring under various driving conditions, the support vector regression method was employed to fit the relationship models between the performance indices and the driving conditions, as well as the suspension geometric parameters (inside diameters of the air line and the connectors). Finally, to tackle the uncertainties of driving conditions in the optimization of suspension geometric parameters, a double-loop multi-objective particle swarm optimization algorithm (DL-MOPSO) was put forward based on the interval uncertainty theory. The simulation results indicate that compared with the longitudinal-connected air suspension using two traditional geometric parameters, the optimization ratios for dynamic load sharing coefficient and root-mean-square acceleration at various spring heights are between −1.04% and 20.75%, and 1.44% and 35.1%, respectively. Therefore, based on the signals measured from the suspension height sensors, through integrated control of inflation/deflation valves of air suspensions, as well as the valves' inside connectors and air lines, the proposed DL-MOPSO algorithm can improve the comprehensive driving performance of the longitudinal-connected three-axle semi-trailer effectively, and in response to changes in driving conditions.

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“…The subsystem models such as suspension, tire, and body are built, and then the whole virtual model of DAT is assembled and simulated through the application of multi-body dynamics software. [4][5][6][7][8] A multi-axle heavy motorized wheel dump truck based on virtual and real prototype experiments integrating Kriging model is proposed in Gong et al, 9 but the approximate model doesn't contain tire and hydraulic parameters. In Michaltsos 10 a full mathematical vehicle model having 7 degrees of freedom (DOF) was developed.…”

Section: Introductionmentioning

confidence: 99%

Damaged-aircraft trailer dynamics simulation and vibration optimization

Hong

Zhang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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As a rescue vehicle, damaged-aircraft trailer is used to move damaged aircraft quickly to restore the normal order of the airport. Several damaged-aircraft trailer parameters such as tire stiffness and damping of the suspension hydraulic system influence the dynamic performance significantly. In this article, a simplified 9 degrees of freedom model of damaged-aircraft trailer is established considering the physical parameters of suspension and tires. The relationships among the parameters of the suspension hydraulic components, the elastic force and damping force are established, and then the optimization model of the whole vehicle is obtained. In order to reduce the secondary damage to the aircraft, the multi-island genetic algorithm is used to optimize the suspension system and tire. During the calculation, the maximum vertical acceleration of damaged-aircraft trailer is taken as objective function for variable parameters of the suspension hydraulic system and the tire. As a result, the performance of the vehicle is greatly improved with the maximum acceleration of 0.2 m/s2 after optimization.

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Ride comfort optimization of a multi-axle heavy motorized wheel dump truck based on virtual and real prototype experiment integrated Kriging model

Cited by 11 publications

References 21 publications

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

Optimization of Geometric Parameters of Longitudinal-Connected Air Suspension Based on a Double-Loop Multi-Objective Particle Swarm Optimization Algorithm

Damaged-aircraft trailer dynamics simulation and vibration optimization

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