Vehicle Sideslip Research Articles

Adaptive Sliding Mode Control of Lateral Stability of Four Wheel Hub Electric Vehicles

Some physical parameters of a hub motor-driven four-wheel electric vehicle will change when the vehicle turns or maneuvers and the parameter change is caused by the change of the driving conditions. An adaptive sliding mode control is proposed in this paper to maintain the vehicle’s stability by compensating for the change of these parameters. The control parameter being adapted is the converging rate of the system state towards the sliding mode. As the Lyapunov method is used, so both the vehicle stability and adaptive rate convergence are guaranteed. Moreover, the hierarchical control structure is adopted for this vehicle stability control system. The above adaptive sliding model control forms the upper-layer; while the lower-layer control is to distribute the upper torque to the four wheels in an optimal way, subject to several constraints. In addition, the best feasible reference of the yaw rate and the vehicle side slip angle are obtained and used in the control system. The developed method is simulated under the CarSim/MATLAB co-simulation environment to evaluate the system performance. The simulation results are compared with the non-adaptive existing sliding mode control, and show that the proposed method is superior under different conditions.

Truck Handling Stability Simulation and Comparison of Taper-Leaf and Multi-Leaf Spring Suspensions with the Same Vertical Stiffness

The lightweight design of trucks is of great importance to enhance the load capacity and reduce the production cost. As a result, the taper-leaf spring will gradually replace the multi-leaf spring to become the main elastic element of the suspension for trucks. To reveal the changes of the handling stability after the replacement, the simulations and comparison of the taper-leaf and the multi-leaf spring suspensions with the same vertical stiffness for trucks were conducted. Firstly, to ensure the same comfort of the truck before and after the replacement, an analytical method of replacing the multi-leaf spring with the taper-leaf spring was proposed. Secondly, the effectiveness of the method was verified by the stiffness tests based on a case study. Thirdly, the dynamic models of the taper-leaf spring and the multi-leaf spring with the same vertical stiffness are established and validated, respectively. Based on this, the dynamic models of the truck before and after the replacement were established and verified by the steady static circular test, respectively. Lastly, the handling stability indexes for the truck were compared by the simulations of the drift test, the ramp steer test, and the step steer test. The results show that the yaw rate of the truck almost does not change, the steering wheel moment decreases, the vehicle roll angle obviously increases, and the vehicle side slip angle slightly increases after the replacement. Thus, the truck with the taper-leaf spring suspension has better steering portability, however, its handling stability performs worse.

A simple and reliable technique to design kinematic-based sideslip estimators

This paper proposes two novel vehicle sideslip estimators, that aim at achieving ease of implementation and tuning, low computational cost and robustness, using only the most common automotive measurements, like vehicle position, acceleration and rotational velocity. The two estimators are only based on the unicycle kinematic model, thus they do not require any knowledge of uncertain or time-varying parameters, like vehicle parameters, or of road conditions, as it usually happens when dynamic models are adopted, and they have been derived by recasting an estimation problem into a linear control problem. Different experiments, ranging from standard driving manoeuvres to drifting driving and autonomous driving, have been performed to demonstrate the effectiveness of the proposal even in particularly critical scenarios, like driving at the limits of vehicle’s handling. A comparison with a state-of-the-art sideslip estimator, using simulation and experimental data, is presented, as well.

Estimation and Simulation Analysis of the Sideslip Angle of Intelligent Vehicle under Unstructured Road Environment

Vehicle sideslip angle is one of the important variables to describe the vehicle's lateral motion state, and it is also difficult to estimate accurately. In order to solve the difficult problem of direct measurement of intelligent vehicle sideslip angle in unstructured road environment, a 3-DOF vehicle Dynamics model was established, Carsim and Matlab were also used to construct the parametric model of vehicle. Based on the extended Kalman filter (EKF) algorithm, the State observer was used to estimate the sideslip angle of the intelligent vehicle. In the case of emergency shift, the estimation effect of State observer is verified by joint simulation. The simulation results show that the proposed method can accurately estimate the sideslip angle of the intelligent vehicle.

An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling.

Influencing Factors on Vehicles Lateral Stability on Tunnel Section in Mountainous Expressway under Strong Wind: A Case of Xi‐Han Highway

When a car is running at high speed, the canyon wind at the bridge‐tunnel junction in the mountainous area brings along the acceleration effect. The aerodynamic lateral force will cause the vehicle sideslip and unsteady steering, which is extremely harmful to driving safety. In this paper, Xi‐Han Expressway is taken as the research object to analyze the influencing factors of vehicle’s lateral stability by combining the theoretical research of the finite element method, automobile aerodynamics, and speed limit with field investigation and simulation test. CarSim software is used for simulation to explore the influence of different positions of the circular curve on vehicle lateral stability. The results show that the wind level affects the tunnel exit’s unfavorable section on the circular curve. The larger the wind level, the larger the proportion of the tunnel exit’s unfavorable section on the circular curve. The proportions of tunnel exit’s unfavorable section on the circular curve under 6–9 wind levels are 33.33%, 38.89%, 55.56%, and 66.67%, respectively. In addition, the lateral stability of vehicles under level 6–8 wind scale is the worst when the tunnel exit is located at 5° position on the circular curve. The results indicate the influence of strong wind on the lateral stability of vehicles in mountainous expressway. The research can optimize the design of the highway tunnel group and provide the basic theory and method basis for the quantitative management and scientific management of the road traffic management department.

Estimation of Vehicle Side Slip Angle by Using Acceleration Sensor

Estimation of Vehicle Side Slip Angle by Using Acceleration Sensor

Yaw Angle Control of Heavy Commercial Road Vehicle with Faulty Brake

Faults in an air brake system affect the performance of Heavy Commercial Road Vehicles (HCRVs). One of the major faults in the air brake system is the “out-of-adjustment”of pushrod due to excessive brake wear, which may cause a significant yaw angle deviation from the current path and an increase in stopping distance. Hence, this paper aims to design a controller that would maintain the vehicle’s directional stability under brake fault scenario. In order to design such a controller, knowledge of vehicle side slip angle is essential, but this is not a measurable quantity. Hence, an Artificial Neural Network (ANN) based estimation scheme for side slip angle prediction is also proposed. As an output regulator problem, Sliding Mode Control (SMC) was used for correction of yaw angle under brake fault scenario through appropriate steering angle input. This controller provided a percentage correction of yaw angle of 93.4 % and 99.8 % respectively for fully laden and fully unladen vehicle on a high friction tire road interface surface, and the same corresponding to a low friction tire road interface was 99.8 % and 98.9 % respectively.

Improvement of vehicle stability using a controller based on reinforcement learning

This paper presents a study on the use of reinforcement learning to control the torque vectoring of a small electric racecar aiming to improve vehicle handling and vehicle stability. The reinforcement-learning algorithm used is Neural Fitted Q Iteration, and the sampling of experiences is based on simulations using the software CarMaker. The cost function is based on the position of the states on the phase-plane of sideslip angle and angular velocity. To investigate the maximum ratio of torque distribution that should be set to guarantee stability as well as the effectiveness of the controller inputs in the learning process, two experiments were done (A and B) with different states and possibilities of torque distribution. The controller A is able to improve the vehicle handling and stability with a significant reduction in vehicle sideslip angle. And it also showed that 70% of torque distribution is enough to keep the vehicle stable.

Vehicle Sideslip Angle Estimation for a Heavy-Duty Vehicle via Extended Kalman Filter Using a Rational Tyre Model

Vehicle sideslip angle is a key state for lateral vehicle dynamics, but measuring it is expensive and unpractical. Still, knowledge of this state would be really valuable for vehicle control systems aimed at enhancing vehicle safety, to help to reduce worldwide fatal car accidents. This has motivated the research community to investigate techniques to estimate vehicle sideslip angle, which is still a challenging problem. One of the major issues is the need for accurate tyre model parameters, which are difficult to characterise and subject to change during vehicle operation. This paper proposes a new method for estimating vehicle sideslip angle using an Extended Kalman Filter. The main novelties are: i) the tyre behaviour is described using a Rational tyre model whose parameters are estimated and updated online to account for their variation due to e.g. tyre wear and environmental conditions affecting the tyre behaviour; ii) the proposed technique is compared with two other methods available in the literature by means of experimental tests on a heavy-duty vehicle. Results show that: i) the proposed method effectively estimates vehicle sideslip angle with an error limited to 0.5 deg in standard driving conditions, and less than 1 deg for a high-speed run; ii) the tyre parameters are successfully updated online, contributing to outclassing estimation methods based on tyre models that are either excessively simple or with non-varying parameters.

Vehicle Sideslip Angle Estimation Based on Fusion of Kinematic Method and Kinematic-geometry Method

Vehicle Sideslip Angle Estimation Based on Fusion of Kinematic Method and Kinematic-geometry Method

Improvement of vehicle stability using a controller based on reinforcement learning

This paper presents a study on the use of reinforcement learning to control the torque vectoring of a small electric racecar aiming to improve vehicle handling and vehicle stability. The reinforcement-learning algorithm used is Neural Fitted Q Iteration, and the sampling of experiences is based on simulations using the software CarMaker. The cost function is based on the position of the states on the phase-plane of sideslip angle and angular velocity. To investigate the maximum ratio of torque distribution that should be set to guarantee stability as well as the effectiveness of the controller inputs in the learning process, two experiments were done (A and B) with different states and possibilities of torque distribution. The controller A is able to improve the vehicle handling and stability with a significant reduction in vehicle sideslip angle. And it also showed that 70% of torque distribution is enough to keep the vehicle stable.

A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches

This article introduces a reliable fusion methodology for vehicle sideslip angle estimation, which only needs the Controller Area Network–Bus signals of production vehicles and has good robustness to vehicle parameters, tire information, and road friction coefficient. The fusion methodology consists of two basic approaches: the kinematic-based approach and the model-based approach. The former is constructed into the extended Kalman filter for transient stage and large magnitude estimation, while the latter is designed to be an adaptive scheme for steady-state and small magnitude estimation. On this basis, combining the advantages of the two methods, a weight allocation strategy is proposed based on the front wheel steering angle and transient characteristics of lateral acceleration and yaw rate. The validity of the method is verified by simulation and experiment, and it is proved that the method can be effectively used for the sideslip angle estimation.

Vehicle sideslip angle estimation based on SVD-UPF algorithm

Vehicle sideslip angle estimation based on SVD-UPF algorithm

Coordinated control of stability and economy based on torque distribution of distributed drive electric vehicle

The torque distribution strategy of distributed drive electric vehicle is only aimed at safety or economy. A multi-target coordinated control method considering stability and economy is proposed to solve the problem of single torque distribution target, which consists of a coordination decision controller, a high-level motion controller, and a low-level allocation controller. The coordination decision controller based on the phase plane method determines whether to adopt a stability or economic control strategy. The high-level motion controller consists of a bicycle model with 2 degree of freedom, a speed tracking controller, a stability controller, and an economic controller to calculate the desired direct yaw moment of the four in-wheel motors. The stability controller based on the fuzzy algorithm tracks the desired vehicle side slip angle and yaw rate calculated by the bicycle model with 2 degree of freedom to control vehicle stability. The economical controller based on a multi-motor loss model optimizes the efficiency of the vehicle’s drive system. The low-level allocation controller is presented to provide optimally distributed torques for each wheel. Finally, the simulation and hardware-in-the-loop testing show that the coordinated control strategy can effectively improve the stability and economy of the distributed drive electric vehicle.

Performance comparison of electric-vehicle drivetrain architectures from a vehicle dynamics perspective

Recent electric vehicle studies in literature utilize electric motors within an anti-lock braking system, traction-control system, and/or vehicle-stability controller scheme. Electric motors are used as hub motors, on-board motors, or axle motors prior to the differential. This has led to the need for comparing these different drivetrain architectures with each other from a vehicle dynamics standpoint. With this background in place, using MATLAB simulations, these three drivetrain architectures are compared with each other in this study. In anti-lock braking system and vehicle-stability controller simulations, different control approaches are utilized to blend the electric motor torque with hydraulic brake torque; motor ABS, torque decomposition, and optimal slip-tracking control strategies. The results for the anti-lock braking system simulations can be summarized as follows: (1) Motor ABS strategy improves the stopping distance compared to the standard anti-lock braking system. (2) In case the motors are not solely capable of providing the required braking torque, torque decomposition strategy becomes a good solution. (3) Optimal slip-tracking control strategy improves the stopping distance remarkably compared to the standard anti-lock braking system, motor anti-lock braking system, and torque decomposition strategies for all architectures. The vehicle-stability controller simulation results can be summarized as follows: (1) higher affective wheel inertia of the on-board and hub motor architecture dictates a higher need of wheel torque in order to generate the tire force required for the desired yaw rate tracking. A higher level of torque causes a higher level of tire slip. (2) Optimal slip-tracking control strategy reduces the tire slip trends drastically and distributes the traction/braking action to each tire with the control-allocation algorithm specifying the reference slip values. This reduces reference tire slip-tracking error and reduces vehicle sideslip angle. (3) Tire slip trends are lower with the hub motor architecture, compared to the other architectures, due to more precise slip control.

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

Robust estimation of vehicle sideslip angle is essential for stability control applications. However, the direct measurement of sideslip angle is expensive for production vehicles. This paper presents a novel sideslip estimation algorithm which relies only on sensors available on passenger and commercial vehicles. The proposed method uses both kinematics and dynamics vehicle models to construct extended Kalman filter observers. The estimate relies on the results provided from the dynamics model observer where the tire cornering stiffness parameters are updated using the information provided from the kinematics model observer. The stability property of the proposed algorithm is discussed and proven. Finally, multiple experimental tests are conducted to verify its performance in practice. The results show that the proposed approach provides smooth and accurate sideslip angle estimation. In addition, our novel algorithm provides reliable estimates of bank angles and lateral acceleration sensor bias.

Virtual sensor for real-time estimation of the vehicle sideslip angle

Purpose The vehicle sideslip angle is an important state of vehicle lateral dynamics and its knowledge is crucial for the successful implementation of advanced driver-assistance systems. Measuring the vehicle sideslip angle on a production vehicle is challenging because of the exorbitant price of a physical sensor. This paper aims to present a novel framework for virtually sensing/estimating the vehicle sideslip angle. The desired level of accuracy for the estimator is to be within +/− 0.2 degree of the actual sideslip angle of the vehicle. This will make the precision of the proposed estimator at par with expensive commercially available sensors used for physically measuring the vehicle sideslip angle. Design/methodology/approach The proposed estimator uses an adaptive tire model in conjunction with a model-based observer. The performance of the estimator is evaluated through experimental tests on a rear-wheel drive vehicle. Findings Detailed experimental results show that the developed system can reliably estimate the vehicle sideslip angle during both steady state and transient maneuvers, within the desired accuracy levels. Originality/value This paper presents a novel framework for vehicle sideslip angle estimation. The presented framework combines an adaptive tire model, an unscented Kalman filter-based axle force observer and data from tire mounted sensors. Tire model adaptation is achieved by making extensions to the magic formula, by accounting for variations in the tire inflation pressure, load, tread-depth and temperature. Predictions with the adapted tire model were validated by running experiments on the Flat-Trac® machine. The benefits of using an adaptive tire model for sideslip angle estimation are demonstrated through experimental tests. The performance of the observer is satisfactory, in both transient and steady state maneuvers. Future work will focus on measuring tire slip angle and road friction information using tire mounted sensors and using that information to further enhance the robustness of the vehicle sideslip angle observer.

Design of an Interacting Multiple Model-Cubature Kalman Filter Approach for Vehicle Sideslip Angle and Tire Forces Estimation

Vehicle states estimation (e.g., vehicle sideslip angle and tire force) is a key factor for vehicle stability control. However, the accurate values of these parameters could not be obtained directly. In this paper, an interacting multiple model-cubature Kalman filter (IMM-CKF) is used to estimate the vehicle state parameters. And improvements about estimation method are achieved in this paper. Firstly, the accuracy of the reference model is improved by building two different models: one is 7-degree-of-freedom (7 DOF) vehicle model with linear tire model, and the other is 7 DOF vehicle model with nonlinear Dugoff tire model. Secondly, the different models are switched by IMM-CKF to match different driving condition. Thirdly, the lateral acceleration correction for sideslip angle estimation is considered, because the sensor of lateral acceleration is easy to be influenced by the gravity on banked road. Then, to compare cubature Kalman filter (CKF) estimation method and IMM-CKF estimation method Hardware-In-Loop (HIL) tests are carried out in the paper. And simulation results show that IMM-CKF methodology can provide accurate estimation values of vehicle states parameters.

Development and evaluation of torque distribution methods for four-wheel independent-drive electric vehicle based on parameter estimation

A hierarchical controller including a vehicle condition parameter estimator for improving the vehicle stability is theoretically applied to a four-wheel independent-drive electric vehicle. This study is aimed at combining the parameters estimation with vehicle stability control and evaluating different optimization algorithms based on the computation time and fluctuation. The hierarchical controller consists of a vehicle condition parameter estimator, active yaw moment controller, and torque distribution controller. The vehicle condition parameter estimator is based on the Unscented Kalman Filter, which avoids the truncation error caused by Taylor expansion to convert a nonlinear system into a linear system. The active yaw moment controller is designed based on the sliding mode control. The controller employs co-control between the vehicle sideslip angle and yaw rate, which properly accounts for both factors. The objective function of the torque distribution controller is based on the tire utilization function and constraint condition. The torque distribution and wheel slip rate are accounted for simultaneously. The torque distribution methods are tested based on nonlinear programming, quadratic programming, and weighted least squares for the sake of comparison. The simulation results demonstrate the accuracy of the proposed vehicle state estimator and the effectiveness of the proposed stability controller.