CLC number: TH133.31
On-line Access: 2017-05-03
Received: 2016-03-30
Revision Accepted: 2016-09-26
Crosschecked: 2017-04-11
Cited: 1
Clicked: 4836
Peng Guo, Jun-hong Zhang. Numerical model and multi-objective optimization analysis of vehicle vibration[J]. Journal of Zhejiang University Science A, 2017, 18(5): 393-412.
@article{title="Numerical model and multi-objective optimization analysis of vehicle vibration",
author="Peng Guo, Jun-hong Zhang",
journal="Journal of Zhejiang University Science A",
volume="18",
number="5",
pages="393-412",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1600124"
}
%0 Journal Article
%T Numerical model and multi-objective optimization analysis of vehicle vibration
%A Peng Guo
%A Jun-hong Zhang
%J Journal of Zhejiang University SCIENCE A
%V 18
%N 5
%P 393-412
%@ 1673-565X
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1600124
TY - JOUR
T1 - Numerical model and multi-objective optimization analysis of vehicle vibration
A1 - Peng Guo
A1 - Jun-hong Zhang
J0 - Journal of Zhejiang University Science A
VL - 18
IS - 5
SP - 393
EP - 412
%@ 1673-565X
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1600124
Abstract: It is crucial to conduct a study of vehicle ride comfort using a suitable physical model, and a precise and effective problem-solving method is necessary to describe possible engineering problems to obtain the best analysis of vehicle vibration based on the numerical model. This study establishes different types of vehicle models with different degrees of freedom (DOFs) that use different types of numerical methods. It is shown that results calculated using the Hamming and runge-Kutta methods are nearly the same when the system has a small number of DOFs. However, when the number is larger, the hamming method is more stable than other methods. The hamming method is multi-step, with four orders of precision. The research results show that this method can solve the vehicle vibration problem. Orthogonal experiments and multi-objective optimization are introduced to analyze and optimize the vibration of the vehicle, and the effects of the parameters on the dynamic characteristics are investigated. The solution F1 (vertical acceleration root mean square of the vehicle) reduces by 0.0352 m/s2, which is an improvement of 7.22%, and the solution F2 (dynamic load coefficient of the tire) reduces by 0.0225, which is an improvement of 6.82% after optimization. The study provides guidance for the analysis of vehicle ride comfort.
The authors presented a dynamic simulation of the different vehicle systems, a study of establishing the 2-DOF, 4-DOF, 7-DOF model were created in a simulation program.
[1]Bae, D.S., Lee, J.K., Cho, H.J., et al., 2000. An explicit integration method for realtime simulation of multibody vehicle models. Computer Methods in Applied Mechanics and Engineering, 187(1-2):337-350.
[2]Baumal, A., McPhee, J., Calamai, P., 1998. Application of genetic algorithms to the design optimization of an active vehicle suspension system. Computer Methods in Applied Mechanics and Engineering, 163(1-4):87-94.
[3]Campbell, C., 1981. Automotive Suspensions. Chapman Hall, London, UK.
[4]Ekoru, J.E.D., Pedro, J.O., 2013. Proportional-integral-derivative control of nonlinear half-car electro-hydraulic suspension systems. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 14(6):401-416.
[5]Gündoğdu, O., 2007. Optimal seat and suspension design for a quarter car with driver model using genetic algorithms. International Journal of Industrial Ergonomics, 37(4):327-332.
[6]Gupta, T.C., 2007. Identification and experimental validation of damping ratios of different human body segments through anthropometric vibratory model in standing posture. Journal of Biomechanical Engineering, 129(4):566-574.
[7]He, Z., Sun, Y., Zhang, G., 2015. Tribilogical performances of connecting rod and by using orthogonal experiment, regression method and response surface methodology. Applied Soft Computing, 29:436-449.
[8]Hegazy, S., Rahnejat, H., Hussain, K., 1999. Multi-body dynamics in full-vehicle handling analysis. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 213(1):19-31.
[9]Ikenaga, S., Lewis, F.L., Campos, J., et al., 2000. Active suspension control of ground vehicle based on a full-vehicle model. American Control Conference, 6:4019-4024.
[10]Jamali, A., Shams, H., Fasihozaman, M., 2014. Pareto multi-objective optimum design of vehicle-suspension system under random road excitations. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 228(3):282-293.
[11]Kadir, Z.A., Hudha, K., Ahmad, F., et al., 2012. Verification of 14DOF full vehicle model based on steering wheel input. Applied Mechanics and Materials, 165:109-113.
[12]Mirzaei, M., Hassannejad, R., 2007. Application of genetic algorithms to optimum design of elasto-damping elements of a half-car model under random road excitations. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 221(4):515-526.
[13]Nasir, M.Z.M., Hudha, K., Amir, M.Z., et al., 2012. Modelling, simulation and validation of 9 DOF vehicles model for automatic steering system. Applied Mechanics and Materials, 165:192-196.
[14]Nigam, S.P., Malik, M., 1987. A study on a vibratory model of a human body. Journal of Biomechanical Engineering, 109(2):148-153.
[15]Rao, S.S., 1996. Engineering Optimization. John Wiley & Sons, New York, USA.
[16]Reddy, P.S., Ramakrishna, A., Ramji, K., 2015. Study of the dynamic behaviour of a human driver coupled with a vehicle. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229(2):226-234.
[17]Schmitke, C., Morency, K., McPhee, J., 2008. Using graph theory and symbolic computing to generate efficient models for multi-body vehicle dynamics. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 222(4):339-352.
[18]Soleymani, M., Montazeri-Gh, M., Amiryan, R., 2012. Adaptive fuzzy controller for vehicle active suspension system based on traffic conditions. Scientia Iranica, 19(3):443-453.
[19]Srinivas, N., Deb, K., 1994. Multiobjective function optimization using nondominated sorting genetic algorithms. Evolutionary Computation, 2(3):221-248.
[20]Sulaiman, S., Samin, P.M., Jamaluddin, H., et al., 2012. Modeling and validation of 7-DOF ride model for heavy vehicle. International Conference on Automotive, Mechanical and Materials Engineering, p.108-112.
[21]Taghirad, H., Esmailzadeh, E., 1998. Automobile passenger comfort assured through LQG/LQR active suspension. Journal of Vibration and Control, 4(5):603-618.
[22]Tamboli, J.A., Joshi, S.G., 1999. Optimum design of passive suspension system of a vehicle subjected to actual random road excitations. Journal of Sound and Vibration, 219(2):193-205.
[23]Thite, A.N., Banvidi, S., Ibicek, T., et al., 2011. Suspension parameter estimation in the frequency domain using a matrix inversion approach. Vehicle System Dynamics, 49(12):1803-1822.
[24]Vaddi, P.K.R., Kumar, C.S., 2014. A non-linear vehicle dynamics model for accurate representation of suspension kinematics. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 229(6):1002-1014.
[25]von Chappuis, H., Mavros, G., King, P.D., et al., 2013. Prediction of impulsive vehicle tyre-suspension response to abusive drive-over-kerb manoeuvres. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 227(2):133-149.
[26]Yu, F., Lin, Y., 2005. Vehicle System Dynamics. Machinery Industry Press, Beijing, China (in Chinese).
[27]Yuen, T.J., Foong, S.M., Ramli, R., 2014. Optimized suspension kinematic profiles for handling performance using 10-degree-of-freedom vehicle model. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 228(1):82-99.
[28]Zong, C., Song, P., Hu, D., 2011. Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 12(6):446-452.
[29]Zuo, L., Nayfeh, S.A., 2003. Structured H2 optimization of vehicle suspensions based on multi-wheel models. Vehicle System Dynamics, 40(5):351-371.
[30]Zuo, L., Zhang, P.S., 2013. Energy harvesting, ride comfort, and road handling of regenerative vehicle suspensions. Journal of Vibration and Acoustics, 135(1):011002.
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