Vehicle Dynamic Control System Research Articles

Real-time estimation of vehicle inertia parameters based on Kalman-bucy filter

Vehicle inertial parameters such as mass and moments of inertia are required for most vehicle dynamic control systems. Due to the wide range variation of these parameters during vehicle operation, accurate estimation of their values in real-time plays an important role in improving the efficiency of vehicle control systems. In this article, the vehicle sprung mass and moments of inertia are estimated in real-time based on a Kalman-Bucy filter algorithm designed for a spatial vibration model of a two-axle truck. This proposed method requires measuring only the vertical, roll, and pitch velocity of the sprung mass and, therefore can reduce the sensor cost significantly. The simulation results for a random roughness road profile according to ISO 8608 class C with step variations in sprung mass and moments of inertia showed that the designed estimator rejected the process and measurement noises and tracked the real vehicle parameters effectively with acceptable errors

Tire normal force estimation using artificial neural networks and fuzzy classifiers: Experimental validation

Tire normal forces play a crucial role in vehicle dynamic control systems (VDCs), and an accurate estimation of them could substantially improve vehicle handling and safety. Total normal forces on tires are the combination of the static and dynamic values. These values change by varying the vehicle static parameters (vehicle mass and CG position), the road grade, and vehicle dynamic states. All of the mentioned forces and parameters are used in the design of vehicle dynamics controllers. Therefore, to have an efficient estimation solution to access all different target controllers, the proposed estimation algorithm is divided into two parts. The first part is an ANN-based vehicle mass and CG position estimation algorithm and the second one is a deep-learning-based algorithm and an integrated hardware–software method to estimate dynamic normal forces. The first part includes two neural network (NN) blocks related to vehicle roll and pitch dynamics. The outputs of the NN blocks are load distributions for the axes associated with each dynamics. Based on the measurement unit data, the vehicle’s maneuvers are classified into one of many categories used for downstream tasks. The rules of the fuzzy classification logic determine which of the mentioned blocks to be activated. At the same time, an experimentally validated 9-DOF vehicle model instantaneously observes the estimated values. In the second part, long short-term memory (LSTM), gated recurrent unit (GRU), and an integrated hardware–software method were developed to assess the tire’s dynamic normal forces during maneuvers. The performed simulations and the results of field tests show that the proposed algorithm can accurately estimate the considered parameters in the presence of noise and disturbances. It is shown that the proposed algorithm is more accurate and faster than the extended Kalman filter and recursive least square estimation methods to estimate static values.

Road slope estimation based on acceleration adaptive interactive multiple model algorithm for commercial vehicles

Road slope is an important external variable in vehicle dynamic control systems. However, it is a challenging problem to estimate road slope accurately for commercial vehicles due to the coupling problem among the mass, road slope and pitch angle. To solve this problem, a novel road slope estimation scheme with the correction of pitch angle is proposed. First, the coupling problem between road slope and body pitch is analyzed from the perspective of sensor signals. Next, the driving conditions are divided into gentle and strong scenarios, abstracted to the smooth driving model (SDM) and intensive driving model (IDM), respectively. SDM alone cannot guarantee accuracy under intensive scenarios, while IDM alone converges slowly under smooth driving scenarios. The acceleration adaptive interactive multiple model (AAIMM) algorithm is then designed to combine the models and determine which model is the most appropriate under different driving intensities. At last, the sensor-less pitch angle correction strategy based on the suspension deformation model is presented and the particle swarm optimization (PSO) algorithm is used to optimize the suspension stiffness off-line. The simulations and road tests indicate the effectiveness and accuracy of the proposed road slope estimation scheme.

Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems.

The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout.

STABILITY CONTROL OF ENERGY SAVING ELECTRIC VEHICLE USING DYNAMIC NONLINEAR SYSTEM STATE ESTIMATION

The purpose is to study the handling stability and energy saving mode of energy saving electric vehicles. First, the differences between electric vehicles and traditional fuel vehicles are analyzed. Moreover, based on active safety technology, the control stability, handling stability and driving safety of electric vehicles are improved. Then, based on the vehicle dynamics theory and the proposed assumptions, the modular electric vehicle dynamics model is established, including the wheel model, tire model and driver model, which can effectively control the error of the vehicle model and reduce the difficulty and complexity of vehicle modeling. Next, based on the model predictive control theory, the vehicle steady-state control strategy is established, which can predict and control the state of the electric vehicle in the compound condition and improve the control stability of the vehicle. Finally, the vehicle dynamic model and steady-state control system are simulated and tested. The experimental results show that the simulation results of the established dynamic model of the electric vehicle in angle step condition and single line shifting condition are consistent with the results of professional software, which shows that the established dynamic model can accurately describe the dynamic state of electric vehicle and provides the basis for the research of vehicle stability control strategy. Experimental results of the control strategy under the condition of double line shifting show that the designed control strategy can accurately track the state of the system and constrain the deviation in a small range, so as to ensure the driving stability of the electric vehicle. This exploration provides a reference for the study of dynamic analysis and handling stability control strategy of electric vehicles.

A Stereo Vision-Aided Receding Horizon Vehicle Dynamic Control System for Independent Drive Vehicles Steered by Wire

This paper proposes a stereo vision-aided receding horizon vehicle dynamic control system for independent drive vehicles steered by wire. First, we propose the receding horizon vehicle dynamic control (RHVDC) to track the desired slip angle and yaw rate of a dynamic vehicle. The RHVDC computes and updates control inputs at each sampling time to minimize a quadratic cost function over the finite future time horizon, allowing it to address the time-varying property of dynamic vehicles. Its stability is shown to be guaranteed with appropriate parameters. Second, unlike the conventional steering systems fixed with the driver’s steering wheels, the SBW system improves the controllability of vehicle dynamics and enables better tracking performance. Third, combining a stereo vision system with existing sensors allows state estimation of dynamic vehicles to be more accurate in noisy environments. The three strategies mentioned above ultimately maximize the tracking performance of dynamic vehicles through improvement in terms of theory, facilities, and recognition. Through the software-in-the-loop simulation (SILS) with CarSim, the proposed RHVDC system has the better tracking performance for controlling both the slip angle and the yaw rate compared with the existing ones. It is also observed that the performance enhancement of the proposed RHVDC system is more outstanding on slippery or more severe roads.

New Integrated Vehicle Dynamics Control System Based on the Coordination of Active Front Steering, Direct Yaw Control, and Electric Differential for Improvements in Vehicle Handling and Stability

New Integrated Vehicle Dynamics Control System Based on the Coordination of Active Front Steering, Direct Yaw Control, and Electric Differential for Improvements in Vehicle Handling and Stability

UKF-based State and tire slip estimation for a 4WD electric vehicle

For performance improvement of modern vehicle dynamic control systems, exact knowledge of the vehicle motion state and the road conditions is essential. Addressing this problem, the development and validation of a state observer for a four-wheel drive electric vehicle is examined in this article. In particular, a reliable and accurate estimation of the tire slip in longitudinal and lateral direction at each wheel is desired. For this, an Unscented Filter is used to estimate the vehicle chassis and wheel dynamics. At the same time, the estimator is able to adapt the tire model according to the current road conditions by using wheel-individual tire-road friction coefficients. In the beginning, details on the used vehicle model as well as the estimator tuning and implementation procedure are given. The validation takes place first in a vehicle simulation environment to point out the performance of the observer especially in severe driving situations, like a sudden change of road friction conditions during a slalom manoeuver. Afterwards, driving tests with an electric four-wheel drive vehicle are carried out to proof the presented observer concept under real-world conditions. All performed driving manoeuvers are described and the test results are presented and evaluated.

Applied nonlinear control of vehicle stability with control and state constraints

For the vehicle dynamic control system, to guarantee directional stability in risky maneuvers, the side-slip angle should be restricted to the admissible range when the yaw rate tracks the proposed desired response for enhanced steerability. Meanwhile, the control input of the external yaw moment produced by asymmetric braking forces should be calculated in the practical range according to the capacity of tire forces. In the present study, a novel constrained controller with input and state constraints is developed. To this aim, a cost function consisting of predicted continuous response of yaw rate tracking error is expanded in terms of current control signal. Concurrently, the state constraint of side slip is transformed to the equivalent constraint of control signal by a novel nonlinear prediction approach. After that, the expanded performance index is analytically minimized in the presence of all input constraints to obtain the control law. The computed yaw moment is optimally distributed to asymmetric braking forces by designing a wheel slip control system. Simulation studies are conducted to evaluate the performance of proposed constrained controller compared with the unconstrained controller and a conventional nonlinear model predictive controller developed in the recent papers using a 14-degree-of-freedom vehicle model which includes suspension system dynamics. The results show that the proposed controller is much faster and easy to solve and implement.

Proposal of the Tire Longitudinal Characteristics Real-Time Estimation Method

For vehicle dynamics control and Autonomous Driving (AD) system, it is important to know the friction coefficient μ of the road surface accurately. It is because the lateral and the longitudinal force characteristics of the tire depend on the road surface condition largely. However, currently, it is difficult to detect tire performance degradation before the deterioration of vehicle dynamics in real time because tire force estimation is usually conducted by comparing the observed vehicle motion with the onboard reference vehicle-model motion. Such conventional estimators do not perform well if there is a significant difference between the vehicle and the model behavior. In this paper, a new tire state estimation method based on this tire longitudinal characteristic is proposed. In addition, the estimator for tire-road surface friction coefficient μ is proposed by using this geometric relationship. Using this method, the friction coefficient value for a real road can be determined from relatively simple calculations. Also, the advantage of this method is that it can be estimated in a small slip region before the tire loses its grip. In addition, this paper explain how to apply and the effect on the actual vehicle.

Road roughness estimation based on discrete Kalman filter with unknown input

ABSTRACTThe road roughness acts as a disturbance input to the vehicle dynamics, and causes undesirable vibrations associated with the ride and handing characteristics. Furthermore, the accurate measurement of road roughness plays a key role in better understanding a vehicle dynamic behaviour and active suspension control systems. However, the direct measurement by laser profilometer or other distance sensors are not trivial due to technical and economic issues. This study proposes a new road roughness estimation method by using the discrete Kalman filter with unknown input (DKF-UI). This algorithm is built on a quarter-car model and uses the measurements of the wheel stroke (suspension deflection), and the acceleration of the sprung mass and unsprung mass. The estimation results are compared to the measurements by laser profilometer in-vehicle test.

Vehicle Dynamic Control for In-Wheel Electric Vehicles Via Temperature Consideration of Braking Systems

−Vehicle dynamic control (VDC) systems play an important role with regard to vehicle stability and safety when turning. VDC systems prevent vehicles from spinning or slipping when cornering sharply by controlling vehicle yaw moment, which is generated by braking forces. Thus, it is important to control braking forces depending on the driving conditions of the vehicle. The required yaw moment to stabilize a vehicle is calculated through optimal control and a combination of braking forces used to generate the calculated yaw moment. However, braking forces can change due to frictional coefficients being affected by variations in temperature. This can cause vehicles to experience stability problems due an improper yaw moment being applied to the vehicle. In this paper, a brake temperature estimator based on the finite different method (FDM) was proposed with a friction coefficient estimator in order to solve this problem. The developed braking characteristic estimation model was used to develop a VDC cooperative control algorithm using hydraulic braking and the regenerative braking of an in-wheel motor. Performance simulations of the developed cooperative control algorithm were performed through cosimulation with MATLAB/Simulink and CarSim. From the simulation results, it was verified that vehicle stability was ensured despite any changes in the braking characteristics due to brake temperatures.

超小型4 輪インホイールモータ車両に対する前輪操舵角制御と駆動/制動トルク制御を統合した車両運動制御システム設計

超小型4 輪インホイールモータ車両に対する前輪操舵角制御と駆動/制動トルク制御を統合した車両運動制御システム設計

Collision mitigation and vehicle transportation safety using integrated vehicle dynamics control systems

The aim of this paper is to investigate the effect of vehicle dynamics control systems (VDCS) on both the collision of the vehicle body and the kinematic behaviour of the vehicle's occupant in case of offset frontal vehicle-to-vehicle collision. A unique 6-degree-of-freedom (6-DOF) vehicle dynamics/crash mathematical model and a simplified lumped mass occupant model are developed. The first model is used to define the vehicle body crash parameters and it integrates a vehicle dynamics model with a vehicle front-end structure model. The second model aims to predict the effect of VDCS on the kinematics of the occupant. It is shown from the numerical simulations that the vehicle dynamics/crash response and occupant behaviour can be captured and analysed quickly and accurately. Furthermore, it is shown that the VDCS can affect the crash characteristics positively and the occupant behaviour is improved.

State estimation in roll dynamics for commercial vehicles

ABSTRACTAccurate lateral load transfer estimation plays an important role in improving the performance of the active rollover prevention system equipped in commercial vehicles. This estimation depends on the accurate prediction of roll angles for both the sprung and the unsprung subsystems. This paper proposes a novel computational method for roll-angle estimation in commercial vehicles employing sensors which are already used in a vehicle dynamic control system without additional expensive measurement units. The estimation strategy integrates two blocks. The first block contains a sliding-mode observer which is responsible for calculating the lateral tyre forces, while in the second block, the Kalman filter estimates the roll angles of the sprung mass and those of axles in the truck. The validation is conducted through MATLAB/TruckSim co-simulation. Based on the comparison between the estimated results and the simulation results from TruckSim, it can be concluded that the proposed estimation method has a promising tracking performance with low computational cost and high convergence speed. This approach enables a low-cost solution for the rollover prevention in commercial vehicles.

Multi-Body Integrated Vehicle-Occupant Models for Collision Mitigation and Vehicle Safety using Dynamics Control Systems

The aim of this paper is to investigate the effect of vehicle dynamics control systems (VDCS) on both the collision of the vehicle body and the kinematic behaviour of the vehicle's occupant in case of offset frontal vehicle-to-vehicle collision. A unique 6-Degree-of-Freedom (6-DOF) vehicle dynamics/crash mathematical model and a simplified lumped mass occupant model are developed. The first model is used to define the vehicle body crash parameters and it integrates a vehicle dynamics model with a vehicle front-end structure model. The second model aims to predict the effect of VDCS on the kinematics of the occupant. It is shown from the numerical simulations that the vehicle dynamics/crash response and occupant behaviour can be captured and analysed quickly and accurately. Furthermore, it is shown that the VDCS can affect the crash characteristics positively and the occupant behaviour is improved.

A Hardware-in-the-Loop Facility for Integrated Vehicle Dynamics Control System Design and Validation

Abstract: Due to the increased number and the complexity of the embedded systems in today’s vehicle, there is ever increasing pressure to reduce the development cost and time to market of such systems. In recent years, Model based Development (MBD) is becoming a main stream in the development of automotive embedded systems, and Hardware-in-the-Loop (HiL) testing is one of the key steps toward the implementation of MBD approach. This paper presents the recent HiL facility that has been developed at Cranfield University. The HiL setup includes real steering and brake smart actuator, high fidelity validated vehicle model, complete rapid control prototyping tool chain, and driver-in-the-loop capability. The applications of HiL setup are including but not limited to: smart actuators system identification; rapid control development and early validation of standalone and/or integrated vehicle dynamics control systems. Furthermore, the facility can be employed for investigation on driver-vehicle interaction at the presence of standalone active steering and/or brake systems as well as various Advanced Driver Assist Systems (ADAS), such as lane keeping or adaptive cruise control systems. The capability of the HiL facility for validation of a several newly developed vehicle dynamics control systems is presented.

Velocity and normal tyre force estimation for heavy trucks based on vehicle dynamic simulation considering the road slope angle

A precise estimation of vehicle velocities can be valuable for improving the performance of the vehicle dynamics control (VDC) system and this estimation relies heavily upon the accuracy of longitudinal and lateral tyre force calculation governed by the prediction of normal tyre forces. This paper presents a computational method based on the unscented Kalman filter (UKF) method to estimate both longitudinal and lateral velocities and develops a novel quasi-stationary method to predict normal tyre forces of heavy trucks on a sloping road. The vehicle dynamic model is constructed with a planar dynamic model combined with the Pacejka tyre model. The novel quasi-stationary method for predicting normal tyre forces is able to characterise the typical chassis configuration of the heavy trucks. The validation is conducted through comparing the predicted results with those simulated by the TruckSim and it has a good agreement between these results without compromising the convergence speed and stability.

Worst-case scenario development based on Sine with Dwell test and evaluation for vehicle dynamics controller in UCC HILS

The current test methods are insufficient to evaluate and ensure the safety and reliability of vehicle systems for all possible dynamic situations, including the worst case scenarios such as rollover, spin-out and so on. Although the known NHTSA Sine with Dwell steering maneuvers have been applied for the vehicle performance assessment, they are not enough to estimate other possible worst case scenarios. Therefore, it is crucial for us to verify the various worst case scenarios, including the existing severe steering maneuvers. This paper includes useful worst case scenarios based upon the existing worst case scenarios mentioned above and worst case evaluation for the vehicle dynamic controller in a simulation basis and UCC HILS. The only human steering angle was selected as a design parameter here and optimized to maximize the index function to be expressed in terms of both yaw rate and side slip angle. The obtained scenarios were enough to generate the worst case scenario to meet NHTSA worst case definition. It has been concluded that the new procedure in this paper is adequate to create other feasible worst case scenarios for a vehicle dynamic control system.

English

This Study was performed during the winter of 2014 at Revi Winter Track in Arjeplog, Sweden on Snow & Ice. The thesis describes how to optimize the intervention of an Vehicle Dynamic Control (VDC) system for a sports car. The problem with VDC today is that it is very often defensively programmed and affects the feel when the car is driven, the intervention of VDC in this case is known as unwanted VDC intervention. What to be researched in this thesis is if an optimization of the program can be done that allows greater body slip without affecting the vehicle dynamics and stability in a negative way. Stability control system (SCS) simulation method which is the combination of virtual plus physical world is most suitable for testing and optimization of active stability control systems like VDC. Optimization and tuning procedure for mitigating the unwanted intervention of the control system during handling manoeuvre test has been achieved through SCS simulation. Field test procedures and results for SUV are described. The field test results are used to validate a simulation model which is then analyzed under severe maneuvering conditions to shed light on dynamic stability issues.