Method for estimating the remaining mileage for the replacement of a brake pad installed in a motor vehicle

Abstract

A method for estimating the remaining mileage before the replacement of a brake pad installed in a motor vehicle, wherein the brake pad comprises a block made of wearable friction material, comprising acquiring a first factory-set data point (Mkr) relating to the maximum mileage for brake pad replacement, a second data point (Xmi) relating to the mileage of the brake pad at the current moment (i), a third data point (Wi.) relating to the wear of the brake pad at the current moment (z), and storing the historical trend of the second data point (Xmi) and the third data point (Wi), and feeding the first, second, and third data points (MKr, Kmi,) Wi) and the historical trend into a processing algorithm that computes an indicator (Kmpred) of the remaining mileage at the current moment (z).

Description

[0001] METHOD FOR ESTIMATING THE REMAINING MILEAGE FOR THE REPLACEMENT OF

[0002] A BRAKE PAD INSTALLED IN A MOTOR VEHICLE

[0003] DESCRIPTION

[0004] The present invention relates to a method for estimating the remaining mileage before the replacement of a brake pad installed in a motor vehicle.

[0005] The term "brake pad" refers to the block of wearable material used in a disc brake or a drum brake. The term "motor vehicle" refers to a car, truck, camper, van, motorcycle, scooter, or any other vehicle that uses brake pads within its braking system.

[0006] It is well known that the brake pads of motor vehicles wear out over time with use, and therefore at a certain point must be replaced in order to maintain consistent braking system performance.

[0007] Checking the wear condition of a brake pad is inconvenient, requires time, and involves specialized labour.

[0008] As a result, since brake pads are rarely inspected for wear, they are typically replaced after a predetermined number of kilometres, rather than when replacement is actually necessary.

[0009] Therefore, brake pads are often replaced prematurely, when they could still travel a significant number of kilometres.

[0010] This occurs because, as mentioned, inspecting the wear of the pads is impractical, and it is therefore preferred to replace them early to avoid the risk of completely depleting the wearable material, which would compromise the effectiveness of the braking system and the safety of the vehicle and its components — such as the brake disc.

[0011] However, calculating the number of kilometres required to fully wear out a brake pad is very complex, due to variations in wear depending on driving style: indeed, one can travel many kilometres on high-speed roads without wearing down the brake pads significantly, as braking is minimal. Conversely, the brake pads can undergo significant wear even over short distances, particularly when frequent or continuous braking is required — such as during downhill driving on mountainous roads or in heavily congested urban areas.

[0012] There are wear detectors available on the market which, through the use of a mechanical component, provide a brake pad replacement alert.

[0013] However, these devices do not provide real-time information on the brake pad wear status (i.e., remaining mileage) and therefore do not allow for preventive maintenance planning.

[0014] Moreover, these devices are not very accurate, as they do not provide continuous feedback but only a binary (on / off) signal, and in some cases may even lead to damage of the braking system due to delayed alerts.

[0015] There is therefore a need for a method that enables real-time estimation of brake pad wear, so that the driver can know the number of kilometres remaining before the pads need to be replaced.

[0016] In addition, being able to access a real-time estimate of the remaining mileage allows the user to coordinate brake pad replacement with other routine maintenance, thereby minimizing vehicle downtime.

[0017] The technical aim of the present invention is therefore to provide a method for estimating the remaining mileage before the replacement of a brake pad that eliminates the drawbacks of known techniques.

[0018] Within the scope of this technical aim, one object of the invention is to provide a method for estimating the remaining mileage before the replacement of a brake pad that allows the driver of a vehicle to know in real time how many kilometres can still be travelled before the brake pads are completely worn.

[0019] Another object of the invention is to provide a method for estimating the remaining mileage before the replacement of a brake pad that allows determination of exactly when a brake pad will be fully worn, so it can be replaced at the optimal time — thus avoiding early replacement (which wastes still-functional pads) or late replacement (which compromises safety). This technical aim, along with these and other objectives, is achieved by implementing a method for estimating the remaining mileage before the replacement of a brake pad according to claim 1. Other features of the present invention are defined in the subsequent claims. Additional features and advantages of the invention will become more apparent from the description of a preferred, though non-exclusive, embodiment of the method for estimating the remaining mileage before the replacement of a brake pad according to the invention, given by way of example and without limitation in the accompanying drawings, in which:

[0020] Figure 1 shows a graph illustrating the evolution over time of the first index;

[0021] Figure 2 shows a graph illustrating the evolution over time of the second index;

[0022] Figure 3 shows a graph illustrating the evolution over time of the third index;

[0023] Figure 4 shows a graph illustrating the evolution over time of the first, second, and third weights;

[0024] Figure 5 shows a graph illustrating the evolution over time of the remaining mileage indicator and the confidence band.

[0025] In all five figures, the y-axis represents the actual driving time of the vehicle.

[0026] With reference to the figures mentioned, a method is shown for estimating the remaining mileage before the replacement of a brake pad installed in a motor vehicle.

[0027] As is known, the brake pad includes a block made of friction material that wears down over time. The method involves acquiring a first data point (MKr), factory-set, relating to the maximum estimated mileage for brake pad replacement, a second data point (Km), relating to the mileage of the brake pad at the current moment z, a third data point (W), relating to the brake pad’s wear at the current moment z.

[0028] The first data point, MKr, corresponds to the maximum estimated mileage of the brake pad, typically indicated or recommended by the manufacturer. This first data point MKr is not a limiting value, as the maximum mileage a brake pad can handle is highly dependent on driving style. Consequently, MKr is only indicative and is mainly useful during the initial phase of the brake pad’s life.

[0029] As the pad continues to wear, the importance of this data point diminishes in favor of the other two: Km, and Wi.

[0030] The second data point, Km}, corresponds to the number of kilometres traveled by the vehicle since the brake pad was installed.

[0031] Kmi is obtained from the vehicle’s onboard sensors, such as the odometer, and / or is calculated by an algorithm using GPS data from an onboard electronic device.

[0032] The algorithm used to compute Kmi from GPS positioning may be executed by the vehicle’s onboard computer, a smartphone, another electronic device, or through cloud computing — thus, it can also run remotely, outside of the vehicle itself.

[0033] The third data point, Wi, corresponds to the instantaneous wear of the brake pad, which can be measured, for example, in millimeters of thinning of the friction material block, or as a percentage of wear relative to the maximum allowable wear.

[0034] The third data point, Wi, is computed by a known-type calculation algorithm powered by data originating from a temperature sensor integrated into the brake pad.

[0035] Alternatively, the third data point, Wi, can be detected by the vehicle's onboard sensors or derived using other known alternative methods for estimating brake pad wear.

[0036] The method then provides for storing the historical evolution of the second data point, Kmi, and the third data point, Wi, in order to retain their time-based progression for future use.

[0037] It is not necessary, however, to store the historical evolution of the first data point, MKr, since it is a fixed, factory-set value that does not change over time and therefore remains constant.

[0038] The method further includes feeding the first data point, MKr, the second data point, Kmi, the third data point, Wi, and the historical trends of the second and third data points into a processing algorithm that computes a Kmpred indicator, representing the remaining mileage at the current time z.

[0039] The processing algorithm of the Kmpred indicator therefore enables the estimation of how many kilometres the brake pad can still travel from the current time z, using the information provided by the first data point MKr, the second data point Km„ the third data point Wi, and the historical trends ofKrrii and Wi.

[0040] The operation of the method is thus conceptually similar to the functioning of remaining range indicators in vehicles, which show the distance that can still be traveled before running out of fuel. The algorithm for processing the Kmpred indicator of remaining mileage at the current time z can be executed by the vehicle’s onboard computer, by a smartphone or other electronic device, or via cloud computing; therefore, it can also be executed remotely and does not necessarily have to run onboard the vehicle.

[0041] Moreover, the processing algorithm for the Kmpred indicator can be executed either in real time as the second and third data points (Krnt, W) are sampled, or at a configurable execution frequency. The operation of the Kmpred processing algorithm is not affected by the execution frequency.

[0042] If the KmPred indicator algorithm is executed via cloud computing, scheduled execution is preferred, in order to process the data collected during a single trip.

[0043] A trip is defined as the period from when the engine is turned on to when it is turned off, regardless of the distance traveled or the time elapsed.

[0044] The KmPred indicator algorithm is capable of autonomously deciding not to execute if it detects that the value of the Kmpred indicator would remain unchanged.

[0045] The KmPred algorithm is able to use both real-time data obtained from the brake pad and data from external sources, or a combination of the two.

[0046] The output of the Kmpred indicator algorithm is the Kmpred indicator of remaining mileage at the current time i, which is composed of a combination of at least two of the following: - a first index (Kmm), which estimates the remaining mileage as the difference between the first data point MKr and the second data point Kmt;

[0047] - a second index (Kmm), which estimates the remaining mileage by computing the ratio between the second data point Km; and the third data point W,;

[0048] - a third index (Kmm), which estimates the remaining mileage by computing the incremental ratio between the second data point Kim and the third data point W,.

[0049] Typically, the combination uses all three indices (Kmm, Kmm, Kmm) simultaneously.

[0050] The method also includes assigning a confidence band (Kmband) to the Kmpred indicator of remaining mileage at the current time i.

[0051] The confidence band Kmband is calculated based on the difference between the maximum and minimum values among the three indices.- Kmm, Kmm, and Kmm.

[0052] The confidence band Kmband allows the accuracy of the Kmpred indicator to be assessed.

[0053] If, at a certain point on the graph, the confidence band Kmband closely overlaps the Kmpred indicator, it means the indicator at that point is very accurate and precise (since the three indices Kmm, Kmm, and Kmm are very similar to one another).

[0054] Conversely, if the confidence band Kmband significantly diverges from the Kmpred indicator at a certain point on the graph, it indicates that the three indices differ greatly from one another, and the KmPred indicator may be less accurate and reliable at that point.

[0055] Furthermore, the confidence band Kmband can be optimized by increasing or decreasing a multiplicative parameter a, thus allowing for greater or lesser sensitivity as needed.

[0056] If the confidence band Kmband is almost entirely overlapping the Kmpred indicator, it may be useful to increase the multiplicative parameter a in order to improve the graphical visualization of the confidence band Kmband-

[0057] In particular, the following formula applies to the confidence band: Kmband= Kmpred± a ■ [max(Kmwl, KmN2,

[0058] The first index Km.Ni is calculated by the following formula:

[0059] Kmwl= MKr — Kmi where, as mentioned above, MKr is the first data point, and Km\ is the second data point.

[0060] The first index, Kmm, therefore estimates the remaining mileage arithmetically based on the maximum mileage indicated by the manufacturer.

[0061] The second index, Kmm, is calculated using the following formula: where, as mentioned above, Kmtis the second data point, Wi is the third data point, and Wioo% is the maximum allowable wear of the brake pad, which can be expressed, for example, in terms of maximum pad thinning in millimeters or as a maximum wear percentage; in the latter case, it usually corresponds to 100%.

[0062] It is essential for the accuracy of the calculations that the units of measurement for Wtand Wwo% are consistent — i.e., both must be in either percentage or millimeters.

[0063] When Wi reaches the value of Wioo%, it means the brake pad is completely worn out.

[0064] In other words, the second index Kmm estimates how many kilometres remain before the brake pad is fully worn out, assuming the driver continues to drive with the same average driving style maintained up to the current moment z.

[0065] The third index Kmm is calculated using the following formula:

[0066] Km, — Kmj_A

[0067] KmN3 =Wi - WiA’Wloo%"Kmiwhere Km, is the second data point, Wi is the third data point, and Km^) and W(,_A) represent the second and third data points at a previous time i~A, used to calculate the incremental ratio, and Wioo% is the maximum allowable wear of the brake pad; this can be expressed, for example, in terms of maximum pad thinning in millimetres or as a maximum percentage of wear; in the latter case, it usually corresponds to 100%.

[0068] It is essential for the correctness of the calculations that the units of measurement for Wi and W(ioo%) are consistent, meaning both must be in either percentage or millimetres

[0069] The third index Kmm has a similar physical meaning to the second index Kmw, with the difference being that it does not consider all the kilometres driven and the total wear of the pad from its initial installation to the current moment z, but only the kilometres driven and wear occurring since time i~A. Thus, it only reflects the average driving style used in the most recent time period J.

[0070] Typically, the time period A corresponds to a few days of driving, while the full interval used in the second index Kmm may span several weeks or months.

[0071] If the time period A were set to include the entire mileage from the initial pad installation, the formula used to calculate the third index Kmm would become identical to that used for the second index Kmm, since Km(, and W^> would both be zero, representing the second Kmi and third data Wi points at the initial moment.

[0072] In other words, the second index Kmm provides a long-term estimate of the remaining mileage, because it takes into account all wear and all kilometres driven up to the current time z, whereas the third index Kmm offers a short-term estimate, considering only the wear and kilometres driven in the most recent i~A interval.

[0073] The combination of the three indices Kmm, Kmm, and Kmm used within the algorithm is a weighted average of the first index Kmm, the second index Kmm, and the third index Kmm-

[0074] In this weighted average the first weight 1 is assigned to the first index Kmm, the second weight 2 is assigned to the second index Kmm, and the third weight 3 is assigned to the third index KHIN3.

[0075] The first, second, and third weights (1, 2, and 3) are either independent or vary in a correlated way based on the historical trend of pad wear. In particular: at the beginning of the brake pad’s life, the first weight 1 is predominant compared to the other two; at an intermediate mileage, the third weight 3 becomes predominant; at the final mileage stage (i.e., when the pad is almost completely worn), the second weight 2 becomes predominant.

[0076] Therefore, the first weight 1 dominates in the early phase of the pad’s life, where the first index Kmm is considered the most reliable.

[0077] As more kilometres are driven, driving style becomes increasingly important, which is why the first index Km.Ni loses relevance (and the first weight 1 tends toward zero).

[0078] At intermediate mileage, short-term driving style (reflected in the third index Knim} becomes the most important factor for estimating remaining mileage, which allows estimating the remaining mileage based on the driving style adopted during the last interval A.

[0079] Consequently, the predominant weight is the third weight, 3.

[0080] When the pad becomes almost completely worn out, however, initial lifetime expectations (first index Krnm) and recent short-term driving style (third index Kmm) become less important; instead, it is essential to estimate the remaining mileage based on a comprehensive overview of the total kilometres driven and the current wear of the pad.

[0081] As a result, long-term driving style (second index KmN2) becomes most relevant, and the second weight 2 becomes predominant.

[0082] An incorrect reading of the first data point, an incorrect input, or even incorrect mileage information provided by the manufacturer will therefore only affect the Kmpred indicator in the early phase of the brake pad’s life; its influence diminishes over time as the vehicle is driven, due to the decreasing relevance of the first weight 1, and the corresponding reduction in the influence of the first index Krnm.

[0083] It has been observed in practice that the method for estimating remaining mileage before replacing a brake pad installed in a motor vehicle, as described in the invention, is particularly advantageous because it allows the vehicle’s driver to accurately estimate the remaining mileage of the brake pads.

[0084] Furthermore, it allows for maximum utilization of the brake pad up to near-complete wear, avoiding premature replacement while still ensuring high reliability, as it can accurately estimate the actual remaining mileage in real time.

[0085] This method also enables planned and predictive maintenance of the braking system.

[0086] The method as conceived is subject to numerous modifications and variations, all of which fall within the scope of the inventive concept. Moreover, all details may be replaced by technically equivalent elements. In practice, the materials used and the dimensions can be of any type, depending on specific needs and the current state of the art.

Claims

CLAIMS1. A method for estimating the remaining mileage before the replacement of a brake pad installed in a motor vehicle, wherein the brake pad comprises a block made of wearable friction material, characterized in that it involves acquiring a first factory-set data point (MKr) relating to the maximum estimated mileage for replacing the brake pad, a second data point (Kmi) relating to the mileage of the brake pad at the current moment (z), a third data point (Wi) relating to the wear of the brake pad at the current moment (z), and in that it includes storing the historical evolution of the second data point (Xm,) and the third data point (Wi), and feeding said first, second, and third data points {MKr, Km,, Wi) along with said historical evolution into a processing algorithm that calculates an indicator (Kmpred) of the remaining mileage at the current moment (z).

2. The estimation method according to claim 1, characterized in that said indicator (Kmpred) is composed of a combination of at least two of the following: a first index (Kmm) that estimates the remaining mileage as the difference between the first data point (MKr) and the second data point (Kmi), a second index (Kmm) that estimates the remaining mileage by computing the ratio between the second data point (Kmi) and the third data point (Wi), a third index (Kmxs) that estimates the remaining mileage by computing the incremental ratio between the second data point (Kmi) and the third data point (Wi).

3. The estimation method according to the preceding claim, characterized in that said first index (Kmm) is calculated using the following formula:Kmwl= MKr — Kmi where MKr is the first data point, and Km, is the second data point.

4. An estimation method according to any one of claims 2 and 3, characterized in that said second index (Kmw) is calculated using the following formula:where Kmm is the second index, Kmt is the second data point, W, is the third data point, and Wioo% is the maximum allowable wear of the brake pad.

5. Estimation method according to any one of claims 2 to 4, characterized in that said third index (KmN3) is calculated using the following formulawhere Km, is the second data point, W, is the third data point, Km^ and Wp-A) represent the second and third data points at a previous time (z'-J) with respect to the current moment (z), in order to calculate the incremental ratio, and Wioo% is the maximum allowable wear of the brake pad.

6. Estimation method according to any one of claims 2 to 5, characterized in that said combination is a weighted average of said first index (Kmm), said second index (KmN2), and said third index (Kmxs).

7. Estimation method according to the preceding claim, characterized in that in said weighted average, a first weight (1) is assigned to said first index (KmNi), a second weight (2) is assigned to said second index (Kmw), and a third weight (3) is assigned to said third index (Kmxs).

8. Estimation method according to the preceding claim, characterized in that at the initial mileage of the brake pad, said first weight (1) is predominant with respect to said second weight (2) and said third weight (3); at an intermediate mileage of the brake pad, said third weight (3) becomes predominant over said first weight (1) and said second weight (2); and at a final mileage of said brake pad, said second weight (2) becomes predominant over said first weight (1) and said third weight (3).

9. Estimation method according to any one of the preceding claims, characterized in that said third data point (Wt) is computed by a calculation algorithm powered by data originating from a temperature sensor integrated into the brake pad, or detected by onboard vehicle sensors.

10. Estimation method according to any one of the preceding claims, characterized in that said second data point (Xrn,) is detected by onboard sensors of the vehicle and / or calculated by an algorithm using GPS positioning data from an electronic device onboard the vehicle, said algorithm being executed by the vehicle's onboard computer, by a smartphone or other electronic device, or via cloud computing.

11. Estimation method according to any one of claims 2 to 10, characterized in that a confidence band (Kmband ) is assigned to said (Kmpred ) indicator of remaining mileage at the current time (i), said confidence band (Kmband) being calculated based on the difference between the maximum and minimum values among said first, second, and third indices (Kmm, Kmm, Kmx3).

12. Estimation method according to any one of the preceding claims, characterized in that said algorithm for computing a (Kmpred) indicator of remaining mileage at the current time (i) is executed by a vehicle’s onboard computer, by a smartphone or other electronic device, or via cloud computing.

13. Estimation method according to any one of the preceding claims, characterized in that the computation algorithm is executed either in real time upon sampling of the second and third data points (Km,, Wi) or at a configurable frequency.

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