Parameters Analyses and Identification for Rubber Bush Based on Theoretical Dynamic Model with Effects of Temperature and Preload
Published April 2, 2019 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
A series connection of the KVBC (Kelvin-Voigt and Bouc-wen) theoretical model of rubber bush in automobile suspension is established. The numerical calculation model is also developed through Matlab/simulation and 9 parameters are identified. Experiments are conducted on the rubber bush on a bench for dynamic and static characteristics and to supply appropriate and reliable data for parameter identification. Based on this, preload and temperature are taken into consideration in an ordinary KVBC model as two important additional factors. As a result, it leads to developing a novel model with new parameter identification, which is validated under different conditions. This new modeling method of rubber bush has three advantages. First, it shows improved accuracy for solving non-linear problems in a multi-body calculation, which is useful for researchers and vehicle engineers. In addition, this new method leads to a very important step for choosing appropriate bush model type before analysis, which depends on the preload value. Furthermore, the model can be used to handle real situations with temperature variation around rubber bushes during simulation, which has been studied for many years for automobile NVH performance versus temperature.
CitationHe, R. and Zhou, H., "Parameters Analyses and Identification for Rubber Bush Based on Theoretical Dynamic Model with Effects of Temperature and Preload," SAE Technical Paper 2019-01-1272, 2019, https://doi.org/10.4271/2019-01-1272.
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- Tian, Y., Multi-Body Dynamics Simulation of Suspension Considering Nonlinear Constitutive Characteristics of Line (Jilin University, 2014).
- Li, L.Y., Parameter Identification, Modeling and Durability Analysis Optimization of Vehicle Suspension System (Huazhong University of Science and Technology, 2013).
- Yu, Z.L., Zhang, L.J., and Luo, Y., “Half-Experienced Parameters Dynamic Model of Rubber Bush,” Journal of Mechanical Engineering 14(14):115-123, 2010.
- Pan, X.Y., An Investigation on Modeling and Calculation Methods for Dynamic Properties of a Rubber Isolator (Zhejiang Technology University, 2009).
- Harris, J. and Stevenson, A., “On the Role of Nonlinearity in the Dynamic Behavior of Rubber Components,” Rubber Chemistry and Technology 59(5):740-764, 1986.
- Wu, J. and ShangGuan, W.B., “Modeling and Application of Dynamic Characteristics for Rubber Isolators Using Viscoelastic Fractional Derivative Model,” Engineering Mechanics 01(1):161-166, 2008.
- Guo, S.H., “Eigen Theory of Viscoelastic Dynamic Based on the Kelvin-Voigt Model,” Applied Mathematics and Mechanics 25(7):723-728, 2004.
- Ma, N. and Zhang, Z.Q., “Dynamic Viscoelasticity and Phenomenological Model of Electrorheological Elastomers,” Journal of Applied Polymer Science, Nov. 5, 2017.
- Saini, A. and Tien, I., “Framework for Probabilistic Assessment of Maximum Nonlinear Structural Response Based on Sensor Measurements: Discretization and Estimation,” Journal of Engineering Mechanics, Sep 2017.
- Dyke, S.J.B.F.S., Sain, M.K. et al., “Phenomenological Model for Magneto rheological Dampers,” Journal of Engineering Mechanics 123(3):230-238, 1997.
- Wang, N., Study on Suspension Rubber Bush Modeling Facing Vehicle Durability (Jilin University, 2011).
- Zawartka, M., “Sensitivity Analysis of the MR Damper Model Parameters on the Vibration Transmissibility Characteristic,” in 15th International Carpathian Control Conference, 2014, 699-704.
- Danko, J., Magdolen, L., and Milesich, T., “Modelling of the Damper Characteristics of the Unmanned Ground Vehicle,” in Transport Means - Proceedings of the International Conference, 2013, 161-164.
- Mythili, S. and Thiyagarajah, K., “Z-Source Inverter Fed Induction Motor Drive Control Using Hybrid Methodology,” Journal of Circuits Systems and Computers, March 2018.
- Zeng, X.H., Li, G.H. et al., “Model Predictive Control-Based Dynamic Coordinate Strategy for Hydraulic Hub-Motor Auxiliary System of a Heavy Commercial Vehicle,” Mechanical Systems and Signal Processing97-120, February 2018.
- Bruns, J. and Dreyer, J., “Dynamic Analysis of a Hydraulic Body Mount with Amplitude and Preload Dependence,” SAE Int. J. Veh. Dyn. Stab. and NVH 1(2):480-487, July 2017, doi:10.4271/2017-01-1909.
- Chen, S., Wang, X., Zhang, Z., Mu, W., and Li, R., “Optimal Design of Laminated-MRE Bearings with Multi-Scale Model,” Smart Materials and Structures 25(10), Oct 2016.
- Li, Y. and Li, L., “Finite Element Design and Analysis of Adaptive Base Isolator Utilizing Laminated Multiple Magneto Rheological Elastomer Layers,” Intell. Mater. Syst. Struct.1861-1870, 2015.
- Gritli, W., Gharsallaoui, H., and Benrejeb, M., “PID-Type Fuzzy Scaling Factors Tuning Using Genetic Algorithm and Simulink Design Optimization for Electronic Throttle Valve,” in 2016 International Conference on Control Decision and Information Technologies, 2016, 216-221.
- Grover, N., Soni, M.K. Simulation and Optimization of VHDL Code for FPGA-Based Design Using Simulink. International Journal of Information Engineering and Electronic Business, v 6, n 3, 22-7, June 2014.