Method for estimating tire / soil adhesion potential

Abstract

The present invention relates to the field of evaluating the rolling parameters of a tire on a surface, and more specifically to estimating the potential grip of a tire on a surface. Thus, the invention relates to a method and a system for such an estimation. Figure for the abstract: Fig 2

Description

Title of the invention: Method for estimating tire / ground grip potential

[0001] The present invention lies in the field of evaluating the rolling parameters of a tire on a rolling surface, and more specifically concerns the estimation of the grip potential of a tire on a surface.

[0002] With the development of on-board technologies, it is becoming possible to obtain more and more information concerning the driving conditions of a vehicle. It is therefore useful, to supply the various driving assistance or safety systems of vehicles, to be able to know in real time the grip potential of a tire on a road, to be able to prevent any risk of skidding due to a loss of grip.

[0003] For the purpose of clarification, it is specified here that throughout the description the symbol q or the term "mu" will be used to designate the coefficient of adhesion of a tire to the rolling surface on which it is located.

[0004] In this field, numerous scientific publications and patent documents are known describing methods for estimating such parameters. For example, we can cite patent application EP3028909A1, which relates to an intelligent method for estimating the level of road grip.

[0005] Existing methods can be classified into two broad categories: - methods using one or more specific sensors installed on vehicles, for example acoustic, thermal or optical sensors, and - methods without sensors, which rely on a reconstruction of information from available vehicle data. The first methods have a major drawback in terms of cost, industrial intrusiveness on vehicles, and equipment maintenance. This drawback is not found in sensorless methods. However, there is currently no sensorless method that can guarantee a good level of estimation under all stress conditions. Thus, a method using an alignment torque value is known, which has the disadvantage of only working for low stresses, because the alignment torque value saturates faster than the lateral force. Another method uses a tire model, but does not take into account the thermal characteristics of the tire, which distorts the determination at low stresses.However, all these existing methods are based on a common principle, that of accurately estimating the maximum adhesion when it is reached or close to it (between 80% and 100% of it). However, none of these methods can estimate maximum grip under low stress conditions, for example when driving conditions are such that the stress is less than 40% of the maximum mu. Machine-learning methods are also known, which do not use any source of information concerning the physical functioning of the tire. However, these methods require very complex learning processes to implement, and no method has yet proven its effectiveness.

[0007] Finally, it has been noted that many methods aim to estimate a grip potential when it has been reached, or close to being reached, in order to trigger immediate corrective action on a vehicle. This is for example the case of the methods implemented by ABS or ESP type systems, now widely deployed on heavy goods vehicles and passenger cars. However, these methods do not allow for a preventive estimation of grip, which would make it possible to modify the vehicle's behavior upstream so as not to have to trigger corrective action.

[0008] The present invention therefore aims to propose a method making it possible to overcome the numerous drawbacks of the state of the art.

[0009] Thus, the invention relates to a method for estimating the grip potential of a tire on a rolling surface, the tire being installed on a vehicle equipped with the method comprising the following steps: - a first step of estimating the force undergone by the tire, based on a vehicle model and a state observer, - a second stage of estimation of a force undergone by the tire, based on a thermomechanical model of the tire, - a step of statistical comparison of the efforts determined during the first and second estimation steps, and - a step of determining, based on the result of this comparison step, a value of the tire / ground grip potential.

[0010] The first estimation step allows us to observe the forces generated by the axle, and transcribed to the wheel center. This step is based on a vehicle model which will be described later using the figures.

[0011] In a preferred embodiment, the vehicle model is a bicycle model, and / or in which the state observer is a Kalman filter.

[0012] The use of a thermomechanical pneumatic model in the second estimation step makes it possible to have a good representation of the different physical phenomena affecting the adhesion potential, such as for example the impact of temperature. This consideration of thermal effects makes it possible to propose a much more reliable method than the methods of the state of the art.

[0013] Indeed, as illustrated in [Fig.l], the adhesion potential corresponds to the maximum of the curve of the longitudinal friction force normalized as a function of the sliding rate. This maximum is indicated by pmax on the curve of [Fig.l], which shows on the abscissa a sliding rate between 0 and 0.4, and on the ordinate the longitudinal friction force Fx divided by the vertical load Fz.

[0014] The estimation of pmax must be able to be done even at low stresses, namely in the linear part of the curve in [Fig.l] (slip less than 0.08). However, it has been found that the slope of this linear part depends on certain quantities, and in particular on the temperature. Thus, not taking thermal effects into account prevents the adhesion potential from being correctly estimated at low stresses. The same applies to other parameters such as pressure, load or wear.

[0015] Depending on the embodiments, several methods can be used to carry out the step of comparing and determining an adhesion potential. Mention may in particular be made of Bayesian approaches, such as for example the so-called Monte-Carlo Markov Chain method. These methods will be described in detail later.

[0016] In one embodiment, the thermomechanical model of the tire comprises a model of the longitudinal forces, the transverse forces, a self-alignment torque and a balance of the elementary shear and sliding forces of the tire at a point of passage between the adherent and sliding contact zones. This model is described in particular in patent applications EP2057567 and EP2062176. However, the invention is not restricted to this embodiment, and any thermomechanical model of the tire can be used. It is however preferable to use a thermomechanical model taking into account at least the inflation pressure, the applied load and the wear of the tire.

[0017] In one embodiment, a method according to the invention comprises, at least before the second estimation step, a step of reducing the pneumatic model. Such a characteristic makes it possible to envisage implementing a method according to the invention in real time.

[0018] This reduction step advantageously comprises the following sub-steps: - generating an observation matrix using the thermodynamic model for different values ​​of the tire parameters, such as for example the load, the pressure or the temperature, - calculating the singular values ​​and vectors of this matrix, - calculating the projection coefficients at the instants of the observations, and - making an interpolation usable for all the parameter values.

[0019] Thus, the use of a reduced model makes it possible to avoid having to calculate the complete model at each time step, which makes it possible to reduce resources. in the necessary calculation, and thus to embed the model in the vehicle for real-time determination.

[0020] The invention also relates to a system for estimating the grip potential of a tire on a rolling surface, the tire being installed on a vehicle, and the system comprising: - means of estimating a force undergone by the tire, based on a vehicle model and a state observer, - means of estimating a force undergone by the tire, based on a thermomechanical model of the tire, - means of statistical comparison of the efforts determined during the first and second estimation stages, and - means of determining, based on the result of this comparison step, a value of the tire / ground grip potential.

[0021] According to one embodiment, the system is such that the different means are installed on the vehicle.

[0022] According to one embodiment, the system further comprises sensors installed on the vehicle.

[0023] Other advantages and embodiments of the invention will be described in more detail, in a non-limiting manner, using the figures, among which: - [Fig. 1], already described, shows a normalized longitudinal friction force curve as a function of the sliding rate, - [Fig.2] is a block diagram of a principle of a method according to the invention, - [Fig.3] and [Fig.4] are representations of the forces acting on a vehicle in a model used in the present invention.

[0024] A method according to the invention comprises several steps, implementing different data, as illustrated in [Fig.2].

[0025] The table below shows the meanings of the different parameters appearing in the figure, as well as the units.

[0026] [Tables 1] Variable Unit Description U mis Longitudinal vehicle speed Wf, wr radis Wheel speed (front and rear) è radis Vehicle pitch speed T p Tr Nm Vehicle engine and braking torque (front and rear) Fxf, Fxr N Longitudinal friction force (front and rear) Fzf, Fzr N Vertical load (front and rear) sr - Slip rate y rad Camber Tg °C Ground temperature °C Air temperature °c Initial internal tire temperature °c Initial tire surface temperature U r max - Grip potential

[0027] The tire in question is installed on a vehicle equipped with different sensors. Starting from the known engine and braking torque of the vehicle, and from data measured by the various sensors, a set of vehicle running parameters 11 is determined in block 1, including a longitudinal speed of the vehicle and a slip rate. Possible disturbances 12 are also taken into account.

[0028] These data are then used to carry out two stages of estimation of the forces undergone by the tire. A first stage, in block 2, consists of determining the forces undergone by the tire, as a function of a vehicle model 21, and of possible disturbances 22.

[0029] A bicycle model will advantageously be used, as shown in [Fig.3], taking into account longitudinal dynamics and pitch variations. To take into account these pitch variations, it is necessary to further consider a suspension model, such as for example that shown in [Fig.4].

[0030] Using the bicycle model and the suspension model leads to the following state representation: [Math 1]

[0031] With [Math 2]

[0033] The state observer used is an extended Kalman filter.

[0034] It is specified here that the main hypotheses of the bicycle model are the following: - front-left steering angles = front-right steering angle, - zero rear steering angles, - vehicle moving on flat ground (no slope).

[0035] Furthermore, in this model, the effects of pitch and roll are often neglected.

[0036] In the present example, the rolling dynamics are neglected but not the pitching because we are considering a suspension system.

[0037] A second step, in block 3, estimates the forces undergone from a thermomechanical model 4. This model is determined from a set of parameters, in particular the temperature. Via this model, the values ​​of mu can be calculated as a function of the slip rate in the pneumatic operating conditions encountered (at the pressure, load, temperature, etc. encountered at the time of the calculation) in order to know the maximum adhesion potential for the value of mu0 supplied to the model. By reproducing this procedure for different settings of mu0, a list of mumax corresponding to different possible ground adhesion levels is obtained.

[0038] In an advantageous embodiment, the initial model is reduced to allow less resource-intensive calculations, and therefore easier to embed directly in a vehicle.

[0039] The results of blocks 2 and 3 are then compared, in a step 5, which allows the determination of an adhesion potential.

[0040] The comparison between the forces estimated in the two previous parts is done here using a Bayesian approach. This type of approach has the advantage of providing, in addition to the sought value, an associated probability. To implement it, it is first necessary to make an assumption about the probability density of the estimated forces, knowing the adhesion potential. As the Kalman filter works by considering Gaussian noise, the probability density considered has a Gaussian form. We thus have [Math 4] i 1 ih \ T i / ~ \A pr[F | e^2{F-Ftire) V [F-Ftire) J

[0042] We then use Bayes' formula to determine the probability of the adhesion potential knowing the friction force, which gives [Math 5]:

[0043] And we thus obtain the coefficient of adhesion by making the weighted sum [Math 6]

[0044] The adhesion potential is then the maximum of this weighted sum.

[0045] In another example, we use not a Bayesian method, but a Monte Carlo Markov Chain method, known as MCMC.

[0046] In a Bayesian method, p is considered as a discrete random variable, thus, the denominator of the Bayes formula is an easily calculable discrete sum. Using the MCMC method, it is possible to consider p as a continuous random variable and therefore to gain in precision. However, in this case, the denominator of the Bayes formula is no longer a discrete sum but an integral which is more complicated to calculate. Thus, the MCMC method proposes to calculate probability density ratios in order to get rid of the denominator. This method also has the advantage of working with low-stress measurements. These measurements in our approach are the forces estimated using the Kalman filter.

[0047] Thus, a method according to the invention makes it possible to provide a reliable estimation of an adhesion potential. The invention has been described in detail for the longitudinal case. Nevertheless, this description is not restrictive, and a similar approach could be envisaged for the lateral, or a combination of the two.

Claims

Claims

1. Method for estimating a potential grip of a tire on a rolling surface, the tire being installed on a vehicle equipped with the method comprising the following steps: - a first step of estimating a force undergone by the tire, as a function of a vehicle model and a state observer, - a second step of estimating a force undergone by the tire, as a function of a thermomechanical model of the tire, - a step of statistical comparison of the forces determined during the first and second estimation steps, and - a step of determining, as a function of the result of this comparison step, a value of the tire / ground grip potential, in which the vehicle model is a bicycle model, and in which the state observer is a Kalman filter.

2. Estimation method according to claim 1, in which the comparison step implements a Bayesian approach method.

3. Estimation method according to claim 1, in which the comparison step implements a method called Monte-Carlo Markov Chain.

4. Estimation method according to one of the preceding claims, in which the thermomechanical model of the tire comprises a model of the longitudinal forces, the transverse forces, a self-alignment torque and a balance of the elementary shear and sliding forces of the tire at a point of passage between the adherent and sliding contact zones.

5. Estimation method according to one of the preceding claims, comprising, at least before the second estimation step, a step of reducing the pneumatic model.

6. Estimation method according to one of the preceding claims, in which all of the steps are carried out in real time.

7. System for estimating the grip potential of a tire on a rolling surface, the tire being installed on a vehicle, and the system comprising: - means for estimating a force undergone by the tire, as a function of a vehicle model and a state observer, - means of estimating a force undergone by the tire, based on a thermomechanical model of the tire, - means of statistical comparison of the efforts determined during the first and second estimation stages, and - means for determining, as a function of the result of this comparison step, a value of the tire / ground adhesion potential, in which the vehicle model is a bicycle model, and in which the state observer is a Kalman filter.

8. Estimation system according to claim 7, wherein the different means are installed on the vehicle.

9. An estimation system according to claim 7 or 8, further comprising sensors installed on the vehicle.

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