Boyuan Li

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.

A novel cost effective method for vehicle tire-road friction coefficient estimation

The tire-road friction coefficient is critical to vehicle longitudinal, lateral and roll dynamics and control because tire is the only contact part between the vehicle body and the road. However, direct measurement of tire-road friction coefficient is impossible in practice. This paper presents a novel cost effective method for vehicle tire-road friction coefficient estimation. This method only needs the measurements of the wheel angular velocity, the traction/brake torque and the longitudinal acceleration, which are all available from the commonly installed sensors in ordinary passenger vehicles, and can be used to estimate the individual tire-road friction coefficient. There are three steps in the proposed method. Firstly, the longitudinal slip ratio is estimated by using a nonlinear filter with the measured wheel angular velocity. Then the tire longitudinal force is estimated by using a Kalman filter with the measured traction/brake torque and longitudinal acceleration. At last, the friction coefficient is estimated by using the recursive least squares (RLS) method and the results obtained from the first two steps. Numerical simulations are provided to validate the effectiveness of the proposed method. It is shown by the simulation results that the proposed method is effective in estimating the tire-road friction coefficient.

A Potential Field Approach-Based Trajectory Control for Autonomous Electric Vehicles With In-Wheel Motors

The studies on the autonomous electric vehicle are quite attractive due to fewer human-induced errors and improved safety in recent years. Extensive research has been done on the autonomous steering control of the mobile robot, but study on the on-road autonomous electric vehicle is still limited. This paper proposes a potential field method to achieve the trajectory control of the autonomous electric vehicle with in-wheel motors. Instead of strictly following a desired path, this method can form a steering corridor with a desired tracking error tolerance and the vehicle can be steered smoothly with less control effort. In this paper, the innovative potential filed function is presented first to determine the desired vehicle yaw angle. Then, according to this desired yaw angle, a two-level trajectory controller is proposed to achieve the trajectory control. Simulation results are shown to prove that this suggested trajectory controller can successfully control the vehicle to move within the desired road boundary and improve the handling and stability performance of the vehicle.

Decision tree assisted EKF for vehicle slip angle estimation using inertial motion sensors

Vehicle side slip angle is a critical variable used in car safety systems like Electronic Stability Control. Due to the practical difficulty in direct measurement of side slip angle, accurate estimation of vehicle side slip angle using available signals is becoming important. This paper presents a novel algorithm for estimating the side slip angle of a vehicle in real time using inertial motion sensors. The algorithm uses a J48 decision tree classifier to assist the Extended Kaiman Filter (EKF) predictions of the vehicle side slip angle. The decision tree classifies the inertial data into classes based on the condition the slip angle is expected to be in. Using the class information asserted by the classifier, the error covariance parameter of the EKF is adjusted to compensate for changes in disturbances and nonlinearities. The results show that the decision tree assisted EKF technique presented in this paper is capable of predicting the slip angle with sound accuracy using inertial motion data.

Non-linear tyre model–based non-singular terminal sliding mode observer for vehicle velocity and side-slip angle estimation

Vehicle velocity and side-slip angle are important vehicle states for the electronic stability programme and traction control system in vehicle safety control system and for the control allocation method of electric vehicles with in-wheel motors. This paper proposes an innovative side-slip angle estimator based on the non-linear Dugoff tyre model and non-singular terminal sliding mode observer. The proposed estimation method based on the non-linear tyre model can accurately present the tyre’s non-linear characteristics and can show advantages over estimation methods based on the linear tyre model. The utilised Dugoff tyre model has a relatively simple structure with few parameters, and the proposed non-linear observer can be applied in various vehicle tyres and various road conditions. Precise determination of the Dugoff tyre model parameters is not required and the proposed observer can still perform good estimation results even though tyre parameters and the tyre–road friction coefficient are not accurate. The proposed non-singular terminal sliding mode observer can achieve fast convergence rate and better estimation performance than the traditional sliding mode observer. At the end of this paper, simulations in various conditions are presented to validate the proposed non-linear estimator.

State and Parameter Estimation of EVs

Abstract This chapter covers the implementation of state- and parameter-estimation techniques for electric vehicles (EVs) in both a mathematical and practical sense. First the reasoning behind such action will be discussed including the uses for EVs and the unique factors that give rise to a demand for such state and parameter estimation. Then highly desirable states and parameters are outlined, followed by an in-depth explanation of the assumption, equations, models, and algorithms required in order to estimate these states and parameters so that they can be utilized by the corresponding control systems implemented in EVs. The key states and parameters that will be discussed are vehicle velocity, vehicle slip angle, road–tire friction coefficient, road gradient, and vehicle mass.