Global-guidance chaotic multi-objective particle swarm optimization method for pneumatic suspension handling and ride quality enhancement on the basis of a thermodynamic model of a full vehicle
Restricted accessResearch articleFirst published online December, 2023
Global-guidance chaotic multi-objective particle swarm optimization method for pneumatic suspension handling and ride quality enhancement on the basis of a thermodynamic model of a full vehicle
In this paper, the parameters of the validated suspension system model of a full-vehicle are tuned through design sensitivity analyses, and then Multi-Objective Particle Swarm Optimization (MOPSO) is used to enhance vehicle ride comfort, which is the vertical whole-body vibrations, and handling features, that is, roll motion and road holding, simultaneously. The parameters of this thermodynamic-based pneumatic suspension system model are comprised of the air spring reservoir volume, orifice resistance, initial volume, and pressure of the pneumatic springs. To enhance the convergence rate, computational times, and diversity of the swarm particles, we have incorporated chaotic dynamics into the MOPSO using the Logistic Map chaotic method to initialize the population and also employed the leader-based global guidance techniques to conduct the potential solutions in each iteration. The analysis of the proposed modeling and optimization results show that the suspension system has been reasonably boosted in terms of vehicle handling and ride comfort. Quantitatively, the RMS acceleration and pitch angle has been reduced by about 71% and 57%, respectively, showing a substantial improvement in passenger comfort. Furthermore, the proposed approach caused an increase in tire road-holding force by about 148% and a reduction of roll angle by 33% which results in an enhancement in vehicle handling, boosting vehicle driving safety.
PrasadVPawaskarDNSeshuP.Controller design and multi-objective optimization of heavy goods vehicle suspension system by geometry-inspired GA. Struct Multidiscipl Optim2021; 64: 89–111.
2.
MashadiBMostaghimiH.Effects of the lateral compliance of the suspension on the vehicle-handling responses. Proc IMechE, Part D: J Automobile Engineering2015; 229: 641–655.
3.
MashadiBEbrahimi-NejadSAbbaspourM.A rolling resistance estimate using nonlinear finite element numerical analysis of a full three-dimensional tyre model. Proc IMechE, Part D: J Automobile Engineering2019; 233(1): 147–160.
4.
NazemianHMasih-TehraniM.Development of an optimized game controller for energy saving in a novel interconnected air suspension system. Proc IMechE, Part D: J Automobile Engineering2020; 234(13): 3068–3080.
5.
CaoKLiZGuY, et al. The control design of transverse interconnected electronic control air suspension based on seeker optimization algorithm. Proc IMechE, Part D: J Automobile Engineering2021; 235(8): 2200–2211.
6.
DiZioPEkchianJKaplanJ, et al. An active suspension system for mitigating motion sickness and enabling reading in a car. Aerosp Med Hum Perform2018; 89(9): 822–829.
7.
PorumamillaHKelkarAGVogelJM.Modeling and verification of an innovative active pneumatic vibration isolation system. J Dyn Syst Meas Control2008; 130: 031001.
8.
SayadiHShokouhiN.New dynamics model for rail vehicles and optimizing air suspension parameters using GA. Sci Iran2009; 16: 496–512.
9.
YinZKhajepourACaoD, et al. A new pneumatic suspension system with independent stiffness and ride height tuning capabilities. Veh Syst Dyn2012; 50: 1735–1746.
10.
Karimi EskandaryPKhajepourAWongA, et al. Analysis and optimization of air suspension system with independent height and stiffness tuning. Int J Autom Technol2016; 17: 807–816.
11.
AwadMNSokarMIAbdrabboSM, et al. Hydro-pneumatic energy harvesting suspension system using a PSO based PID controller. SAE Int J Commer Veh2018; 11: 223–234.
12.
ZhuHYangJZhangY.Modeling and optimization for pneumatically pitch-interconnected suspensions of a vehicle. J Sound Vib2018; 432: 290–309.
13.
YinYRakhejaSBoileauP-E.Multi-performance analyses and design optimisation of hydro-pneumatic suspension system for an articulated frame-steered vehicle. Veh Syst Dyn2019; 57: 108–133.
14.
ChenYPetersonAWAhmadianM.Achieving anti-roll bar effect through air management in commercial vehicle pneumatic suspensions. Veh Syst Dyn2019; 57(12): 1775–1794.
15.
LiZWangYDuH, et al. Modelling and analysis of full-vehicle hydro-pneumatic suspension system considering real-gas polytropic process. Mech Syst Signal Process2022; 165: 108406.
16.
DamavandiADMasih-TehraniMMashadiB.Configuration development and optimization of hydraulically interconnected suspension for handling and ride enhancement. Proc IMechE, Part D: J Automobile Engineering2022; 236(2–3): 381–394.
17.
ShojaeefardMHKhalkhaliAFaghihianH, et al. Optimal platform design using non-dominated sorting genetic algorithm II and technique for order of preference by similarity to ideal solution; application to automotive suspension system. Eng Optim2018; 50: 471–482.
18.
Nariman-ZadehNSalehpourMJamaliA, et al. Pareto optimization of a five-degree of freedom vehicle vibration model using a multi-objective uniform-diversity genetic algorithm (MUGA). Eng Appl Artif Intell2010; 23: 543–551.
19.
SeifiAHassannejadRHamedMA.Use of nonlinear asymmetrical shock absorbers in multi-objective optimization of the suspension system in a variety of road excitations. Proc IMechE, Part K: J Multi-body Dynamics2017; 231: 372–387.
20.
AtaeiMAsadiEGoodarziA, et al. Multi-objective optimization of a hybrid electromagnetic suspension system for ride comfort, road holding and regenerated power. J Vib Control2017; 23: 782–793.
21.
SertEBoyrazP.Optimization of suspension system and sensitivity analysis for improvement of stability in a midsize heavy vehicle. Eng Sci Technol Int J2017; 20: 997–1012.
22.
ChengY-CLeeC-K.Integration of uniform design and quantum-behaved particle swarm optimization to the robust design for a railway vehicle suspension system under different wheel conicities and wheel rolling radii. Acta Mech Sin2017; 33: 963–980.
23.
GenoveseAStranoSTerzoM.Design and multi-physics optimization of an energy harvesting system integrated in a pneumatic suspension. Mechatronics2020; 69: 102395.
24.
SunZShiYWangN, et al. Mechanism and optimization of a novel automobile pneumatic suspension based on dynamic analysis. Electronics2021; 10(18): 2232.
25.
XuXLiuHJiangX, et al. Uncertainty analysis and optimization of quasi-zero stiffness air suspension based on polynomial chaos method. Chin J Mech Eng2022; 35(1): 1–9.
FakhraeiJKhanloHMGhayourM.Analysis of the passengers effect on chaotic vibrations of a nonlinear full vehicle model. Mod Mech Eng2015; 15: 173–184.
28.
PedroJODangorMDahunsiOA, et al. Differential evolution-based PID control of nonlinear full-car electrohydraulic suspensions. Math Probl Eng2013; 2013: 1–13.
29.
OzcananSAtahanAO.Minimization of accident severity index in concrete barrier designs using an ensemble of radial basis function metamodel-based optimization. Eng Optim2021; 22: 485–519.
30.
MollajafariMShahhoseiniHS.An efficient ACO-based algorithm for scheduling tasks onto dynamically reconfigurable hardware using TSP-likened construction graph. Appl Intell2016; 45(3): 695–712.
31.
MollajafariMShahhoseiniHS.Cost-optimized GA-based heuristic for scheduling time-constrained workflow applications in infrastructure clouds using an innovative feasibility-assured decoding mechanism. J Inf Sci Eng2016; 32(6): 1541–1560.
32.
MashadiBMajidiMPourabdollah DizajiH.Optimal vehicle dynamics controller design using a four-degrees-of-freedom model. Proc IMechE, Part D: J Automobile Engineering2010; 224(5): 645–659.
33.
MollajafariMShahhoseiniHS.A repair-less genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware. Int Rev Comput Software2011; 6(2): 206–212.
34.
MollajafariMShojaeefardMH.TC3PoP: a time-cost compromised workflow scheduling heuristic customized for cloud environments. Cluster Comput2021; 24: 2639–2656.
35.
CvokIŠkugorBDeurJ.Control trajectory optimisation and optimal control of an electric vehicle HVAC system for favourable efficiency and thermal comfort. Eng Optim2021; 22: 83–102.
36.
NingYPengZDaiY, et al. Enhanced particle swarm optimization with multi-swarm and multi-velocity for optimizing high-dimensional problems. Appl Intell2019; 49(2): 335–351.
37.
TharwatAHousseinEHAhmedMM, et al. MOGOA algorithm for constrained and unconstrained multi-objective optimization problems. Appl Intell2018; 48(8): 2268–2283.
38.
NireshJArchanaNAnand RajG.Optimisation of linear passive suspension system using MOPSO and design of predictive tool with artificial neural network. Stud Inform Control2019; 28(1): 105–110.
39.
FuGWangCZhangD, et al. A multiobjective particle swarm optimization algorithm based on multipopulation coevolution for weapon-target assignment. Math Probl Eng2019; 2019: 1–11.
40.
ZhangHLiuTYeX, et al. Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems. Eng Comput. Epub ahead of print 10 January 2022. DOI: 10.1007/s00366-021-01545-x
41.
PengYHeSSunK.Parameter identification for discrete memristive chaotic map using adaptive differential evolution algorithm. Nonlinear Dyn2022; 107(1): 1263–1275.
42.
AbualigahLDiabatA.Chaotic binary group search optimizer for feature selection. Expert Syst Appl2022; 192: 116368.
43.
FengYTengG-FWangA-X, et al. Chaotic inertia weight in particle swarm optimization. In: Proceedings of the 2nd international conference on innovative computing, information and control (ICICIC '07), Kumamoto, Japan, 2007, p.475.
44.
International Organization of Standardization (ISO 2631-1). Mechanical vibration and shock – evaluation of human exposure to whole-body vibration – Part 1: General requirements. Geneva, Switzerland: International Organization of Standardization, 1997.
45.
GuoPZhangJH.Numerical model and multi-objective optimization analysis of vehicle vibration. Journal of Zhejiang University-Science A2017; 18(5): 393–412.
46.
InamdarKV.Vehicle Suspension optimization for stochastic inputs. MSc Thesis, The University of Texas at Arlington, 2012.
47.
International Organization of Standardization (ISO 8608). Mechanical vibration — road surface profiles. Geneva, Switzerland: International Organization of Standardization, 2016.
48.
ShiraziKHJaafariSMDerakhshanM, et al. Road-holding and ride performance optimization using Bee’s algorithm. In: ASME international mechanical engineering congress and exposition, Denver, Colorado, USA, 2011, vol. 54938.
49.
OuattaraA.Methods of eco-design methodology for multiobjective optimization and multiple-criteria decision analysis [Méthodologie d'éco-conception de procédés par optimisation multiobjectif et aide à la décision multicritère]. Ph.D. Thesis, Univerity de Toulouse, Toulouse, France, 2011.
50.
AfshariAMojahedMYusuffRM.Simple additive weighting approach to personnel selection problem. Int J Innov Manag Technol2010; 1: 511–515.
51.
SavithaKChandrasekarC.Vertical handover decision schemes using SAW and WPM for network selection in heterogeneous wireless networks. Glob J Comput Sci Technol2011; 11: 19–24.
52.
AhmadiAAHallGPapachristodoulouA, et al. Improving efficiency and scalability of sum of squares optimization: recent advances and limitations. In: 2017 IEEE 56th annual conference on decision and control (CDC), Melbourne, VIC, Australia, 2017, pp.453–462. IEEE.
