Estimation of vehicle sideslip angle and tire-road friction coefficient based on magnetometer with GPS

This paper presents a method that estimates the vehicle sideslip angle and a tire-road friction coefficient by combining measurements of a magnetometer, a global positioning system (GPS), and an inertial measurement unit (IMU). The estimation algorithm is based on a cascade structure consisting of a sensor fusing framework based on Kalman filters. Several signal conditioning techniques are used to mitigate issues related to different signal characteristics, such as latency and disturbances. The estimated sideslip angle information and a brush tire model are fused in a Kalman filter framework to estimate the tire-road friction coefficient. The performance and practical feasibility of the proposed approach were evaluated through several tests.     

Comparative study of vehicle tyre–road friction coefficient estimation with a novel cost-effective method

This paper qualitatively and quantitatively reviews and compares three typical tyre–road friction coefficient estimation methods, which are the slip slope method, individual tyre force estimation method and extended Kalman filter method, and then presents a new cost-effective tyre–road friction coefficient estimation method. Based on the qualitative analysis and the numerical comparisons, it is found that all of the three typical methods can successfully estimate the tyre force and friction coefficient in most of the test conditions, but the estimation performance is compromised for some of the methods during different simulation scenarios. In addition, all of these three methods need global positioning system (GPS) to measure the absolute velocity of a vehicle. To overcome the above-mentioned problem, a novel cost-effective estimation method is proposed in this paper. This method requires only the inputs of wheel angular velocity, traction/brake torque and longitudinal acceleration, which are all easy to be measured using available sensors installed in passenger vehicles. By using this method, the vehicle absolute velocity and slip ratio can be estimated by an improved nonlinear observer without using GPS, and the friction force and tyre–road friction coefficient can be obtained from the estimated vehicle velocity and slip ratio. Simulations are used to validate the effectiveness of the proposed estimation method.     

Robust estimation of maximum tire-road friction coefficient considering road surface irregularity

An accurate estimation of the maximum tire-road friction coefficient may provide higher performance in a vehicle active safety control system. Unfortunately, real-time tire-road friction coefficient estimation is costly and necessitates additional sensors that must be installed and maintained at all times. This paper proposes an advanced longitudinal tire-road friction coefficient estimation method that is capable of considering irregular road surfaces. The proposed algorithm uses a stiffness based estimation method, however, unlike previous studies, improvements were made by suggesting a third order model to solve problems related to nonlinear mu-slip curve. To attain the tire-road friction coefficient, real-time normalized force is obtained from the force estimator as exerted from the tire in the low slip region using the recursive least squares method. The decisive aspect of using the suggested algorithm lies in its low cost and versatility. It can be used under irregular road conditions due to its capability of easily obtaining wheel speed and acceleration values from production cars. The newly improved algorithm has been verified to computer simulations as well as compact size cars on dry asphalt conditions.     

Real-Time Longitudinal Location Estimation of Vehicle Center of Gravity

The longitudinal location of a vehicle’s center of gravity (CG) is used as an important parameter for vehicle safety control systems, and can change considerably according to various driving conditions. Accordingly, for the better performance of vehicle safety control systems, it is essential to obtain the accurate CG location. However, it is generally difficult to acquire the value of this parameter directly through sensors due to cost reasons. In this study, a practical algorithm for estimating vehicle’s longitudinal CG location in real time is proposed. This algorithm is derived based only on longitudinal motion of the vehicle, excluding excessive lateral, yaw and roll movements of the vehicle. Moreover, the proposed algorithm has main differences from previous studies in that it does not require information such as vehicle mass, vehicle moments of inertia, road grade or tire-road surface friction, which are difficult to acquire. In the proposed algorithm, the relationship between the ratio of rear-to-front tire longitudinal force and the corresponding wheel slips are used to determine the CG location. To demonstrate a practical use of the proposed algorithm, the ideal brake force distribution is tested. The proposed CG estimation algorithm and its practical use are verified via simulations and experiments using a test vehicle equipped with electro-mechanical brakes in the rear wheels. It is shown that the estimated CG locations are close to the actual ones, and that the deceleration can be maximized by the ideal brake force distribution.     

Sliding-mode-based parameter identification with application to tire pressure and tire-road friction

This paper presents a method of simultaneous estimation of tire pressure and tire-road friction. A sliding-mode scheme is designed to identify the system state and the parameter variation of a torsional tire system, which greatly depend on the change in tire pressure. Then, the recursive least-squares method with a forgetting facto is used to estimate the parameter variations of the tire system and the tire-road friction force without a friction model using the information retrieved from the equivalent input for sliding motion. A simulation study is performed to illustrate the effectiveness of the proposed method.     

A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations

A sliding-mode observer is designed to estimate the vehicle velocity with the measured vehicle acceleration, the wheel speeds and the braking torques. Based on the Burckhardt tyre model, the extended Kalman filter is designed to estimate the parameters of the Burckhardt model with the estimated vehicle velocity, the measured wheel speeds and the vehicle acceleration. According to the estimated parameters of the Burckhardt tyre model, the tyre/road friction coefficients and the optimal slip ratios are calculated. A vehicle adaptive sliding-mode control (SMC) algorithm is presented with the estimated vehicle velocity, the tyre/road friction coefficients and the optimal slip ratios. And the adjustment method of the sliding-mode gain factors is discussed. Based on the adaptive SMC algorithm, a vehicle's antilock braking system (ABS) control system model is built with the Simulink Toolbox. Under the single-road condition as well as the different road conditions, the performance of the vehicle ABS system is simulated with the vehicle velocity observer, the tyre/road friction coefficient estimator and the adaptive SMC algorithm. The results indicate that the estimated errors of the vehicle velocity and the tyre/road friction coefficients are acceptable and the vehicle ABS adaptive SMC algorithm is effective. So the proposed adaptive SMC algorithm can be used to control the vehicle ABS without the information of the vehicle velocity and the road conditions.     

Emergency Braking Control with an Observer-based Dynamic Tire/Road Friction Model and Wheel Angular Velocity Measurement

Summary A control scheme for emergency braking of vehicles is designed. The tire/road friction is described by a LuGre dynamic friction model. The control system output is the pressure in the master cylinder of the brake system. The controller utilizes estimated states for a feedback control law that achieves a near maximum deceleration. The state observer is designed using linear matrix inequality (LMI) techniques. The analysis shows that using the wheel angular speed information exclusively is not sufficient to rapidly estimate the velocity and relative velocity, due to the fact that the dynamical system is almost unobservable with this measurement as output. Findings are confirmed by simulation results that show that the estimated vehicle velocity and relative velocity converge slowly to their true values, even though the internal friction state and friction parameters converge quickly. The proposed control system has two main advantages when compared with an antilock braking system (ABS): (1) it produces a source of a priori information regarding safe spacing between vehicles that can be used to increase safety levels in the highway; and (2) it achieves a near optimal braking strategy with less chattering.     

Emergency Braking Control with an Observer-based Dynamic Tire/Road Friction Model and Wheel Angular Velocity Measurement

Summary A control scheme for emergency braking of vehicles is designed. The tire/road friction is described by a LuGre dynamic friction model. The control system output is the pressure in the master cylinder of the brake system. The controller utilizes estimated states for a feedback control law that achieves a near maximum deceleration. The state observer is designed using linear matrix inequality (LMI) techniques. The analysis shows that using the wheel angular speed information exclusively is not sufficient to rapidly estimate the velocity and relative velocity, due to the fact that the dynamical system is almost unobservable with this measurement as output. Findings are confirmed by simulation results that show that the estimated vehicle velocity and relative velocity converge slowly to their true values, even though the internal friction state and friction parameters converge quickly. The proposed control system has two main advantages when compared with an antilock braking system (ABS): (1) it produces a source of a priori information regarding safe spacing between vehicles that can be used to increase safety levels in the highway; and (2) it achieves a near optimal braking strategy with less chattering.     

Literature review and fundamental approaches for vehicle and tire state estimation*

ABSTRACT

Most modern day automotive chassis control systems employ a feedback control structure. Therefore, real-time estimates of the vehicle dynamic states and tire-road contact parameters are invaluable for enhancing the performance of vehicle control systems, such as anti-lock brake system (ABS) and electronic stability program (ESP). Today's production vehicles are equipped with onboard sensors (e.g. a 3-axis accelerometer, 3-axis gyroscope, steering wheel angle sensor, and wheel speed sensors), which when used in conjunction with certain model-based or kinematics-based observers can be used to identify relevant tire and vehicle states for optimal control of comfort, stability and handling. Vehicle state estimation is becoming ever more relevant with the increased sophistication of chassis control systems. This paper presents a comprehensive overview of the state-of-the-art in the field of vehicle and tire state estimation. It is expected to serve as a resource for researchers interested in developing vehicle state estimation algorithms for usage in advanced vehicle control and safety systems.     

Novel electronic braking system design for EVS based on constrained nonlinear hierarchical control

Both environment protection and energy saving have attracted more and more attention in the electric vehicles (EVs) field. In fact, regarding control performance, electric motor has more advantages over conventional internal combustion engine. To decouple the interaction force between vehicle and various coordinating and integrating active control subsystems and estimate the real-time friction force for Advanced Emergency Braking System (AEBS), this paper’s primary intention is uniform distribution of longitudinal tire-road friction force and control strategy for a Novel Anti-lock Braking System (Nov- ABS) which is designed to estimate and track not only any tire-road friction force, but the maximum tire-road friction force, based on the Anti-Lock Braking System (ABS). The longitudinal tire-road friction force is computed through real-time measurement of breaking force and angular acceleration of wheels. The Magic Formula Tire Model can be expressed by the reference model. The evolution of the tire-road friction is described by the constrained active-set SQP algorithm with regard to wheel slip, and as a result, it is feasible to identify the key parameters of the Magic Formula Tire Model. Accordingly, Inverse Quadratic Interpolation method is a proper way to estimate the desired wheel slip in regards to the reference of tireroad friction force from the top layer. Then, this paper adapts the Nonlinear Sliding Mode Control method to construct proposed Nov-ABS. According to the simulation results, the objective control strategy turns out to be feasible and satisfactory.     

基于四轮轮边驱动电动车的路面附着系数估算方法

路面附着系数是影响车辆行驶安全性的重要因素,利用轮边驱动电动汽车驱动力矩可以对路面利用附着系数进行观测。当观测到μ-λ曲线接近于峰值点时,将该时刻的轮胎利用附着系数作为路面峰值附着系数,并根据识别的路面峰值附着系数进行驱动防滑控制。该方法能够有效防止轮胎滑转,提高车辆行驶稳定性。     

车速和路面附着系数的滚动时域估计

基于Dugoff轮胎模型建立了车辆的非线性动力学方程,并给出了路面附着系数的约束条件.针对车速和路面附着系数约束的非线性估计,提出了一种基于滚动优化原理的滚动时域估计法(MHE),并给出了MHE法的具体步骤.在不同路面上对MHE法进行了多种工况的实验验证,并在同样条件下与扩展Kalman滤波法进行了比较.实验结果表明,MHE法的估计性能优于扩展Kalman滤波法.     

Robust road friction estimation during vehicle steering

Automated vehicles require information on the current road condition, i.e. the tyre–road friction coefficient for trajectory planning, braking or steering interventions. In this work, we propose a framework to estimate the road friction coefficient with stability and robustness guarantee using total aligning torque in vehicle front axle during steering. We first adopt a novel strategy to estimate the front axle lateral force which performs better than the classical unknown input observer. Then, combined with an indirect measurement based on estimated total aligning torque and front axle lateral force, a non-linear adaptive observer is designed to estimate road friction coefficient with stability guarantee. To increase the robustness of the estimation result, criteria are proposed to decide when to update the estimated road conditions. Simulations and experiments under various road conditions validate the proposed framework and demonstrate its advantage in stability by comparing it with the method utilising the wide-spread Extended Kalman Filter.     

Road profile input estimation in vehicle dynamics simulation

Vehicle motion simulation accuracy, such as in accident reconstruction or vehicle controllability analysis on real roads, can be obtained only if valid road profile and tire-road friction models are available. Regarding road profiles, a new method based on sliding mode observers has been developed and is compared with two inertial methods. Experimental results are shown and discussed to evaluate the robustness of our approach.     

Cooperative Estimation Using Vehicle Communications

Summary Each vehicle on a section of highway is potentially a driving condition 'sensor.' For example, a vehicle's speed give can give a clue about the traffic conditions in its section of roadway. By 'cooperative estimation,' we mean a system that uses a communication network to combine the experience of many vehicles into parameter estimates that are more useful than the estimates that any individual vehicle could generate by itself. This paper demonstrates the cooperative estimation concept by showing how it can be used to estimate traffic conditions and road friction without using roadside sensors.     

A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

ABSTRACT

Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence.     

Nonlinear tire-road friction control based on tire model parameter identification

A hierarchical control structure is a more suitable structural scheme for integrated chassis control. Generally, this type of structure has two main functions. The upper layer manages global control and force allocation, while the bottom layer allocates realized forces with 4 independent local tire controllers. The way to properly allocate these target forces poses a difficult task for the bottom layer. There are two key problems that require attention: obtaining the nonlinear time-varying coefficient of friction between the tire and different road surfaces and accurately tracking the desired forces from the upper layer. This paper mainly focuses on longitudinal tire-road friction allocation and control strategies that are based on the antilock braking system (ABS). Although it is difficult to precisely measure longitudinal tire-road friction forces for frequently changing road surface conditions, they can be estimated with a real-time measurement of brake force and angular acceleration at the wheels. The Magic Formula model is proposed as the reference model, and its key parameters are identified online using a constrained hybrid genetic algorithm to describe the evolution of tire-road friction with respect to the wheel slip. The desired wheel slip, with respect to the reference tire-road friction force from the top layer, is estimated with the inverse quadratic interpolation method. The tire-road friction controller of the extended anti-lock braking system (Ext-ABS) is designed through use of the nonlinear sliding mode control method. Simulation results indicate that acceptable modifications to changes in road surface conditions and adequate stability can be expected from the proposed control strategy.     

A new parameter identification methodology for a modal analysis test of a rail vehicle

The validation of vehicle mathematical models is a key part of the virtual acceptance process since it is essential to ensure a precise representation of the reality. The model validation procedure should include validation of stationary but also dynamic tests. However, parameter identification from on-track tests is a challenging task due to the non-controlled excitation and the great variability of the test results. Thus, an alternative solution by means of a vehicle modal analysis is proposed, developing a parameter identification methodology for dynamic vehicle model parameters. This methodology calculates estimated values of the vehicle model parameters that have an influence on the excited vehicle vibration modes. Moreover, a new criterion for taking into account the effect of the measurement uncertainties on the selection process of the vehicle parameters is developed. Finally, experimental results show that not only estimations of the suspension stiffness parameters can be obtained, but damping values and structural frequencies from the vehicle bodies can also be estimated.     

Vehicle sideslip estimation and compensation for banked road

The sideslip driving status is of fundamental importance to the stability of a vehicle. This paper presents a practical vehicle sideslip driving status estimation method that uses ESP (electronic stability program) sensors. ESP sensors such as wheel speed, lateral acceleration, yaw rate and steering wheel angle sensors are used to determine the sideslip driving status and distinguish a banked road. This estimation algorithm contains front-rear sideslip and banked road detection methods. The proposed sideslip estimation algorithm was designed to use the analytical redundancy of these sensors and Lagrange interpolation methods. The performance and effectiveness of the proposed estimation and compensation algorithm were investigated using vehicle tests. This paper presents the results of two cases that were used for the experimental verification: a curved flat road and banked road.     

Effect of vehicle model on the estimation of lateral vehicle dynamics

A methodology is presented for estimating vehicle handling dynamics, which are important to control system design and safety measures. The methodology, which is based on an extended Kalman filter (EKF), makes it possible to estimate lateral vehicle states and tire forces on the basis of the results obtained from sinusoidal steering stroke tests that are widely used in the evaluation of vehicle and tire handling performances. This paper investigates the effect of vehicle-road system models on the estimation of lateral vehicle dynamics in the EKF. Various vehicle-road system models are considered in this study: vehicle models (2-DOF, 3-DOF, 4-DOF), tire models (linear, non-linear) and relaxation lengths. Handling tests are performed with a vehicle equipped with sensors that are widely used by vehicle and tire manufacturers for handling maneuvers. The test data are then used in the estimation of the EKF and identification of lateral tire model coefficients. The accuracy of the identified values is validated by comparing the RMS error between experimentally measured states and regenerated states simulated using the identified coefficients. The results show that the relaxation length of the tire model has a notable impact on the estimation of lateral vehicle dynamics.