IMECE2002-32157 GPS-BASED REAL-TIME IDENTIFICATION OF TIRE-ROAD FRICTION COEFFICIENT

ABSTRACT Vehicle control systems such as collision avoidance, adaptive cruise control and automated lane-keeping systems as well as ABS and stability control systems can benefit significantly from being made "road-adaptive". The estimation of tire-road friction coefficient at the wheels allows the control algorithm in such systems to adapt to external driving conditions. This paper develops a new tire-road friction coefficient estimation algorithm based on measurements related to the lateral dynamics of the vehicle. A lateral tire force model parameterized as a function of slip angle, friction coefficient, normal force and cornering stiffness is used. A real-time parameter identification algorithm that utilizes measurements from a differential GPS system and a gyroscope is used to identify the tire-road friction coefficient and cornering stiffness parameters of the tire. The advantage of the developed algorithm is that it does not require large longitudinal slip in order to provide reliable friction estimates. Simulation studies indicate that a parameter convergence rate of one second can be obtained. Experiments conducted on both dry and slippery road indicate that the algorithm can work very effectively in identifying a slippery road. Two other new approaches to realtime tire road friction identification system are also discussed in the paper

A multisensing setup for the intelligent tire monitoring

The present paper offers the chance to experimentally measure, for the first time, the internal tire strain by optical fiber sensors during the tire rolling in real operating conditions. The phenomena that take place during the tire rolling are in fact far from being completely understood. Despite several models available in the technical literature, there is not a correspondently large set of experimental observations. The paper includes the detailed description of the new multi-sensing technology for an ongoing vehicle measurement, which the research group has developed in the context of the project OPTYRE. The experimental apparatus is mainly based on the use of optical fibers with embedded Fiber Bragg Gratings sensors for the acquisition of the circumferential tire strain. Other sensors are also installed on the tire, such as a phonic wheel, a uniaxial accelerometer, and a dynamic temperature sensor. The acquired information is used as input variables in dedicated algorithms that allow the identification of key parameters, such as the dynamic contact patch, instantaneous dissipation and instantaneous grip. The OPTYRE project brings a contribution into the field of experimental grip monitoring of wheeled vehicles, with implications both on passive and active safety characteristics of cars and motorbikes

Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

A diagnosis-based approach for tire-road forces and maximum friction estimation

International audienceA new approach to estimate vehicle tire forces and road maximum adherence is presented. Contrarily to most of previous works on this subject, it is not an asymptotic observer based estimation, but a combination of elementary diagnosis tools and new algebraic techniques for filtering and estimating derivatives of noisy signals. In a first step, instantaneous friction and lateral forces will be computed within this framework. Then, extended braking stiffness concept is exploited to detect which braking efforts allow to distinguish a road type from another. A weighted Dugoff model is used during these “distinguishable” intervals to estimate the maximum friction coefficient. Very promising results have been obtained in noisy simulations and real experimentations for most of driving situations

Real-Time Vehicle Parameter Estimation and Adaptive Stability Control

This dissertation presents a novel Electronic Stability Control (ESC) strategy that is capable of adapting to changing vehicle mass, tire condition and road surface conditions. The benefits of ESC are well understood with regard to assisting drivers to maintain vehicle control during extreme handling maneuvers or when extreme road conditions such as ice are encountered. However state of the art ESC strategies rely on known and invariable vehicle parameters such as vehicle mass, yaw moment of inertia and tire cornering stiffness coefficients. Such vehicle parameters may change over time, especially in the case of heavy trucks which encounter widely varying load conditions. The objective of this research is to develop an ESC control strategy capable of identifying changes in these critical parameters and adapting the control strategy accordingly. An ESC strategy that is capable of identifying and adapting to changes in vehicle parameters is presented. The ESC system utilizes the same sensors and actuators used on commercially-available ESC systems. A nonlinear reduced-order observer is used to estimate vehicle sideslip and tire slip angles. In addition, lateral forces are estimated providing a real-time estimate of lateral force capability of the tires with respect to slip angle. A recursive least squares estimation algorithm is used to automatically identify tire cornering stiffness coefficients, which in turn provides a real-time indication of axle lateral force saturation and estimation of road/tire coefficient of friction. In addition, the recursive least squares estimation is shown to identify changes in yaw moment of inertia that may occur due to changes in vehicle loading conditions. An algorithm calculates the reduction in yaw moment due to axle saturation and determines an equivalent moment to be generated by differential braking on the opposite axle. A second algorithm uses the slip angle estimates and vehicle states to predict a Time to Saturation (TTS) value of the rear axle and takes appropriate action to prevent vehicle loss of control. Simulation results using a high fidelity vehicle modeled in CarSim show that the ESC strategy provides improved vehicle performance with regard to handling stability and is capable of adapting to the identified changes in vehicle parameters

Vehicle Dynamics, Lateral Forces, Roll Angle, Tire Wear and Road Profile States Estimation - A Review

Estimation of vehicle dynamics, tire wear, and road profile are indispensable prefaces in the development of automobile manufacturing due to the growing demands for vehicle safety, stability, and intelligent control, economic and environmental protection. Thus, vehicle state estimation approaches have captured the great interest of researchers because of the intricacy of vehicle dynamics and stability control systems. Over the last few decades, great enhancement has been accomplished in the theory and experiments for the development of these estimation states. This article provides a comprehensive review of recent advances in vehicle dynamics, tire wear, and road profile estimations. Most relevant and significant models have been reviewed in relation to the vehicle dynamics, roll angle, tire wear, and road profile states. Finally, some suggestions have been pointed out for enhancing the performance of the vehicle dynamics models

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Most existing ESC (Electronic Stability Control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because the sensors are too expensive. For this reason, sideslip angle estimation has been widely discussed in the relevant literature. The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. In this paper, we propose a novel observer based on ANFIS, combined with Kalman Filters in order to estimate the sideslip angle, which in turn is used to control the vehicle dynamics and improve its behavior. For this reason, low-cost sensor measurements which are integrated into the actual vehicle and executed in real time have to be used. The ANFIS system estimates a "pseudo-sideslip angle" through parameters which are easily measured, using sensors equipped in actual vehicles (inertial sensors and steering wheel sensors); this value is introduced in UKF in order to filter noise and to minimize the variance of the estimation mean square error. The estimator has been validated by comparing the observed proposal with the values provided by the CARSIM model, which is a piece of experimentally validated software. The advantage of this estimation is the modeling of the non-linear dynamics of the vehicle, by means of signals which are directly measured from vehicle sensors. The results show the effectiveness of the proposed ANFIS+UKF-based sideslip angle estimator

OPTYRE—Real time estimation of rolling resistance for intelligent tyres

The study of the rolling tyre is a problem framed in the general context of nonlinear elasticity. The dynamics of the related phenomena is still an open topic, even though few examples and models of tyres can be found in the technical literature. The interest in the dissipation effects associated with the rolling motion is justified by their importance in fuel-saving and in the context of an eco-friendly design. However, a general lack of knowledge characterizes the phenomenon, since not even direct experience on the rolling tyre can reveal the insights of the correlated different dissipation effects, as the friction between the rubber and the road, the contact kinematics and dynamics, the tyre hysteretic behaviour and the grip. A new technology, based on fibre Bragg grating strain sensors and conceived within the OPTYRE project, is illustrated for the specific investigation of the tyre dissipation related phenomena. The remarkable power of this wireless optical system stands in the chance of directly accessing the behaviour of the inner tyre in terms of stresses when a real-condition-rolling is experimentally observed. The ad hoc developed tyre model has allowed the identification of the instant grip conditions, of the area of the contact patch and allows the estimation of the instant dissipated power, which is the focus of this paper