Vehicle Dynamics Model Research Articles

Mechanism and performance of a passive auxiliary steering device for low-floor light rail vehicle

For low-floor light rail vehicles, a passive auxiliary steering device (ASD) is developed aiming at relieving the independently rotating wheelset (IRW) excessive wear and improving IRW bogie system running safety on curve line. The ASD can be briefly seen as some longitudinal springs arranged between a carbody with IRW and a carbody ahead or behind it to increase the vehicle horizontal flexural stiffness. Consequently, the vehicle will possess the characteristics of both single-type vehicle and traditional vehicle. When passing curved track, the yaw angle between the adjacent carbodies deforms the springs to generate moment which will reduce the unbalanced centrifugal force of the carbody with IRW. As a result, the centering ability of the IRW bogie is improved. The vehicle-track coupled dynamics model for a 6-module single-type low-floor light rail vehicle is developed and the simulations of vehicle passing through tangent and curve lines with and without ASD are carried out. The results indicate that the ASD can improve the IRW restoring capability on tangent track and the IRW negotiation performance on medium and large curved tracks. Besides, larger spring preload, larger steering stiffness and smaller steering clearance are preferred to guarantee the higher performance of this ASD.

Multiple model fault diagnosis and fault tolerant control for the launch vehicle’s attitude control system

Abstract For the launch vehicle attitude control problem, traditional methods can seldom accurately identify the fault types, making the control method lack of pertinence, which largely affects the effect of attitude control. This paper proposes an active fault tolerant control strategy, which mainly includes fault diagnosis and fault tolerant control. In the fault diagnosis part, a small deviation attitude dynamics model of the launch vehicle is established, Kalman filters with different structures are designed to detect and isolate faults through residual changes, and the fault quantity of the actuator is further estimated. In the fault tolerant control part, the following control scheme is adopted according to the above diagnostic information: when the sensor fault is detected, the sensor measurement data is reconstructed; when the actuator fault is identified, the control allocation matrix is reconstructed. Simulation results show that the proposed method can effectively diagnose sensor fault and actuator faults, and significantly improve attitude tracking accuracy and control adjustment time.

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

Constructing critical control parameters to keep vehicle away from divergent loss of stability

This paper describes a proposed methodology for the vehicle dynamics modelling and analysis of the stationary states for a two-axle road vehicle subject to a constant external lateral force and a resulting moment acting about a vertical axis passing through the mass centre of the vehicle. The classic nonlinear bicycle model is used and analyses are performed to investigate the stabilisation of straight-line motion through the automated correction of steering inputs to the front wheels. The approach taken here is based on the Pevsner-Pacejka graph-analytical method analysis of a stability set of stationary states in combination with an applicable theory of bifurcation. The disturbing lateral force is typically due to the pressure resulting from a strong side wind gust. The resolution of this force will act through the centre of pressure, taking the vehicle in side profile. The centre of pressure is unlikely to coincide with the centre of mass. This results in the additional disturbing yaw moment acting about a vertical axis through the centre of mass of the vehicle. Depending on the vehicle configuration this moment may introduce a dangerous oversteer response that most drivers would find difficult to manage.

Research on DCT shift strategy for various driving style based on “driver-vehicle-cloud” machine learning

In order to solve the problem that the DCT static shift strategy cannot adapt to the difference in driving style, the driving style identification model based on multi-dimensional data mining and intelligent algorithm heavily depends on vehicle terminal data storage and calculation, an intelligent shift strategy based on “driver-vehicle-cloud” cooperative control is proposed. Firstly, the dynamic model of the DCT vehicle is analyzed, the primary shift schedule is calculated, and a method to adaptively modify the shifting schedule of DCT according to driving style is proposed. Then, many vehicle driving data are collected, cleaned, and reconstructed by wavelet denoising and other methods, and a driving style database with 80-dimensional features is constructed. Five essential features are selected by the ReliefF method, and the driving style recognition model is constructed by combining random forest, support vector machine, naive Bayesian, and other algorithms. Finally, the support vector machine model with the highest precision is selected, and the “driver-vehicle-cloud” collaborative control system is deployed using cloud computing and vehicle-cloud collaborative technology. The experiment car test shows that the system can identify the driver’s driving style in real time and realize the differential shift schedule and driving experience of DCT.

Research on fault-tolerant control methods for Bi-rotor vehicles under actuator failure

A PID-self-impedance fault-tolerant controller based on the inner and outer loop structure is developed for a new form of bi-rotor vehicle, taking into account the fault-tolerant control problem when the actuator fails. First, the dynamics model and actuator fault model of the bi-rotor vehicle are established, the PID active fault-tolerant controller with control parameter switching is selected for the outer loop, and the self-immune controller is selected for the inner loop. Second, when a fault occurs, the outer loop PID controller’s settings are switched to the controller parameters of the relevant fault state based on the extent of the fault, and the parameters are matched with the robust controller of the inner loop to achieve fault-tolerant control. Finally, simulation experiments show that the designed fault-tolerant controller can still achieve stable tracking of position and attitude in the presence of actuator faults. This indicates that the flight control system is robust to fault-induced disturbances and effectively improves the vehicle’s fault-tolerant performance.

Analysis of Energy Consumption for electric motorcycles depending on vehicle configuration and driving cycles

This article presents an analysis of energy consumption for battery electric motorcycles (BEM) based on vehicle configuration and driving cycles. The study investigates the impact of two architectures: rear-wheel regenerative (RWR), and two-wheel regenerative (2WR) on consumption. For the analysis, the study considered the effect of various parameters on the longitudinal vehicle dynamics such as speed profiles (WMTC, real-world profile), longitudinal road slope (from -5° to 5° with 0.5° step increment), and 5 different brake distributions. The research employs simulation techniques using the Matlab/Simulink environment to develop a simplified longitudinal vehicle dynamics model (1 DOF) and for the implementation of a serial brake-blending control strategy. The results of the analysis provide energy potential regeneration and insight into the sensitivity of energy consumption to road slope variations, powertrain working points, and potential energy harvesting. The findings contribute to a valuation of the factors that influence energy consumption in electric motorcycles and have implications for the development of vehicle architectures through accurate range assessment on real-world riding conditions and provide valuable information for powertrain components right-sizing.

Estimation of the pavement skid resistance by automated vehicle based on one-dimensional convolutional neural network

ABSTRACT The pavement skid resistance is critical to the driving safety of a road. Automated vehicles, which have high requirements for environmental perception, are not yet commonly equipped with an estimation module that could monitor the real-time skid resistance, leading thus to instability in the steering and braking control. In this paper, we propose a data-driven approach incorporating vehicle dynamics implementing real-time pavement skid resistance estimation for autonomous driving. Feature data is collected through the automated vehicle dynamics calibration process, and the selection of features is essentially based on the use of the vehicle dynamics model. To extract the features of one-dimensional timing signals, a temporal one-dimensional convolutional neural network is proposed to improve the stability and robustness of the estimation and minimise the effect of small perturbations and noises. In addition, given the insufficient data that may be faced in practical situations, a transfer learning approach is further proposed to improve the model generalisation. Training data is collected in an autonomous driving simulation platform and the effectiveness of the method is then verified based on this platform. Satisfactory performance is demonstrated for different driving scenarios and under varied road conditions.

Pitch motion suppression of electric vehicle active suspensions based on multibody dynamics

Active suspension control strategies are proposed to suppress pitch motion and address the issue of motion sickness among electric vehicle passengers. In this work, we investigate the performance of the linear quadratic regulator (LQR), variable universe fuzzy control, adaptive-network-based fuzzy inference system (ANFIS), and fuzzy PID control under continuous acceleration and deceleration and bumpy road conditions. First, a 14-degrees-of-freedom (DOF) dynamics model of an electric vehicle is developed based on a semirecursive multibody dynamics method, which was originally proposed by Javier García de Jalón. The vehicle dynamics simulation is performed to accurately model the vehicle states and responses. Second, the LQR, variable universe fuzzy control, ANFIS, and Fuzzy PID control algorithms are tailored via vehicle states, including pitch, rate, and acceleration. Their performances are investigated and compared in detail. Finally, the robustness of the proposed control strategies is further discussed based on various speed bump parameters and initial velocities. The results indicate that the proposed control strategies are capable of suppressing the pitch motion of electric vehicles, enhancing the riding comfort under diverse operating conditions, thereby reducing the likelihood of motion sickness among passengers of electric vehicles.

Dynamic analysis of ultra-low frequency carbody swaying for a high-speed rail vehicle: Causes and measures

This paper investigated on the ultra-low frequency carbody swaying (ULFCS) of a high-speed railway vehicle through field tests and numerical simulations. In the field tests, the vibration characteristics, the dynamics performance, the wheel-rail contact condition, and the track condition were analyzed to realize the manifestation as well as the possible cause of this abnormal vibration. The excessive low equivalent conicity caused by the abnormal wheel tread wear arouse suspicion for this issue. To find out the mechanism and propose the solving measures, the multi-body dynamic (MBD) model of the high-speed vehicle was developed and verified by applying the measured wheel/rail profiles and carbody acceleration. Based on the developed model, the linearized and nonlinear researches were performed. The results indicate that the carbody hunting, caused by the frequency coupling between the hunting mode and the carbody lower sway mode, is the deep reason of the ULFCS, and this issue can be solved by increasing the equivalent conicity and decreasing the roll stiffness of the anti-roll torsion bar. Furthermore, the ULFCS can pose a potential threat to running safety once the violent carbody swaying gives rise to the wheel-rail contact point entering the wheel flange area. Finally, the optimized measures that reducing the hardness of the abrasive block, changing the working mode of the tread cleaning device, and decreasing the anti-roll stiffness were proposed and verified by the on-track test.

Intelligent vehicle trajectory tracking control based on physics-informed neural network dynamics model

In order to solve the accuracy problem of trajectory tracking control method based on data-driven model, an intelligent vehicle trajectory tracking control method based on physics-informed neural network (PINN) vehicle dynamics model is proposed. Aiming at the problem of poor interpretability of data-driven model, a vehicle dynamics model based on the PINN is established, and the physics-driven deep learning method is used instead of the data-driven deep learning method to obtain the dynamic characteristics of the intelligent vehicle, to benefit from both the physical-based method and the data-driven method. A sequential training method is also proposed to solve the coupling problem when training multiple PINNs simultaneously. The model takes the nonlinearity of the neural network model and physical interpretability into consideration compared to the standard neural network model. Then, based on the PINN vehicle dynamics model, a trajectory tracking controller based on the iterative linear quadratic regulator (ILQR) control algorithm is developed. The optimal control law is derived by optimizing the ILQR control algorithm to implement the intelligent vehicle’s precise and stable tracking for the desired trajectory. The Levenberg-Marquardt (LM) algorithm and line search technology are used and damping factor adjustment rules are set up to enhance the convergence performance of the ILQR control algorithm. In order to verify the effectiveness of the proposed method, the simulation is conducted under the condition of double lane change. The simulation results demonstrate that the proposed method can track the reference trajectory accurately under the limited conditions. Its control performance is much better than other algorithms.

Real-time multibody simulation of vehicle wheel suspensions of different topologies with elastokinematic properties

AbstractDriving simulators are becoming increasingly important in vehicle development, as they allow test drivers to evaluate the driving characteristics of a vehicle at an early stage of the development process. Vehicle dynamics models used for such real-time applications are usually simplified to achieve sufficiently low computation times, especially with respect to the elastokinematic properties of the wheel suspension. This contribution introduces a real-time capable multibody simulation model for wheel suspensions taking into account their elastokinematics. The underlying data model allows the parametrization of different suspension topologies. Linear-implicit integration methods enable real-time capable integration of the underlying numerically stiff equation of motion without major model simplifications. It is shown how the computational effort of linear-implicit integration can be reduced by explicitly taking the suspension topology into account. A versatile process for the analytical online-linearization of the equation of motion of arbitrary suspension topologies is presented. Both a double wishbone and a multilink suspension are simulated with very high accuracy in real-time on a standard PC.

Modeling and analysis of vehicle stability region based on Lyapunov and coordinated control

The timely intervention of the assisted driving system is the key to improving the handling stability and roll stability of the vehicle, and the vehicle stability region serves as the core basis for determining the intervention timing of the assisted driving system. With the aim of modeling and analyzing the vehicle stability region, a three-degree-of-freedom (3-DOF) vehicle dynamics model including yaw, roll, and lateral motions, as well as a nonlinear Magic Formula tire model are established in this paper. Based on this, a simplified but improved cubic tire model is developed to accurately fit the tire lateral force of Magic Formula tire model within a larger range of slip angles. Subsequently, using the Lyapunov method, the roll stability region and the yaw stability region are respectively constructed, and the accuracy verification and robustness analysis of the established stability region are conducted in the Matlab/Simulink environment. Finally, a model-free adaptive control method is employed to keep the vehicle state within the stability region, without tracking specific vehicle state objectives. The study in this paper can provide theoretical support for stability boundary determination, formulation of intervention timing for assisted driving stability control, and coordination control of vehicle stability and anti-rollover with local compatibility or even conflicts.

Co-simulation of giant magnetostrictive autonomous emergen-cy braking system for intelligent vehicles based on Prescan/Simulink /CarSim

The combination of giant magnetostrictive actuator (GMA) and autonomous Emergency brak-ing system (AEB) can achieve the lightweight body and fast braking of intelligent vehicle. In this essay, simulation scenarios are established in PreScan, AEB algorithm and GMA actuator are combined in simulink, vehicle dynamics model is provided by CarSim, and co-simulation analysis of CCRm, CCRs and CCRb working conditions is carried out. When AEB adopts TTC algorithm only, in the stationary vehicle scenario (CCRs) where the vehicle approaches the front at a certain speed, the relative distance between the two vehicles is between 1.86 and 2.85m until stopping. When the front vehicle is driving slowly and the ego vehicle is faster than the front (CCRm), the relative distance between the two vehicles until stopping is between 2.28 and 2.83m; When the ego vehicle is driving at the same speed as the front, the front vehicle suddenly braking (CCRb), and the relative distance between the two vehicles until stopping is 1.98~2.54m; The above scenarios can achieve effective collision avoidance, and the safety distance is within an effective and reasonable range. In the scenario of two vehicles following at the same speed and close distance, TTC algorithm fails. AEB adopts TTC algorithm and Seungwukmoonalgo-rithm for collaborative control to achieve effective collision avoidance. The co-simulation re-sults show that the AEB system can intervene in time and avoid collisions effectively under the above three working conditions, and the relative distance between the two vehicles until stop-ping is between 1.86 and 2.85m, which verifies the timeness and effectiveness of the braking of the giant magnetostrictive actuator when AEB algorithm intervenes, and significantly improves the active safety performance of the vehicle.

Model Predictive Control for Trajectory Planning Considering Constraints on Vertical Load Variation

To address the issue of centrifugal force affecting the vertical load during the stability and trajectory planning of autonomous vehicles during high-speed cornering and obstacle avoidance, a model predictive control of trajectory planning and tracking is proposed that considers the roll factor using only a two-degrees-of-freedom vehicle dynamics model. Firstly, a trajectory planning controller is designed. As a predictive model, a dual-track two-degrees-of-freedom vehicle dynamics model is established. This model describes the relationship between tire lateral forces and vertical loads using a quadratic nonlinear tire model. To reflect the actual dynamic state of the vehicle, the controller incorporates a nonlinear constraint that considers vertical load variations. The nonlinear optimization problem is transformed into a simplified quadratic programming problem by using the Jacobian matrix method to linearize the constraints. By fitting a fourth-degree polynomial curve to the discrete points calculated by the replanning algorithm, an optimal collision-free trajectory is obtained. Secondly, an MPC trajectory tracking controller is designed to control the vehicle in real time along the optimal trajectory from the planning, incorporating control quantity constraints, control increment constraints, and lateral angle constraints to maintain the vehicle’s motion state. We transform the trajectory tracking control problem into a quadratic programming problem, solving for the optimal control sequence for the autonomous vehicle to track the trajectory, achieving an optimized solution and rolling time domain control. Finally, the effectiveness of the vehicle’s obstacle avoidance planning and tracking under high-speed double-lane-change maneuver conditions is validated using the Simulink simulation platform. The results indicate that the designed planning and tracking controllers effectively improve the obstacle avoidance planning and tracking control for high-speed autonomous vehicles.

Research on an Intelligent Vehicle Trajectory Tracking Method Based on Optimal Control Theory

This study aims to explore an intelligent vehicle trajectory tracking control method based on optimal control theory. Considering the limitations of existing control strategies in dealing with signal delays and communication lags, a control strategy combining an anthropomorphic forward-looking reference path and longitudinal velocity closure is proposed to improve the accuracy and stability of intelligent vehicle trajectory tracking. Firstly, according to the vehicle dynamic error tracking model, a linear quadratic regulator (LQR) transverse controller is designed based on the optimal control principle, and a feedforward control strategy is added to reduce the system steady-state error. Secondly, an anthropomorphic look-ahead prediction model is established to mimic human driving behavior to compensate for the signal lag. The double proportional–integral–derivative (DPID) control algorithm is used to track the longitudinal speed reference value. Finally, a joint simulation is conducted based on MatLab/Simulink2021b and CarSim2019.0 software, and the effectiveness of the control strategy proposed in this paper is verified by constructing a semi-physical experimental platform and carrying out a hardware-in-the-loop test. The simulation and test results show that the control strategy can significantly improve the accuracy and stability of vehicle path tracking, which provides a new idea for future intelligent vehicle control system design.

Vertical dynamics analysis and multi-objective optimization of electric vehicle considering the integrated powertrain system

Integrated powertrain system, mainly composed of driving motor and reducer, is one of the excitation sources to electric vehicle vibration but is limited in coupling with the vertical vehicle dynamic in the existing research, which restricts the reliability of dynamics investigation of vehicle and integrated powertrain system. This work proposes a novel vertical dynamic model of the electric vehicle considering the integrated powertrain system, enabling the way to access the coupled vibration among the chassis, unsprung mass, and integrated powertrain system. The road irregularity, unbalanced magnetic pull, and gear mesh excitation are developed and are further applied to the novel vehicle dynamic model with ten degrees of freedom (DOFs). Then, the dynamic response between the novel model and traditional one is compared. Further, the effects of the stiffness and damping of suspension and mounting on the vibration of chassis and integrated powertrain system are explored by numerical calculation. Finally, a multi-objective optimization model is proposed and is driven by the dragonfly algorithm to minimize the vibration of chassis and integrated powertrain system. The research results shown that there will be present the new complex dynamic characteristics with considering the excitation from the integrated powertrain system. The suspension and mounting parameters have the nonlinear effects on the vehicle vibration. And the system vibration can be globally optimized with the optimization algorithm.

Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions

In the actual implementation of autonomous vehicle controller and related applications, it is difficult to obtain all the actual parameters of the vehicle. Considering factors such as uneven pavement and different pavement conditions, it is difficult to accurately establish the vehicle dynamic system model. Based on the non-singular terminal sliding mode and adaptive control theory, this paper establishes a trajectory tracking control strategy for an autonomous vehicle with unknown parameters and unknown disturbances. Firstly, the complex trajectory tracking problem is decoupled from the position and heading angle tracking problem, and the preview error equation is established. Secondly, a non-singular terminal sliding mode (NTSM) controller is established to stabilize the trajectory tracking error to the origin in a finite time, and adaptive laws are proposed to estimate the unknown vehicle parameters to adapt to environmental changes. Through the CarSim–Matlab platform, typical working conditions are implemented to verify the proposed controller. Our experimental outcomes affirm that the NTSM controller effectively guarantees the autonomous vehicle’s accurate following of the reference path, ensuring smooth control inputs throughout the entire process.

Research on regenerative braking control of electric vehicles based on game theory optimization.

The energy-efficient, clean, and quiet attributes of electric vehicles offer solutions to conventional challenges related to resource scarcity and environmental pollution. Consequently, thorough research into harmonizing energy recuperation during braking, enhancing vehicle stability, and ensuring occupant comfort in electric vehicles is imperative for their effective advancement. The study introduces a regenerative braking control strategy for electric vehicles founded on game theory optimization to enhance braking performance and optimize braking energy utilization. Develop a regenerative braking control approach based on the dynamic model of an electric vehicle equipped with hub motors. Employing game theory, we establish participants, control variables, strategy sets, benefit functions, and constraints to optimize the coefficient K for regenerative braking. The efficacy and superiority of the control strategy model are validated through joint simulations using Matlab/Simulink and AVL Cruise. Research findings indicate: (1) Speed tracking error remains below 3% in both NEDC and CLTC-P simulations, underscoring the effectiveness of the dynamic model and control strategy devised in this study. (2) The energy recovery rate achieved by the game theory-based optimization strategy surpasses that of the Cruise self-contained strategy and fuzzy control strategy by 18.06% and 4.5% in the NEDC simulation, and by 13.48% and 3.85% in the CLTC-P simulation, respectively. The adhesion coefficient curves implemented on the front and rear axles, derived from the game theory optimization control strategy, closely approximate the ideal adhesion coefficient curve, leading to a substantial enhancement in the car's braking stability. The degree of jerk magnitude regulated by the game theory optimization strategy consistently falls within the ±3 m/s³ threshold, resulting in a considerable enhancement in the comfort of vehicle occupants. These outcomes underscore the efficacy of the game theory-based optimized control strategy in enhancing energy recovery, braking stability, and comfort throughout the braking process of the vehicle.

Modeling and Subjective Evaluation Method of Driveability for Fuel Cell Vehicles

Aiming at the demand for subjective evaluation of driveability for fuel cell vehicles, the modeling and evaluation method of driveability for fuel cell vehicles were studied in this paper. Firstly, a real-time model of the fuel cell vehicle powertrain system was established, which included the fuel cell model, power battery model, DC/DC converter model and drive motor model. Secondly, it was integrated with the vehicle dynamics model to form a virtual prototype of a fuel cell vehicle. And a virtual subjective evaluation platform for fuel cell vehicles was built by combining the virtual prototype and high-fidelity driving simulator. Thirdly, a subjective evaluation method of driveability for fuel cell vehicles was proposed, which included the starting performance, acceleration performance, uniform speed performance and tip-in/tip-out performance. Finally, the paper used the platform and method mentioned above to conduct subjective evaluations of the fuel cell vehicles.