Vehicle Dynamic Behavior Research Articles

Optimization of the handling behaviour of an electric car by adjusting the kinematic gains of suspension

Abstract In this paper, within the scope of development of an electric car on a vehicle platform that is borned as internal combustion engine is presented. The vehicle dynamics behaviour changes due to the change of weight distribution and centre of gravity. Replacing the internal combustion engine with an electric motor, plus the addition of the battery moves the center of gravity towards the rear of the vehicle. Due to the increase in the rear axle load, the tendency of the vehicle to oversteer increases. To overcome this phenomenon, the rear suspension kinematic gains (camber, toe change due to vertical movement of the wheel) are adjusted thanks to the multi-link suspension. In the fist part of the study the platform is measured and Admas Car model is correlated depends on measured handling metrics. Subsequently, centre of gravity changes are applied to the verified Adams Car model. Also main platform handling metrics is defined as design targets and rear axle kinematic gains are defined as design parameters. A design of experiments (DOE)-based optimization study was performed via Adams®/Insight commercial software to achieve required handling metrics which satisfy design targets. Kinematic gains of the suspension system is analyzed via Adams car and sensitivity deepens on harpoints is determined. Design space for hardpoints is determined depends on mechanical design. The lowest and highest points are determined on the x, y and z axis and experiment list are designed according to DOE. Maneuvers that are for measuring the handling metrics are performed one after the other by changing the parameters according to the boundary conditions comes from DOE. A regression mathematical model is obtained and optimum parameters are calculated for the handling behaviour optimisation. Optimum parameters are applied on the Adams Car model again and handling manoeuvres are repeated. Results show that, although weight distribution changes, handling behaviours remain the same as based platform.

Modeling the Influence of Internal Factors on Vehicular Mobility Performance

Traffic mobility is significantly influenced by road conditions and the internal factors governing vehicular performance. Internal factors play a crucial role in vehicular mobility, influencing vehicles operate and performance. While existing vehicular mobility models primarily address external factors, external factors such as governor, engine, gear train and differential unit remain underexplored. This study formulates a comprehensive model to address this gap, focusing on the internal components that affect vehicular mobility. The proposed model employs both analytical and numerical methods to derive transfer functions for these subsystems using Matrix Laboratory (MATLAB), aiming to capture the dynamic behavior of vehicles under various conditions. The model was evaluated by examining vehicle transaction times across different road surface qualities, measured by the International Roughness Index (IRI) values of 3.5, 3.0, and 2.0, and tractive forces ranging from 4500 N to 17750 N, with applied pedal forces of 50 N, 100 N, 150 N and 200 N. Results indicated that higher tractive forces lead to reduced transaction times, with IRI values of 3.5 m/km showing a decrease from 122.0 seconds to 31.0 seconds as tractive forces increased from 4500 N to 17750 N. Similarly, for IRI values of 2.0 m/km, the transaction times reduced from 67.5 seconds to 7.5 seconds under the same conditions. The analysis further demonstrated that increased applied pedal forces correspond to higher tractive forces, thereby enhancing vehicular mobility and performance. These findings highlight the critical role of internal factors in optimizing vehicular mobility and performance, suggesting that internal subsystem dynamics should be integrated into future mobility models for more accurate and comprehensive assessments.

Increasing the energy efficiency of an electric vehicle powered by hydrogen fuel cells

The article considers fuel cell electric vehicle energy efficiency improvement by means of traction battery remanent state of charge modern control algorithms application which based on fuzzy logic. Fuel cell vehicle traction system used as experimental setup for the study includes brushless dc motor, lithium-ion batteries pack and electronic differential approach implementation. The results of simulation modelling in MATLAB-Simulink software comparing conventional PID-controller based and fuzzy logic based controller for vehicle speed control systems are presented. Fuzzy logic controller provides an electric vehicle dynamic behavior tuning for different driving modes automatically transferring the control system to economy mode when it possible. However, the control system is able to provide fast dynamic behavior when it is required. The proposed control algorithm is tested by means of developed experimental setup. Experimental results validate conducted simulation modelling results and the proposed control algorithm. Application of the proposed fuzzy logic control algorithm to control the electric vehicle dynamic behavior allows energy efficiency improvement and driving range increase.

Dynamic behavior of different kinds of rack vehicles

For the sake of improving climbing ability of railway vehicle, gear-rack system is added to traditional vehicle to form rack vehicle. Aiming at the influence of the different gear-rack systems on the rack vehicle dynamic behavior, this article considers the gear-rack meshing impact based on the analysis of the generation mechanism of the gear-rack meshing excitation. The gear-rack meshing model and four kinds of rack vehicle coupled dynamics models are established, and the model is verified by experiments. Based on the model, the gear-rack nonlinear meshing behavior and rack vehicle dynamics characteristic of two kinds of Strub system, double row teeth Abt system and Locher system under different slopes is explored. and the dynamic characters of the rack vehicle system is analyzed. Finally, the optimal gear-rack system is obtained by comparing the dynamic behavior of different vehicles. The results show that: The dynamic behavior of rack vehicles with different gear-rack systems is significantly different, and Locher system has the optimal dynamic behavior. The gear-rack nonlinear meshing behavior and the vehicle safety of the coaxial Strub system and the double row teeth Abt system are poor, which are obviously affected by the track irregularity. The impact of the gear-rack nonlinear contact force of the coaxial Strub system reaches 29.8 kN. The maximum wheel/rail vertical force of the double row teeth Abt system reaches 51.4 kN. The vehicle stability of the different shaft Strub system is the worst, and the maximum stability index is 1.368. The conclusion of this paper provides theoretical support for the design and safe operation and maintenance of the world’s mountainous rack railway.

Investigating nonlinear dynamic properties of a swing arm rubber joint and their effects on the dynamic behavior of high-speed trains

The rubber joints mounted on swing arms provide the main part of the yaw stiffness for wheelsets and thus have a significant influence on the dynamic behaviour of railway vehicles. However, their nonlinear dynamic properties concerning the frequency, amplitude, and temperature were not fully considered in previous studies, most of which adopt simple models such as the Kelvin–Voigt model to represent rubber components. This study aims to investigate the radial nonlinear properties of swing arm rubber joints under various conditions and assess their effects on the dynamic performance of high-speed trains. A nonlinear rubber spring model which combines the fractional derivative Zener model with Berg’s friction model is established. To achieve a better fit to the measurements of a swing arm rubber joint, an optimisation-based method is employed to identify the model parameters. The viscous and frictional effects of the rubber joint are compared at different frequencies, amplitudes, and temperatures, by determining the predominant element of the nonlinear model. Additionally, the nonlinear rubber spring model is integrated into a high-speed vehicle dynamic model to investigate the effects of the nonlinear properties of swing arm rubber joints on the vehicle dynamic behaviour through the MATLAB/SIMPACK co-simulation method.

A Functional Observer Approach to Accurate Road Bank Estimation

Understanding critical situations and risks while driving ensures a safe drive, this will only be achieved by vehicle dynamic behavior and a good knowledge of road attributes, an unknown inputs functional observer is developed and presented in this study to estimate the vehicle’s unmeasured states in real-time and the unknown input, it is mainly about the road bank angle and it is based on a measured output and a known input. The design conditions for functional observer proposed are expressed by Lyapunov-Krasovskii stability theory using convergence conditions and solving linear matrix inequalities (LMIs). The solutions to these LMIs are used to determine the observer parameters.

Sensitivity Analysis of the Coupled Dynamic Behavior of Vehicles in Collision with Semirigid Guardrails

Sensitivity Analysis of the Coupled Dynamic Behavior of Vehicles in Collision with Semirigid Guardrails

Vehicle Dynamics in Electric Cars Development Using MSC Adams and Artificial Neural Network

Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety.

Real-Time Measurement of Track Irregularities Using an Instrumented Axle and Kalman Filtering Techniques

Abstract A model-based methodology for the estimation of both lateral and vertical track irregularities is presented. This methodology, based on Kalman filter techniques, was developed for an independent and compact measuring system comprising an instrumented axle equipped with a limited set of low-cost sensors: a 3D gyroscope, a linear variable differential transformer (LVDT) distance sensor, and an encoder. The instrumented axle can be used on any railway vehicle traveling at moderate forward speed to provide measurements in real-time. The proposed methodology, combined with the instrumented axle, enables precise and prompt measurement of track irregularities. An experimental campaign carried out on a 1:10 scale track facility at the University of Seville validated both the system and the methodology. In the testing, 80 m of scaled track was measured at an operational speed of V = 0.65 m/s in just 2 min. Simulation estimates for track irregularities compared against the measured data from the testing showed a good performance of the proposed methodology, with maximum errors of 0.45 mm in the short wavelength range D1, the range most influential to vehicle dynamic behavior.

Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm

Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles directly. Therefore, the estimation of sideslip angle has been extensively discussed in the relevant literature. Accurate modeling is complicated by the fact that vehicles are highly nonlinear. This article combines a radial basis function neural network with an unscented Kalman filter to propose a new sideslip angle estimation method for controlling the dynamic behavior of vehicles. Considering the influence of input data type and sensor ease of measurement factors on the results, a two-degrees-of-freedom vehicle nonlinear dynamic model was established, and a radial basis function neural network estimation algorithm was designed. In order to reduce the impact of noise and improve the reliability of the algorithm, the neural network algorithm was combined with the Kalman filter. The information collected from low-cost sensors for actual vehicle operation (longitudinal vehicle speed, steering wheel angle, yaw rate, lateral acceleration) was trained using a radial basis function neural network to obtain a “pseudo slip angle”. The “pseudo slip angle”, yaw rate, and lateral acceleration are input as observations of the Kalman filter. The sideslip angle obtained from different observation methods was compared with the values provided by the Carsim 2020. The experiment shows that the sideslip angle estimator based on the radial basis function neural network and unscented Kalman filter achieves the optimal effect.

Position design of the trailing arm bushing on torsion beam suspension accounting for vehicle transient handling performance and uncertainties

The bushing of the trailing arm on torsion beam suspension plays a pivotal role in vehicle dynamic behavior. In this paper, the connection between bushing position and vehicle dynamic response is elucidated. According to the simulation results, the impact of the bushing position on the transient performance of vehicle is more pronounced at low handling frequencies, and different index under the same bushing position are not always optimal. To design the bushing position that is better for evaluation indexes, this paper formulates the design problem of the bushing position as a multi-objective optimization problem. Due to the influence of actual production and processing, inevitable errors in bushing position may result in vehicle performance not meeting design requirements. Therefore, this paper takes into consideration the uncertainties and conducts robust multi-objective optimization to design the bushing position. To address the computational burden associated with robust optimization, the RBF approximation model is developed in this paper. Finally, the optimization problem is solved with the NSGA-II intelligent algorithm. The optimization results show that the bushing position designed by robust multi-objective optimization results in vehicle with stronger anti-roll performance and better robustness for each evaluation index. It is more suitable for practical applications.

Study of Half Car Model of Different Private Vehicles on Three Bumps Road

Speed bumps are constructed on the road to control the vehicle speed. While passing through the bumps, the vehicle dynamic behaviour is critical to ride comfort and it is challenging for researcher. The present study is on the performance of four different private vehicles with four different speeds (10kmph, 20kmph, 30kmph and 40kmph) on three speed bumps. A half car model is developed in Matlab/Simulink®. The weighted RMS accelerations of these vehicles are evaluated as per vehicle ride comfort ISO-2631-1-1997. Linear control strategies are implemented to the model to improve the ride comfort.

A Review of Wheel-Rail Dynamic Behavior on Curved Tracks

With a speedy advancement in vehicle speed and transport capability, a huge challenge has been imposed to improve railway vehicle dynamic behaviour and track design to ensure the optimum performance of railway vehicles and tracks. To safely negotiate the vehicle speed on circular and transition curve tracks several aspects such as wheel-rail wear, smooth ride and vehicle stability need to be analysed. The investigators have paid great attention to exploring the compatibility among vehicle stability and curving ability, parametric analysis and optimization of curving performance, minimising the wheel-rail wear and curve squeal noise etc. The present paper focuses on reviewing these past efforts made by the researchers.

A trailer car dynamics model considering brake rigging of a high-speed train and its application

AbstractBrake systems are essential for the speed regulation or braking of a high-speed train. The vehicle dynamic performance under braking condition is complex and directly affects the reliability and running safety. To reveal the vehicle dynamic behaviour in braking process, a comprehensive trailer car dynamics model (TCDM) considering brake systems is established in this paper. The dynamic interactions between the brake system and the other connected components are achieved using the brake disc–pad frictions, brake suspension systems, and wheel–rail interactions. The force and motion transmission from the brake system to the wheel–rail interface is performed by the proposed TCDM excited by track irregularity. In addition, the validity of TCDM is verified by experimental test results. On this basis, the dynamic behaviour of the coupled system is simulated and discussed. The findings indicate that the braking force significantly affects vehicle dynamic behaviour including the wheel–rail forces, suspension forces, wheelset torsional vibration, etc. The dynamic interactions within the brake system are also significantly affected by the vehicle vibration due to track irregularity. Besides, the developed TCDM can be further employed to the dynamic assessment of such a coupled mechanical system under different braking conditions.

A Beta-less Approach for Vehicle Cornering Stiffness Estimation

With an increasing number of driver assistance functions and the upcoming trend of autonomous driving, knowledge of the current state and changeable parameters of the vehicle becomes increasingly essential. Particularly significant parameters with regard to vehicle lateral dynamics are the cornering stiffnesses, which describe the tire lateral forces depending on the current slip angles. Thus, the cornering stiffness characterizes the current lateral force of a tire, which heavily varies over different tires and decreases by tire wear. However, its knowledge is crucial for describing the vehicle dynamics behavior. Therefore, vehicle control algorithms and driver assistance functions would highly benefit from knowledge of the vehicle cornering stiffness values, which could increase driving comfort and safety. For that purpose, this paper introduces an online estimation algorithm for the tire cornering stiffness values. Only typical vehicle series sensors are required for the algorithm, which ensures its applicability for series vehicles. Finally, vehicle measurements are conducted in order to validate the proposed estimation algorithm by means of the resulting cornering stiffnesses.

Estimation of Vehicle Dynamic Response from Track Irregularity Using Deep Learning Techniques

To improve the quality of track maintenance work, it is a desire to estimate vehicle dynamic behavior from track geometry irregularities. This paper proposes a deep learning model to predict vehicle responses (e.g., vertical wheel-rail forces, wheel unloading rate, and car body vertical acceleration) using deep learning techniques. In the proposed CA-CNN-MUSE model, convolutional neural networks (CNNs) are used to learn features of track irregularities, and multiscale self-attention mechanisms (MUSE) are employed to capture the long-term and short-term trends of sequences. Coordinate attention (CA) is introduced into CNN to focus on important interchannel relationships and important spatial mileage points. The experiments were performed on a multibody simulation model of the vehicle system and the measured data of the actual high-speed line. The results show that the CA-CNN-MUSE has high prediction accuracy for vertical vehicle responses and fast computation speed. The predicted time-domain waveforms and power spectral densities (PSDs) agree well with the actual vehicle responses. The main features of the lateral vehicle responses can also be captured by the proposed method, yet the results are not as good as the vertical ones.

Multiobjective optimization framework for designing a vehicle suspension system. A comparison of optimization algorithms

Recent advances in robotics and digital technologies in the automotive industry, allow the integration of vehicle systems with their virtual twins, thus facilitating their modelling and optimization. As a result, the systems design time and manufacturing costs are substantially reduced, while their performance, safety and fatigue life are expanded.This work presents a multiobjective optimization framework for developing an optimal design of a front double wishbone vehicle suspension system based on a four-bar mechanism. This is carried out by coupling several computer-aided design tools (CAD) and computer-aided engineering (CAE) software. The 3D CAD model of the lower control arm of the suspension system is made using SolidWorks®, the Finite Element Analysis (FEA) of the suspension assembly is modelled using ANSYS® Workbench, while the multibody kinetic and dynamic of the designed suspension system is analysed using MSC ADAMS®. They are embedded in a multidisciplinary optimization design framework (modeFrontier®) with the aim of determining the optimal hardpoint locations of a lower control arm by minimizing the chassis pitch accelerations to improve the passengers’ comfort, reducing the volume and mass of the suspension system to increase the vehicle stability and manoeuvrability, while decreasing the maximum stresses to extend the system fatigue life and enhancing safety.The methodology has been successfully applied to several driving scenarios entailing different vehicle dynamics manoeuvres with the aim to find the Pareto optimal front, and to analyse the suspension assembly performance together with the vehicle dynamic behaviour. Results show that the use of such approach may significantly improve the design of the suspension system. Furthermore, a comparison of different optimization strategies and algorithms is performed.

Simulation of the thermo-mechanical behaviour of tread braked railway wheels by means of a 2D finite element model

The friction heat generated at the wheel-shoe interface during tread braking operations leads to a temperature rise of the wheel tread surface. Fast cooling as well as residual thermal stresses and strains can cause damages to the wheel surface, thus negatively affecting the vehicle dynamic behaviour and strongly reducing the wheel life. Modelling the thermo-mechanical interaction of the wheel-block pair is thus of paramount importance to improve the reliability of braking manoeuvres. The paper shows the development of a decoupled thermo-mechanical model and its implementation in ANSYS Mechanical APDL, which can compute the contact pressure at the wheel-shoe interface and the resulting wheel temperature field produced during braking operations, considering friction heating, air convection cooling and the rail chill effect.

Investigation of Lateral and Vertical Direction Dynamic Responses of a Full Car Model Exposed to Sine Road Input

In this study, the full car model, which is exposed to a sinusoidal road input, has been examined to ensure the driving safety of the vehicles and the comfort of the passengers. In this context, the full car, including lateral and vertical movements, is modeled with fourteen degrees of freedom. The second-order equations of motion of the modeled vehicle were obtained using the Lagrangian method and reduced to first-order equations of motion using the state-space form. Then, these equations of motion were solved precisely in the time domain using the Euler method in the Matlab environment. To examine the lateral movements and the rotations of the vehicle in the pitch and roll axis, a sine wave with a wavelength of 5 m and an amplitude of 0.1 m was applied to the wheels of the vehicle. In the analysis, the right front wheel is exposed to a sinusoidal road input at t=0 s, while the left front wheel is exposed to a sinusoidal road input after the vehicle moves 2.5 m. Thus, both the pitch and roll motion of the vehicle will be examined in detail. In the study, four different vehicle masses 500 kg, 1000 kg, 2000 kg, and 3000 kg were taken into consideration and the effect of different vehicle masses on passenger and vehicle dynamic behaviors was investigated. In addition, the situation of passing vehicles at variable speeds from the given disruptive road input has also been examined. The maximum dynamic responses of the passenger and the vehicle were examined when the vehicle speed changed from 3 m/s to 50 m/s by 0.1 m/s. In the study, it has been observed that the vehicle mass and certain vehicle speed have effects on the vertical and lateral displacements and accelerations of the passenger and the vehicle

Mobility Prediction of Off-Road Ground Vehicles Using a Dynamic Ensemble of NARX Models

Abstract Mobility prediction of off-road autonomous ground vehicles (AGV) in uncertain environments is essential for their model-based mission planning, especially in the early design stage. While surrogate modeling methods have been developed to overcome the computational challenge in simulation-based mobility prediction, it is very challenging for a single surrogate model to accurately capture the complicated vehicle dynamics. With a focus on vertical acceleration of an AGV under off-road conditions, this article proposes a surrogate modeling approach for AGV mobility prediction using a dynamic ensemble of nonlinear autoregressive models with exogenous inputs (NARX) over time. Synthetic vehicle mobility data of an AGV are first collected using a limited number of high-fidelity simulations. The data are then partitioned into different segments using a variational Gaussian mixture model to represent different vehicle dynamic behaviors. Based on the partitioned data, multiple surrogate models are constructed under the NARX framework with different numbers of lags. The NARX models are then assembled together dynamically over time to predict the mobility of the AGV under new conditions. A case study demonstrates the advantages of the proposed method over the classical NARX models for AGV mobility prediction.