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Dynamic Behaviour of a 7 DoF Passenger Car Model


Affiliations
1 Mech. Engg. Dept., Aditya Inst. of Tech. and Management, Tekkali, Andhra Pradesh, India
2 Mech. Engg. Dept., Maharishi Markandeshwar University, Mullana, India
 

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Conventional vehicle suspension systems, which are passive in nature consists of springs with constant stiffness and dampers with constant damping coefficient. These suspension systems cannot meet the characteristics such as ride comfort, road handing and suspension deflection during abnormal road conditions simultaneously. Active and semi-active suspension systems are the solutions to achieve the desired suspension characteristics. Since, active system is bulky and requires high energy for working, a semi-active suspension system is considered in the present work to analyze vehicle traversing over various road profiles for ride comfort. Mathematical model of a 7 DoF passenger car is formulated using Newton’s method. A semi-active suspension system with skyhook linear control strategy avoids the road excitations at resonant frequencies by shifting the natural frequencies of the model by varying damping coefficients based on the vehicle response for different road conditions where the excitations could be harmonic, transient and random. Modal analysis is carried out to identify the un-damped natural frequencies and mode shapes for different values of damping. The above analyses are carried out through analytical and numerical methods using MATLAB and ANSYS software respectively and the results obtained from both are in good agreement.

Keywords

Dynamic Response, Modal Analysis, Natural Frequency, Mathematical Model, ANSYS, MATLAB.
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  • R.P. Kumar and S.R. Kulkarni. 2014. Comparative analysis of multiple controllers for semi-active suspension system, Proc. of 2nd Int. Conf. on Emerging Research in Computing, Information, Communication and Applications, Nitte Meenakhsi Institute of Tech., Bangalore, India.
  • X. Dong, M. Yu and C. Liao. 2010. Comparative research on semi-active control strategies for magnetorheological suspension, Non Linear Dynamics, 59(3), 433-453. https://doi.org/10.1007/s11071-009-9550-8.
  • M.A. Eltantawie. 2012.Decentralized neuro-fuzzy control for half-car with semi-active suspension system, Int. J.Automotive Technology, 13(3), 423-431. https://doi.org/ 10.1007/s12239-012-0039-y.
  • M.A. Karkoub and M. Zribi. 2006. Active/semi-active suspension control using magnetorheological actuators, Int. J. Systems Science, 37(1), 35-44. https://doi.org/10.1080/00207720500436344.
  • S.M. Savaresi, E. Silani and S. Bittanti. 2005. An optimal control algorithm for comfort–oriented semi-active suspensions, ASME Trans. J. Dyn. Sys. Meas. & Control, 127(2), 218-229. https://doi.org/10.1115/1.1898241.
  • S. Turkay and H. Akcay. 2010. Tire damping effect on h2 optimal control of half-car active suspensions, J.Vibration and Acoustics, 132, 1-4. https://doi.org/10.1115/1.4000767.
  • V. Goga and M. Klucik. 2012. Optimization of vehicle suspension parameters with use of evolutionary computation, Procedia Engineering, 48, 174-179.https://doi.org/10.1016/j.proeng.2012.09.502.
  • X.M. Sun, Y. Chu, J. Fan and Q. Yang. 2012. Research of simulation on the effect of suspension damping on vehicle ride, Energy Procedia, 17, 145-151. https://doi.org/10.1016/j.egypro.2012.02.075.
  • L.H. Zong, X.L. Gong, C.Y. Guo and S.H. Xuan. 2012. Inverse neuro-fuzzy MR damper model and its application in vibration control of vehicle suspension system, Int. J. Vehicle Mech. and Mobility, Vehicle Sys. Dynamics, 50(7), 1025-1041. https://doi.org/10.1080/ 00423114.2011.645489.
  • L. Dugard, O. Sename, S. Aubouet and B.Talon. 2012.Full vertical car observer design methodology for suspension control applications, Control Engineering practice, 20, 832-845. https://doi.org/10.1016/j.coneng prac.2012.04.008.
  • A. Unger, F. Schimmack, B. Lohmann and R. Schwarz.2013. Application of LQ-based semi-active suspension control in a vehicle, Control Engineering Practice, 21, 1841-1850. https://doi.org/10.1016/j.conengprac.2013.06.006.
  • N.M. Ghazaly and A.O. Moaaz. 2014. The future development and analysis of vehicle active suspension system, IOSR J. Mechanical and Civil Engineering, 11, 2014, 16-25.
  • H.J. Kim, H.S. Yang and Y.P. Park. 2002. Improving the vehicle Performance with active suspension using roadsensing algorithm, Computers and Structures, 80, 15691577. https://doi.org/10.1016/S0045-7949(02)00110-4.
  • A.J. Qazi, A. Khan, M.T. Khan and S. Noor. 2013. A parametric study on performance of semi-active suspension system with variable damping coefficient limit, AASRI Procedia, 4, 154-159. https://doi.org/ 10.1016/j.aasri.2013.10.024.
  • Y. Liu, T.P. Waters and M.J. Brennan. 2003. A comparison of semi-active damping control strategies for vibration isolation of harmonic disturbances, J. Sound & Vibration, 280(1-2), 21-39. http://dx.doi.org/10.1016/ j.jsv.2003.11.048
  • B. Assadsangabi and M. Eghtesad. 2009. Hybrid sliding mode of control of semi-active suspension systems, Smart Materials and Structures, 18, 250-270. https://doi.org/10.1088/0964-1726/18/12/125027.
  • T.R.M. Rao, G.V. Rao, K.S. Rao and A. Purushottam. 2010. Analysis of passive and semi-active controlled suspension systems for ride comfort in an Omnibus passing over a speed bump, Int. J. Research of Reviews in Applied Sciences, 5, 7-17.
  • J. Wang and C. Song. 2013. Computer Simulation on Fuzzy control of semi-active suspension system based on the whole vehicle, Int. J. Multimedia & Ubiquitous Engg., 8(6), 217-228. https://doi.org/10.14257/ijmue. 2013.8.6.22.
  • L. Balamurugan, J. Jancirani, and M.A. Eltanrawie. 2014. Generalized magnetorheological (MR) damper model and its application in semi-active control of vehicle suspension system, Int. J. Automotive Technology, 15(3), 419-427. https://doi.org/10.1007/s12239-014-0044-4.
  • S. Dutta, S. Narahari and G. Chakraborty. 2013. Semiactive vibration isolation of a quarter car model under random road excitations using magnetorheological damper, Proc. 1st Int. and 16th National Conf. on Machines and Mechanisms, IIT Roorkee, India.

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  • Dynamic Behaviour of a 7 DoF Passenger Car Model

Abstract Views: 448  |  PDF Views: 264

Authors

S. Palli
Mech. Engg. Dept., Aditya Inst. of Tech. and Management, Tekkali, Andhra Pradesh, India
R. C. Sharma
Mech. Engg. Dept., Maharishi Markandeshwar University, Mullana, India
P. P. D. Rao
Mech. Engg. Dept., Aditya Inst. of Tech. and Management, Tekkali, Andhra Pradesh, India

Abstract


Conventional vehicle suspension systems, which are passive in nature consists of springs with constant stiffness and dampers with constant damping coefficient. These suspension systems cannot meet the characteristics such as ride comfort, road handing and suspension deflection during abnormal road conditions simultaneously. Active and semi-active suspension systems are the solutions to achieve the desired suspension characteristics. Since, active system is bulky and requires high energy for working, a semi-active suspension system is considered in the present work to analyze vehicle traversing over various road profiles for ride comfort. Mathematical model of a 7 DoF passenger car is formulated using Newton’s method. A semi-active suspension system with skyhook linear control strategy avoids the road excitations at resonant frequencies by shifting the natural frequencies of the model by varying damping coefficients based on the vehicle response for different road conditions where the excitations could be harmonic, transient and random. Modal analysis is carried out to identify the un-damped natural frequencies and mode shapes for different values of damping. The above analyses are carried out through analytical and numerical methods using MATLAB and ANSYS software respectively and the results obtained from both are in good agreement.

Keywords


Dynamic Response, Modal Analysis, Natural Frequency, Mathematical Model, ANSYS, MATLAB.

References





DOI: https://doi.org/10.4273/ijvss.9.1.12