Estimation of the tyre slip ratio for two-wheel-drive or four-wheel-drive vehicles by means of bayesian filters

Abstract

The invention relates to a system for estimating the slip ratio of a tyre mounted in driving condition on a vehicle, the system being characterised in that the system comprises: - at least one tyre mounted on a vehicle; - at least one communication device communicating data provided by the signals corresponding to one or more electronic systems on board the vehicle; - a communication network comprising at least one communication server allowing the execution of programmed instructions stored in a memory of one or more processors of the system for implementing a method for estimating the slip ratio; characterised in that the system comprises an estimator of the slip ratio comprising a Bayesian selection, the estimator further comprising: -a speed estimator; - a longitudinal force estimator; and - a pseudo-slip stiffness estimator of the tyre.

Description

[0001]Description Title: Estimation of the slip ratio of tires for two-wheel drive or four-wheel drive vehicles using Bayesian filters Technical Field The invention relates to a method for estimating the slip ratio of tires for two-wheel drive (or "2 wheel drive" or "2WD") and four-wheel drive (or "4 wheel drive" or "4WD") vehicles. More particularly, the invention relates to a method for estimating the slip ratio, an estimation model of which is constructed on the basis of a Bayesian filter. Context In the field of vehicle dynamics,Slip is the relative motion between a tire mounted on a vehicle and the rolling surface on which the tire is traveling. This slip can be generated either by the tire's rotational speed (which is higher or lower than the free rolling speed) or by the tire's plane of rotation being at an angle to its direction of travel (or "slip angle"). When a force is applied to a tire, it produces a frictional force resulting from the interaction between the tire and the rolling surface. A tire cannot produce a frictional force to accelerate the vehicle without wheel torque. Thus, the frictional force can be expressed as a ratio of wheel torque to the frictional force (called the "coefficient of friction"). A rolling tire has a slip ratio,which corresponds to the relative speed difference between the linear speed of the top of a tire (i.e., the points of the tread in contact with the belt) and the forward speed of the vehicle (i.e., the linear speed of travel on the ground). The friction potential of a tire represents the amount of friction remaining before the tire begins to skid on the road. Knowing this quantity during a journey is particularly advantageous, notably for the development of autonomous vehicles and for improving the performance of driver assistance systems (see Vincent Mussot,“Tire friction potential estimation combining Kalman filtering and Monte-Carlo Markov Chain Model Learning”. https: / / theses.hal.science / tel-03615055 (March 21, 2022)(or “the Mussot reference”). Typical active safety systems that control vehicle dynamics rely on real-time monitoring of signals such as wheel angular velocities, steering angle, lateral acceleration, and rotation rate around the vertical axis (called the “yaw rate”). These signals are communicated by one or more on-board communication devices on the vehicle (e.g., at least one CAN (or “Controller Area Network”) bus) allowing multiple vehicle systems (including, but not limited to, powertrain control, airbags, ABS,traction control and stability control) to communicate with each other in real time. It is well known that communication devices on board a vehicle can electronically communicate several parameters of its operation (including, without limitation, its speed, its acceleration and the estimation of a rolling radius of a tire mounted on the vehicle). As an example, the prior art discloses the use of data collected by a CAN bus in the modeling of tires in the process of rolling (see US Pat. No. 10,603,962 and publication US2022 / 0063347 which relate to tire wear estimation systems where each disclosed system incorporates at least one tire mounted to a vehicle and a CAN bus system disposed on the vehicle for constructing one or more wear models). The prior art models are sensitive to the uncertainty of each model and thus susceptible to the unpredictable influence of unknown external conditions. For example, in conditions where the vehicle is close to the grip limit, it operates in the non-linear range of the tire curve (i.e., large slip angles) where a small variation in the contact patch between the tires and the rolling surface can induce a rapid variation in the available grip. At the same time, there are other conditions for which the vehicle is stable (see Tommaso Novi, Renzo Capitani and Claudio Annicchiarico,“An Integrated ANN-UKF Vehicle Slideslip Angle Estimation Based on IMU Measurements”, Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, DOI: 10.1177 / 0954407018790646 (August 2018). As also indicated in the prior art, a common vehicle dynamics-based method for sideslip angle estimation is Bayesian selection (e.g., employing a Kalman filter (or KF). This is due not only to the KF's ability to directly utilize input and measurement noise information. In addition, the KF is robust, stable, and relatively simple to implement, allowing it to be used for both linear and nonlinear systems (see Daniel Chindamo, Basilio Lenzo, and Marco Gadola, "On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations," Applied Sciences,https: / / doi.org / 10.3390 / app8030355 (1 March 2018)). Although the concept of tire slip angle is applied with respect to lateral force production, it is also necessary to consider the tire slip ratio which concerns the amount of slip a tire experiences relative to a slipping condition (and therefore also takes the longitudinal direction into account). Knowledge of the tire slip ratio is therefore important for stability control (e.g., with respect to braking and independent wheel torque). Thus, in order to have the most accurate estimate possible,The disclosed invention relates to a slip ratio estimator comprising a Bayesian selection based on a kinematic model and a wheel dynamics block. Summary of the invention The invention relates to a system for estimating the slip ratio of a tire mounted in rolling condition on a vehicle with at least one front wheel and at least one rear wheel,the system comprising: - at least one tire mounted in rolling condition on a vehicle; - at least one communication device on board the vehicle communicating data provided by the signals corresponding to one or more electronic systems on board the vehicle; and - a communication network comprising at least one communication server making it possible to execute programmed instructions stored in a memory of one or more processors of the slip ratio estimation system to implement a method for estimating the slip ratio of the tires mounted on the vehicle; - characterized in that the slip ratio estimation system comprises a slip ratio estimator comprising a Bayesian selection,and in that the slip ratio estimator further comprises: - a speed estimator; - a longitudinal force estimator; and - a tire pseudo-slip stiffness estimator. In certain embodiments of the system of the invention, during the slip ratio estimation method, one or more processors introduce into the slip ratio estimator the models comprising; - the speed of the front and rear wheels ω, f / r estimated using a Kalman filter (KF); - the rotation rate of the front and rear wheels ^̇ ^ / ^ estimated using a KF; - the initial calculation of the front and rear slip rate Sf / rcalc; and - the final estimate of the tire slip rate S f / rusing a second KF. In certain embodiments of the system of the invention, during the slip ratio estimation method, the models introduced into the slip ratio estimator are constructed from data comprising: - the signals from the communication device(s) on board the vehicle, comprising; - the speed of the front wheels ω fCAN represented by the measured speeds of the right front wheel, the left front wheel or the average of the two; - the rear wheel speed ωrCAN represented by the measured speeds of the right rear wheel, the left rear wheel or the average of the two; - the front wheel torque TwfCAN represented by the sum of the torques applied to the right and left wheels; and - the rear wheel torque T wrCANrepresented by the sum of the torques applied to the right and left wheels; and constant vehicle parameters, including: - the loaded radius of the tire R charge ; - the moment of inertia of the wheel Iw; and - the pseudo-slip stiffness KX of the tire. In certain embodiments of the system of the invention: - the KF comprises a kinematic model of the type: [Math 1] ^ ^ ^^^ ^ ^ ̇ ^^^ ^ ^1 Δ^ ^ = ^ . ^ ^ 0 1 ^̇ ^ - Where: - Δ^ is the time interval between two measurements; and - x is the input of the KF block including either the wheel speed ω f / r or the tire slip rate S f / r ; - and the wheel dynamics block expresses the slip ratio according to a mathematical model of the type: [Math 5] - For the rear wheels: ^ ^ ^ − 2^ ^ ^̇ ^ = ^^ = ^ ^ ^^^^^ , ^ ^^ ^ ^^ ^ ^^^^^^ [Math 6] - For the front wheels: - Where: - Kxf / r represents the pseudo-slip stiffness of the tire; - Iw represents the inertia of the wheel; - R charge represents the loaded radius of the tire; - Twf / r represents the front and rear wheel torque respectively; and - Fxf / r represents the front and rear longitudinal forces acting on the tire. In certain embodiments of the system of the invention, the speed estimator expresses the speed according to a mathematical model of the type: [Math 10]: - Where: - S f represents the estimated front slip rate; - ωf represents the front wheel speed; and - Rr represents the rolling radius of the front tire; and a mathematical model of the type: [Math 11]: Where: - Sr represents the estimated rear slip rate; - ωr represents the speed of the rear wheel; and - Rr represents the rolling radius of the rear tire. In certain embodiments of the system of the invention, the longitudinal force estimator expresses the longitudinal forces according to a mathematical model of the type: [Math 12]: ^ ^^ = ^ ^^ ^ ^ and a mathematical model of the type: [Math 13]: ^ ^^ = ^ ^^ ^ ^ . In some embodiments of the system of the invention, the longitudinal force estimator expresses the longitudinal forces according to a mathematical model of the extended Kalman filter (EKF) type according to a mathematical model of the type: [Math 14]: - Where: - ρ represents the air density; - S represents the frontal surface of the vehicle; - C d represents the drag coefficient; - ^ ^^ represents the forward longitudinal force; - F xrrepresents the rear longitudinal force; - Froll represents the rolling resistance; and - m represents the mass of the vehicle. In some embodiments of the system of the invention, the system further comprises at least one sensor disposed in the tire that measures the parameters of the tire. In some embodiments of the system of the invention, the at least one communication device on board the vehicle comprises at least one CAN bus. The invention also relates to a method for estimating the slip ratio of tires mounted on the vehicle implemented by the disclosed slip ratio estimation system, characterized in that the method comprises the following steps: - a step of introducing to the slip ratio estimation system the models comprising; - the speed of the front and rear wheels ωf / r estimated using a Bayesian selection; - the rotation rate of the front and rear wheels ^̇ ^ / ^estimated using Bayesian selection; - the initial calculation of the front and rear slip rate Sf / rcalc; and - the final estimate of the tire slip rate S f / r using Bayesian selection; - and a step of constructing the models introduced from the data comprising: - the signals of at least one communication device on board the vehicle, comprising; - the speed of the front wheels ωfCAN represented by the measured speeds of the right front wheel, the left front wheel or the average of the two; - the speed of the rear wheels ω rCAN represented by the measured speeds of the right rear wheel, the left rear wheel or the average of the two; - the front wheel torque T wfCANrepresented by the sum of the torques applied to the right and left wheels; and - the rear wheel torque TwrCAN represented by the sum of the torques applied to the right and left wheels; and constant vehicle parameters, including: - the loaded radius of the tire Rcharge; - the moment of inertia of the wheel Iw; and - the pseudo-slip stiffness K X of the tire. In certain embodiments of the method of the invention, the step of introducing the slip ratio estimation system comprises the introduction of models comprising; - the speed of the front and rear wheels ω f / r estimated using a Kalman filter (KF); - the rotation rate of the front and rear wheels estimated using a KF; and - the final estimation of the tire slip ratio Sf / r using a second KF. In certain embodiments of the method of the invention, the method further comprises a speed estimation step, during which, for the calculation of the rear force, a speed estimator of the slip ratio estimation system expresses the speed according to a mathematical model of the type: [Math 10]: - Where: - Sf represents the estimated forward slip rate; - ω f represents the speed of the front wheel; and - Rr represents the rolling radius of the front tire; and a mathematical model of the type: [Math 11]: Where: - Sr represents the estimated rear slip rate; - ωr represents the speed of the rear wheel; and - Rr represents the rolling radius of the rear tire. In certain embodiments of the method of the invention, for the estimation of the front force, the longitudinal force estimator expresses the longitudinal forces according to a mathematical model of the extended Kalman filter (EKF) type according to a mathematical model of the type: [Math 14]: - Where: - ρ represents the air density; - S represents the frontal area of ​​the vehicle; - Cd represents the drag coefficient; - ^ ^^represents the front longitudinal force; - Fxr represents the rear longitudinal force; - Froll represents the rolling resistance; and - m represents the mass of the vehicle. In certain embodiments of the method of the invention, the method further comprises a step of estimating the pseudo-slip stiffness Kx of the tire, during which an estimator of the pseudo-slip stiffness K xof the slip ratio estimation system represents a virtual measurement of which: - for rear wheels, the pseudo-slip stiffness estimator Kx constructs the virtual measurement from the estimated rear slip rate and the estimated wheel speed rate before using a KF; and - for front wheels, the pseudo-slip stiffness estimator Kx constructs the virtual measurement from the estimated front force and the estimated front slip rate with a KF. Other aspects of the invention will become apparent from the following detailed description.Brief Description of the Drawings The nature and various advantages of the invention will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which like reference numerals designate like parts throughout, and in which: [Fig 1] Figure 1 shows the components of a tire in a meridian plane. [Fig.2] Figure 2 shows a schematic view of a slip ratio estimator employed in a method of estimating the slip ratio of tires of the invention. [Fig.3] Figure 3 shows a diagram of a velocity estimator which is used in the slip ratio estimator of Figure 2. [Fig.4] Figure 4 shows a diagram of a force estimator which is used in the slip ratio estimator of Figure 2. [Fig.5] Figure 5 represents a diagram of a pseudo-slip stiffness estimator that is used in the slip ratio estimator of Figure 2. [Fig.6] Figure 6 represents a diagram incorporating the interactions of the estimators of Figures 3, 4 and 5 in the slip ratio estimator of Figure 2. Detailed Description When considering the characteristics of a tire that is the subject of a slip ratio estimation, its geometry must be taken into account. A tire is an object with a known geometry generally comprising several superimposed layers of rubber (or "layers"), as well as a metal or textile fiber structure constituting a reinforcing carcass of the tire structure. The nature of the rubber and the nature of the reinforcement are chosen according to the desired final characteristics.Figure 1 includes a schematic representation of a tire 10 comprising, in a conventional manner, two circumferential beads intended to allow the tire to be attached to a rim. Each bead comprises an annular reinforcing bead wire. The constitution of a tire is typically described by a representation of its constituents in a meridian plane, that is to say a plane containing the axis of rotation of the tire. The radial, axial and circumferential directions respectively designate the directions perpendicular to the axis of rotation of the tire, parallel to the axis of rotation of the tire, and perpendicular to any meridian plane. The expressions “radially”, “axially” and “circumferentially” respectively mean “in a radial direction”, “in the axial direction” and “in a circumferential direction” of the tire.The expressions "radially inner" and "respectively radially outer" mean closer, respectively further, from the axis of rotation of the tire, in a radial direction. The tire 10 also comprises a tread 12, added to the outer surface of the tire. The tread 12 intended to come into contact with a ground via a rolling surface 12a. The tire 10 further comprises a crown reinforcement comprising a working reinforcement 14 and a hoop reinforcement 16, the working reinforcement 14 having working layers represented by the layers 14a and 14b. The tire 10 also comprises two sidewalls (a sidewall 18 being represented in FIG. 1) and two fillers 20 reinforced with a bead wire 22. A radial carcass layer 24 extends from one bead to the other, surrounding the bead wire in a known manner.The tread 12 comprises reinforcements consisting, for example, of superimposed layers comprising known reinforcing threads. In embodiments, the tire may include a rubber 26 which evacuates the static electricity produced during rolling. The tread 12 is delimited, in the radial direction, by two circumferential surfaces, the most radially outer of which is the rolling surface 12a and the most radially inner of which is called the tread base surface. The tread base surface (or “bottom surface”) is defined as the surface translated from the rolling surface radially inward by a radial distance equal to the tread depth. It is common for this depth to be decreasing on the most axially outer circumferential portions (called “shoulders”) of the tread 12.In addition, the tread of a tire is delimited, in the axial direction, by two lateral surfaces. The tread is further constituted by one or more rubber compounds. The expression "rubber compound" designates a rubber composition comprising at least one elastomer and a filler. Referring to the figures, in which the same numbers identify identical elements, Figure 2 represents a schematic view of a slip ratio estimator used in a method for estimating the slip ratio of tires (or "estimation method" or "method") of the invention. The performance of the slip ratio estimator was carried out for 2WD vehicles through a characterization campaign included in the group comprising numerical simulation and experimental measurements. The case of 4WD vehicles works in the same way and should therefore give similar results.In order to operate the tire slip ratio estimation, the slip ratio estimator needs the following vehicle signals (representing the measured values): - Signals from one or more on-board communication devices in the vehicle (e.g., at least one CAN bus available on the vehicle); - Front wheel speed ω. fCAN represented by the measured speeds of the right front wheel, the left front wheel or the average of the two; - The speed of the rear wheels ω rCAN represented by the measured speeds of the right rear wheel, the left rear wheel or the average of the two; - The front wheel torque TwfCAN represented by the sum of the torques applied to the right and left wheels; and - The rear wheel torque T wrCANrepresented by the sum of the torques applied to the right and left wheels. Furthermore, in order to operate the tire slip ratio estimation, the slip ratio estimator needs the assumed constant vehicle parameters including: - the loaded radius Rload which is measurable directly on the vehicle; - the wheel moment of inertia Iw; and - the pseudo-slip stiffness K X of the tire. The estimation method of the invention does not require additional sensors compared to those already present on all standard vehicles. In the slip ratio estimator, the following models are introduced: - the speed of the front and rear wheels ωf / r estimated using a Kalman filter (or "(KF)"); - the rotation rate of the front and rear wheels ^̇ ^ / ^ estimated using a KF; - the initial calculation of the forward and reverse slip rate S f / rcalc; and - the final estimation of the tire slip ratio Sf / r using a second KF. This approach allows estimating the slip ratio under high and low speed conditions, without making assumptions about the undriven wheel. This estimation is obtained using a commercial sensor with a potentially low signal-to-noise ratio (or “SNR”) and a low frequency. The measurements used in this estimator include wheel torques and speed measurements of both wheels (front and rear). It is understood that the estimator could use other available measurements (e.g., acceleration). Referring again to Figure 2, the slip ratio estimator comprises two main blocks. The first block comprises a Bayesian selection of the Kalman filter (KF) type based on a kinematic model.Throughout this description, reference to the Kalman filter is understood to include a variety of Kalman filters (including, for example, the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the constrained Kalman filter (CKF). The purpose of a KF is to provide an estimate of the state xk and its covariance matrix Pk using a description of the model and noisy measurements zk. To achieve this goal, two steps are performed: (1) first, a prediction step where the model equations are used to predict the value of the state at time k; and (2) second, a correction step is performed where the prediction is modified to take into account the measurement vector at time k.Thus, to introduce the parameters used and to explain why KFs are particularly suitable for this case, we consider a kinematic model of the type: [Math 1] ^. ^^^ ^ ^ ^ ^1 Δ^ ^ ^̇ ^^^ = ^ . ^ ^ 0 1 ^̇ ^ - Where: - Δ^ is the time interval between two measurements; and - x is the input of the KF block (i.e. the wheel speed ω f / rlet the tire slip ratio Sf / r. KFs are used here because they are theoretically optimal in that they provide an unbiased estimate with minimal variance. They are also recursive and can therefore be easily implemented directly on the vehicle for live estimation of tire slip ratio. The second main block of the slip ratio estimator includes a wheel dynamics block. This block is used to generate a virtual slip ratio measurement using the estimated wheel speed rate and the torque applied to the wheel combined with the vehicle parameters. Considering the single bicycle / chain model of the vehicle, the wheel dynamics equation yields the following equations: [Math 2] - For rear wheels: [Math 3] - For front wheels: - Where: - Iw represents the inertia of the wheel; - Rload represents the loaded radius of the tire; - T wf / r represents the front and rear wheel torque respectively; and - Fxf / r represents the front and rear longitudinal forces acting on the tire. Using the approach that assumes that F x = K x S for a small slip ratio (S < 0.1), a virtual measure of the slip ratio can be obtained as follows: [Math 4] From this approach, we obtain, for each pair of wheels, the following calculations: [Math 5] - For the rear wheels: [Math 6] - For the front wheels: Now, the KF block can be used with the virtual slip ratio measurement to obtain the final slip ratio estimate. There are different approaches to estimate the tire slip ratio, most of them are classified into one of two categories including a model-based approach (like KF) and a data-driven approach (like machine learning). For the model-based approach, there are two main approaches. The first approach involves using the slip ratio definition Sf / r: [Math 7] This definition is mainly based on the precise estimation of the speed of the front or rear wheel ω f / r , the rolling radius Rr and the longitudinal speed of the vehicle v x . The rolling radius differs from the loaded radius presented previously. The assumption that Rr = R chargecan be done, but it is associated with a significant increase in uncertainty. The skilled person understands that methods exist to estimate the rolling radius from the loaded radius. For the model-based approach, the second approach concerns, for two-wheel drive (2WD) vehicles, that the slip ratio is assumed to be zero on the non-driven axle (e.g., for a front-wheel drive car): [Math 8] Thus, the new equation for the forward slip ratio becomes: [Math 9] − It is understood that everything could be reversed depending on a front-wheel drive or rear-wheel drive case. The second approach is notably less sensitive to state estimation errors (often five to ten times less sensitive). Due to this assumption, there will always be a lag in the front slip estimation, because the rear slip is not zero (for example, a bias of the order of 5% of the observed value can be observed). This approach, which is used by the slip ratio estimator, is also much less sensitive to changes in states and parameters. If a torque measurement is available for each axle, this approach can therefore work for four-wheel drive (4WD) vehicles. The approach that is used for slip ratio estimation can also be used for speed estimation.Referring now to Figure 3, Figure 3 represents a diagram of a speed estimator which is used in the slip ratio estimator during the realization of the method of the invention. During the realization of the estimation method of the invention, a virtual measurement of the speed can be obtained by using the estimated slip ratio in the standard definition of slip ratio as follows: [Math 10]:. - Where: - S f represents the estimated forward slip rate; - ω f represents the speed of the front wheel; and - Rr represents the rolling radius of the front tire. The same result can be obtained using the rear wheels: [Math 11]: 1 + ^ ^ = ^ ^ ω ^ ^^ Where: - Sr represents the estimated back slip rate; - ω rrepresents the speed of the rear wheel; and - Rr represents the rolling radius of the rear tire. In Figure 3, it is recalled that the values ​​a xm , ω fCAN , ω rCAN , T wfCAN and T wrCAN represent the measured values ​​(these values ​​obtained, for example, from a CAN bus type communication device on board the vehicle). The R values charge , K x and I wrepresent the assumed constant values. Using the virtual vehicle speed measurement together with acceleration measurements from the communication device (e.g., CAN bus communication device(s)) onboard the vehicle can provide a better estimate of the vehicle speed and correct for the offset in the estimate. In addition, this approach is useful in case of speed sensor failure. For example, GPS (Global Positioning System) signals are often lost in tunnels. If the speed estimation algorithm includes a KF with a kinematic model, and GPS is used to measure the vehicle speed, then there will be a significant offset between the estimated speed and the actual value.Referring now to Figure 4, Figure 4 represents a diagram of a force estimator that is used in the slip ratio estimator during the realization of the method of the invention. During the estimation method of the invention, the longitudinal forces can be estimated using the estimated states (including speed, wheel dynamics and slip ratio) according to the following equations. For the calculation of the rear force: [Math 12]:. The same result can be obtained using the front tires: [Math 13]: For forward force estimation, an Extended Kalman Filter (EKF) is proposed with a kinematic model for velocity and acceleration estimation. A kinematic model is proposed for forward force estimation according to a mathematical model of the type: [Math 14]: - Where: - ρ represents the air density; - S represents the frontal surface of the vehicle; - C d represents the drag coefficient; - Fxf represents the forward longitudinal force; - Fxr represents the rear longitudinal force; - F roll represents the rolling resistance; and - m represents the mass of the vehicle. In Figure 4, it is recalled that the values ​​a xm , ω fCAN , ω rCAN , T wfCAN and T wrCAN represent the measured values ​​(these values ​​obtained, for example, from the communication device(s) on board the vehicle). The values ​​Rcharge, Kx , Iw , Froll and m represent the values ​​assumed to be constant. Referring now to Figure 5, Figure 5 represents a diagram of an estimator of the pseudo-slip stiffness Kx which is used in the estimator of the slip ratio during the realization of the method of the invention. During the method of the invention, an initial value of Kx is used in the force estimator (see Figure 4). Once the estimate of Kx is substantially convergent, the estimated value of Kx can be used for the force estimator. For rear wheels, the virtual measure is constructed from the estimate of the rear slip rate and the estimate of the wheel speed rate before using a Bayesian selection (e.g., a Kalman filter KF). For front wheels, the virtual measure is constructed from the estimated front force and the estimated front slip rate with a KF. In Figure 5, it is recalled that the values ​​a xm , ω fCAN , ω rCAN , T wfCAN and T wrCAN represent the measured values ​​(those values ​​obtained, for example, from the communication device(s) on board the vehicle). The R values charge , K x , I w , F rolland m represent the presumed constant values. Referring now to Figure 6, Figure 6 represents a diagram of the entire slip ratio estimator which is schematically represented in Figure 2. The slip ratio estimation method of the invention is implemented by a system which comprises the slip ratio estimator. The system further comprises at least one communication network (or "network") which manages the data incoming to the system from various sources (for example, from at least one communication device of the CAN bus type which manages the data provided by the electronic systems on board the vehicle (for example, the ABS system) and / or from one or more sensors mounted on the tire and chosen from the sensors of the TPMS (or "Tire Pressure Monitoring System") type) and of the TMS (or "Tire Mounted Sensor") type).It is also understood that the communication network of the system carrying out the estimation method of the invention can manage the data entering the system from one or more wireless identification devices (for example, of the RFID type, or "Radio Frequency Identification Device") which could be mounted on a tire and interrogated by external communication devices (for example, to provide the serial numbers of the tires which are recorded in the transponder memory). The communication network incorporates one or more communication servers (or "servers") each comprising one or more processors operatively connected to a memory. The processor(s) of the system comprise an analysis application execution module whose processor(s) are capable of executing programmed instructions stored in the memory to carry out the steps of the estimation method of the invention.The memory is configured to store an application for analyzing data representative of the operating characteristics of an identified tire. The term "identified tire" (in the singular or plural) is used herein to refer to a tire that is mounted on an identified vehicle (being a tire still in service on the identified vehicle). The identified tire may include one or more sensors known to generate or capture data, such as data corresponding to an operational environment of the identified vehicle or a portion thereof. The sensors may include a set of sensors to provide data regarding the operating characteristics of the identified tire.The sensors may include, for example, speed sensor(s), acceleration sensor(s), traction-related sensor(s), braking-related sensor(s), and / or a combination of sensors to collect data regarding one or more aspects of the dynamic situation of the identified tire. The sensors may also provide stored data regarding the identification of the identified tire (including, without limitation, its production, distribution and / or storage origin, its production date, its retreading history if applicable and its mounting position and history). Input data to the system performing the estimation method of the invention may include general information regarding the identified tire.General information includes stored data regarding the identification of the identified tire (including, without limitation, its production origin, distribution and / or storage, production date, retreading history if applicable and its mounting position and history). The corresponding data of an identified tire could be managed by an entity that manages the use of one or more vehicles to which a tire of the same type is mounted (for example, such an entity may include one or more persons and / or one or more companies) and / or the manufacturer of such tires. More direct measurements at the level of an assembled assembly may also be considered (by "assembled assembly" it is understood that the tire-brake-wheel assembly is concerned).For example, calibrating a periodic signal at the wheel revolution makes it possible to extract a longitudinal dimension, characteristic of the contact between the tire casing and the ground for a given mounted assembly. In this case, a sensor sensitive to the radial and / or longitudinal deformation of the tire is considered (for example, a sensor of the accelerometer or piezoelectric element type). It is understood that other equivalent devices can also be used.The term "processor" (or, alternatively, the term "programmable logic circuit") refers to one or more devices capable of processing and analyzing data and including one or more software programs for processing them (e.g., one or more integrated circuits known to those skilled in the art as being included in a computer, one or more controllers, one or more microcontrollers, one or more microcomputers, one or more programmable logic controllers (or "PLCs"), one or more application-specific integrated circuits, one or more neural networks, and / or one or more other known equivalent programmable circuits). The processor includes one or more software programs for processing the data captured by the system that performs the estimation method of the invention as well as one or more software programs for identifying and locating variances and identifying their sources to correct them.In the system that performs the estimation method of the invention, the memory may include both volatile and non-volatile memory devices. The non-volatile memory may include solid-state memories, such as NAND flash memory, "live" memory (or "keep-alive memory" or "KAM") for saving various operating variables while the processor is powered off, magnetic and optical storage media, or any other suitable data storage device that retains data when the system (and / or a facility incorporating the system) is turned off or loses power. The volatile memory may include static and dynamic RAM that stores program instructions and data, including a learning application.The processor may also refer to a reference (e.g., a size chart of various tires) to make a final determination of a parameter or parameters of the identified tire. The reference may include known tire parameters corresponding to a plurality of known commercially available tires. The tire reference may include measurements corresponding to a plurality of commercially available tires. For example, for a tire of size 225 / 50R17, the number "225" identifies the tire cross-sectional area in millimeters, the number "50" indicates the sidewall aspect ratio, and the measurement "R17" represents the rim diameter in inches (being approximately 43.18 centimeters).As used herein, the term “method” may include one or more steps performed by at least one computer system having one or more processors to execute instructions that perform the steps. Unless otherwise indicated, any sequence of steps is given by way of example and does not limit the methods described to any particular sequence. The slip ratio estimation method of the invention may be done by PLC control and may include preprogramming of management information. For example, a setting of the method may be associated with the parameters of an identified tire and / or the properties of the vehicles to which the identified tire is intended to be mounted. The system performing the estimation method of the invention (and / or a facility incorporating this system) may easily repeat one or more steps of the estimation method in a predetermined order.The system implementing the estimation method of the invention (and / or a facility incorporating this system) may include pre-programming of management information. For example, a process setting may be associated with the parameters of the typical vehicles on which the system operates. In embodiments of the invention, the system implementing the estimation method of the invention (and / or a facility incorporating this system) may receive voice commands or other audio data representing, for example, a step or a stop of the process. A generated response may be represented audibly, visually, tactilely (for example, using a haptic interface) and / or virtually and / or augmentedly. This response, associated with the corresponding data, may be recorded in at least one neural network. For all embodiments of the system, a monitoring system could be implemented.At least a portion of the monitoring system may be provided in a portable device such as a mobile network device (e.g., a mobile phone, a laptop computer, one or more network-connected wearable devices (including "augmented reality" and / or "virtual reality" devices), network-connected wearables, and / or any combinations and / or equivalents).In one embodiment, the slip ratio estimation method of the invention may comprise a step of training a system to recognize values ​​representative of the predicted slip ratios (e.g., values ​​of the expected slip speed, force and stiffness under the preconditions) and to make a comparison with targeted values ​​(e.g., the slip ratio values ​​associated with the values ​​of the slip speed, force and stiffness already estimated for the identified tire under substantially similar operating conditions). Each step of the training may include a classification generated by self-learning means. This classification may include, without limitation, the parameters of the mounted tires, the vehicle configurations, and the expected values ​​at the end of an ongoing estimation process.It is understood that several distinct learning methods are possible, including supervised learning (in which the algorithm trains on a set of labeled data and modifies itself until it is able to obtain the desired result), unsupervised or semi-supervised learning (in which the data is not labeled so that the network can adapt to increase the algorithm's accuracy), reinforcement learning (in which the algorithm is reinforced for positive results and punished for negative results), and active learning (in which the algorithm gradually requests examples and labels to refine its prediction) (see https: / / www.lebigdata.fr / reseau-de-neurones-artificiels-definition). The terms "at least one" and "one or more" are used interchangeably. Ranges that are presented as being "between a and b" encompass the values ​​"a" and "b".Although particular embodiments of the disclosed apparatus have been illustrated and described, it will be understood that various changes, additions, and modifications may be practiced without departing from the spirit and scope of the present disclosure. Accordingly, no limitations should be imposed on the scope of the disclosed invention except those set forth in the appended claims.

Claims

- 1 - Claims 1. A system for estimating the slip ratio of a tire mounted in rolling condition on a vehicle with at least one front wheel and at least one rear wheel, the system comprising: - at least one tire mounted in rolling condition on a vehicle; - at least one communication device on board the vehicle communicating data provided by the signals corresponding to one or more electronic systems on board the vehicle; and - a communication network comprising at least one communication server making it possible to execute programmed instructions stored in a memory of one or more processors of the slip ratio estimation system to implement a method for estimating the slip ratio of the tires mounted on the vehicle;- characterized in that the slip ratio estimation system comprises a slip ratio estimator comprising a Bayesian selection, and in that the slip ratio estimator further comprises: - a speed estimator; - a longitudinal force estimator; and - a tire pseudo-slip stiffness estimator.

2. The slip ratio estimation system of claim 1, wherein, during the slip ratio estimation method, one or more processors input into the slip ratio estimator the models comprising; - the front and rear wheel speed ωf / r estimated using a Kalman filter (KF); - the front and rear wheel rotation rate ^̇; ^ / ^estimated using a KF; - the initial calculation of the front and rear slip rate Sf / rcalc; and - the final estimation of the tire slip rate Sf / r using a second KF.

3. The slip ratio estimation system of claim 1 or claim 2, wherein, during the slip ratio estimation process, the models introduced into the slip ratio estimator are constructed from data comprising: - 2 - - the signals from the on-board communication device(s) on the vehicle, including; - the front wheel speed ωfCAN represented by the measured speeds of the right front wheel, the left front wheel or the average of the two; - the rear wheel speed ω rCANrepresented by the measured speeds of the right rear wheel, the left rear wheel or the average of the two; - the front wheel torque TwfCAN represented by the sum of the torques applied to the right and left wheels; and - the rear wheel torque TwrCAN represented by the sum of the torques applied to the right and left wheels; and constant vehicle parameters, including: - the loaded radius of the tire Rload; - the wheel moment of inertia Iw; and - the pseudo-slip stiffness KX of the tire.

4. The slip ratio estimation system of claim 2 or claim 3, wherein: - the KF comprises a kinematic model of the type: [Math 1] ^ ^ ^ ^ ^^ ^ ^1 Δ^ ^ ^̇ ^^^ = ^ . ^ 0 1 ^̇ ^ ^ - Where: - Δ^ is the time interval between two measurements; and - x is the input of the KF block including either the wheel speed ωf / r or the tire slip rate S f / r; - and the wheel dynamics block expresses the slip ratio according to a mathematical model of the type: [Math 5] - For the rear wheels: - 3 - [Math 6] - For the front wheels: - Where: - K xf / r represents the pseudo-slip stiffness of the tire; - Iw represents the inertia of the wheel; - Rload represents the loaded radius of the tire; - Twf / r represents the torque of the front and rear wheels respectively; and - F xf / r represents the front and rear longitudinal forces acting on the tire.

5. The slip ratio estimation system of claim 4, wherein the speed estimator expresses the speed according to a mathematical model of the type: [Math 10]: - Where: - S frepresents the estimated front slip rate; - ωf represents the front wheel speed; and - Rr represents the rolling radius of the front tire; and a mathematical model of the type: [Math 11]: Where: - S r represents the estimated back slip rate; - ω r represents the speed of the rear wheel; and - Rr represents the rolling radius of the rear tire. - 4 - 6. The slip ratio estimation system of claim 5, wherein the longitudinal force estimator expresses the longitudinal forces according to a mathematical model of the type: [Math 12]: ^ ^^ = ^ ^^ ^ ^ and a mathematical model of the type: [Math 13]: ^ ^^ = ^ ^^ ^ ^7. The slip ratio estimation system of claim 6, wherein the longitudinal force estimator expresses the longitudinal forces according to a mathematical model of the extended Kalman filter (EKF) type according to a mathematical model of the type: [Math 14]: - Where: - ρ represents the air density; - S represents the frontal area of ​​the vehicle; - Cd represents the drag coefficient; - ^ ^^ represents the front longitudinal force; - Fxr represents the rear longitudinal force; - Froll represents the rolling resistance; and - m represents the mass of the vehicle.

8. The slip ratio estimation system of any one of claims 1 to 7, further comprising at least one sensor disposed in the tire that measures the parameters of the tire. - 5 - 9. The slip ratio estimation system of any one of claims 1 to 8, wherein the on-board communication device(s) on the vehicle comprise at least one CAN bus.

10. A method for estimating the slip ratio of the tires mounted on the vehicle implemented by the slip ratio estimation system of any one of claims 1 to 9, characterized in that the method comprises the following steps: - a step of introducing to the slip ratio estimation system the models comprising; - the speed of the front and rear wheels ω f / r estimated using Bayesian selection; - the rotation rate of the front and rear wheels ^̇ ^ / ^ estimated using Bayesian selection; - the initial calculation of the forward and backward slip rate S f / rcalc ; and - the final estimate of the tire slip rate S f / rusing Bayesian selection; - and a step of constructing the models introduced from the data comprising: - the signals from at least one communication device on board the vehicle, comprising; - the speed of the front wheels ω fCAN represented by the measured speeds of the right front wheel, the left front wheel or the average of the two; - the rear wheel speed ωrCAN represented by the measured speeds of the right rear wheel, the left rear wheel or the average of the two; - the front wheel torque TwfCAN represented by the sum of the torques applied to the right and left wheels; and - the rear wheel torque TwrCAN represented by the sum of the torques applied to the right and left wheels; and constant vehicle parameters, including: - the loaded radius of the tire Rload; - the moment of inertia of the wheel I w ; and - the pseudo-slip stiffness KX of the tire. - 6 - 11. The slip ratio estimation method of claim 10, wherein the step of introducing to the slip ratio estimation system comprises introducing models comprising; the speed of the front and rear wheels ω f / r estimated using a Kalman filter (KF); - the rotation rate of the front and rear wheels estimated using a KF; and - the final estimation of the tire slip ratio Sf / r using a second KF.

12. The slip ratio estimation method of claim 11, further comprising a speed estimation step, during which, for calculating the rear force, a speed estimator of the slip ratio estimation system expresses the speed according to a mathematical model of the type: [Math 10]: - Where: - Sf represents the estimated front slip rate; - ωf represents the front wheel speed; and - Rr represents the rolling radius of the front tire; and a mathematical model of the type: [Math 11]: Where: - S r represents the estimated back slip rate; - ω r represents the speed of the rear wheel; and - Rr represents the rolling radius of the rear tire. - 7 - 13. The method for estimating the slip ratio of claim 12, wherein, for estimating the forward force, the longitudinal force estimator expresses the longitudinal forces according to a mathematical model of the extended Kalman filter (EKF) type according to a mathematical model of the type: [Math 14]: - Where: - ρ represents the air density; - S represents the frontal area of ​​the vehicle; - Cd represents the drag coefficient; - ^ ^^represents the front longitudinal force; - Fxr represents the rear longitudinal force; - Froll represents the rolling resistance; and - m represents the mass of the vehicle.

14. The method for estimating the slip ratio of claim 13, further comprising a step of estimating the pseudo-slip stiffness K x of the tire, during which an estimator of the pseudo-slip stiffness K x of the slip ratio estimation system represents a virtual measurement of which: - for the rear wheels, the pseudo-slip stiffness estimator K x constructs the virtual measure from the estimated rear slip rate and the estimated front wheel speed rate using a KF; and - for front wheels, the pseudo-slip stiffness estimator Kx constructs the virtual measure from the estimated front force and the estimated front slip rate with a KF.

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