Device and method for calculating and / or monitoring tire wear rate

Abstract

A method for calculating the wear rate of a tire implemented on a computer, comprising providing an aggregation of residual tread depth (RTD) evaluations based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle, generating a mathematical model for the wear rate of the tire based at least in part on the provided aggregation of RTD evaluations, and calculating the wear rate of the tire based at least in part on the generated mathematical model for the wear rate of the tire.

Description

Technical Field

[0001] The present disclosure generally relates to methods and apparatuses implemented on a computer for calculating and / or monitoring the wear rate of tires.

Background Art

[0002] The wear rate of tires is an essential factor contributing to traffic safety. If tire replacement is too late, it may even lead to dangerous traffic situations or accidents, with the potential for serious injury or death, and an increased risk of liability for compensation. Therefore, being able to replace tires at just the right time is not only a safety issue but also an economic one. Missing the appropriate time for tire replacement can result in significant costs due to potential accidents and damages, and premature tire replacement can lead to a waste of valuable resources. Appropriate tire life cycle management is also an issue of sustainability.

[0003] Determining the appropriate time for tire replacement is also an important success factor in vehicle logistics or fleet management. By being able to schedule tire replacement for vehicles in a timely manner, or by scheduling tire replacement in combination with the maintenance of other components of the vehicle, the downtime can be minimized as much as possible, and as a result, cost reduction and reliability improvement can be achieved, especially in terms of commercial applications. For example, when replacing tires, other components of the vehicle with only a short remaining life can also be replaced. In particular, when managing a fleet that includes multiple long-haul trucks, appropriately monitoring the wear rate of tires is important for determining, for example, which of the available trucks is most suitable for a particular route.

[0004] Therefore, being able to accurately predict the appropriate time for tire replacement is an important factor for making mobility and the transportation of goods safer, more environmentally friendly, reliable, and more economical.

[0005] In recent years, various methods and systems for monitoring the wear rate of vehicle tires based on tire-integrated sensors and data from vehicle dynamics have been developed. However, for at least some specific applications, there is still a need to improve the accuracy of tire wear estimation.

[0006] European Patent No. 3800072A1 describes a technique for measuring the tire tread wear rate by utilizing the trend of the peak value of tire acceleration. According to an embodiment of the present disclosure, a tire wear measuring device includes a signal receiver configured to measure the acceleration inside the tire for each point inside the tire, a signal analyzer configured to receive a signal from the signal receiver and estimate the tire tread wear rate using the peak value of the longitudinal acceleration perpendicular to the tire axis direction among the acceleration signals inside the tire, a transmitter configured to receive the analysis information regarding the tire tread wear rate from the signal analyzer and transmit the analysis information, and a control module configured to receive the analysis information from the transmitter and generate a control signal for the vehicle to which the tire is attached. The physical changes have been verified using a flexible ring tire model which is a mathematical model.

[0007] European Patent No. 3741589A1 discloses a tire wear estimation device. This tire wear estimation device includes a tire measurement system adapted to be attached to the inner surface of the tire or the inner surface of the tire. The tire measurement system includes a sensor adapted to detect the physical characteristics of the tire and an acquisition system for sampling the signal of the sensor, the acquisition system being adapted to acquire the perturbation of the sampling data induced by the tire contact patch for the sensor attached to the inner surface or the outer surface of the tire. In this acquisition system, since the sampling rate is sufficiently high, at least one vibration indicating the tread depth of the tire can be detected from the sample data within and / or around the perturbation.

[0008] With the rise of IoT (Internet of Things) products, a more robust and versatile toolchain is now available than ever before. Based on IoT technology, by handling tire mount sensors, a list of tire information and algorithms during use can be obtained. This can use tire mount sensor predictions as input to generate a more reliable and accurate tire wear estimation model, which can accurately predict the remaining tread depth, remaining mileage, or remaining time until tire replacement. SUMMARY OF THE INVENTION

[0009] The above object is achieved by various methods and apparatuses according to the present disclosure, which are computer-implemented methods and apparatuses for calculating and / or monitoring the wear rate of a tire.

[0010] According to a first aspect, the present disclosure provides a method for calculating the wear rate of a tire, including providing an aggregation of remaining tread depth (RTD) evaluations based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle; generating a mathematical model for the wear rate of the tire based at least in part on the provided aggregation of RTD evaluations; and calculating the wear rate of the tire based at least in part on the generated mathematical model for the wear rate of the tire.

[0011] According to an example of the first aspect, providing an aggregation of RTD evaluations further includes, for each of the RTD evaluations included in the provided aggregation of RTD evaluations, performing a series of aggregations based at least in part on data from at least one sensor attached to at least one tire of the vehicle, where the vehicle is within a preselected time span and / or within a travel distance from a predetermined point in time during operation of the vehicle and / or at least one tire of the vehicle is within a predetermined travel distance, performing a series of RTD evaluations, removing one or more outliers from the series of RTD evaluations performed, and calculating an average value with one or more outliers removed for the series of RTD evaluations performed. The average value can be any average value such as a Pythagorean mean, median, mode, midrange, weighted average, etc.

[0012] According to a further example of the first aspect, the provided aggregation of RTD evaluations includes two or more RTD evaluations.

[0013] According to another example of the first aspect, generating a mathematical model for the tire wear rate based at least in part on the provided aggregation of RTD evaluations further includes selecting one from a plurality of pre-stored algorithms based at least in part on the provided aggregation of RTD evaluations.

[0014] According to yet another example of the first aspect, selecting one from a plurality of pre-stored algorithms based at least in part on at least a portion of the provided aggregation of RTD evaluations to generate a mathematical model for the tire wear rate includes providing the aggregation of RTD evaluations as an input to the plurality of algorithms, executing the plurality of algorithms on the provided aggregation of RTD evaluations, and selecting one algorithm from the plurality of algorithms based at least in part on executing the plurality of algorithms.

[0015] According to another example of the first aspect, the data obtained from at least one sensor attached to at least one tire of a vehicle includes at least one of radial acceleration, rolling lateral acceleration, footprint size estimate, air pressure, tire temperature, tire measurement estimate.

[0016] According to another example of the first aspect, the method further includes providing information about the vehicle and verifying the output of a mathematical model for the tire wear rate based at least in part on the telematics information provided for the vehicle.

[0017] According to a further example of the first aspect, the telematics information about the vehicle includes at least one of vehicle usage, tire air pressure, tractor load, region, country, longitudinal acceleration, lateral acceleration, speed, GPS coordinates, odometer, road type, load, tire air pressure, gear shift, engine speed, wheel speed, throttle / brake pedal position, tire temperature, outside air temperature, steering angle.

[0018] According to another example of the first aspect, the tire wear rate includes at least one of the estimated remaining tread depth of the tire, the remaining driving distance of the tire, and the remaining time until tire replacement according to a set minimum tread depth.

[0019] In an example of the first aspect, the method further includes reporting at least one of the calculated tire wear rate, the estimated remaining tread depth of the tire, the remaining driving distance of the tire, and the remaining time until tire replacement according to a set minimum tread depth to a control system.

[0020] In a further example of the first aspect, the control system is disposed inside the vehicle.

[0021] In a further example of the first aspect, the control system is disposed outside the vehicle.

[0022] According to another embodiment of the first aspect, the aggregation of the provided RTD measurements is stored inside the vehicle or within one or more tires of the vehicle.

[0023] According to another embodiment of the first aspect, the aggregation of the provided RTD measurements is stored outside the vehicle.

[0024] According to a second aspect, the present disclosure provides an apparatus for calculating a tire wear rate, the apparatus comprising means for providing an aggregation of residual tread depth (RTD) assessments based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle, means for generating a mathematical model for the tire wear rate based at least in part on the provided aggregation of RTD assessments, and means for calculating the tire wear rate based at least in part on the generated mathematical model for the tire wear rate.

[0025] In an example of the second aspect, the apparatus comprises means configured to execute any of the methods disclosed herein.

[0026] Further benefits and advantages of the present invention will become apparent from a careful reading of the detailed description with appropriate reference to the accompanying drawings.

Brief Description of the Drawings

[0027]

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[0028] The present disclosure provides a computer-implemented method and apparatus for calculating a tire wear rate. The computer-implemented method and apparatus according to the present disclosure provide many advantages.

[0029] It should be noted that in the translation of the text in item , the number in "" is retained as "" in the translation because it is not clear what the specific intention of this number is in the original text. If it is a figure number, it may need to be adjusted according to the actual situation. Also, for the text tags like , they are directly retained as they are as required.As a method for estimating tire wear, for example, a method of estimating based on vehicle dynamics information sensed by a sensor attached to a vehicle tire and a statistical model has been developed. However, in some cases, there may be a bias in the estimated tire wear amount due to vehicle dynamics effects. Therefore, the estimation accuracy is not high in all cases.

[0030] To improve the accuracy of tire wear estimation, an approach based on vehicle telematics information, that is, the state of the vehicle's odometer, may be used. Generally, by relying on the vehicle's odometer, the driving distance of the tire can be estimated with very high accuracy. However, depending on the vehicle usage scenario, such as trucks (e.g., for lift axles) or vehicles that change tires many times during the typical life of a tire (e.g., changing from summer tires to winter tires), this conventional approach is more laborious. There are mainly three reasons for this.

[0031] First, when the tire management system depends on vehicle telematics information, it is necessary to pair new tires with each vehicle after tire replacement. In a large fleet, this procedure becomes an additional effort for the fleet manager and errors leading to inconsistent fleet management data are likely to occur.

[0032] Second, in order to accurately predict the driving distance and wear rate of trailer tires, individual ad-hoc monitoring systems are required. In the case of trucks, odometer data is only available for the tractor tires and not for the trailer. When the trailer is detached from the tractor and / or connected to another tractor, without individual monitoring, the accuracy of the tire wear estimation model decreases.

[0033] Thirdly, when the tire is attached to a lift axle, each method requires additional input data. If information about the state of the axle is not available to the model, the accuracy of the estimated number of kilometers each tire has traveled, and thus the estimated wear rate, also decreases.

[0034] The underlying invention discloses a computer-implemented method for calculating the wear state of a tire that combines the simplicity of a first approach based on tire sensors and the improved accuracy of a second approach based on vehicle telematics information. At the same time, the additional effort associated with the second approach can be significantly reduced.

[0035] A computer-implemented method for calculating the wear rate of a tire comprises providing an aggregation of residual tread depth (RTD) evaluations based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle, generating a mathematical model for the wear rate of the tire based at least in part on the provided aggregation of RTD evaluations, and calculating the wear rate of the tire based at least in part on the generated mathematical model for the wear rate of the tire.

[0036] Figures 1a, 1b, and 1c show the difference between the kilometers traveled by the vehicle itself and the kilometers traveled by the tires that are part of the lift axle. Thus, it shows a comparison between an approach based on vehicle telematics information performed without properly monitoring the lift axle of the truck and the method for calculating the wear rate of the tire disclosed herein. The horizontal axis of each of Figures 1a, 1b, and 1c represents time in units of time of a typical working day of the truck. The vertical axis of each of Figures 1a, 1b, and 1c represents the estimated travel distance of a particular axle of the truck. Further, the solid black line in each of Figures 1a, 1b, and 1c represents the estimated value of the travel distance by the conventional method. The dashed black line in each of Figures 1a, 1b, and 1c represents the estimated travel distance based on the computer-implemented method described herein.

[0037] As can be seen from Figure 1a, in the morning, the truck begins to drive towards the loading point located approximately 50 km away (during this distance, since the third axle is lifted (raised), the driving distance of each tire on the lifted axle is zero). At the loading point, the trailer is loaded. The driving distance of the tires on the lifted axle is actually 0 km. However, each truck has already driven approximately 50 km. When the truck is loaded, the lifted third axle is lowered to the ground and the load is distributed to all the tires. In many cases, when the truck is loaded, it heads to one or more locations for unloading.

[0038] In Figure 1a, the area indicated by (1) shows that the empty vehicle is heading towards the loading point. This vehicle has driven 50 km with the axle lifted. Therefore, the odometer reading has increased by 50 km, but the driving distance of the lifted tires remains zero.

[0039] In the example of Figure 1b, two different locations are identified. The first location is the one reached around 12:00, where the truck is only partially unloaded, so the third axle remains on the ground. The second location is the one reached around 14:00, where the truck is completely unloaded.

[0040] In Figure 1b, the area indicated by (2) shows the time when the vehicle is making normal deliveries with the lift axle pressed against the ground after loading. The first delivery is made at 12:00. The second delivery is made at 14:00, where the remaining goods are delivered. The odometer reading and the driving distance of the tires on the lift axle since the vehicle was first loaded were 220 km.

[0041] As shown in FIG. 1c, after the truck is completely unloaded, the third axle is lifted again and the truck returns to its original position. When the vehicle telematics information is used and the driving distance is simply estimated based on the state of the truck's odometer, the approach based on vehicle telematics shows significant drawbacks at this point. Compared with such a conventional method, it can be seen that there is a difference, where the driving distance of the lifted tire is 220 km (the horizontal dotted line at the bottom), while the driving distance of the vehicle is 420 km (the horizontal dotted line at the top). As a result, if the tire life is estimated using the approach based on vehicle telematics without using a monitoring system for the lifted axles of the trailer, the estimated driving distance of the tire will be much larger than the actual driving distance.

[0042] In FIG. 1c, the area indicated by (3) shows that the vehicle is empty after completing all deliveries. The lift axle is lifted and the vehicle drives 150 km back to the deposit. The odometer increases by 150 km, but each tire on the lifted axle has only driven 0 km during this time. The total driving distance of the odometer is 420 km, and the total driving distance of each tire of the lift axle is 220 km.

[0043] According to the invention on which the present application is based, it helps to avoid such errors and can more accurately determine the optimal time to replace the tires of the vehicle. As a result, according to the invention on which the present application is based, people's movement and the transportation of goods can be carried out more safely, environmentally friendly, reliably and cost - effectively.

[0044] [Estimation of remaining tread depth based on Tire Mount Sensor (TMS) information] FIG. 2 is a block diagram of a first system for estimating the remaining tread depth of at least one tire of a vehicle.

[0045] This system includes a Tire Mounting Sensor (TMS) that can acquire a list of in-use information of the tire. The in-use information of the tire includes at least one of radial acceleration, rolling lateral acceleration, estimated footprint size, air pressure, tire temperature, and tire measurement estimate. The in-use information of the tire is provided by the TMS to a statistical model that estimates the remaining tread depth of the tire.

[0046] Furthermore, tire information including at least one of tire manufacturer, tire pattern, tire specifications, tire size, tire mounting position, and tire retread information is provided to a statistical model that estimates the remaining tread depth of the tire.

[0047] Based at least in part on the in-use data and tire information of the tire, the statistical model estimates the remaining tread depth.

[0048] Figure 3 is a flowchart of a second system for estimating the remaining tread depth of at least one tire of a vehicle. The second system is an extended version of the first system.

[0049] Additional information may be provided to the statistical model for estimating the remaining tread depth. The additional information includes at least one of vehicle manufacturer, vehicle chassis, vehicle load, acceleration, speed, GPS coordinates, road type, engine load, gear shift, engine speed, wheel speed, throttle / brake pedal position, tire temperature, outside air temperature, steering wheel angle, and additional RTD measurements during operation.

[0050] After estimating the remaining tread depth based at least in part on the additional information, in-use data of the tire, and tire information, the model output may be further verified based at least in part on the additional information.

[0051] FIG. 4 is a block diagram of a third system that calculates a wear rate of a tire based on a first system or a second system that estimates a remaining tread depth of at least one tire of a vehicle. An aggregation of remaining tread depth evaluations performed at least in part based on data obtained from at least one sensor attached to one or more tires of the vehicle is provided to an adaptive algorithm suitable for generating a mathematical model of the wear rate of the tire based at least in part on the provided aggregation of RTD evaluations. In some examples, the system further provides data obtained from at least one sensor attached to at least one tire of the vehicle to the adaptive algorithm. The adaptive algorithm calculates an estimated remaining mileage of the wear rate of the tire or the tire life.

[0052] FIG. 5a is a diagram showing a first aspect of providing an aggregation of remaining tread depth evaluations based at least in part on data obtained from one or more sensors attached to one or more tires of a vehicle. The horizontal axis in FIG. 5a shows the mileage (KM), and the vertical axis shows the remaining tread depth. The upper left corner represents a state where the remaining tread depth matches the technical data when the tire is new, that is, when the tire is sold and the mileage of the tire is zero. As the mileage increases, the remaining tread depth decreases. For example, in the case of a new tire, the remaining tread depth is about 13 mm.

[0053] At a predetermined point in time during the use of the vehicle, or when the mileage of the tire reaches a predetermined value, a series of remaining tread depth evaluations is started. Each remaining tread depth evaluation in the series of remaining tread depth evaluations is performed at least in part based on data obtained from at least one sensor attached to at least one tire of the vehicle. The series of evaluations is within a selected time span and / or within the mileage from a predetermined point in time during the operation of the vehicle and / or the at least one tire of the vehicle is fully executed within a predetermined mileage.

[0054] For example, for at least one tire of a vehicle, a predetermined mileage is about 0 km, i.e., the tire is new. The preselected mileage is 1000 km. At the 0 km point, a first series of a plurality of remaining tread depth evaluations (see the red circles in Fig. 5a) is started. The plurality of tread depth evaluations are each performed within 1000 km, which is the preselected mileage.

[0055] Separate from the first series of RTD evaluations, Fig. 5a shows the provision of a plurality of additional series of RTD evaluations. For example, in Fig. 5a, three additional series of RTD evaluations at mileage 5000 km, 10000 km, and 15000 km are highlighted (see the red circles in Fig. 5a).

[0056] Fig. 5b shows another aspect of a method for providing an aggregation of remaining tread depth evaluations based at least in part on data obtained from one or more sensors attached to one or more tires of a vehicle. Again, the horizontal axis in Fig. 5b shows mileage (KM) and the vertical axis shows remaining tread depth. To reduce potential bias caused by the influence of vehicle dynamics in a plurality of series of RTD evaluations, outliers in the series of RTD evaluations performed are removed. For example, within each series of RTD evaluations, a group of single RTD evaluations showing a higher variance within the same system than others may be reduced to one point. The removal of outliers for each series of RTD evaluations can be done immediately after a series of RTD evaluations is completed, or periodically, e.g., within a preselected time span or within a preselected tire mileage, or when a series of evaluations is required as input for further calculations and / or estimations.

[0057] FIG. 5c shows a third aspect that provides an aggregation of remaining tread depth evaluations based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle. After removing outliers in a series of RTD evaluations, for each of the series of RTD evaluations, the average value of the RTD is calculated. The average value can be any average value, such as a Pythagorean mean, median, mode, midrange, weighted average, etc. The calculation of the average value may be performed immediately after a series of RTD evaluations is completed, or periodically, for example, within a preselected span or within a preselected tire mileage, or when each average value is required as input for further calculations and / or estimations.

[0058] FIG. 6 shows an aspect of generating a mathematical model of tire wear rate based at least in part on an aggregation of provided RTD measurements. Here, one of a plurality of pre-stored algorithms is selected. Selecting one of a plurality of pre-stored algorithms can include providing an RTD evaluation aggregation as input to the plurality of algorithms, executing the plurality of algorithms on the provided aggregation of RTD measurements, and selecting one algorithm from the plurality of algorithms based at least in part on the execution of the plurality of algorithms.

[0059] As an example, FIG. 6 shows three different graphs for three different pre-stored algorithms. For each of the three graphs, the remaining tread depth is shown on the vertical axis and the vehicle odometer and / or the mileage (KM) of one or more tires read from a tire mount sensor is shown on the horizontal axis. Again, the upper left corner of each graph represents the state where the tire is new, i.e., the remaining tread depth matches the technical data at the time of tire sale and the tire mileage is zero. As the mileage increases, the remaining tread depth decreases.

[0060] In each graph, each black dot represents the average value for a series of RTD evaluations, as described in connection with FIGS. 5a - 5c. For example, in the first graph, three average values for the RTD evaluation are shown, in the second graph, seven average values for the RTD evaluation are shown, and in the third graph, similarly, seven average values for the RTD evaluation are calculated.

[0061] The three algorithms shown in the three graphs above can be executed in series or at least partially in parallel. These algorithms are suitable for finding the optimal algorithm for generating a mathematical model of the tire wear rate, based at least in part on the aggregation of the provided RTD measurement values.

[0062] More generally, the example shown in FIG. 6 provides two or more aggregations of the RTD evaluation, based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle, after which various pre - stored algorithms such as a three - point algorithm, linear regression, exponential regression, logarithmic regression, or neural network can be executed at three points. Among the pre - stored and executed algorithms, the algorithm closest to the provided aggregation of the RTD evaluation is selected, and the wear rate of the tire is calculated based at least in part on the generated mathematical model of the tire wear rate.

[0063] In one aspect, the pre - stored algorithms can be continuously modified, improved, or replaced / updated by each provider.

[0064] [Provision of Aggregation of Remaining Tread Depth Evaluation, or Report of Tire Wear Rate] Figures 7a, 7b, and 7c show different ways of providing an aggregation of RTD evaluations or reporting a calculated tire wear rate. The aggregation of RTD evaluations and the transmission of data such as the calculated tire wear rate are illustrated by the black arrows. The black arrows may alternatively or additionally represent the provision of the aggregation of RTD evaluations or the transmission of the calculated tire wear rate when the calculations are performed inside the vehicle or by components outside the vehicle not shown in FIGS. 7a - 7c, i.e., the output of the applied mathematical model. In any case, the dashed arrows represent the provision of the aggregation of RTD evaluations, or the reporting of the calculated tire wear rate, or the model output.

[0065] As shown in FIG. 7a, in one aspect, the mathematical model of the tire wear rate may be generated outside the vehicle. Similarly, the tire wear rate may be calculated outside the vehicle, e.g., in a cloud computing environment. In this example, the aggregation of RTD evaluations can be provided to a cloud computing device. Inside the cloud computing device, the generation of the model and the calculation of the tire wear amount are performed, and the calculated tire wear amount can be reported to at least each vehicle.

[0066] As shown in FIGS. 7b and 7c, additionally or alternatively, the report may be transmitted to a user computing device such as a smartphone, notebook, PC, tablet, etc. or a fleet management platform.

[0067] In other examples, such as those shown in FIGS. 7b and 7c, the wear rate of the tire may be calculated within the vehicle or within the tires of the vehicle. In this case, the calculated wear rate of the tire may be reported from the vehicle or the tires of the vehicle to at least one of a user computing device or a fleet management platform. The fleet management platform / software may be stored and executed on a cloud computing device / server, respectively. In each fleet management software, there may be a digital representation of each vehicle in a fleet of at least one vehicle. The digital representation of the vehicle may be composed of technical data of the vehicle such as the brand, vehicle model, number of axles, tires in use, etc. The wear rate of the tire can be calculated using one of the methods according to the present disclosure, and the calculated wear rate of the tire can be reported to a fleet management application.

[0068] Within a fleet management application, such as that shown in FIG. 8, the calculated wear rate of the tire can be associated with the digital representation of each vehicle. This association can be done for each tire.

[0069] As a result, the application may be able to display the wear rate of each tire of the vehicle. In some embodiments, the remaining time or mileage of each tire of the vehicle may be displayed. Based on the displayed information, the user of the vehicle management platform may be provided with detailed information regarding at least one of the wear rate of the tire, remaining tread depth, remaining mileage, and remaining life time calculated for each tire of the vehicle. A warning message may be issued to the driver, fleet manager, or other user when certain conditions are met.

[0070] [A method implemented on a computer for calculating and / or monitoring the wear rate of a tire] FIG. 9a is a flowchart showing a method 900 for calculating and / or monitoring the wear rate of a tire implemented on a tire wear rate computer. The operations of this computer-implemented method may be implemented by a computing device.

[0071] At 910, it provides an aggregation of remaining tread depth (RTD) evaluations based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle.

[0072] At 920, it generates a mathematical model for the wear rate of a tire based at least in part on the provided aggregation of RTD evaluations.

[0073] At 930, it calculates the wear rate of a tire based at least in part on the generated mathematical model for the wear rate of the tire.

[0074] Figure 9b is a flowchart further showing the provision of an aggregation of RTD evaluations.

[0075] At 911, for each of the RTD evaluations included in the provided aggregation of RTD evaluations, it performs a series of RTD evaluations based at least in part on data from at least one sensor attached to at least one tire of a vehicle.

[0076] At 912, it removes one or more outliers from the series of RTD evaluations performed.

[0077] At 913, for the series of RTD evaluations performed, with one or more outliers removed, it calculates an average value.

[0078] In a further aspect, the provided aggregation of RTD evaluations includes two or more RTD evaluations.

[0079] In a further aspect, generating a mathematical model for the wear rate of a tire based at least in part on the provided aggregation of RTD evaluations further includes selecting one from a plurality of pre - stored algorithms based at least in part on the provided aggregation of the noted RTD evaluations.

[0080] In a further aspect, selecting one from a plurality of pre - stored algorithms based on at least a part of the provided aggregation of RTD evaluations to generate a mathematical model for the tire wear rate further includes providing the aggregation of RTD evaluations as an input to the plurality of algorithms, executing the plurality of algorithms on the provided aggregation of RTD evaluations, and selecting one algorithm from the plurality of algorithms based at least in part on executing the plurality of algorithms.

[0081] In a further aspect, the data obtained from at least one sensor attached to at least one tire of a vehicle includes at least one of radial acceleration, rolling lateral acceleration, footprint size estimate, air pressure, tire temperature, tire measurement estimate.

[0082] In a further aspect, the method further includes providing information about a vehicle and verifying an output of a mathematical model for the tire wear rate generated based at least in part on the telematics information provided about the vehicle.

[0083] In a further aspect, the telematics information about the vehicle includes at least one of vehicle usage situation, tire air pressure, tractor load, region, country, longitudinal acceleration, lateral acceleration, speed, GPS coordinates, odometer, road type, load, tire air pressure, gear shift, engine speed, wheel speed, throttle / brake pedal position, tire temperature, outside air temperature, steering wheel angle.

[0084] In a further aspect, the tire wear rate includes at least one of the estimated remaining tread depth of the tire, the remaining driving distance of the tire, and the remaining time until tire replacement according to a set minimum tread depth.

[0085] In a further aspect, the method further includes reporting to a control system at least one of the calculated tire wear rate, the estimated remaining tread depth of the tire, the remaining driving distance of the tire, and the remaining time until tire replacement according to a set minimum tread depth.

[0086] In a further aspect, the control system is disposed inside the vehicle.

[0087] In a further aspect, the control system is disposed outside the vehicle.

[0088] In a further aspect, the aggregation of the provided RTD measurement values is stored inside the vehicle or in at least one tire of the vehicle.

[0089] In a further aspect, the aggregation of the provided RTD measurement values is stored outside the vehicle.

[0090] In a further aspect, an apparatus for calculating a tire wear rate is provided. The apparatus includes means for providing an aggregation of remaining tread depth (RTD) evaluations, at least partially based on data obtained from at least one sensor attached to at least one tire of a vehicle; means for generating a mathematical model for the tire wear rate, at least partially based on the provided aggregation of RTD evaluations; and means for calculating the tire wear rate, at least partially based on the generated mathematical model for the tire wear rate.

[0091] In a further aspect, the apparatus includes means configured to execute any of the methods described herein.

Claims

1. A method (900) for calculating the tire wear rate, implemented in a computer, (910) To provide an aggregation of remaining tread depth (RTD) evaluation based at least in part on data acquired from at least one sensor attached to at least one tire of the vehicle, (920) To generate a mathematical model for the tire wear rate based at least in part on the aggregation of the provided RTD evaluations, Calculating the tire wear rate based at least in part on the generated mathematical model for the tire wear rate (930) Methods that include...

2. Providing the aggregation of the RTD evaluation (910) further means (911) Performing a series of RTD evaluations for each of the RTD evaluations included in the aggregate of provided RTD evaluations, at least in part, based on the data from the at least one sensor attached to the at least one tire of the vehicle, wherein the vehicle is within a pre-selected time span and / or within a distance traveled from a predetermined point in time during the operation of the vehicle and / or the at least one tire of the vehicle is within a predetermined distance traveled. The removal of one or more outliers from the series of RTD evaluations performed (912), With respect to the series of RTD evaluations performed, the mean value is calculated after removing one or more outliers (913) The method according to claim 1, including the method described in claim 1.

3. The method according to claim 1, wherein the aggregation of the provided RTD evaluations includes two or more RTD evaluations.

4. Generating the mathematical model for the tire wear rate based at least partially on the aggregation of the provided RTD evaluations is: The method according to claim 3, further comprising selecting one from a plurality of pre-stored algorithms based at least in part on the aggregation of the provided RTD evaluations.

5. Based on at least a portion of the aggregated RTD evaluations provided, one of several pre-stored algorithms is selected to generate the mathematical model for the tire wear rate. The aggregated RTD evaluation is provided as input to multiple algorithms, The above-mentioned multiple algorithms are executed on the aggregated RTD evaluations provided, Selecting one algorithm from the aforementioned plurality of algorithms, at least in part, based on executing the aforementioned plurality of algorithms. The method according to claim 4, further comprising:

6. The method according to claim 1, wherein the data obtained from at least one sensor attached to at least one tire of the vehicle includes at least one of radial acceleration, rolling lateral acceleration, footprint size estimate, air pressure, tire temperature, and tire measurement estimate.

7. To provide telematics information for the aforementioned vehicle, With respect to the said vehicle, verify the output of the mathematical model for the tire wear rate that was generated, based at least in part on the telematics information provided. The method according to claim 1, further comprising:

8. The method according to claim 7, wherein the telematics information for the vehicle includes at least one of the following: vehicle usage, tire pressure, tractor load, region, country, longitudinal acceleration, lateral acceleration, speed, GPS coordinates, odometer, road type, load, tire pressure, gear, engine speed, wheel speed, throttle / brake pedal position, tire temperature, ambient temperature, and steering angle.

9. The method according to claim 1, wherein the tire wear rate includes at least one of the following: the estimated remaining tread depth of the tire, the remaining mileage of the tire, and the remaining time until the tire is replaced according to a set minimum tread depth.

10. The method according to claim 9, further comprising reporting to a control system at least one of the calculated tire wear rate, the estimated remaining tread depth of the tire, the remaining mileage of the tire, and the remaining time until the tire is replaced according to the set minimum tread depth.

11. The method according to claim 10, wherein the control system is located inside the vehicle.

12. The method according to claim 10, wherein the control system is located outside the vehicle.

13. The method according to claim 1, wherein the aggregated RTD measurements provided are stored inside the vehicle or in at least one of the tires of the vehicle.

14. The method according to claim 1, wherein the aggregated RTD measurement values ​​provided are stored outside the vehicle.

15. A device for calculating the tire wear rate, Means for providing an aggregated assessment of remaining tread depth (RTD) based at least partially on data acquired from at least one sensor attached to at least one tire of a vehicle, A means for generating a mathematical model for the tire wear rate based at least in part on the aggregation of the provided RTD evaluations, A means for calculating the tire wear rate, based at least in part on the generated mathematical model for the tire wear rate. A device equipped with the following features.

16. The apparatus according to claim 15, comprising means configured to carry out any of the methods described in claims 1 to 14.

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