Tire–road friction coefficient estimation based on designed braking pressure pulse

2021 ◽  
pp. 095440702098358
Author(s):  
Juqi Hu ◽  
Subhash Rakheja ◽  
Youmin Zhang
Keyword(s):  
Friction Coefficient ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Vehicle Speed ◽  
Two Stage ◽  
Slip Ratio ◽  
Road Friction ◽  
Vehicle Motion ◽  
Road Friction Coefficient

Knowledge of tire–road friction coefficient (TRFC) is valuable for autonomous vehicle control and design of active safety systems. This paper investigates TRFC estimation on the basis of longitudinal vehicle dynamics. A two-stage TRFC estimation scheme is proposed that limits the disturbances to the vehicle motion. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure for reliable estimation of TRFC with minimal interference with the vehicle motion. This stage also provides a qualitative estimate of TRFC. In the second stage, tire normal force and slip ratio are directly calculated from the measured signals, a modified force observer based on the wheel rotational dynamics is developed for estimating the tire braking force. A constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the TRFC for achieving rapid convergence and enhanced estimation accuracy. The effectiveness of the proposed methodology is evaluated through CarSim™-MATLAB/Simulink™ co-simulations considering vehicle motions on high-, medium-, and low-friction roads at different speeds. The results suggest that the proposed two-stage methodology can yield an accurate estimation of the road friction with a relatively lower effect on the vehicle speed.

scholarly journals Calculation Algorithm of Tire-Road Friction Coefficient Based on Limited-Memory Adaptive Extended Kalman Filter

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Bin Huang ◽  
Xiang Fu ◽  
Sen Wu ◽  
Song Huang
Keyword(s):  
Kalman Filter ◽  
Friction Coefficient ◽  
Covariance Matrix ◽  
Extended Kalman Filter ◽  
Estimation Accuracy ◽  
Limited Memory ◽  
Road Friction ◽  
Adaptive Extended Kalman Filter ◽  
Road Friction Coefficient ◽  
Real Vehicle

In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed. By combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old measurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh covariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference to accurately track the breaking status of system. Therefore, problems, including large filter error and divergence caused by incorrect model, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the different road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF results. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation accuracy of algorithm, and increase algorithm robustness.

Robust adaptive anti-slip regulation controller for a distributed-drive electric vehicle considering the driver’s intended driving torque

2017 ◽  
Vol 232 (4) ◽  
pp. 562-576 ◽  
Author(s):  
Zhuoping Yu ◽  
Renxie Zhang ◽  
Xiong Lu ◽  
Chi Jin ◽  
Kai Sun
Keyword(s):  
Friction Coefficient ◽  
Electric Vehicle ◽  
State Feedback Control ◽  
Slip Ratio ◽  
The Road ◽  
Road Friction ◽  
Distributed Drive Electric Vehicle ◽  
Robust Adaptive ◽  
Driving Torque ◽  
Road Friction Coefficient

A robust adaptive anti-slip regulation controller which consists of two components, namely a road friction coefficient estimator and a wheel dynamics controller, is designed for distributed-drive electric vehicles. The road friction coefficient estimator is based on the latest non-affine parameter estimation theory to achieve the peak road friction coefficient. Also, working conditions for the road friction coefficient estimator are proposed to avoid the estimation error caused by a small slip ratio. According to the results of the road friction coefficient estimator, the desired reference slip ratio is obtained and the key parameters of the robust adaptive anti-slip regulation controller are modified to make sure that the road conditions can be made full use of. Then, according to the desired reference slip ratio, a state feedback control law with a conditional integrator is designed on the basis of the Lyapunov stability theory for a wheel dynamics controller by analysis of the non-linear characteristics of the tyres and the driver’s intended driving torque and constraints from the ground–tyre adhesion. In addition, it achieves smooth switching between optimal driving and the driver’s intended driving torque rather than normal switching logic. Multi-condition simulations and experiments show that the controller is adaptive to different road conditions, can improve the driving efficiency of the vehicle and can ensure stability of the vehicle. Finally, with comparative experiments, the distributed-drive electric vehicle with a robust adaptive anti-slip regulation controller proves to be more efficient than the traditional vehicle with a traditional anti-slip regulation controller.

Wireless Piezoelectric Sensor for the Measurement of Tire Deformations and the Estimation of Slip Angle

2009 ◽  
Author(s):  
Gurkan Erdogan ◽  
Lee Alexander ◽  
Rajesh Rajamani
Keyword(s):  
Friction Coefficient ◽  
Piezoelectric Sensor ◽  
Slip Angle ◽  
Lateral Deformation ◽  
Test Rig ◽  
Slip Ratio ◽  
Lateral Deflection ◽  
Road Friction ◽  
Tire Forces ◽  
Road Friction Coefficient

This paper introduces a wireless piezoelectric tire sensor whose readings can be utilized for the estimation of various tire variables such as slip angle, slip ratio, tire forces and tire road friction coefficient. In this paper, the proposed sensor is demonstrated for the estimation of tire slip angle. Lateral deformation of the tire is decoupled from radial and longitudinal tire deformations using a special sensor design. The decoupled lateral deflection profile of the tire is employed to estimate the slip angle. A new tire test rig is constructed to experimentally evaluate the performance of the developed sensor. Results show that the tire sensor can accurately estimate slip angles up to values of 5.0 degrees.

scholarly journals Slip Control of Electric Vehicle Based on Tire-Road Friction Coefficient Estimation

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Gaojian Cui ◽  
Jinglei Dou ◽  
Shaosong Li ◽  
Xilu Zhao ◽  
Xiaohui Lu ◽  
...  
Keyword(s):  
Friction Coefficient ◽  
Electric Vehicle ◽  
Slip Ratio ◽  
Braking Performance ◽  
Slip Control ◽  
Coefficient Estimation ◽  
Road Friction ◽  
Braking Torque ◽  
Novel Method ◽  
Road Friction Coefficient

The real-time change of tire-road friction coefficient is one of the important factors that influence vehicle safety performance. Besides, the vehicle wheels’ locking up has become an important issue. In order to solve these problems, this paper comes up with a novel slip control of electric vehicle (EV) based on tire-road friction coefficient estimation. First and foremost, a novel method is proposed to estimate the tire-road friction coefficient, and then the reference slip ratio is determined based on the estimation results. Finally, with the reference slip ratio, a slip control based on model predictive control (MPC) is designed to prevent the vehicle wheels from locking up. In this regard, the proposed controller guarantees the optimal braking torque on each wheel by individually controlling the slip ratio of each tire within the stable zone. Theoretical analyses and simulation show that the proposed controller is effective for better braking performance.

scholarly journals Path tracking of autonomous vehicle based on adaptive model predictive control

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141988008 ◽  
Author(s):  
Fen Lin ◽  
Yuke Chen ◽  
Youqun Zhao ◽  
Shaobo Wang
Keyword(s):  
Friction Coefficient ◽  
Working Conditions ◽  
Autonomous Vehicle ◽  
Least Square ◽  
Vehicle Speed ◽  
Adaptive Model ◽  
Recursive Least Square ◽  
Tracking Accuracy ◽  
Road Friction ◽  
Road Friction Coefficient

In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimated tire cornering stiffness is used to update vehicle dynamics model and the estimated road friction coefficient is used to update the road adhesion constraint. Moreover, the control parameters consist of prediction horizon, control horizon, and sampling time, all of which are set according to vehicle speed. A co-simulation based on MATLAB/Simulink and CarSim is conducted. The simulation results illustrate that the proposed controller has a great adaptive ability to changing working conditions, especially to speed and road conditions.

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