Real-time Estimation of Road Friction Coefficient for the Electric Vehicle

2012 ◽  
Author(s):  
Lin Cheng ◽  
Wang Gang ◽  
Cao Wan-Ke ◽  
Zhou Feng-Jun
Keyword(s):  
Friction Coefficient ◽  
Real Time ◽  
Electric Vehicle ◽  
Time Estimation ◽  
Road Friction ◽  
Real Time Estimation ◽  
Road Friction Coefficient

Real-time estimation of tire–road friction coefficient based on lateral vehicle dynamics

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2444-2457
Author(s):  
Juqi Hu ◽  
Subhash Rakheja ◽  
Youmin Zhang
Keyword(s):  
Kalman Filter ◽  
Friction Coefficient ◽  
Kalman Filters ◽  
Time Estimation ◽  
Lane Change ◽  
Low Friction ◽  
Road Friction ◽  
Real Time Estimation ◽  
Double Lane Change ◽  
Road Friction Coefficient

This study proposes a two-stage framework for real-time estimation of tire–road friction coefficient of a vehicle on the basis of lateral dynamics of the vehicle. The estimation framework employs a new cascade structure consisting of an extended Kalman filter and two unscented Kalman filters to reduce the computational burden. In the first stage, extended Kalman filter is utilized to estimate lateral velocity of the vehicle and thereby both the front and rear tires’ side-slip angles. In the second stage, a two–unscented Kalman filters sub-framework is formulated in sequence to observe both the front- and rear-axle tire forces, and to subsequently identify their respective tire–road friction coefficient, regarded as two unknown states. All the measured signals required in the study could be realized from the conventional on-board sensors. Typical double-lane change and single-lane change maneuvers were designed and the developed algorithm was verified through CarSim–MATLAB/Simulink software platform considering high-, mid-, and low-friction road conditions. The simulation results show that the proposed method can yield accurate and rapid estimations of the tire–road friction coefficient for mid- and low-friction road conditions even under a single-lane change maneuver, although double-lane change maneuver is needed to accurately estimate the tire–road friction coefficient for high-friction road condition.

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.

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