Full Vehicle Model Research Articles

Influence of suspension hysteresis characteristics on vehicle vibration performance

To analyze the influence of the leaf spring hysteresis characteristics on the vehicle body vibration performance, it is necessary to take the physical nonlinear factors into account in the suspension dynamic modeling analysis. The hysteresis characteristics of the leaf spring are caused by the contact and friction between the spring pieces. Besides that, the damping elements of the suspension system are also strongly nonlinear. And hence this article presents a generalized Maxwell-slip damper (GMD) model, which can represent the general hysteresis characteristics of the suspension system. The GMD model incorporates spring stiffness and nonlinear damping in addition to spring friction using the Maxwell model. Then the effects of various parameters on the hysteresis characteristics of GMD model are analyzed and verified by simulation and bench experiments. In addition, an eight degree of freedom (8-DOF) full vehicle model capturing some frictional characteristics was established to study vehicle vibration performance under random road excitation. At the same time, the actual vehicle test is conducted under different road conditions. Ultimately, the results of the nonlinear suspension model have a reasonable agreement with the experimental results, which further demonstrates the credibility of the proposed GMD model. That is, the full vehicle dynamic model with friction force is entirely accurate and useful. The proposed nonlinear hysteresis model may be instructive for accessing the vehicle vibration response to further study the direct effects of friction on vehicle handling and driver feedback.

Particle Swarm Optimization and Real-Road/Driving-Cycle Analysis Based Powertrain System Design for Dual Motor Coupling Electric Vehicle

In this study, a planetary gear based dual motor coupling electric vehicle is proposed, which achieves higher system efficiency by enabling motor working under high operating efficiency area. Firstly, the dynamic characteristics of the proposed configuration are analyzed and the reasonable working modes are established. Secondly, the optimal dual motor parameters are derived according to the statistical analysis on the typical driving conditions and the collected real road driving data. Especially, the optimal parameters of planetary gear and final transmission ratio are obtained using particle swarm optimization algorithm. Finally, based on the developed mode shift algorithm, the dual motor coupling full vehicle model is developed and the vehicle performance is analyzed using MATLAB/Simulink. For the UDDS (Urban Dynamometer Driving Schedule) driving cycle, it is seen from the simulation results of motor operating points that the proposed dual motor configuration is mostly operated under the high efficiency range, and the power consumption is significantly reduced by 7.6% compared with the single motor configuration. For the NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Cycle) and real road driving conditions, the proposed dual motor configuration also achieves system efficiency improvement of 5.0%~16.3%, which confirms the validity of the proposed configuration and its corresponding parameter matching and control algorithm development.

Compensating Delays and Noises in Motion Control of Autonomous Electric Vehicles by Using Deep Learning and Unscented Kalman Predictor

Accurate knowledge of the vehicle states is the foundation of vehicle motion control. However, in real implementations, sensory signals are always corrupted by delays and noises. Network induced time-varying delays and measurement noises can be a hazard in the active safety of over-actuated electric vehicles (EVs). In this paper, a brain-inspired proprioceptive system based on state-of-the-art deep learning and data fusion technique is proposed to solve this problem in autonomous four-wheel actuated EVs. A deep recurrent neural network (RNN) is trained by the noisy and delayed measurement signals to make accurate predictions of the vehicle motion states. Then unscented Kalman predictor, which is the adaption of unscented Kalman filter in time-varying-delay situations, combines the predictions of the RNN and corrupted sensory signals to provide better perceptions of the locomotion. Simulations with a high-fidelity, CarSim, full-vehicle model are carried out to show the effectiveness of our RNN framework and the entire proprioceptive system.

Autonomous Wary Collision Avoidance

Handling of critical situations is an important part in the architecture of an autonomous vehicle. A controller for autonomous collision avoidance is developed based on a wary strategy that assumes the least tire-road friction for which the maneuver is still feasible. Should the friction be greater, the controller makes use of this, and performs better. The controller uses an acceleration-vector reference obtained from optimal control of a friction-limited particle, whose applicability is verified by using numerical optimization on a full vehicle model. By employing an analytical tire model of the tire-road friction limit, to determine slip references for steering, and body-slip control, the result is a controller where the computation of its output is explicit, and independent of the actual tire-road friction. When evaluated in real-time on a high-fidelity simulation model, the developed controller performs close to that achieved by offline numerical optimization.

Modelling and simulation of an integrated human-vehicle system with non-linear cushion contact force

Driver comfort is related to human-seat interaction forces and the seat suspension system. For a human body subjected to vertical seat vibration, the head exhibits vertical as well as fore-aft motion, which leads to high forces in the neck. In order to capture these effects, a detailed 12 degrees of freedom two-dimensional seated human body model with inclined backrest support is developed to study direct and cross-axis seat to head transmissibility. The human model is then integrated with a non-linear cushion-human interaction model. The cushion parameters are obtained using a genetic algorithm and a global criterion-based scheme to minimize the least square difference between experimental contact force-time signal and analytical results. Subsequently, the human body model, along with a non-linear seat cushion model, are incorporated into a full vehicle model. Seat suspension parameters are obtained by minimizing the seat effective amplitude transmissibility and human body comfort factors. Both random road excitation and sudden bumps on the road have been studied. The influence of the human body's position in the vehicle and parameter sensitivity with respect to cushion-human interaction force, vertical and fore-aft head acceleration are investigated for multi-compression damper with an inclined damper-seat suspension system. The angle of inclination of the damper has been found to be the parameter with the highest sensitivity. These results and the modelling methodology are expected to lead to a better way of analyzing biodynamic response of the driver.

Modeling of Full Vehicle to Evaluate the Impact of Electric Powertrain on Steering Performance

The study of dynamic behavior of Electric Power Steering System (EPAS) is very critical to understand the overall handling of the vehicle. The appropriate feedback from the steering to the driver is crucial so that the driver get the precise steering feel in order to control the vehicle with ease during all kinds of maneuvers. For a maximum tire life, the steering system should maintain, a proper tire road contact, and proper tire geometry with respect to the chassis both during cornering and straight-ahead driving. The driver should be able to turn the vehicle with ease during low as well as high speed maneuver. A complete steering system dynamic model is developed and is integrated with the full vehicle model using AMESIM 1D simulation platform. The simulation is carried out as per the guidelines laid by the ISO 7401 standard criterion. In this project we have performed the simulation on three different vehicle models in which a behavioral comparison of EPAS between their Conventional & Electric variants is performed. A thorough implementation of various vehicle SPMM parameters and KnC files is done in AMESim simulation model. Finally a list of Performance Aggregate Target Parameters is calculated. The resulting PAT parameter values are then compared with the test data and an appropriate design modification has been suggested accordingly.

Vehicle tip-over prevention using gain-scheduled State-dependent Riccati Equation–based anti-rollover controller

The most hazardous kind of vehicle crash among all road accidents is vehicle rollover. Present-day rollover prevention systems in commercial vehicles mitigate rollover by preventing any wheel lift-off from the ground. These systems make use of actuators such as differential brakes and demand all the wheels on the ground for satisfactory operation. Such systems are not effective in recovering a vehicle from intense rollover scenarios where the wheels on one side are lifted off the ground, and the vehicle is about to rollover to the other side after reaching the tip-over point. A few studies have investigated the possibility of reinstating a vehicle at the tip-over point with the wheels on a side lifted off. The high complexity and computation time of the optimal control strategies such as nonlinear model predictive controller make it unsuitable for real-time implementations. This study proposes a novel gain-scheduled State-dependent Riccati Equation–based optimal anti-rollover controller for reinstating a vehicle from the tip-over point. An inverted double pendulum on a cart vehicle model is used as the plant model. The anti-rollover controller is found to be presentable as a two-dimensional gain-scheduled lookup table with specific state dependencies in existence. It eliminates the necessity of solving the nonlinear performance index minimization problem online. State-dependent Riccati Equation method adequately accounts for the nonlinearities involved, yet possesses a small computational time per sample. The anti-rollover controller is evaluated with a 10 degrees of freedom full vehicle model with a nonlinear pure slip tyre model that incorporates the dynamical effects neglected in the controller formulation. Finally, the anti-rollover controller is evaluated in real-life initial conditions using a sophisticated pick-up truck model obtained from TruckSim® software through a co-simulation with the anti-rollover controller setup in MATLAB®/Simulink® environment. The State-dependent Riccati Equation controller was found to be effective in reinstating the higher-order models from the tip-over point in all the case studies conducted.

Parallelized Multiscale Off-Road Vehicle Mobility Simulation Algorithm and Full-Scale Vehicle Validation

Abstract In this work, a scalable parallel computing scheme for the hierarchical multiscale off-road vehicle mobility simulation capability is developed with the hybrid message passing interface (MPI)/OpenMP framework, and it is validated against full-scale vehicle test data. While the hierarchical multiscale modeling approach has been introduced to high-fidelity off-road mobility simulations to eliminate limitations of existing single-scale deformable terrain models, computational complexities associated with the large dimensionality of multibody vehicle equations, involving nonlinear finite element tires and multiscale terrain models, need to be addressed for use in full-scale vehicle mobility predictions. To this end, a co-simulation framework for the multiscale off-road vehicle mobility model is proposed by exploiting the moving soil patch technique. This allows for systematically extracting four moving soil patches for four tires in a vehicle model from the deformable terrain domain, and time integrations of the four tire–soil subsystems are performed concurrently to enable computational speedup. Furthermore, an automated updating scheme for multiscale moving soil patches for a full vehicle model is developed, considering the multipass effect in various vehicle maneuvering scenarios. To demonstrate the off-road mobility prediction capability using the proposed parallelized multiscale vehicle–terrain interaction simulation algorithm, full-scale vehicle validation is presented for the vehicle drawbar pull as well as variable grade hill climb tests on soft soil.

Path tracking control of 4-wheel-steering autonomous ground vehicles based on linear parameter-varying system with experimental verification

In this study, a novel 4-wheel-steering electric vehicle is proposed as an autonomous ground vehicle. It aims to study the path tracking control algorithm of the 4-wheel-steering autonomous ground vehicle for intelligent driving.. Path tracking model is built for path tracking controller design based on a single track model. Besides, the linear parameter-varying system model is constructed to make the path tracking controller adaptive to different longitudinal velocities and road friction coefficients. Furthermore, a linear quadratic regulator controller for path tracking is designed and stability analysis is carried out. To eliminate the error caused by disturbance, feedforward control is combined with a linear quadratic regulator controller. To verify the path tracking performance of the designed controller, numerical simulations are carried out based on a high-fidelity and full-vehicle model constructed in CarSim. Moreover, real road experiments are performed. Both the simulation results and experiment results show that the designed controller has good path tracking performance. In addition, the path tracking controller shows good robustness to deal with different longitudinal velocities and road friction coefficients.

Sliding Mode Control of Laterally Interconnected Air Suspensions

This paper presents a control method based the lateral interconnected air suspension system, in order to improve the road handling of vehicles. A seven-DOF (Degree of freedom) full-vehicle model has been developed, which considers the features of the interconnected air suspension system, for example, the modeling of the interconnected pipelines and valves by considering the throttling and hysteresis effects. On the basis of the well-developed model, a sliding mode controller has been designed, with a focus on constraining and minimizing the roll motion of the sprung mass caused by the road excitations or lateral acceleration of the vehicle. Moreover, reasonable road excitations have been generated for the simulation based on the coherence of right and left parts of the road. Afterwards, different simulations have been done by applying both bumpy and random road excitations with different levels of roughness and varying vehicle lateral accelerations. The simulation results indicate that the interconnected air suspension without control can improve the ride comfort, but worsen the road handling performance in many cases. However, by applying the proposed sliding mode controller, the road handling of the sprung mass can be improved by 20% to 85% compared with the interconnected or non-interconnected mode at a little cost of comfort.

Modeling and dynamic analysis of a 6 x 6 heavy military truck by adaptive model predictive control with application to NATO lane change test course

In this paper, the computer-aided vehicle dynamic analysis of a 6x6 heavy military truck is presented and examined. For the analysis, a MATLAB/Simulink® platform is used to design and model a truck. The vehicle configuration taken into account for the analysis is the powertrain (engine, gear box, transfer gear, differential), suspension, steering system and tire model according to the Pacekja 89’ formulation. In addition, the effect of the rolling resistance and drag is considered, in order to represent the vehicle behavior as real as possible. The longitudinal dynamic and lateral dynamic are formulated. First, the longitudinal dynamic model is established by means of implementation of the weight transfer function. The vehicles are considered as rigid bodies with 1 degree of freedom. Second, the vehicular planar model with three wheels, well known as bicycle model, is applied following the North Atlantic Treaty Organization double line change maneuver test reaching 3 degree of freedom. The driver behavior is represented by using an adaptive model predictive control varying the longitudinal velocity. The forces for braking, inertia of the rotating components, the energy lost in the powertrain, and the effect of dive squat and rollover. The numerical simulation results are shown and compared with a full-vehicle model formed by using Mechanical Simulation Corporation’s truckSIM®. There were chosen simulation scenarios applied to the model to observe the effects of different parameters concerning the dynamic behavior, and also prepared in truckSIM® environment. The main contributions of this article are the development of the vehicular model, through the use of block diagrams in a reliable and relatively simple programming code such as MATLAB/Simulink®, with innovative tools used in the control of autonomous vehicle driving and the flexibility to adapt said model to different environmental conditions and different vehicle parameters.

A generalized method for three-dimensional dynamic analysis of a full-vehicle model

Dynamic performances of the vehicle are significantly influenced by the suspension mechanisms. An understanding of the effects of the suspension kinematics and statics (or, briefly, kinestatics) is crucial to improve the dynamic performances of a vehicle. However, the suspension kinestatics is often neglected in the dynamic analysis. This paper presents a generalized full-vehicle model for the three-dimensional dynamic analysis, which consists of two pairs of the front and rear spatial suspension mechanisms. Each suspension is represented by a corresponding instantaneous screw joint supporting the vehicle body at any instant. The full-vehicle model is viewed to be a 6-degree-of-freedom spatial parallel mechanism. As the spatial parallel mechanism, the kinematics and statics of the full-vehicle model are analysed using the theory of screws. Taking the suspension kinestatics and tyre dynamics into consideration, the dynamic equations of the full-vehicle model are formulated in terms of the Lagrangian equations. As immediate applications, the dynamic behaviours of a vehicle are simulated and evaluated under two different road disturbances, respectively. By comparing with the simulation results from two other widely used methods, it confirms the validity of the theoretical method.

Real-time multibody modeling and simulation of a scaled bogie test rig

In wheel–rail adhesion studies, most of the test rigs used are simplified designs such as a single wheel or wheelset, but the results may not be accurate. Alternatively, representing the complex system by using a full vehicle model provides accurate results but may incur complexity in design. To trade off accuracy over complexity, a bogie model can be the optimum selection. Furthermore, only a real-time model can replicate its physical counterpart in the time domain. Developing such a model requires broad expertise and appropriate software and hardware. A few published works are available which deal with real-time modeling. However, the influence of the control system has not been included in those works. To address these issues, a real-time scaled bogie test rig including the control system is essential. Therefore, a 1:4 scaled bogie roller rig is developed to study the adhesion between wheel and roller contact. To compare the performances obtained from the scaled bogie test rig and to expand the test applications, a numerical simulation model of that scaled bogie test rig is developed using Gensys multibody software. This model is the complete model of the test rig which delivers more precise results. To exactly represent the physical counterpart system in the time domain, a real-time scaled bogie test rig (RT-SBTR) is developed after four consecutive stages. Then, to simulate the RT-SBTR to solve the internal state equations and functions representing the physical counterpart system in equal or less than actual time, the real-time simulation environment is prepared in two stages. To such end, the computational time improved from 4 times slower than real time to 2 times faster than real time. Finally, the real-time scaled bogie model is also incorporated with the braking control system which slightly reduces the computational performances without affecting real-time capability.

Design of Fuzzy Super Twisting Sliding Mode Control Scheme for Unknown Full Vehicle Active Suspension Systems Using an Artificial Bee Colony Optimization Algorithm

AbstractThis article proposes a new intelligent control scheme that uses the Fuzzy Super Twisting Sliding Mode Concept (FSTSMC) and PID controller tuned with the Artificial Bee Colony (ABC) algorithm to control a full vehicle active suspension system with new convergence proof. Suspension systems are utilized to provide vehicle safety and improve comfortable driving. The effects of road roughness transmitted by tires to the vehicle body can be reduced by using suspension systems. In this work super twisting sliding mode is combined with a fuzzy system to design a robust control method. The super twisting sliding mode concept is utilized to limit and minimize the chattering problem and the fuzzy system is used for estimating the unknown parameters and uncertainty in the suspension system components (spring, damper, and actuator). The advantage of such combination is that it can handle the uncertainties and nonlinearities efficiently. The PID controller is used to create the required force to be produced by the actuator. The proposed control scheme consists of four similar sub control schemes, one for each of the four corners of the vehicle. All parameters in the sub control schemes are optimized using the ABC algorithm. The designed control system is applied for a full vehicle model with 8 degrees of freedom. Simulation results show large reduction in the vibration of the vehicle body when passing on disturbance and also show good robustness properties when tested for different road conditions. Passive and active suspension systems using sliding modecontrol (SMC) and the proposed FSTSMC are compared to test the efficiency and the ability of the proposed control scheme to achieve safe and comfortable driving for a random road profile.

A condensed dynamic model of a heavy-duty truck for optimization of the powertrain mounting system considering the chassis frame flexibility

The existing powertrain mounting models of the heavy-duty truck for optimizing the mounts parameters cannot well describe the deformation of the chassis frame. To overcome this disadvantage, a new full vehicle model is proposed which embraces the view of system modeling and includes the frame flexibility. The model can achieve optimal parameters of the Powertrain Mounting System for isolating the vibration transmitted from the powertrain to the chassis. A model reduction technique, improved reduced system, is used to obtain a reduced model of the frame to represent its original large-scale finite element analysis model for the accessibility of time-efficient solutions of the model. The reduced frame model is integrated with the powertrain mounting and suspension model to form the full dynamic model of the vehicle. The accuracy and effectiveness of the proposed model are evaluated by its original vehicle model built in software ADAMS and an existing rigid model with rigid foundation. A hybrid model optimization strategy is also presented to tune the dynamic parameters of the powertrain mounts using the developed coupling model. The simulation results show that the proposed coupling model has better representation of the dynamic characteristics of the real vehicle system, and the presented hybrid model optimization strategy can obtain better optimization results compared with the existing rigid model with rigid foundation. In addition, the application of the proposed model can also be extended to the vibration control and the structural fatigue prediction.

Investigation of the Effect of Tire Deformation on Open-Wheel Aerodynamics

<div class="section abstract"><div class="htmlview paragraph">This paper introduces a finite element (FE) approach to determine tire deformation and its effect on open-wheeled racecar aerodynamics. In recent literature tire deformation was measured optically. Combined loads like accelerating at corner exit are difficult to reproduce in wind tunnels and requires several optical devices to measure the tire deformation. In contrast, an FE approach is capable of determining the tire deformation in combined load states accurately. The FE tire model was validated using computer tomography images, 3D scan measurements, contact patch measurements and stiffness measurements. The deformed shape of the FE model was used in a computational fluid dynamics (CFD) simulation. A sensitivity study was created to determine the effect of the tire deformation on aerodynamics for unloaded and loaded tires. In addition, the influence of these tire deformations was investigated in a CFD study using a full vehicle model. The CFD model was validated through full scale wind tunnel tests as well as on-road tests. Finally, a straight-line and a steady state cornering maneuver based on a vehicle dynamics simulation were simulated using this combined approach of FE and CFD. The tire deformation included proper wheel load, wheel speed and slip angle for each wheel. The CFD full vehicle model took chassis slip angle, body roll angle and wheel steering angle into account in order to match the real driving situation. The results show that realistic tire deformations provide better insight into the effect of rotating wheels on aerodynamics of full vehicles, especially that of race cars.</div></div>

Jacobian approach to the kinestatic analysis of a full vehicle model with application to cornering motion analysis

The Jacobian approach to the kinestatic analysis of a planar suspension mechanism has been previously presented. In this paper, the theory is extended to three-dimensional kinestatic analysis by developing a full kinematic model and viewing it as a spatial parallel mechanism. The full kinematic model consists of two pairs of the front (double wishbone) and rear (multi-link) suspension mechanisms together with a newly developed ground-wheel contact model. The motion of each wheel of four suspension mechanisms is represented by the corresponding instantaneous screw at any instant. A vehicle is considered to be a 6-degrees-of-freedom spatial parallel mechanism whose vehicle body is supported by four serial kinematic chains. Each kinematic chain consists of a virtual instantaneous screw joint and a kinematic pair representing ground-wheel contact model. The kinestatic equation of the 6-degrees-of-freedom spatial parallel mechanism is derived in terms of the Jacobian. As an important application, a cornering motion of a vehicle is analysed under the assumption of steady-state cornering. A numerical example is presented to illustrate how to determine the optimal locations of strut springs for the least roll angle in cornering motion using the proposed method.

Active Fault Diagnosis Method for Vehicles in Platoon Formation

This paper presents an active fault diagnosis (AFD) method with reduced excitation for detection and identification of sensor faults of vehicles in a platoon formation. By introducing a probing signal into the platooning, it will allow an active excitation of the system, reveling a residual component, with the same frequency, that can be explored to obtain a fault identification of specific system faults. A supervisor is introduced to monitor the platoon behavior and activate the auxiliary input whenever the system natural excitation is insufficient for a clear fault diagnosis. This solution will allow the fault diagnosis to behave as active or passive through the adaptive signal provided by the supervisor. A dual Youla-Jabr-Bongiorno-Kucera (YJBK) matrix transfer function, also known as fault signature matrix (FSM) is investigated to get a fault diagnosis. In order to obtain an online identification of specific faults in the system, a Taylor approximation of the FSM is pursued. Computational simulations with a high-fidelity full-vehicle model, provided by CarSim , are carried out to demonstrate the effectiveness of the proposed active approach. A direct comparison between an active and a passive behavior in the same scenario shows that the active fault diagnosis method outperforms the passive approach whenever the dynamic behavior does not provide sufficient excitation. Furthermore, the excitation supervisor is able to significantly reduce the amount of artificial excitation introduced into the system ensuring a more energy efficient active fault diagnosis.

Dynamic Pavement Response Analysis under Moving Truck Loads with Random Amplitudes

AbstractThe objective of this paper is to analyze dynamic responses of asphalt pavements induced by random loads under moving truck wheels due to surface roughness. A full-vehicle model was applied...

Dynamic Behavior and Force Analysis of the Full Vehicle Model using Newmark Average Acceleration Method

Dynamic Behavior and Force Analysis of the Full Vehicle Model using Newmark Average Acceleration Method