Fuzzy Multi-Objective Optimization of Passive Suspension Parameters

2010 ◽  
Vol 2 (1) ◽  
pp. 87-100 ◽  
Author(s):  
Chong-zhi Song ◽  
You-qun Zhao
Keyword(s):  
Multi Objective Optimization ◽  
Passive Suspension ◽  
Multi Objective ◽  
Suspension Parameters

Multi-objective optimization of quarter car passive suspension design in the frequency domain based on PSO

2016 ◽  
Vol 33 (5) ◽  
pp. 1422-1434 ◽  
Author(s):  
Herbert Martins Gomes
Keyword(s):  
Frequency Domain ◽  
Optimization Design ◽  
Heuristic Algorithms ◽  
Computational Effort ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Passive Suspension ◽  
Multi Objective ◽  
Suspension Parameters ◽  
Quarter Car

Purpose – The purpose of this paper is to investigate the optimum design of a quarter car passive suspension system using a particle swarm optimization algorithm in order to minimize the applied loads and vibrations. Design/methodology/approach – The road excitation is assumed as zero-mean random field and modeled by single-sided power spectral density (PSD) based on international standard ISO 8608. The variance of sprung mass displacements and variance of dynamic applied load are evaluated by PSD functions and used as cost function for the optimization. Findings – The advantages in using this methodology are emphasized by an example of the multi-objective optimization design of suspension parameters and the results are compared with values reported in the literature and other gradient based and heuristic algorithms. The paper shows that the algorithm effectively leads to reliable results for suspension parameters with low computational effort. Research limitations/implications – The procedure is applied to a quarter car passive suspension design. Practical implications – The proposed procedure implies substantial time savings due to frequency domain analysis. Social implications – The paper proposes a procedure that allows complex optimization designs to be feasible and cost effective. Originality/value – The design optimization is performed in the frequency domain taking into account standard defined road profiles PSD without the need to simulate in the time domain.

Multi-objective bionics design method of passive suspension parameters based on hybrid behavior game

2010 ◽  
Vol 42 (3) ◽  
pp. 371-386 ◽  
Author(s):  
Wang Lu ◽  
Xie Neng Gang ◽  
Song Chong Zhi ◽  
Bao Jia Han ◽  
Cen Yu Wan
Keyword(s):  
Design Method ◽  
Passive Suspension ◽  
Multi Objective ◽  
Suspension Parameters

Multi-Objective Optimization of Vehicle Air Suspension Based on Simulink-Mfile Mixed Programming

2014 ◽  
Vol 509 ◽  
pp. 63-69 ◽  
Author(s):  
Jin Hui Li ◽  
Jie He ◽  
Xu Hong Li
Keyword(s):  
Ride Comfort ◽  
Optimization Method ◽  
Vehicle Suspension ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Road ◽  
Suspension Parameters ◽  
Air Suspension ◽  
Mixed Programming ◽  
Road Roughness

In order to reduce the road damage of heavy trucks, comprehensively considering ride comfort and road friendliness, the multi-objective optimization method of vehicle suspension parameters with non-linear air spring was presented based on Simulink-Mfile mixed programming. The simulation model including vehicle dynamics module, road roughness module, ride comfort and road friendliness evaluation index modules was constructed in Simulink platform, and the multi-objective optimization model was developed in Mfile program which took the linear weighted sum of ride comfort and road friendliness indexes as the objective. Then the suspension parameters were optimized with genetic algorithm (GA). The results showed that, compared with before optimization, the vehicle ride comfort and road friendliness could be synthetically improved. And with the Simulink-Mfile mixed programming method, the optimization of nonlinear vehicle suspension could be successfully solved in time domain, which could provide a new idea for vehicle suspension design.

Multi-objective optimization of the suspension parameters in the in-wheel electric vehicle

2021 ◽  
pp. 1-8
Author(s):  
Ruihua Li
Keyword(s):  
Electric Vehicle ◽  
Ride Comfort ◽  
Impact Force ◽  
The Body ◽  
Particle Swarm Algorithm ◽  
Vertical Acceleration ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Suspension Parameters ◽  
Vertical Impact

The hub motor significantly increases the unsprung mass of electric in-wheel vehicles, which deteriorates the ride comfort and safety of vehicles and which can be effectively improved by optimizing the main suspension parameters of vehicles reasonably, so a multi-objective optimization method of main suspension parameters based on adaptive particle swarm algorithm is proposed and the dynamic model of a half in-wheel electric vehicle is established. Taking the stiffness coefficient of the suspension damping spring and damping coefficient of the damper as independent variables, the vertical acceleration of the body, the pitch acceleration and the vertical impact force of the hub motor as optimization variables, and the dynamic deflection of the suspension and the dynamic load of the wheel as constraint variables, the multi-objective optimization function is constructed, and the parameters are simulated and optimized under the compound pavement. The simulation results show that the vertical acceleration and pitch acceleration are reduced by 20.2% and 18.4% respectively, the vertical impact force of the front hub motor is reduced by 3.7%, and the ride comfort and safety are significantly improved.

Design and optimization of a semi-active suspension system for a two-wheeled vehicle using a full multibody model

2016 ◽  
Vol 231 (4) ◽  
pp. 630-646 ◽  
Author(s):  
A Khadr ◽  
A Houidi ◽  
L Romdhane
Keyword(s):  
Ride Comfort ◽  
Optimization Procedure ◽  
Active Suspension ◽  
Suspension System ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Passive Suspension ◽  
Multi Objective ◽  
Wheeled Vehicle ◽  
Passive Suspension System

This paper focuses on the design and the optimization of a semi-active suspension system used in a full dynamic model of a two-wheeled vehicle. The two-wheeled vehicle is considered as a multibody system. The equations of motion are obtained by applying an approach used widely in the robotic modeling field. Two basic strategies, called the continuous skyhook and the modified skyhook, are used to control the semi-active suspension system. Using the developed model, a multi-objective optimization procedure, based on Genetic Algorithms (NSGA-II), is proposed. The objective is to optimize the parameters of the two control laws of the semi-active suspension systems, in order to improve the ride comfort and the safety. To study the effectiveness of this approach, the results of the optimization are used in different simulations and the results are compared with those obtained from a simulation of a two-wheeled vehicle equipped with a passive suspension system. The results show that both control strategies of the semi-active suspension system give an improvement compared to the passive suspension system. Moreover, the multi-objective optimization results show that the simplified law “Modified Skyhook” ensures a higher ride safety, whereas the “Continuous Skyhook” is more effective in obtaining a higher level of ride comfort.

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