Method for determining a compensated acceleration gradient of a motor vehicle tire, method for monitoring the wear of a motor vehicle tire, and motor vehicle for implementing said methods
Patent Information
- Application Number
- FR2023002952
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2023-03-28
- Publication Date
- 2025-02-14
- Estimated Expiration
- 2043-03-28
Smart Images
Figure 00000018_0000 
Figure 00000018_0001
Abstract
Description
Title of the invention: Method for determining a compensated acceleration gradient of a motor vehicle tire, method for monitoring the wear of a motor vehicle tire and motor vehicle for implementing said methods Technical field
[0001] The present invention relates to a method for determining a compensated acceleration gradient of a motor vehicle tire, a method for monitoring the wear of a motor vehicle tire and a motor vehicle for implementing said methods. Prior art
[0002] Conventionally, the tire has at its outer surface a zone called “tread”, corresponding to the outer surface of the tire which is in contact with the road.
[0003] The tread includes a relief, also called a "sculpture", which allows in particular the evacuation of rainwater, snow, dust and heat, in order to limit the loss of grip of the tire or to avoid aquaplaning.
[0004] As the vehicle travels, the tire's tread wears down and becomes smooth, increasing the risk of loss of grip. Therefore, once the vehicle reaches a certain level of wear, it is necessary to replace the worn tire with a new one.
[0005] In order to detect the wear of a tire, it is known to use methods for monitoring the state of wear of the tire.
[0006] A first type of method, called “direct methods”, makes it possible to deduce the deterioration of the tire through the use of a device which wears at the same time as the tire wears.
[0007] These methods can be manual, by means of a colored wear strip integrated into the tread. This solution is however unsatisfactory insofar as the vehicle owner must visually check his tire in order to determine whether it needs to be replaced. Also, he must think about checking his tires himself. However, a person of average attention does not check his tires systematically and risks traveling, after a certain time, on board a vehicle equipped with worn tires offering little grip, which presents a clear danger.
[0008] Also, there is a growing need to autonomously monitor the wear of a tire, particularly in the context of vehicle fleet management, a model which is part of the growing development of new modes of mobility for which the driver is not the owner and where monitoring and maintenance are carried out by specific organizations on large sets of vehicles. Also, autonomous monitoring of the wear of a tire applies even more to autonomously driven vehicles.
[0009] Among the autonomous methods for monitoring tire wear, direct methods are also known, making it possible to deduce the deterioration of the tire and in particular implemented by measuring variations in the impedance of tubes which pass through the tire.
[0010] A second type of method, called “indirect methods”, makes it possible to deduce a state of wear of the tire by exploiting data from one or more parameters of the wheel or the motor vehicle.
[0011] Among the indirect methods of automatic monitoring of tire wear, there is a method known under the name "TDM", an English acronym for "Thread Depth Monitoring".
[0012] This method mainly uses signals from the systems embedded in the motor vehicle to deduce the state of wear of the tires.
[0013] These on-board systems include, in particular: - a positioning system, known as “GPS”, the English acronym for “Global Positioning System”, which enables the speed of the motor vehicle to be acquired, - a wheel speed sensor, known as “WSS”, the English acronym for “Wheel Speed Sensor”, and used to acquire the rotation speed of the wheels of the motor vehicle, - a tire pressure monitoring module, known as “TPMS”, the English acronym for “Tire Pressure Monitoring System”, which allows the tire pressure to be acquired.
[0014] Among the indirect methods for automatically monitoring tire wear, there is also known a method known under the name "TDS", an acronym for "Thread Depth Sensing", which aims to exploit more directly a signal which is perceived by the TPMS module and by which the state of wear of the tires will be drawn almost exclusively from this signal.
[0015] Unlike the aforementioned so-called “TDM” method, where a set of information acquired by different sensors of the motor vehicle is compiled, the so-called “TDS” method is applied as close as possible to the source of the parameter that one wishes to monitor, which makes it possible to reduce the chain of causality used to deduce a state. wear of the tires of the motor vehicle. Thus, thanks to the so-called "TDS" method, the chain of causality is much shorter and is therefore more precise than that obtained by the so-called "TDM" method.
[0016] A “TDS” method known from the prior art consists of exploiting the acceleration signal in the contact zone of the tire with the ground, to deduce therefrom a state of wear of the tire. This signal, also called “acceleration gradient in the contact zone”, corresponds to the acceleration signal seen by the TPMS module which is mounted in the tire and which is fixed at the level of the tread of the tire. The accelerometer of the TPMS module follows the acceleration gradient during the rotation of the wheel on which the tire to be monitored is mounted.
[0017] The value of the acceleration gradient, and the interpretation made of it, indicates the state of wear of the tire being monitored.
[0018] However, other parameters dependent on the context of use of the vehicle influence the value of the acceleration gradient and are likely to distort the interpretation of the acceleration gradient that can be made.
[0019] These parameters are, for example, the tire pressure, the load applied to the tire, the footprint quotient (known as “FQP”).
[0020] It is therefore appropriate to take into consideration, in the algorithm for calculating the acceleration gradient, the values of the parameters identified as being likely to influence the value of the acceleration gradient.
[0021] A solution aimed at learning these parameters in advance through specific preliminary tests carried out in the factory and then integrating them into the acceleration gradient calculation algorithm would not be optimal.
[0022] First of all, such a solution would be too long and too expensive to implement.
[0023] Then, the compensation parameters applied in the algorithm for calculating the acceleration gradient would apply systematically regardless of the tire considered, and this independently of the changes that there could be during the life of the tire. Statement of the invention
[0024] The present invention aims to overcome the drawbacks of the prior art, and to do so relates to a method for determining a compensated acceleration gradient (Gradcomp) of a motor vehicle tire, said motor vehicle comprising a set of sensors capable of acquiring signals representative of variables considered in a group comprising at least: the pressure of said tire, the footprint quotient of the wheel on which said tire is mounted and the speed of said motor vehicle, said acceleration gradient
[0025]
[0026]
[0027]
[0028]
[0029]
[0030]
[0031] compensated (Gradeomp being a function of: - a raw acceleration gradient (Gradbnit), - of momentary values (K^ of the variables considered - of reference values \ of the variables considered, G' / ) said method being remarkable in that the compensated acceleration gradient (Gradeomp) is also a function of compensation factors (Cj com{), specific to said variables considered and obtained by the execution of a learning phase according to which: - when said motor vehicle is in operation for each of said variables, sets of values of the raw acceleration gradient (Gradhr[lt) are acquired (step El) as a function of the evolution of said variables, - we calculate (step E2), from each of said sets of acquired values, said compensation factors (GY Thus, the present invention makes it possible to take into account, for the calculation of the acceleration gradient, the changes in the behavior of the tire throughout its lifetime within the motor vehicle. In other words, the present invention provides for acquiring online, that is to say during the life of the tire mounted on the vehicle in operation, sets of values of the acceleration gradient as a function of the evolution of the variables identified as influencing the value of the acceleration gradient. Based on these sets of acquired values, the compensation factors specific to each variable are calculated. The compensation factors are thus updated throughout the life of the tire. The integration of these updated compensation factors into the algorithm for calculating the compensated acceleration gradient makes it possible to take into account changes in behavior that occur during the life of the tire, regardless of the type of tire. Measuring the value of these parameters therefore makes it possible to compensate for the value of the acceleration gradient, and therefore to refine the conclusions relating to the estimated state of wear of the tire which results from it. According to optional characteristics of the method for determining a compensated acceleration gradient of a tire according to the invention: - in a first embodiment, said learning phase is executed continuously; - in a second embodiment, said acquisition step (step El) is carried out over a limited period;
[0032]
[0033] - said learning phase is initiated following a step of detecting one or more predefined trigger events (step Eli); - said triggering events are chosen from a group comprising at least: the age of said tire, the mounting / dismounting of said tire, the mounting / dismounting of the wheel comprising said tire, a predetermined distance traveled by said tire, a lack of parallelism of the wheel on which said tire is mounted, a detection of variation in the average temperature of said tire; - the method comprises a supervision phase (step E14) initiated when no triggering event has been previously detected and according to which, when said motor vehicle is in operation, over a limited period and for each of said variables, sets of values of the raw acceleration gradient {Gradb as a function of the evolution of said variables are acquired; - said supervision phase (step E12) is subsequent to the learning phase and, to determine the compensated acceleration gradient (Gradeomp), the compensation factors (Ci com ) acquired during said learning phase are used; - said step according to which the compensation factors (C- 1 from ri rr of each of the sets of acquired values (step E2) is obtained by applying a multilinear regression to each of said sets of acquired values (step El); - said learning phase comprises, prior to the step of executing the multilinear regression, a “severe” filtering step applied to the sets of values of the raw acceleration gradient (Grad^^) acquired; - said supervision phase includes a “low severity” to “moderate severity” filtering step applied to the sets of values of the raw acceleration gradient (Gradbnit) acquired. The invention also relates to a method for monitoring the wear of a motor vehicle tire, comprising a step of calculating the compensated acceleration gradient of said tire, remarkable in that said step of calculating the compensated acceleration gradient of said tire is obtained by the method of determining a compensated acceleration gradient of a tire according to the invention. The invention also relates to a motor vehicle which is remarkable in that it comprises hardware and / or software means for implementing said methods according to the invention. Brief description of the drawings
[0034] Other characteristics, aims and advantages of the invention will appear on reading the detailed description which follows, for the understanding of which reference will be made to the appended drawings in which:
[0035] [Fig.l] is a schematic view of a tire of a wheel of a motor vehicle according to the invention.
[0036] [Fig.2] shows a curve representative of the value of the radial acceleration Z of a tire pressure monitoring module to be monitored as a function of time l.
[0037] [Fig.3] shows a comparison of acceleration gradient signals of a first pneumatic and a second pneumatic.
[0038] [Fig.4] details the steps of a learning phase of the determination process of a compensated acceleration gradient according to the invention.
[0039] [Fig.5] is a graph representing a flowchart describing an example of an algorithm which can be implemented to execute the method for determining a compensated acceleration gradient according to the invention. Description of the embodiments
[0040] In the remainder of the description, elements having an identical structure or similar functions are designated by the same reference.
[0041] Reference is made to [Fig.l] which illustrates a tire 1 of a wheel of a motor vehicle according to the invention.
[0042] The tire 1, mounted in a motor vehicle wheel (not shown) rests on a ground 3.
[0043] The tire 1 has a nominal radius Rn defined by the radius of the tire 1 when the wheel is not loaded, i.e. not mounted in the vehicle.
[0044] The loaded tire 1 is deformed at the contact zone 5 between the tire 1 and the ground 3, at the level of its tread.
[0045] At this contact zone, the radius of the tire 1 is defined by the radius under load Rc, which corresponds to the distance between the axis of rotation 7 of the wheel and the ground 3.
[0046] The tire 1 comprises a module 9 for monitoring the tire pressure according to the invention, known under the name “TPMS”, an English acronym for “Tire Pressure Monitoring System” and making it possible to acquire the tire pressure.
[0047] In the wheel position illustrated in [Fig.l], the TPMS module 9 is in contact with the ground 3.
[0048] Figure 2 shows the curve showing an example of the value of the radial acceleration Z seen by the TPMS module 9 as a function of time r, for a complete rotation of the wheel on which the tire 1 is mounted.
[0049] The periodic signal representing the acceleration curve fluctuates around a static value Zo equal to the product of the nominal radius R„ defined by the formula next: Zo = R^w 2
[0050] Where: Zo is a static value of the wheel acceleration, Rn is the nominal radius of the wheel, eu is the rotational speed of the wheel.
[0051] At the moment when the TPMS module 9 almost comes into contact with the ground when the tire 1 rotates in the direction of the arrow shown in [Fig.l], a pinch zone 11 of the tire 1 (visible in [Fig.l]) is generated between the tire 1 and the ground 3.
[0052] This pinch zone 11 results in a local reduction in the nominal radius Rn of the tire 1 and in an increase in the rotation speed eu.
[0053] Given that the radial acceleration Z is equal to the product of the nominal radius Rtl of the tire 1 by the square of the rotation speed eu, the increase in the rotation speed eu leads to an increase in the radial acceleration Z seen by the TPMS module 9.
[0054] This increase in radial acceleration Z seen by the TPMS module 9 is represented in FIG. 2 by a local peak in radial acceleration Zj seen by the TPMS module 9 at the pinch zone 11.
[0055] When the TPMS module 9 has crossed the pinch zone 11 and arrives in the contact zone 5 with the ground 3, the acceleration Z2 seen by the TPMS module 9 is zero, the TPMS module 9 no longer being in motion.
[0056] In the same way, when the TPMS module 9 emerges from the contact zone 5 with the ground 3, a pinch zone 13 of the tire 1 (visible in FIG. 1) is generated between the tire 1 and the ground 3. This pinch zone 13 also results in a local reduction in the nominal radius Rn of the tire 1, leading to an increase in the radial acceleration Z seen by the TPMS module 9, represented in FIG. 2 by a local peak of radial acceleration Z3 at the level of the pinch zone 13.
[0057] The phenomenon reflecting the radial acceleration peaks Z] or Z3 seen by the TPMS module 9 is commonly called “overshoot” in English.
[0058] The portion 15 of the acceleration curve between Z and Z2 defines an entry acceleration gradient Gradmrtie in the contact zone 5 with the ground 3 and the portion 17 of the acceleration curve between Z2 and Z3 defines an exit acceleration gradient Gradmrtie of the contact zone 5 with the ground 3.
[0059] The acceleration gradient in the contact zone defines a signal commonly called “footprint” in English.
[0060] Refer to [Fig.3] showing a comparison of a “footprint” SI signal of a first tire, shown in bold, and a “footprint” signal S2 of a second tire.
[0061] The entry acceleration gradients Gradentrée and exit Gradsortie of the second tire each have a slope greater than those obtained for the first tire, which means that the radius of curvature of the second tire at the pinch zones 11, 13 is less than that of the first tire.
[0062] The method for determining a compensated acceleration gradient according to the invention is implemented in a motor vehicle (not shown in the figures) equipped with hardware and software means adapted to implement said method.
[0063] The software means comprise a computer program code means, comprising in particular the algorithm implemented to execute the method of the invention.
[0064] The hardware means comprise a set of sensors capable of acquiring signals representative of physical variables identified as influencing the value of the acceleration gradient.
[0065] The physical variables identified as influencing the value of the acceleration gradient may be so-called “instantaneous” variables, which are directly linked to the movement dynamics of the motor vehicle.
[0066] Among the instantaneous variables, we have identified, in particular: - the load applied to the wheel: in fact, when the motor vehicle is loaded, the load is taken up by the tire 1, particularly at the pinch zones 11, 13, - the footprint quotient: the footprint quotient of the wheel on the ground is defined by the ratio between the width of the contact patch on the ground and the rotation periodicity. Thus, the footprint quotient on the ground will be greater for a tire with a higher load or lower inflation pressure than for a tire with a lower load or higher inflation pressure, - inertial forces: it has been observed that the pinching of the tire 1 in its pinching zones 11,13 increases as a function of the wheel rotation speed, - the pressure / temperature couple: a tire tends to present a pinch at the pinch zones 11,13 which decreases according to its inflation pressure.
[0067] The physical variables identified as influencing the value of the acceleration gradient can also be so-called “quasi-static” variables, identified as being, in particular: - the thickness of the tire, - the age of the tire, - the mileage, - wheel installation parameters: problems with balancing, alignment or parallelism of the wheels of the motor vehicle.
[0068] These variables affect how the tire will react at the pinch zones 11, 13 with the ground.
[0069] According to the invention, the compensated acceleration gradient is a function of: - of the raw acceleration gradient at a time t, representative of the radial acceleration seen by the TPMS 9 module near the contact zone with the ground, - of momentary values of the variables considered, - of reference values of the variables considered, and - compensation factors specific to the variables considered updated throughout the life of the tire.
[0070] The raw acceleration gradient measurements acquired in the context of the present invention are: - the input acceleration gradient Gradentrée, or - the output acceleration gradient Gradsortje, or - the relative difference between the input acceleration gradient Gradentrée and the output gradient Gradsorüe.
[0071] The method which is the subject of the invention provides for obtaining the compensation factors by carrying out learning phases initiated throughout the life of the tire.
[0072] [Fig.4] details the steps of a learning phase of the method of the invention.
[0073] The learning phase comprises a first step E1 of signal acquisition, followed by a second step E2 of calculating the compensation factors from the acquired signals.
[0074] The signal acquisition step E1 aims to acquire, when said vehicle is in operation, for each variable identified as influencing the value of the acceleration gradient, sets of raw acceleration gradient values as a function of the evolution of these variables.
[0075] For example, when the variables monitored are the tire pressure of the wheel, the quotient of the wheel footprint on the ground and the speed of the vehicle, the evolution of the acceleration gradient is monitored as a function of each of these variables.
[0076] These variables are directly accessible by the TPMS module 9. Thus, the TPMS module 9 according to the invention comprises hardware and software means adapted to implement the method of the invention when the variables monitored are the tire pressure of the wheel, the footprint quotient of the wheel on the ground and the speed of the vehicle. The software means comprise a computer program code means, including in particular the algorithm implemented to execute the method of the invention, while the hardware means comprise an electronic module electronics comprising a tire pressure sensor and a radial accelerometer adapted to monitor wheel acceleration.
[0077] Thus, in an example of execution of the method according to the invention, the values of the acceleration gradient are acquired, thanks to the TPMS module 9, for each wheel of the vehicle and for a given period: - depending on the measured pressure values, - depending on the vehicle speed values, - depending on the ground footprint quotient values.
[0078] We thus obtain two- or three-dimensional point clouds representing: - the values of the acceleration gradient as a function of the pressure values measured over a given period, - the values of the acceleration gradient as a function of the vehicle speed values over a given period, - the values of the acceleration gradient as a function of the footprint quotient values.
[0079] When the values of the acceleration gradients are acquired, step E2 is initiated, according to which the compensation factors specific to each variable are calculated from each of the sets of acquired values.
[0080] This calculation step is carried out by means of mathematical functions including in particular a multilinear regression, applied to each set of acquired values and at the end of which a linear regression line representative of the compensation factor of the variable considered is obtained.
[0081] For reasons of memory resource limitations, the algorithm that determines the value of the compensation factor for the variable considered (step E2) is recursive, which means that the calculation is updated after each new measurement. More precisely, step E2 is an intermediate update iteration of the compensation factor considered. Step E2 is applied as many times as necessary until the end of learning conditions are reached. The value of the compensation factor of the last iteration then becomes the value retained for the calculation of the compensated acceleration gradient.
[0082] The same procedure is followed for all the vehicle's tires and for all the variables monitored.
[0083] The compensated acceleration gradient is then calculated by the following formula: Gradcomp = G radbrut ( t ) + 52 C i_com p *1 Kire r ” ( H]
[0084] Where: Gradcomp is the compensated acceleration gradient of the tire, Grad bru1(t) is the gross acceleration gradient of the tire at time t. Ci comp is the compensation factor specific to the variable i considered, Ki is a momentary value of the variable i considered, K; is a reference value of the variable i considered. lrej
[0085] Thus, when the variables monitored are tire pressure, wheel footprint quotient, and vehicle speed, the compensated acceleration gradient is calculated by the following formula: Gradc(mp = Gradbrat(l) + Cf^f P(iï)+CFPQ_ccmp^ + CvJvnf-v( / j)
[0086] According to one arrangement of the invention, the step of calculating the compensation factors during the learning phase can be supplemented by a filtering step initiated prior to the step of executing the multilinear regression.
[0087] The type of filtering can be so-called “severe” filtering and is applied to the sets of values of the raw acceleration gradient acquired as a function of the variables considered, in order to guarantee learning with input signals that are as clean as possible.
[0088] Filtering is obtained by using a statistical criterion related to the average standard deviation of the acceleration gradient. To do this, we choose to reject an instantaneous value of the acceleration gradient when it deviates from a predetermined value depending on this standard deviation.
[0089] For example, if the current value of the acceleration gradient is greater than the absolute value of three times the standard deviation, then it is rejected.
[0090] Thanks to the method according to the present invention, the instantaneous and quasi-static variables, identified as influencing the value of the acceleration gradient, are compensated in the algorithm for calculating the acceleration gradient, which makes it possible to only retain the wear of the tire in the calculation of the acceleration gradient, independently of the context of use of the tire.
[0091] According to one execution of the method of the invention, the acquisition step (step E1) can be executed over a limited period.
[0092] According to one arrangement of the invention, the learning phase can be initiated following the detection of triggering events.
[0093] As non-limiting examples, the events triggering a new learning phase are notably chosen from a group comprising at least: - the age of the tire: the TPMS module includes a clock indicating that a predetermined period has elapsed, then initiating a new learning phase, - the mounting / dismounting of the tire: this event can be detected by the TPMS module when the TPMS module detects that the tire pressure becomes atmospheric then returns to a predetermined tire inflation pressure, - the fitting / removal of a wheel: this event can be detected using the electronic calculator of a central computing unit of the motor vehicle, which detects a change in the location of a wheel in the vehicle, - a distance traveled: the TPMS module calculates the mileage traveled and issues an alert when a predetermined distance traveled is reached, - a wheel alignment defect: this event can be detected using the electronic calculator of the central computing unit of the motor vehicle, - detection of average temperature variation: this event can, for example, be detected when the temperature difference over a given period is greater than a predetermined threshold.
[0094] According to one arrangement of the invention, when no triggering event has been detected, a so-called supervision phase is initiated rather than a learning phase.
[0095] According to this supervision phase, sets of raw acceleration gradient values are acquired as a function of the evolution of the retained variables but the compensation factors are not calculated.
[0096] The compensation factors used for calculating the compensated acceleration gradient are then those acquired and stored during the learning phase. The supervision phase is therefore preferably subsequent to the learning phase.
[0097] In the case where the supervision phase is not subsequent to the learning phase and the compensation factors have not yet been calculated, compensation factors provided by the manufacturer for a type of tire considered can be used.
[0098] When a supervision phase is carried out, a less severe filtering than that applied for the learning phase is applied to the sets of values of the acquired raw acceleration gradient. This is a so-called “low-severity” to “moderately severe” filtering. As for the “severe” filtering, the “low-severity” to “moderately severe” filtering is applied using a statistical criterion related to the average standard deviation of the acceleration gradient, by rejecting an instantaneous value of the acceleration gradient when it deviates from a predetermined value depending on this standard deviation.
[0099] If the current value of the acceleration gradient is greater than the absolute value of three times the standard deviation, then it is rejected.
[0100] According to an alternative embodiment of the method of the invention, the acquisition step (step E1) can be executed continuously. In this embodiment, there is no longer any need for a trigger element to initiate the learning phase. The learning is then continuous, which means that there is no distinction between the learning phase and the tracking phase. In this way, the compensation parameters are updated at each iteration and are directly taken into account for the calculation of the compensated acceleration gradient.
[0101] Reference is made to [Fig.5] showing a flowchart describing an example of an algorithm that can be implemented to execute the method for determining a compensated acceleration gradient of a vehicle tire according to the invention.
[0102] The algorithm runs in successive iterations. The learning phase is initiated by default.
[0103] The first step of the algorithm is a step of waiting for the next iteration (step E10). When the cycle reaches a new iteration, the algorithm moves on to a step E11 during which the events that may have occurred since the last iteration are evaluated. To do this, the algorithm asks the question of whether at least one of the triggering events listed above in the description has occurred.
[0104] If the answer is yes, then a learning phase is initiated during which all previously calculated compensation factors are reset, i.e. all previously acquired compensation factors are erased.
[0105] In the learning phase, signals representative of physical variables identified as influencing the value of the acceleration gradient are acquired (step E1) using the dedicated sensors.
[0106] Multilinear regression is then applied to the values of the acceleration gradient as a function of the variables considered, in order to calculate the compensation factors (step E2).
[0107] We return to the initial step E10 and a new iteration of the algorithm is initiated with the previously calculated compensation factors stored.
[0108] The question of whether a triggering event is detected is asked (step El 1).
[0109] If the answer is no, then we continue with the previous phase, here a learning phase, (step E12).
[0110] The algorithm then asks the question of whether this is an end-of-learning step (step E13).
[0111] If the answer is no, we return to the initial step E10 and a new iteration of the algorithm is initiated.
[0112] If the answer is yes, the compensation factors are memorized and a supervision phase is switched to (step E14).
[0113] The algorithm then returns to the initial step E10 and a new iteration is initiated with the acquired compensation factors stored in step E14.
[0114] According to a particular application of the invention, the calculation of the compensated acceleration gradient measured by the method which has just been described is used in order to implement a method for monitoring the wear of a motor vehicle tire.
[0115] It should be noted that the determination of the compensated acceleration gradient can find other applications than that of monitoring the wear of a tire. For example, the determination of the compensated acceleration gradient can be used to detect a loss of grip on the ground of the motor vehicle, which may occur for example in an aquaplaning situation.
[0116] It goes without saying that the present invention is not limited to the sole embodiments of this method for determining a compensated acceleration gradient of a motor vehicle tire, of this method for monitoring the wear of a motor vehicle tire and of this motor vehicle for the implementation of said methods, described above solely as illustrative examples, but on the contrary it embraces all the variants involving the technical equivalents of the means.
Claims
Claims
1. Method for determining a compensated acceleration gradient (Gradcomp) of a motor vehicle tire (1), said motor vehicle comprising a set of sensors capable of acquiring signals representative of variables considered in a group comprising at least: the pressure of said tire (1), the footprint quotient of the wheel on which said tire (1) is mounted and the speed of said motor vehicle, said compensated acceleration gradient (Gradcomp) being a function of: - a raw acceleration gradient (Grad hrM), - momentary values (K() of the variables considered - reference values (g \ of the variables considered, lref) said method being characterized in that the compensated acceleration gradient (Gradcomp) is furthermore a function of compensation factors (Ç comp), specific to said variables considered and obtained by the execution of a learning phase according to which: - one acquires (step El),when said motor vehicle is in operation for each of said variables, sets of values of the raw acceleration gradient (Gradhtia) as a function of the evolution of said variables, - said compensation factors (C. Y, are calculated (step E2), from each of said sets of acquired values,
2. Method according to claim 1, characterized in that said learning phase is executed continuously.
3. Method according to claim 1, characterized in that said acquisition step (step E1) is carried out over a limited period.
4. Method according to claim 2, characterized in that said learning phase is initiated following a step of detecting one or more predefined trigger events (step Eli).
5. Method according to claim 4, characterized in that said triggering events are chosen from a group comprising at least: - the age of said tire (1), - the mounting / dismounting of said tire (1), - the mounting / dismounting of the wheel comprising said tire (1), - a predetermined distance traveled by said tire (1), - a lack of parallelism of the wheel on which said tire (1) is mounted, - detection of variation in the average temperature of said tire (1).
6. Method according to one of claims 4 or 5, characterized in that it comprises a supervision phase (step E14) initiated when no triggering event has been previously detected and according to which, when said motor vehicle is in operation, over a limited period and for each of said variables, sets of values of the raw acceleration gradient {Gradh as a function of the evolution of said variables are acquired.
7. Method according to claim 6, characterized in that said supervision phase (step E12) is subsequent to the learning phase and in that, to determine the compensated acceleration gradient (Gradf omp), the compensation factors (C- acquired during said learning phase are used.
8. Method according to any one of claims 1 to 7, characterized in that said step according to which the compensation factors (r\ | are calculated from each of the sets of acquired values (step E2) is obtained by applying a multilinear regression to each of said sets of acquired values (step E1).
9. Method according to claim 8, characterized in that said learning phase comprises, prior to the step of executing the multilinear regression, a “severe” filtering step applied to the sets of values of the raw acceleration gradient (Gradbt.ut) acquired.
10. Method according to any one of claims 6 to 9, characterized in that said supervision phase comprises a “low severity” to “moderate severity” filtering step applied to the sets of values of the raw acceleration gradient (Gradhru^ acquired.
11. Method for monitoring the wear of a motor vehicle tire (1), comprising a step of calculating the compensated acceleration gradient of said tire (1), characterized in that said step of calculating the compensated acceleration gradient of said tire (1) is obtained by the method according to any one of claims 1 to
12. IV. Motor vehicle characterized in that it comprises means hardware and / or software for implementing the method according to any one of claims 1 to 11.