Vehicle Suspension Parameters Research Articles

Optimization of electric vehicle suspension parameters using improved artificial fish swarm algorithm

This study proposes a solution to reduce vertical vibrations and body pitching in response to random road surface excitations. To achieve these objectives, a half-vehicle model of an electric vehicle (EV) is developed to determine optimal parameters for both the EV suspension system and the driver's seat suspension system. An Improved Artificial Fish Swarm Algorithm (IAFSA) is implemented using MATLAB software to optimize these suspension parameters. The optimization aims to minimize the root mean square (RMS) values of three objective functions: vertical driver's seat acceleration (aws), vertical vehicle body acceleration (awb), and pitching vehicle body acceleration (awphi). The optimization results reveal that the values of these three objective functions decrease when using the optimized suspension parameters compared to the original suspension settings. Specifically, the aws, awb and awphi values are reduced by 15.44%, 11.46%, and 8.65%, respectively, when the vehicle travels on an ISO road class B at a speed of 20 m/s with a full load. Furthermore, the peak amplitude values of as, ab, and aphi in the frequency domain are also reduced with the optimized suspension parameters compared to the original settings under the specified conditions.

Vehicle suspension tuning for bridge-friendliness and influence on coupled vehicle-bridge system frequency

A novel study on the mitigation of bridge response via tuning of vehicle suspension parameters is presented. The coupled vehicle-bridge interaction (VBI) system consists of a quarter car model with a semi-active skyhook suspension. Transmissibility functions of the time-varying VBI system are derived and an extended fixed points principle is applied to obtain optimally tuned values of the vehicle suspension parameters for bridge-friendliness. Performances of both the optimally tuned passive and semi-active suspensions are assessed in terms of the dynamic responses, energetics and interacting force of the VBI system, and are shown to achieve excellent bridge-friendliness. Effects of vehicle suspension parameters on the temporal variation of the coupled bridge frequency are also studied. It is found that the bridge frequency depends strongly on two key physical parameters: vehicle-bridge frequency ratio and vehicle damping ratio. Moreover, variations of the bridge frequency during the vehicle passage are small when the tuned suspensions are used. This new finding may have critical implications in future research in bridge frequency identification using a moving vehicle.

Non-Invasive Identification of Vehicle Suspension Parameters: A Methodology Based on Synthetic Data Analysis

In this study, we introduce an innovative approach for the identification of vehicle suspension parameters, employing a methodology that utilizes synthetic and experimental data for non-invasive analysis. Central to our approach is the application of a basic local optimization algorithm, chosen to establish a baseline for parameter identification in increasingly complex vehicle models, ranging from quarter-vehicle to half-vehicle (bicycle) models. This methodology enables the accurate simulation of the vehicle dynamics and the identification of suspension parameters under various conditions, including road perturbations such as speed bumps and curbs, as well as in the presence of noise. A significant aspect of our work is the ability to process real-world data, making it applicable in practical scenarios where data are obtained from onboard sensor equipment. The methodology was developed in MatLab, ensuring portability across platforms that support this software. Furthermore, the study explores the application of this methodology as a tool for denoising, enhancing its utility in real-world data analysis and predictive maintenance. The findings of this research provide valuable insights for vehicle suspension design, offering a cost-effective and efficient solution for dynamic parameter identification without the need for physical disassembly.

Research on optimal matching of vehicle suspension parameters for improving vehicle ride comfort on bump road

A rigid-flexible dynamics model is built. In view of established 7-DOF vibration model, the expressions of the output indexes are derived and the influence mechanism of suspension parameters on ride comfort is revealed. The effects of different vehicle speeds on ride comfort on bump road are studied in the frequency and time domains by analyzing the suspension dynamic disturbance by taking the vertical acceleration of the foot floor and seat rail, the wheel dynamic loads, and the suspension dynamic deflections of the suspension as the objects of analysis. The effects of dynamic changes in the suspension spring and damper parameters on the time and frequency domain response indices are analyzed. Two window functions are used on the bump road to process and analyze the time domain charts of the front and rear of foot floors and seat rails in the vertical direction, and the [Formula: see text] values of the global and local vibrations of the foot floor and seat rail obtained are used as one of the evaluation indicators of ride comfort. The evaluation index of the total vehicle considering local and global vibration is determined, and the suspension parameters are collaboratively optimized by genetic algorithm to improve vehicle ride comfort.

Development of an algorithm for refineding parameters vehicle suspensions

The research results of the oscillations for sprung and unsprung masses in the frequency range are presented. The amplitude-phase frequency characteristics of individual masses during vibrations with follow-up presentation of transient processes, and the main results on the refinement of vehicle suspension parameters are determined. The function of the damping time of vehicle oscillations is considered. The time function a program algorithm that can be used when designing a vehicle to find the minimum value is developed. The purpose of the work is to rice the comfort of the vehicle by minimizing the damping time of the sprung mass oscillations. Keywords oscillation frequency, suspension system, static and dynamic suspension deflection, oscillation damping time, suspension parameters refinement algorithm

Passenger vehicle suspension parameters influence on vehicle – track model solutions

The simulation tests results of the rail vehicle - track system model are presented in this article. The purpose of the research was to determine the influence of chosen vehicle suspension element parameters on stability and safety of motion. Simulation model of 4-axle passengers coach was created with use the VI-Rail software. Damping component of the second stage elastic-damping element in the longitudinal direction was selected. For two values of the damping parameter applied, such a few system parameters were determined: critical velocity, values of solutions in a wide velocity range, lateral wheelset-track forces and values of safety factor against derailment. The vehicle motion was simulated along a straight track and curved track with a radius of R = 3000, 4000 and 6000m. Comparison of vehicle model features for particular damping component values were done. The results are presented in the form of diagrams illustrating changes in the tested system parameters as a function of vehicle velocity.

Maneuver-based deep learning parameter identification of vehicle suspensions subjected to performance degradation

A novel parameter identification method was proposed for vehicle suspensions subjected to performance degradation. The proposed method does not require the measurement of the stiffness and damping coefficients of suspensions. Instead, it uses vehicle states to calculate the stiffness and damping coefficients based on an efficient multibody model and inverse dynamics. First, a full-vehicle system was modelled using a semirecursive multibody formulation, and the dynamic properties of suspensions, chassis frame, and tires were considered. Second, dynamic simulations on a bumpy road were performed, and vehicle state data were collected. A deep neural network (DNN) model, whose inputs and outputs were vehicle states and suspension parameters, was developed. The DNN model can estimate the stiffness and damping coefficients based on vehicle states measured by sensor networks. The parameter identification was achieved by deep learning of the relationship between vehicle states and suspension parameters in a given maneuver. Finally, the model accuracy was investigated in terms of different DNN inputs, data samples, and hidden layers. The results showed that the DNN model predicts accurate stiffness and damping coefficients in real time. This maneuver-based parameter identification method can be used for the condition-based monitoring or fault diagnosis of vehicle suspensions subjected to performance degradation.

Adaptive Fuzzy PID Control Strategy for Vehicle Active Suspension Based on Road Evaluation

The paper proposes a fuzzy proportion integral differential (PID) control strategy based on road estimation to meet the different performance requirements of active suspension for comfort and safety under different road conditions in view of the uncertainty of vehicle suspension parameters and random road perturbations. First, flexible neural tree (FNT) is used to estimate the vertical disturbance of road within uncertain vehicle parameters in which the Fourier transform of the autocorrelation function is used to fit the road power spectrum density (PSD). In order to compensate for the delay and realize the real-time estimate of road conditions, an online fuzzy evaluation strategy for road condition is developed by integrating the fitted road PSD and the real-time suspension performance. After that, an adaptive fuzzy PID control strategy is developed based on the adaptive suspension control performance, which is supposed to realize the adaptive adjustment of control parameters for different road conditions. The simulation experimental results show that the online fuzzy evaluation strategy can effectively reflect the change in road condition, and the proposed adaptive fuzzy PID control strategy can work for adjusting the parameters adaptively, according to the changes in road conditions and therefore meet the control performance requirements under different road conditions.

Simulation of Active Wheelset Steering for an Electric Locomotive

Reduction of the force interaction between vehicle and track is strongly desirable for many reasons. Because conventional methods of reduction of wheel-rail contact forces, based on tuning of vehicle suspension parameters or utilising various mechanisms acting between wheelsets, bogie-frames and car-body, reach their limits, active controlled systems are becoming a promising solution of the problem. The paper is created within the project which aim is to design a system of active wheelset steering for an electric four-axle locomotive. The project consists of three main stages: multibody simulations, scaled roller rig experiments and field tests. One of the important elements of wheelset steering system is the estimation of the actual track curvature. The paper focuses on the track curvature calculation based on the rotation of the bogies towards the bogie-frame. The method of track curvature calculation is proposed and assessed across varying track radiuses, vehicle speeds and friction conditions.

A Monte Carlo Parametric Sensitivity Analysis of Automobile Handling, Comfort, and Stability

This paper investigates the bandwidth sensitivity of automobile handling, comfort, and stability based on Monte Carlo sensitivity simulations. Performed bandwidth sensitivity simulations include the effects of vehicle geometry and suspension parameters on lateral acceleration, roll angle, front/rear sideslip angles, and yaw rate angle, including both time- and frequency-domain sensitivity analyses. To replicate actual automobile responses, a full-vehicle roll-oriented suspension seven-degree-of-freedom (7-DOF) model was developed and implemented considering a 2-DOF planar two-track model with a nonlinear Pacejka tire model. During the Monte Carlo simulations, 10 mm and 20 mm amplitude sine-wave excitations were used for the left and right sides, respectively, and the frequency was uniformly sampled over the range of 0–30 Hz. Simultaneously, each investigated vehicle parameter varied by ±25% relative to the reference model parameters. These simulations illustrate the sensitivity of the lateral acceleration, roll angle, yaw angle, and sideslip angles to their parameter variations. The results confirm that the road excitation frequency, tire properties, vehicle geometry, and suspension parameters significantly influence the vehicular lateral and roll stabilities when considering the lower and higher peaks and the frequency bandwidths of the lateral and roll stabilities. Interestingly, the longitudinal location of the center of gravity and the tire properties can achieve more significant peak lateral stability responses, as represented by the front and rear sideslip angles and the frequency bandwidth, compared to the other vehicle parameters at high frequencies. Choosing the correct tire properties and vehicle geometry, as well as suspension characteristics, plays an essential role in increasing the vehicular lateral stability and the rollover threshold. Variations in the studied parameters allow for higher vehicular stability when a vehicle is driven on random road surfaces.

Wheel Profile Optimization of Speed-up Freight Train Based on Multi-population Genetic Algorithm

The geometric shape of the wheel tread is mathematically expressed, and geometric parameters affecting the shape of the wheel were extracted as design variables. The vehicle dynamics simulation model was established based on the vehicle suspension parameters and track conditions of the actual operation, and the comprehensive dynamic parameters of the vehicle were taken as the design objectives. The matching performance of the wheel equivalent conicity with the vehicle and track parameters was discussed, and the best equivalent conicity was determined as the constraint condition of the optimization problem; a numerical calculation program is written to solve the optimization model based on a multi-population genetic algorithm. The results show that the algorithm has a fast calculation speed and good convergence. Compared with the LM profile, the two optimized profiles effectively reduce the wheelset acceleration and improve the lateral stability of the bogie and vehicle stability during straight running. Due to the optimized profile increases the equivalent conicity under larger lateral displacement of the wheelset, the lateral wheel-rail force, derailment coefficient, wheel load reduction rate, and wear index are reduced when the train passes through the curve line. This paper provides a feasible way to ensure the speed-up operation of a freight train.

Identification of Road Profile Parameters from Vehicle Suspension Dynamics for Control of Damping

Concept of symmetry covers physical link between road profile form, vehicle dynamic characteristics, and speed conjunction. Symmetry frame between these items is asymmetric itself and has no direct expression, but it affects a vibration level on the vehicle and driving comfort. Usually, we can change only the vehicle’s speed to achieve desired vibrations level of the driver and passengers. Recently, vehicle dynamic characteristics can be changed depending on its damping system structure, but these solutions are limited by construction and control possibilities and evidently represented by symmetric dependency between road input and the resulting acceleration of the vehicle. The main limitation of this process is to have a reliable value of the existing road profile that is mainly defined by road category but unpredictable for each road distance. Functional road profile calculations are provided in this article, where power spectral density (further-PSD) and waviness of the road play the main role in delineating road profile parameters. Furthermore, the transfer function system was created using full car dynamic model analysis. Values on vehicle suspension’s effects on acceleration were obtained from vehicle speed and road roughness. Acceleration values and transfer function were used to calculate PSD value quickly and practically. This calculated result can be formed as a control value to the vehicle damping control. In addition, the provided methodology became useful to determine road quality for adjustment of vehicle suspension parameters and set safe driving characteristics, which became part of driver assistant systems or autonomous driving mode.

Pavement roughness evaluation method based on the theoretical relationship between acceleration measured by smartphone and IRI

ABSTRACT Smartphones have integrated various sensors (e.g. GPS, acceleration sensor) and can be used to evaluate roads roughness. In this study the theoretical algorithm, in which the vehicle suspension parameters were included, would be established to calculate International Roughness Index (IRI) from acceleration using smartphones. Firstly, based on Android Studio 2.0, a client application installed on smartphones was developed. Next, the physical parameters of a quarter actual vehicle model (QAVM) were identified utilizing the front wheel drop test in accordance with complex modal theory. Then, the relationships among acceleration, IRI and the profile elevation were established through the derivation of theoretical formula. Finally, a validation analysis was implemented to evaluate the feasibility and reliability of this methodology. The validation experimental results on three asphalt pavement segments indicated that the collected acceleration data utilizing smartphone were basically consistent with that by accelerometers. Meanwhile, IRI calculated by the proposed theoretical methodology was close to that obtained by digital survey vehicle (DSV) and the maximum relative error between them was below 10%. Overall, due to the consideration of vehicle dynamic characteristics, the proposed method could improve the measurement accuracy and enable various vehicles to be used for road roughness evaluation.

Analysis of bio-dynamic model of seated human subject and optimization of the passenger ride comfort for three-wheel vehicle using random search technique

Ride comfort is the major concern to the roadway vehicle passengers, travelling in as it affects their health and efficiency to work. In the present study, a 9 DoF model of a three-wheel vehicle is developed with Lagrangian approach to investigate its ride behavior when subjected to random surface irregularities. The irregularities of the track are measured with a three-wheeled setup equipped with profilometer known as opto-coupler. The present model is validated in two ways, first by comparing the vertical-lateral PSD acceleration received from simulation and actual testing and second by comparing vertical seat to head transmissibility obtained from analysis (VSTH) with past reported studies. A 7 DoF bio-dynamic model of the seated human subject is formulated and integrated with the vehicle model, ride comfort of the vehicle and human body segments are assessed based on ISO specifications. Passenger Ride Comfort is optimized through non-linear optimization using Random Search Technique. The modified values of vehicle suspension parameters are presented to obtain optimum passenger comfort based on ISO-2631-1 criteria.

Optimization of vehicle suspension parameters based on ride comfort and stability requirements

The use of optimization methods in engineering is growing, allowing the best possible way to fulfill the requirements of the project. For vehicle suspensions, there are various conditions, which involve comfort, safety, stability, maneuverability, among others. A safety and stability evaluation is carried out by several tests, including Double Lane Change. In this maneuver, the vehicle must change lanes quickly twice, allowing it to be assessed for stability in sudden movements. For ride comfort, it is common for the design to be based on the vehicle’s natural vibration frequencies. In this context, this work aims to present a methodology for optimizing the suspension parameters of a vehicle, based on the natural frequencies of vibration and the simulation of a Double Lane Change maneuver. For that, it is employed vertical and lateral dynamics mathematical models, with hypotheses that allow the adequate adaptation to the represented phenomena. Finally, Particle Swarm Optimization (PSO) is used, which is a stochastic algorithm, based on nature. It has low computational cost, with reasonable results, allowing the parameters to be estimated and comprising the two objectives simultaneously.

Nonlinear Stability of Rail Vehicles Traveling on Vibration-Attenuating Slab Tracks

Abstract Various vibration-attenuating slab tracks have been introduced into urban railways to minimize the negative effects of train-induced ground vibration and noise. However, compared with traditional slab tracks, vibration-attenuating slab tracks usually have a lower overall stiffness, which reduces the vehicle lateral stability. This paper presents an investigation of the nonlinear hunting stability of fast metro rail vehicles traveling on vibration-attenuating slab tracks. A three-dimensional vehicle–track coupled model considering different vibration-attenuating slab tracks is developed to investigate the nonlinear hunting behavior of metro vehicles running on different elastic vibration-attenuating tracks. The nonlinear critical speed and wheelset hunting limit cycle of two types of metro vehicles traveling on four typical types of vibration-attenuating tracks are compared in detail. The influences of vehicle–track system parameters, including rail fastener stiffness and vehicle suspension parameters, on the vehicle lateral nonlinear stability are reported. The results show that the flexibility of vibration-attenuating slab tracks leads to a large wheelset limit cycle and lowers the nonlinear critical speed. Increasing track lateral stiffness and designing appropriate vehicle suspension parameters can improve the lateral stability of rail vehicles traveling on vibration-attenuating slab tracks.

Frequency Domain Control Simulation of In-Wheel Motor Electric Vehicle

Aiming at the optimization of the suspension structure and control strategy of electric vehicle driven by hub motor, a magnetorheological suspension fuzzy control form based on the idea of ceiling was proposed to optimize the suspension system. Taking a hub motor driven electric vehicle as the research object, a vehicle simulation model was established in Matlab/Simulink. Taking the vertical acceleration, pitching Angle acceleration and roll Angle acceleration of the vehicle as the optimization target, the smoothness of the vehicle was simulated and analyzed and compared with the initial hub motor drive form. The simulation results show that the proposed optimization method is effective and provides a new way to further study the optimization method of hub motor driven electric vehicle suspension parameters.

Sensitivity analysis of three-wheel vehicle’s suspension parameters influencing ride behavior

In this article coupled vertical–lateral 9 degree-of-freedom model of a three-wheel vehicle formulated using Lagrangian dynamics is presented in order to determine its vertical and lateral ride. The model is justified by correlating the power spectral density vertical and lateral acceleration results determined from analysis with the same obtained from experimental measurements. The ride comfort of the vehicle is evaluated on the basis of ISO 2631-1 criteria. The sensitivity of vehicle suspension parameters on vertical and lateral ride behavior is analyzed, and it is noticed that rear-suspension damping coefficient, front-tire stiffness, rear-tire stiffness, front-tire damping coefficient, and rear-tire damping coefficient are critical parameters for vertical power spectral density acceleration. Rear-suspension stiffness, rear-suspension damping coefficient, front-tire stiffness, and rear-tire stiffness are critical parameters for lateral power spectral density acceleration.

Vertical Vibration Simulation Analysis of In-wheel Motor Electric Vehicle Suspension

To analyze the vertical vibration characteristics of in-wheel motor electric vehicle suspension, through MATLAB/ Simulink for simulation and analysis, a vertical kinetic model of 1/4 vehicle is built. Without changing the original premise of vehicle suspension parameters, vehicle vertical vibration acceleration 􀝕􁈷 of in-wheel motor as unsprung mass and wheel dynamic load 􀜨􀯧 are in turn to be simulated, to study the influence of the increase of the unsprung mass on the vertical vibration of the suspension because of the introduction of in-wheel motor. The research results provide a reference for improving the NVH characteristics of in-wheel motor electric vehicle.

Comparative Optimization Study on Vehicle Suspension Parameters for Rider Comfort Based on RSM and GA

This paper present on optimal vehicle seat suspension design for a quarter car model to reduce vibrations. With the aid of MATLAB/Simulink a simulation model is achieved for further process into Response Surface Methodology and Genetic Algorithm. For the Response surface methodology, an experimental design was chosen in order to obtain the proper modelling equation. Later this modeled equation was served as objective function for further process into genetic algorithm to choose the best values of three control variables Parallelly numerical optimization through Response surface methodology was done to compare the results of both optimization techniques i.e. through Genetic algorithm and Response surface methodology. The results indicated that both techniques are capable of locating good conditions to evaluate optimal setting of sprung mass, spring stiffness and damping co-efficient.