Vehicle Dynamics Control Research Articles

Modeling and Verification of Tire Nonlinearity Effect on Accuracy of Vehicle Yaw Rate Calculation

<div class="section abstract"><div class="htmlview paragraph">The desired yaw rate is a vital target parameter for vehicle stability control, which is currently determined as a steady-state yaw rate by the linear single-track vehicle model. Tire nonlinearity deteriorates the effect of vehicle stability control at larger lateral acceleration. This paper proposes a new calculation method of the steady-state yaw rate considering the tire nonlinearity based on the brush tire model. To validate and verify the proposed method, step steering tests of the target vehicle under different lateral accelerations are carried out on a real proving ground. The results show that when the lateral acceleration is relatively small, the difference between the calculation results of the proposed method and the traditional one is not apparent, and both methods can provide a good estimation for the steady-state yaw rate; however, when the lateral acceleration is relatively large, the difference becomes apparent. It can be shown that the linear tire model cannot be able to calculate the steady-state yaw rate accurately, while the brush tire model agrees well with the observation. This difference shows the importance of considering tire nonlinearity in vehicle stability control, especially in extreme driving conditions. By analyzing the existence of the solution of the yaw rate equation, this paper finds that the vehicle speed upper limit under which the vehicle can be able to achieve steady-state steering is also affected by the tire nonlinearity. A Simulink vehicle model is built to verify the steady-state vehicle speed upper limit for vehicles with different steering characteristics. The results of the modeling and tests provide certain guidelines for developing a vehicle stability control system.</div></div>

Hybrid Kinematic-Dynamic Sideslip and Friction Estimation

AbstractVehicle sideslip and tyre/road friction are crucial variables for advanced vehicle stability control systems. Estimation is required since direct measurement through sensors is costly and unreliable. In this paper, we develop and validate a sideslip estimator robust to unknown road grip conditions. Particularly, the paper addresses the problem of rapid tyre/road friction adaptation when sudden road condition variations happen. The algorithm is based on a hybrid kinematic-dynamic closed-loop observer augmented with a tyre/road friction classifier that reinitializes the states of the estimator when a change of friction is detected. Extensive experiments on a four wheel drive electric vehicle carried out on different roads quantitatively validate the approach. The architecture guarantees accurate estimation on dry and wet asphalt and snow terrain with a maximum sideslip estimation error lower than 1.5 deg. The classifier correctly recognizes 87% of the friction changes; wrongly classifies 2% of the friction changes while it is unable to detect the change in 11% of the cases. The missed detections are due to the fact that the algorithm requires a certain level of vehicle excitation to detect a change of friction. The average classification time is 1.6 s. The tests also indicate the advantages of the friction classifiers on the sideslip estimation error.

Impact of the tyre dynamics on autonomous vehicle path following control with front wheel steering and differential motor torque

AbstractThis paper investigates the impact of tyre dynamics on autonomous vehicle dynamic control. The commonly used 2 degrees of freedom bicycle model and a detailed vehicle model considering tyre dynamics are built, respectively, to describe the vehicle dynamics. The two models are firstly compared with the response to front wheel steering input and differential motor torque input. The transient vehicle body vibrations are clearly demonstrated by the detailed model but overlooked by the bicycle model. The influence of tyre nonlinearity on path offset is also revealed. Then, the path following control strategy is designed based on the linear quadratic regulator control to compare the two models from the path following performance perspective. The double lane change simulation is conducted. The results show that at low vehicle speed, the path following errors of two models are very close, which proves the rationality of using the bicycle model to represent the vehicle dynamics in mild and moderate driving conditions. However, when the path following is conducted with high vehicle speed, the required lateral tyre force is large, so the tyres work with large slip angles and the tyre nonlinearity generates significant deviation on the path following errors between the two models.

Fault-Tolerant Predictive Control With Deep-Reinforcement-Learning-Based Torque Distribution for Four In-Wheel Motor Drive Electric Vehicles

This article proposes a fault-tolerant control (FTC) method for four in-wheel motor drive electric vehicles considering both vehicle stability and motor power consumption. First, a seven-degree-of-freedom vehicle nonlinear model integrating motor faults is built to design a hierarchical FTC control scheme. The control structure is composed of two levels: an upper level nonlinear model-predictive controller and a lower level fault-tolerant coordinated controller. The upper level controller provides an appropriate reference in terms of additional yaw moment and vehicle longitudinal force, required for vehicle stability control, to the lower level controller. This latter aims at distributing the four-wheel torques taking into account both vehicle stability and power consumption. Specifically, the weighting factor involved in the optimization-based design of the lower level controller is determined online by the randomized ensembled double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning reinforcement learning algorithm to achieve an optimal control strategy for the whole vehicle operating range. Moreover, the tradeoff between vehicle stability and power consumption is analyzed, and the necessity of using reinforcement learning is discussed. Numerical experiments are performed under various driving scenarios with a high-fidelity CarSim vehicle model to demonstrate the effectiveness of the proposed control method. Via a comparative study, we highlight the advantages of the new FTC control method over many related existing control results in terms of improving the vehicle stability and driver comfort as well as reducing the power consumption.

The straight-line stability analysis of the vehicle with the electronic stability control

BACKGROUND: At present time, branches of industry have been increasingly filled with electronics, capable of controlling different processes in order to achieve demanded paremeters values. Automotive industry, safety issues of which such as vehicle stability and handling are paid much attention, have not become an exclusion. Vehicle safety improvement is one of the areas of development of modern auto-motive industry. Manufacturers permanentally increase the number of control systems and raise the level of control on wheeled vehicle motion parameters in order to achieve the highest possible level of stability and handling. Using of various all-wheel-drive systems becomes more relevant and reasonable. Traction force distribution between all wheels improves dynamic performance indicators, makes cross-country ability better in similar road conditions and allows using algorithms, improving handling and course-keeping ability of a vehicle.&#x0D; AIMS: Determination of variation range of the control parameter in order to ensure stable straight-line motion of a vehicle with controlled transmission.&#x0D; MATERIALS AND METHODS: The two-axled vehicle, which transmission has an ability of torque distribution between front and rear axles, is considered in the article. The system of diiferential equations in terms of middle wheels side slip angles was made up in the study. The LinardChipart algebraic cri-terion was used to define variation ranges of the control parameter in order to ensure stable straight-line motion of the vehicle.&#x0D; RESULTS: The obtained variation ranges of the control parameter are included in the adjustment range of modern torque distribution mechanisms, consequently, it is possible to speak about ability to ensure stability of straight-line motion of a full-wheel-driven vehicle with currently existing mechanisms. The scientific novelty of the study lies in analytical determination of variation range of the control parameter for the case of straight-line motion of a vehicle equipped with the electronic stability control.&#x0D; CONCLUSIONS: Practical value of the study lies in establishment of ability to ensure stability of straight-line motion of a full-wheel-driven vehicle with currently existing mechanisms.

A Study on Self-Sustained Vibrations of a Tire Operating above Peak Friction

ABSTRACT When a tire operates at a side slip level above peak friction, large vibrations in the produced forces and moments are observed. In the lateral direction, these vibrations are typically centered at frequencies close to 50 Hz and significantly affect the force that is produced by the tire. As an example, high dynamics vehicle maneuvers with an electronic stability program control system in the loop can be affected by this behavior. To accurately reproduce the tire response in these operating conditions, it is important to employ a model that can capture this phenomenon. Based on analysis of nonlinear, unstable systems and limit cycle phenomenon, a theory is presented that provides a physical background for the source of vibrations. The theory also gives insight in how the frequency and amplitude of the vibrations are influenced by the tire physical characteristics and the operating conditions. It is found that the most dominant characteristics are the shape of the steady-state force response of the tire around peak friction, the contact patch mass, the carcass damping and stiffness. Simulations with physical models of different complexity levels, as well as with the commercial Simcenter Tire MF-Tyre/MF-Swift tire software model, validate the theory and demonstrate how the vibrations can be reproduced in a simulation environment. The simulation results are compared with tire measurements in different operating conditions, validating the exposed theory and employed models.

Vehicle Stability Control Through Pre-Emptive Braking

Vehicle Stability Control Through Pre-Emptive Braking

SIMULATION OF RANDOM EXTERNAL DISTURBANCE ACTING ON THE CAR BODY IN THE URGENT BRAKING MODE

Topicality. When creating ESP (Electronic Stability Program) car stability systems, the main task is to determine the values of the varied parameters of the EBD (Electronic Brake Distribution) electronic block, which ensure the required accuracy of stabilization of the car body in urgent braking mode relative to the given direction of movement. The purpose of the article. To solve the problem of parametric synthesis of the EBD block, it is necessary to create a simulation model of external disturbances acting on the car body from the side of the road surface. Well-known simulation models are created for car movement modes on a random surface with a constant speed. At the same time, random external disturbances are stationary in nature. The urgent braking mode is characterized by an intense decrease in the current speed of the car. Results. The paper proposes a method of constructing a random function of an external disturbance acting on the car body in urgent braking mode. It is proved that this perturbation is non-stationary in nature; the forming dynamic link, the input of which is supplied with single "white noise", and the output of which is an external disturbance, is an oscillating dynamic link, the gain and time constants of which depend on the current value of the car's speed during urgent braking.

Research on control strategy of vehicle stability based on dynamic stable region regression analysis.

The intervention time of stability control system is determined by stability judgment, which is the basis of vehicle stability control. According to the different working conditions of the vehicle, we construct the phase plane of the vehicle's sideslip angle and sideslip angular velocity, and establish the sample dataset of the stable region of the different phase planes. To reduce the complexity of phase plane stable region division and avoid large amount of data, we established the support vector regression (SVR) model, and realized the automatic regression of dynamic stable region. The testing of the test set shows that the model established in this paper has strong generalization ability. We designed a direct yaw-moment control (DYC) stability controller based on linear time-varying model predictive control (LTV-MPC). The influence of key factors such as centroid position and road adhesion coefficient on the stable region is analyzed through phase diagram. The effectiveness of the stability judgment and control algorithm is verified by simulation tests.

Longitudinal and lateral coupling vehicle stability controller designed based on piecewise T-S fuzzy model

Due to tire force coupling and load transfer coupling during the stability control process, the vehicle longitudinal and lateral dynamics shows particularly obvious nonlinear coupling characteristics, which will deteriorate the performance of the stability controller. This paper proposes a Vehicle Stability Controller considering the vehicle longitudinal and lateral coupling characteristics. First, a piecewise guidance method, including the Adaptive Linear Regression Algorithm and Dimensionless Performance Evaluation Weighting Function, is developed to ensure the Takagi-Sugeno fuzzy model piecewise approaches the nonlinear characteristics of the original system and obtains the multiple linear fuzzy subsystems. Then, an H∞ robust controller with dynamic output feedback is used in each fuzzy subsystem based on the identification result of the driving intention, and the Linear Matrix Inequality is applied to obtain the controller gains. Subsequently, the Parallel Distributed Compensation architecture is adopted to integrate the outputs of all fuzzy subsystems, and vehicle stability controller is realized by using a rule-based control distribution strategy. The proposed controller is evaluated by simulations and hardware in the loop tests, and the results demonstrate satisfactory longitudinal following ability and lateral stability.

Model-Based Estimation of Vehicle Center of Gravity Height and Load

AbstractWith 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 more and more important. One particularly significant parameter with regard to vehicle dynamics is the center of gravity (COG) height, which mainly accounts for its roll dynamics in combination with the mass of the vehicle. Thus, a highly increased vehicle mass and COG height might lead to rollover during cornering, which underlines the need for accurate knowledge of the load of the vehicle. Based on that, an improved rollover prevention could be implemented, for instance, by enhancing the electronic stability program (ESP) of the vehicle. Therefore, the main contribution of this work is the model-based online estimation of the additional load of the vehicle, comprising its COG position and mass. This is achieved by applying a joint extended Kalman Filter (EKF) for the simultaneous state and parameter estimation. Based on a nonlinear model with roll, pitch, and vertical dynamics, an accurate and reliable estimation is possible. One major novelty of this work is the consideration of air suspension systems on top of conventional steel spring suspension systems. Therefore, a nonlinear air spring model with sufficient complexity is proposed, making it suitable for real-time applications. Further, a system theoretical observability analysis allows for an online adaptation of the Kalman Filter weights in order to account for different driving situations. The proposed estimation method is tested and validated by considering a wide range of driving situations, considering distinct loading conditions on both a test track and public roads. The estimation accuracy lies within roughly 50 kg and 1.5 cm for the vehicle mass and COG height, respectively.

Cooperative Strategy of Trajectory Tracking and Stability Control for 4WID Autonomous Vehicles Under Extreme Conditions

Trajectory tracking and stability control are two essential functions of autonomous vehicles, and there is inevitable mutual interference between them, especially under extreme conditions. Towards addressing this challenge, this paper proposes a novel cooperative strategy of trajectory tracking and stability control for four-wheel independent drive (4WID) autonomous vehicles. An adaptive trajectory tracking controller is designed based on the model predictive control (MPC) theory, in which the tire cornering stiffness is modified in real-time leverages the lateral force estimated by the square-root cubature Kalman filter (SCKF). Then, the vehicle stability controller is designed based on the sliding mode control (SMC), in which the tire force saturation constraint and the relative weight of yaw rate and sideslip angle are considered. Furthermore, a weight adaptive criterion with normalized stability index is defined to develop the cooperative strategy of trajectory tracking and stability control. The feasibility and adaptability of the proposed method to different extreme conditions are verified by hardware-in-the-loop (HIL) test and CarSim-Simulink co-simulation. Finally, an experimental case is presented to demonstrate the realizability of the proposed method.

Overview of Key Technologies for Torque Ripple Suppression in Four- Wheel In-Wheel Motor Drive Electric Vehicles

Aims and Objectives: The stability control of the four-wheel hub motor-driven electric vehicle is of great significance to the safety of the driver during the driving process, and the torque fluctuation is an important factor that affects the stability control of the vehicle. Therefore, this paper reviews the key technologies and difficulties of torque ripple suppression for in-wheel motor-driven electric vehicles from two aspects: rational design of the in-wheel motor structure to suppress the torque fluctuation of the motor and the in-wheel motor torque control distribution strategy. Methods: Through the analysis of the structural characteristics of the motor, the structural optimization design of the pole slot, cogging, core shape, and magnetic pole shape is used to suppress the torque fluctuation of the motor, and the methods of previous scholars are unified and explained; From the motor control strategy, summed up the scholars in the motor torque ripple suppression method strategy; From the perspective of motor torque control allocation strategy, a series of related strategies are elaborated. The previous researchers proposed the neural network PID electronic differential speed torque comprehensive control strategies, electronic differential control algorithms based on sliding mode control, automotive electronic stability program control algorithms based on hierarchical coordinated control strategies, and other control strategies and algorithms. Results: Based on the theoretical model, the theoretical model is verified and tested through software simulation or test platform. The errors of all simulation and test results in the literature and the theoretical model are within the acceptable range. Conclusion: The theoretical model has been verified on the software simulation or test platform, which proves the feasibility and effectiveness of the theoretical model, thereby suppressing torque fluctuations and improving the stability of the vehicle. Finally, the development direction of the key technology of torque ripple suppression for four-wheel in-wheel motor-driven electric vehicles has been prospected.

Estimated potential death and disability averted with vehicle safety interventions, Association of Southeast Asian Nations.

To evaluate road safety in member countries of the Association of Southeast Asian Nations and estimate the benefits that vehicle safety interventions would have in this group of countries. We used a counterfactual analysis to assess the reduction in traffic deaths and disability-adjusted life years (DALYs) lost if eight proven vehicle safety technologies and motorcycle helmets were entirely in use in countries of the Association of Southeast Asian Nations. We modelled each technology using country-level incidence estimations of traffic injuries, and the prevalence and effectiveness of the technology to calculate the reduction in deaths and DALYs if the technology was fitted in the entire vehicle fleet. The availability of electronic stability control, including the antilock braking systems, would provide the most benefits for all road users with estimates of 23.2% (sensitivity analysis range: 9.7-27.8) fewer deaths and 21.1% (9.5-28.1) fewer DALYs. Increased use of seatbelts was estimated to prevent 11.3% (8.11-4.9) of deaths and 10.3% (8.2-14.4) of DALYs. Appropriate and correct use of motorcycle helmets could result in 8.0% (3.3-12.9) fewer deaths and 8.9% (4.2-12.5) fewer DALYs. Our findings show the potential of improved vehicle safety design and personal protective devices (seatbelts and helmets) to reduce traffic deaths and disabilities in the Association of Southeast Asian Nations. These improvements can be achieved by vehicle design regulations and creating consumer demand for safer vehicles and motorcycle helmets through mechanisms such as new car assessment programmes and other initiatives.

Coordinated Lateral Stability Control of Autonomous Vehicles Based on State Estimation and Path Tracking

This paper proposes a coordinated control system of autonomous vehicles’ lateral stability based on state estimation and path tracking, which contributes to enhancing path-tracking capability and at the same time maintaining the stability of autonomous vehicles driving in complex and dynamic road conditions. First, an extended Kalman filter observer is designed to estimate the vehicle parameters. Then, based on the single-point preview theory, an adaptive preview time model is built for the accuracy of path tracking. When designing the direct yaw-moment controller, sideslip angle and yaw rate, two critical parameters for lateral dynamic stability control are considered, and the single-wheel braking method is utilized to reasonably distribute the additional yaw moment to a certain wheel. To make full use of active front steering and direct yaw-moment control, the critical steering angle of the front wheel is determined, and a switching function is formulated. Co-simulation shows that the suggested coordinated control system can effectively strengthen maneuvering stability and simultaneously satisfy tracking accuracy.

Dynamic Loading Mechanism and Stability Control of Gob-Side Entry Retaining with Thick and Hard Roof: Insights from Numerical Simulation and Field Test

Dynamic Loading Mechanism and Stability Control of Gob-Side Entry Retaining with Thick and Hard Roof: Insights from Numerical Simulation and Field Test

An Anti-Skid Control System Based on the Energy Method for Decentralized Electric Vehicles

Anti-slip control, as a fundamental technique of vehicle stability control, prevents loss of control of vehicles, especially under extreme driving conditions. However, current control methods fail to suppress vehicle slippage when steering. Therefore, a new anti-slip control approach for four-wheel independent-drive electric vehicles (EVs) based on the energy method is proposed. This approach makes full use of the distribution of motor energy between the body and the wheels during vehicle turning, being able to adjust the driving torque of each wheel. Simulation results validate that the proposed approach can prevent wheel slip when the vehicle steers on slippery roads. Furthermore, simulations also show that the proposed control strategy can maintain high control performance when the motor flux linkage varies.

Integrated Adhesion Coefficient Estimation of 3D Road Surfaces Based on Dimensionless Data-Driven Tire Model

The tire/road peak friction coefficient (TRPFC) is the core parameter of vehicle stability control, and its estimation accuracy significantly affects the control effect of active vehicle safety. To estimate the peak adhesion coefficient accurately, a new method for the comprehensive adhesion coefficient of three-dimensional pavement based on a dimensionless data-driven tire model is proposed. Firstly, in order to accurately describe the contact state between the three-dimensional road surface and the tire during driving, stress distribution and multi-point contact are introduced into the vertical dynamic model and a new tire model driven by dimensionless data is established based on the normalization method. Secondly, the real-time assessment of lateral and longitudinal adhesion coefficients of three-dimensional pavement is realized with the unscented Kalman filter (UKF). Finally, according to the coupling relationship between the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient, a fuzzy reasoning strategy of fusing the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient is designed. The results of vehicle tests prove that the method proposed in this paper can estimate the peak adhesion coefficient of pavement quickly and accurately.

Research on Longitudinal Control Algorithm of Adaptive Cruise Control System for Pure Electric Vehicles

The vehicle longitudinal control algorithm is the core function of the adaptive cruise control system, whose main task is to convert vehicle acceleration and deceleration requirements into vehicle driving and braking commands so that the vehicle can quickly and accurately track the desired acceleration. Traditional longitudinal control algorithms rely on accurate vehicle dynamic modeling or complex controller parameter calibrations. To overcome those difficulties, a longitudinal control algorithm based on RBF-PID is proposed in this paper. The algorithm uses the RBFNN (radial basis function neural network), which can simply and quickly approximate any complex nonlinear system, to identify the Jacobian information of the vehicle and perform parameter tuning for PID control and achieve vehicle longitudinal control with self-tuning capability. Finally, the algorithm of this paper is verified by the joint simulation of Matlab/Simulink and Carsim. The results show that this algorithm has a better response rate and anti-jamming capability than the traditional PID control and can achieve accurate and rapid tracking of the desired acceleration.

Tire Slip H∞ Control for Optimal Braking Depending on Road Condition.

Tire slip control is one of the most critical topics in vehicle dynamics control, being the basis of systems such the Anti-lock Braking System (ABS), Traction Control System (TCS) or Electronic Stability Program (ESP). The highly nonlinear behavior of tire-road contact makes it challenging to design robust controllers able to find a dynamic stable solution in different working conditions. Furthermore, road conditions greatly affect the braking performance of vehicles, being lower on slippery roads than on roads with a high tire friction coefficient. For this reason, by knowing the value of this coefficient, it is possible to change the slip ratio tracking reference of the tires in order to obtain the optimal braking performance. In this paper, an H∞ controller is proposed to deal with the tire slip control problem and maximize the braking forces depending on the road condition. Simulations are carried out in the vehicular dynamics simulator software CarSim. The proposed controller is able to make the tire slip follow a given reference based on the friction coefficient for the different tested road conditions, resulting in a small reference error and good transient response.