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 a hybrid electromagnetic suspension system for ride comfort, road holding and regenerated power

2016 ◽  
Vol 23 (5) ◽  
pp. 782-793 ◽  
Author(s):  
Mansour Ataei ◽  
Ehsan Asadi ◽  
Avesta Goodarzi ◽  
Amir Khajepour ◽  
Mir Behrad Khamesee
Keyword(s):  
Pareto Front ◽  
Ride Comfort ◽  
Optimization Method ◽  
Suspension System ◽  
Multi Objective Optimization ◽  
Damping Force ◽  
Nsga Ii ◽  
Electromagnetic Suspension ◽  
Multi Objective ◽  
Electromagnetic Component

This paper reports work on the optimization and performance evaluation of a hybrid electromagnetic suspension system equipped with a hybrid electromagnetic damper. The hybrid damper is configured to operate with hydraulic and electromagnetic components. The hydraulic component produces a large fail-safe baseline damping force, while the electromagnetic component adds energy regeneration and adaptability to the suspension. For analyzing the system, the electromagnetic component was modeled and integrated into a 2DOF quarter-car model. Three criteria were considered for evaluating the performance of the suspension system: ride comfort, road holding and regenerated power. Using the genetic algorithm multi-objective optimization (NSGA-II), the suspension design was optimized to improve the performance of the vehicle with respect to the selected criteria. The multi-objective optimization method provided a set of solutions called Pareto front in which all solutions are equally good and the selection of each one depends on conditions and needs. Among the given solutions in the Pareto front, a small number of cases, with different design purposes, were selected. The performances of the selected designs were compared with two reference systems: a conventional and a nonoptimized hybrid suspension system. The results show that the ride comfort and road holding qualities of the optimized hybrid system are improved, and the regenerated power is considerably increased.

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.

The Multi-Objective Optimization Design of Vehicle Suspension System Based on ADAMS

2012 ◽  
Vol 472-475 ◽  
pp. 1932-1936
Author(s):  
Zhi Jian Gou
Keyword(s):  
Dynamic Load ◽  
Optimization Design ◽  
Optimization Method ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Multi Objective Optimization ◽  
System Parameters ◽  
Multi Objective ◽  
Full Vehicle ◽  
Vehicle Suspension System

In order to improve the riding and handling of the vehicle，the full-vehicle dynamical model of a certain vehicle is established by means of the software Adams/Car．The design of multi-objective optimization was used for Suspension system parameters on the base of the dynamical model．The optimized results show that riding of the vehicle is retained and dynamic load of wheel is improved obviously．It can be concluded that the optimization method is feasible for the optimization design of suspension system parameters．the investigation also supply the basis of theory for design considering the matching of the suspension system parameters.

Multi-objective optimization of chassis integrated system for electric wheel vehicle

2018 ◽  
Vol 233 (1) ◽  
pp. 7-17 ◽  
Author(s):  
Wanzhong Zhao ◽  
Zunsi Yang ◽  
Chunyan Wang
Keyword(s):  
Dynamic Models ◽  
Ride Comfort ◽  
Optimization Method ◽  
Steering System ◽  
Integrated System ◽  
Multi Objective Optimization ◽  
Evaluation Indexes ◽  
Multi Objective ◽  
Vehicle Dynamic Model ◽  
Optimization Efficiency

In order to improve the overall performance of the electric wheel vehicle, this paper researches the multi-objective optimization method of the chassis integrated system. The dynamic models of integrated system including differential steering system, differential brake system, and active suspension system are established. In order to verify the validity of the vehicle dynamic model and ensure the correctness of the optimization analysis results, the model validation is implemented. Considering the coupling relationship among subsystems, the performance evaluation indexes of steering road feel, steering sensitivity, and suspension ride comfort are deduced under steering and braking conditions. To alleviate the subjectivity in the selection of objective weighting, the deviation sort polymerization method is used to convert the multi-objective model into a single-objective one based on the linear weighted polymerization. Aiming at the optimization characteristic of chassis integrated system, an adaptive weight particle swarm optimization algorithm is proposed to improve the optimization efficiency and convergence. The optimization results show that the optimized chassis integrated system can obtain favorable steering road feel, better steering sensitivity, and suspension ride comfort.

Student Admission Assesment using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)

2018 ◽  
Author(s):  
Rivalri Kristianto Hondro ◽  
Mesran Mesran ◽  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Initial Step ◽  
Optimization Method ◽  
Ratio Analysis ◽  
Multi Objective Optimization ◽  
Prospective Students ◽  
Multi Objective

Procurement selection process in the acceptance of prospective students is an initial step undertaken by private universities to attract superior students. However, sometimes this selection process is just a procedural process that is commonly done by universities without grouping prospective students from superior students into a class that is superior compared to other classes. To process the selection results can be done using the help of computer systems, known as decision support systems. To produce a better, accurate and objective decision result is used a method that can be applied in decision support systems. Multi-Objective Optimization Method by Ratio Analysis (MOORA) is one of the MADM methods that can perform calculations on the value of criteria of attributes (prospective students) that helps decision makers to produce the right decision in the form of students who enter into the category of prospective students superior.

Multi-objective optimization of optimal capacitor allocation in radial distribution systems

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Sayed Mir Shah Danish ◽  
Mikaeel Ahmadi ◽  
Atsushi Yona ◽  
Tomonobu Senjyu ◽  
Narayanan Krishna ◽  
...  
Keyword(s):  
Radial Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Reactive Power ◽  
Stability Index ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Size ◽  
Multi Objective Optimization ◽  
Multi Objective

AbstractThe optimal size and location of the compensator in the distribution system play a significant role in minimizing the energy loss and the cost of reactive power compensation. This article introduces an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using multi-objective optimization method. A new objective function different from literature is adapted to enhance the overall system voltage stability index, minimize power loss, and to achieve maximum net yearly savings. However, the capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Load sensitive factor (LSF) has been used to predict the most effective buses as the best place for installing compensator devices. IEEE 34-bus and 118-bus test distribution systems are utilized to validate and demonstrate the applicability of the proposed method. The simulation results obtained are compared with previous methods reported in the literature and found to be encouraging.

scholarly journals Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 107
Author(s):  
Rongchao Jiang ◽  
Zhenchao Jin ◽  
Dawei Liu ◽  
Dengfeng Wang
Keyword(s):  
Ride Comfort ◽  
Lightweight Design ◽  
Dynamic Performance ◽  
Vehicle Dynamic ◽  
Multi Objective Optimization ◽  
Original Design ◽  
Dynamic Simulations ◽  
Nsga Ii ◽  
Torsion Beam ◽  
Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

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