Experimental Evaluation of Damping and Stiffness in Optimized Active Sport Utility Vehicle Suspension Systems

Shaimaa Awad; Eid Ouda Awad; Wael Galal Ata; Samir M. El-Demerdash; Ahmed Shehata Gad

doi:10.15282/ijame.21.3.2024.2.0885

Authors

Shaimaa Awad Faculty of Engineering at Mataria, Helwan University, Cairo, Egypt
Eid Ouda Awad Faculty of Engineering at Mataria, Helwan University, Cairo, Egypt
Wael Galal Ata Head of Tanks Department, Mechanical Engineering Branch, Military Technical College, Cairo, Egypt
Samir M. El-Demerdash Faculty of Engineering at Mataria, Helwan University, Cairo, Egypt
Ahmed Shehata Gad Faculty of Engineering at Mataria, Helwan University, Cairo, Egypt

DOI:

https://doi.org/10.15282/ijame.21.3.2024.2.0885

Keywords:

SUV coil spring, SUV oil/air mixed damper, Vehicle suspension model, Hybrid controller design

Abstract

On a flat city road, the specificity of the suspension poses a challenge. Sport utility vehicles (SUVs) have less flexibility compared to regular cars, despite providing a firm grip on asphalt and concrete. Constant adjustments to the center of gravity of SUVs are necessary. Surprisingly, they are less reliable and stable in urban settings due to this characteristic. In this study, a mathematical model of the suspension system of SUVs based on the Newtonian approach is introduced and validated with data from experiments carried out by the MTS machine system on the mono-tube (oil/air) mixed damper element. The model accurately predicts the performance of this damper, commonly used in SUVs, across various operating conditions, including different frequencies. A coil spring element, serving as a passive suspension unit with this damper, is also tested experimentally under similar conditions. By integrating passive suspension elements with the active actuator, the proposed modified design reduces the power consumption needed for the actuator to function and ensures a certain level of reliability. The effectiveness and performance of the modified active suspension system in comparison to the traditional passive suspension system are assessed using three different strategies: hybrid PID-LQR, linear quadratic regulator (LQR), and proportional-integral-derivative (PID). The genetic algorithm is utilized to determine the optimal parameter values for each controller by minimizing a cost function, maximizing performance, and minimizing energy consumption. Simulation results demonstrate that the active suspension system controlled by the PID-LQR controller offers significantly improved ride comfort and vehicle stability compared to other systems. This suggests that the performance of the active suspension system is greatly enhanced by the combined application of the hybrid PID-LQR controller compared to other systems.

References

J. D.J. Lozoya-Santos, J. C. Tudon-Martinez, R. Morales-Menendez, O. Sename, A. Spaggiari and R. Ramírez-Mendoza, “A general modeling approach for shock absorbers: 2 Dof Mr Damper case study,” Frontiers in Materials, vol. 7, p. 590328, 2021.

S.W.R. Duym, “Simulation tools, modelling and identification, for an automotive shock absorber in the context of vehicle dynamics,” Vehicle System Dynamics, vol. 33, pp. 261–285, 2000.

M. Shams, R. Ebrahimi, A. Raoufi and B. J. Jafari, “CFD-Fea analysis of hydraulic shock absorber valve behavior,” International Journal of Automotive Technology, vol. 5, pp. 615−622, 2007.

N. Lavanya, P. Rao And M. Reddy, “Design and analysis of a suspension coil spring for automotive vehicle,” international journal of engineering research and applications, vol. 9, pp. 151-157, 2014.

L. Dai, M. Chi, H. Gao and J. Sun, “An investigation into the modeling methodology of the coil spring,” Hindawi Shock and Vibration, vol. 2020, no. 1, p. 8814332, 2020.

A. M. Wittek, D. Gąska, B. Łazarz and T. Matyja, “Coil springs in passenger cars – general theoretical principles and structural requirements,” The Archives of Automotive Engineering, vol. 7, no. 2, pp. 141-159, 2016.

H.B. Pawar, A.R. Patil and. S.B. Zope, “Design and analysis of a front suspension coil spring for three wheeler vehicle,” International Journal Of Innovations In Engineering Research and Technology, vol. 3, pp. 1-10, 2016.

N. Singh, “General review of mechanical springs used in automobiles suspension system,” International Journal Of Advanced Engineering Research and Studies, vol. 115, p. 122, 2013.

M. Rahman and S.B. Abdullah, “Numerical and experimental study on optimization of coil springs used in vehicles’ suspension system,” Journal Of Engineering Advancements, vol. 2, pp. 169-174, 2021.

Y. Suda, S. Nakada and K. Nakano, “Hybrid suspension system with skyhook control and energy regeneration,” Vehicle System Dynamics, vol. 28, pp. 619-634, 1998.

B. Ebrahimi, H. Bolandhemmat, M. B. Khamesee and F. Golnaraghi, “A hybrid electromagnetic shock absorber for active vehicle suspension systems,” Vehicle System Dynamics, vol. 49, pp. 311–332, 2011.

K. Efatpenah, J. H. Beno and S. P. Nichols, “Energy requirements of a passive and an electromechanical active suspension system,” Vehicle System Dynamics, vol. 34, pp. 437–458, 2000.

H.H. Yuan Li, J. Z. Jiang, S. Burrow, S. Neild and A. Conn, “Enhancing the trade-off between ride comfort and active actuation requirements via an inerter-based passive-active-combined automotive suspension,” Vehicle System Dynamics, vol. 62, no. 3, pp. 556-579, 2023.

W. Huang, M. Ahmadian, A. Rahim and L. Steiginga, “Dynamics performance of long combination vehicles with active control systems,” Vehicle System Dynamics, vol. 61, no. 7, pp.1831-1880, 2023.

S. Liu, T. Zheng, D. Zhao, R. Hao and M. Yang, “Strongly perturbed sliding mode adaptive control of vehicle active suspension system considering actuator non-linearity,” Vehicle System Dynamics, vol. 60, no. 2, pp. 597-616, 2022.

V. Muniandy, P. Samin and H. Jamaluddin, “Application of a self-tuning fuzzy Pi–Pd controller in an active anti-roll bar system for a passenger car,” Vehicle System Dynamics, vol. 53, no. 11, pp. 1641-1666, 2015.

H. Gao, L. Jézéquel, E. Cabrol and . B. Vitry, “Chassis durability and comfort trade-off at early stage of project by virtual proving ground simulation,” Vehicle System Dynamics, vol. 60, no. 6, pp. 2124-2144, 2022.

G. Li, R. Wu, X. Deng, L. Shen and Y. Yao, “Suspension parameters matching of high-speed locomotive based on stability/comfort pareto optimization,” Vehicle System Dynamics, vol. 60, no. 11, pp. 3848-3867, 2022.

M. Čorić, J. Deur, L. Xu, H.E. Tseng and D. Hrovat, “Optimisation of active suspension control inputs for improved vehicle ride performance,” Vehicle System Dynamics, vol. 54, no. 7, pp. 1004-1030, 2016.

A.S. Gad and E.-D. Samir, “Effect of optimal fuzzy models for pneumatic magnetorheological suspension system on ride performance under different conditions,” SAE International Journal of Vehicle Dynamics, Stability, and NVH, vol. 6, pp. 421-440, 2022.

A. Shehata Gad, “Preview model predictive control controller for magnetorheological damper of semi-active suspension to improve both ride and handling,” SAE International Journal of Vehicle Dynamics, Stability, and NVH, vol. 4, pp. 305-326, 2020.

A. Shehata Gad, S. Darakhshan Jabeen, and W. Galal Ata, “Damping magnetorheological systems based on optimal neural networks preview control integrated with new hybrid fuzzy controller to improve ride comfort,” SAE Int. J. Veh. Dyn., Stab., and NVH, vol. 7, no. 4, pp. 1-24, 2023.

H. Han, B. Yim, N. Lee and Y. Kim, “Prediction of ride quality of a maglev vehicle using a full vehicle multibody dynamic model,” Vehicle System Dynamics, vol. 47, no. 10, pp. 1271–1286, 2009.

G. Geetharamani, K. Dhinakaran, J. Selvaraj and S.C. Ezhil Singh, “Sport-utility vehicle prediction based on machine learning approach,” Journal of Applied Research and Technology, vol. 19, pp. 184-193, 2021.

A. Khajepour and A. Soltani, “A coordinated control system for truck cabin suspension based on model predictive control,” International Journal Of Heavy Vehicle Systems, vol. 29, p. 518–536, 2022.

J. Liang, Y. Lu, D. Pi, G. Yin, W. Zhuang, F. Wang, J. Feng and C. Zhou, “A decentralized cooperative control framework for active steering and active suspension: Multi-agent approach,” IEEE Transactions on Transportation Electrification, vol. 8, pp. 1414-1429, 2022.

“Machine Learning for Autonomous Vehicle's Trajectory Prediction: A Comprehensive Survey, Challenges, And Future Research Directions,” Vehicular Communications, vol. 46, p.100733, 2024.

T.A. Nguyen, “A novel approach with a fuzzy sliding mode proportional integral control algorithm tuned by fuzzy method (FSMPIF),” Scientific Reports, vol. 13, no. 1, p.7327, 2023.

D.N. Nguyen and T.A. Nguyen, “A novel hybrid control algorithm sliding mode-Pid for the active suspension system with state multivariable,” Complexity, vol 2022, no. 1, p. 9527384, 2022.

T.A. Nguyen, “Improving the stability of the passenger vehicle by using an active stabilizer bar controlled by the fuzzy method,” Hindawi Complexity, vol 2021, no. 1, p. 6569298, 2021.

Z.J. Fu and X.Y. Dong, “H∞ optimal control of vehicle active suspension systems in two time scales,” Journal For Control, Measurement, Electronics, Computing and Communications, vol. 62, pp. 1-15, 2021.

H. Yang, B.-G. Kim, J.-S. Oh and G.-W. Kim , “Simultaneous estimation of vehicle mass and unknown road roughness based on adaptive extended kalman filtering of suspension systems,” Electronics, vol. 11, no. 16, pp. 1-15, 2022.

Y. He, Y. Zhou, D. Wang, S. Liu and X. Wei, “Stabilization analysis of a class of nonlinear time delay systems with time-varying full-state constraints,” Journal for Control, Measurement, Electronics, Computing and Communications, vol. 64, no. 3, pp. 496-507, 2023.

S.Y. Han, J.F. Dong, J. Zhou and Y.-H. Chen, “Adaptive fuzzy Pid control strategy for vehicle active suspension based on road evaluation,” Electronics, vol. 11, no. 6, p. 921, 2022.