Apparatus and method for calculating and / or monitoring tire wear rate for a vehicle - Patents.com

Abstract

1. A computer-implemented method for calculating a tire wear rate of a vehicle, the method comprising: acquiring technical data of at least one tire of the vehicle; acquiring the technical data of the vehicle; acquiring data of at least one in-operation measurement of at least one characteristic of the at least one tire of the vehicle; and calculating a tire wear rate based at least in part on the acquired technical data of the at least one tire of the vehicle, the acquired vehicle technical data, and the acquired data of the at least one in-operation measurement of the at least one characteristic of the at least one tire of the vehicle according to a self-tuning mathematical tire wear model.

Description

[Technical field]

[0001] This disclosure is generally directed to computer-implemented methods and apparatus for calculating and / or monitoring tire wear rates for a vehicle. [Background technology]

[0002] Tire wear rate is an essential factor contributing to road safety. Changing tires too late can lead to dangerous traffic situations or even accidents, potentially resulting in serious injury or death as well as increased liability risk. The ability to change tires in a timely manner is therefore not only a safety issue, but also an economic one: missing the right moment to change tires can be costly due to potential accidents or damages, and changing them too early can add to fleet costs. Proper tire life cycle management is also a sustainability issue, since changing tires too early can lead to the waste of valuable resources.

[0003] Determining the right time to change tires is also a key success factor in vehicle logistics or fleet management. Scheduling the tire change of a vehicle in a timely manner, or being able to schedule the tire change in conjunction with the maintenance of other components of the vehicle, can minimize downtime, resulting in cost savings and increased reliability, especially in commercial applications. For example, when changing tires, other components of the vehicle that have a short remaining life can also be changed. Especially when managing a fleet that includes several long-distance trucks, proper monitoring of the wear rate of tires is important, for example, to determine which of the available trucks is best suited for a particular route.

[0004] Therefore, being able to accurately predict the right time to change a tire is a key factor in making mobility and the transportation of goods safer, greener, more reliable and more economical.

[0005] Conventional methods for monitoring tire wear rates by estimating tire wear are based on purely static mathematical models or expensive dedicated sensors in the tire, or a combination of both. The use of purely static mathematical models leads to a lack of accuracy and therefore makes it impossible to achieve the goal of accurately predicting the right time for tire replacement. To overcome the shortcomings of the approaches inherent in purely static mathematical models, sensor-based methods have been developed. However, each sensor is expensive and requires extra effort when changing tires. Furthermore, proper communication between tire sensors and the vehicle's electronics involves additional factors that complicate the entire system. Furthermore, when changing tires, the tire sensors may also need to be replaced. This not only leads to high costs but also to increased waste and therefore an increased environmental footprint.

[0006] US 2010 / 0133963 A1 discloses a method for making available data relating to tires of a vehicle, whereby vehicle data and / or tire data are made available by a vehicle memory unit of the vehicle, whereby the vehicle data are acquired by sensors arranged on the vehicle. The vehicle data and / or tire data comprise information on at least one characteristic of the tires of the vehicle. A tire wear probability for the tires of the vehicle is obtained as a function of the vehicle data and / or tire data.

[0007] US Patent No. 5,999,336 provides a tire wear estimation system based on sensor data. In the disclosed system, at least one sensor is attached to a tire to generate a first predictor variable. A look-up table or database stores data regarding a second predictor variable. One of the predictor variables includes at least one vehicle effect. A model receives the predictor variables and generates an estimated wear rate for the at least one tire.

[0008] US Patent No. 5,999,943 discloses a vehicle integrated predicted tread life indicator system and method of operation. In the disclosed method, data relating to one or more tread depth measurements is received. The one or more tread depth measurements are taken by a measurement device external to the vehicle. The one or more tread depth measurements represent the tread depth of at least one tread of at least one tire of the vehicle. The method includes associating a respective distance value with each of the one or more tread depth measurements and accessing a model correlating the one or more tread depth measurements to a predicted tread depth. The method includes determining, based at least in part on the model, an estimated distance at which the predicted tread depth is expected to equal or exceed a tread depth threshold. The method includes providing the estimated distance to a notification system of the vehicle. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] US Patent Application Publication No. 2017 / 0001482 [Patent Document 2] US Patent Application Publication No. 2018 / 0272813 [Patent Document 3] US Patent Application Publication No. 2019 / 0009618 Summary of the Invention [Problem to be solved by the invention]

[0010] With the advent of big data and machine learning, a more robust and versatile tool chain is available today. Based on these technologies, the tire wear rate of a vehicle can be monitored with greater precision, allowing accurate predictions of the remaining tread depth, the remaining mileage, or the remaining time until a tire change. [Means for solving the problem]

[0011] The above objectives are achieved by the present disclosure of various computer-implemented methods and apparatus for calculating and / or monitoring the rate of tire wear on a vehicle.

[0012] According to a first aspect, the present disclosure provides a first computer-implemented method for calculating and / or monitoring a tire wear rate of a vehicle. The method includes acquiring technical data of at least one tire of the vehicle, acquiring the technical data of the vehicle, acquiring data of at least one in-operation measurement of at least one characteristic of the at least one tire of the vehicle, and calculating a tire wear rate based at least in part on the acquired technical data of the at least one tire of the vehicle, the acquired vehicle technical data, and the acquired vehicle telematics information according to a self-tuning mathematical tire wear model. The computer-implemented method enables an accurate representation of tire wear by the self-tuning mathematical tire wear model.

[0013] According to one example of the first aspect, the method may further include selecting one of a plurality of pre-stored algorithms for calculating the tire wear rate.

[0014] According to yet another example of the first aspect, the method may further include executing a plurality of pre-stored algorithms for calculating a tire wear rate, and selecting from the plurality of algorithms an algorithm that produces a tire wear rate calculation that is closest to a tire wear rate based on data of the at least one in-operation measurement obtained.

[0015] According to another example of the first aspect, the self-tuning mathematical tire wear model is tuned based on data from at least one in-operation measurement of at least one tire of the vehicle.

[0016] According to yet another example of the first aspect, the self-tuning mathematical tire wear model is tuned based on a plurality of calculated tire wear rates, the plurality of calculated tire wear rates being based on data acquired from a plurality of vehicles, the acquired data including technical data of at least one tire of each of the vehicles, the technical data of each of the vehicles, and data of at least one in-operation measurement of at least one characteristic of the at least one tire of each of the vehicles.

[0017] According to another example of the first aspect, the method includes estimating a remaining tread depth and / or a remaining mileage of the tire and / or a remaining time until replacement according to a set minimum tread depth based on the calculated tire wear rate.

[0018] According to another example of the first aspect, the method includes reporting to a control system at least one of a calculated tire wear rate, an estimated remaining tread depth, a remaining mileage of the tire, and a remaining time until replacement according to a set minimum tread depth.

[0019] In one example of the first aspect, the control system is located in a vehicle.

[0020] In yet another example of the first aspect, the control system is located external to the vehicle and enables collection from a plurality of vehicles of at least calculated tire wear rates, estimated remaining tread depths, remaining mileage of the tires, and remaining time until replacement according to a set minimum tread depth.

[0021] In yet another example of the first aspect, performing an in-operation measurement of at least one characteristic of at least one tire of the vehicle includes measuring a remaining tread depth during operation, and preferably relating the measured remaining tread depth to an odometer wear rate of the vehicle.

[0022] According to another / second aspect, the present disclosure provides a second computer-implemented method of calculating and / or monitoring a tire wear rate of a vehicle, the method including the steps of transmitting technical data of at least one tire of the vehicle, transmitting the technical data of the vehicle, transmitting data of at least one operational measurement of at least one characteristic of the at least one tire of the vehicle, and obtaining a calculated tire wear rate based at least in part on the transmitted technical data of the at least one tire of the vehicle, the transmitted technical data of the vehicle, and the transmitted data of the at least one operational measurement of the at least one characteristic of the at least one tire of the vehicle, the calculated tire wear rate being calculated according to a self-tuning mathematical tire wear model.

[0023] According to one example of the second aspect, the method includes estimating the remaining tread depth and / or the remaining mileage of the tire and / or the remaining time until replacement according to a set minimum tread depth based on the calculated tire wear rate.

[0024] According to another example of the second aspect, the method includes reporting to a control system at least one of a calculated tire wear rate, an estimated remaining tread depth, a remaining mileage of the tire, and a remaining time until replacement according to a set minimum tread depth.

[0025] In one example of the second aspect, the control system is located in a vehicle.

[0026] In yet another example of the second aspect, the control system is located external to the vehicle and enables collection from a plurality of vehicles of at least calculated tire wear rates, estimated remaining tread depths, remaining mileage of the tires, and remaining time until replacement according to a set minimum tread depth.

[0027] In one example of the second aspect, the technical data for at least one tire of the vehicle includes at least one of a tire manufacturer, a tire model, a tire pattern, a tire specification, a tire size, a tire mounting position, retread information, and a tire batch number.

[0028] In yet another example of the second aspect, performing an in-operation measurement of at least one characteristic of at least one tire of the vehicle includes measuring a remaining tread depth during operation, and preferably relating the measured remaining tread depth to an odometer wear rate of the vehicle.

[0029] According to another / third aspect, the present disclosure provides a third computer-implemented method for calculating and / or monitoring a tire wear rate of a vehicle. The method includes obtaining technical data of at least one tire of the vehicle, obtaining the vehicle technical data, obtaining vehicle telematics information, and calculating a tire wear rate based at least in part on the obtained technical data of the at least one tire of the vehicle, the obtained vehicle technical data, and the obtained vehicle telematics information according to a data-driven mathematical tire wear model. The computer-implemented method enables an accurate representation of tire wear by the data-driven mathematical tire wear model.

[0030] According to one example of the third aspect, the method may further include obtaining data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle, where calculating the tire wear rate further includes calculating a tire wear rate based at least in part on the obtained data of the at least one in-operation measurement of the at least one characteristic of the at least one tire of the vehicle. With the in-operation measurements, the present invention further enables more accurate assessment of tire wear and, as described in more detail below, more accurate prediction of total tire life into the future.

[0031] Advantageously, the step of calculating the tire wear rate further comprises the step of selecting one of a plurality of pre-stored algorithms for calculating tire wear after obtaining data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle. Preferably, the pre-stored algorithm represents an algorithm that performs sufficiently well for calculating tire wear under predetermined conditions.

[0032] According to yet another example of the third aspect, the step of selecting one of a plurality of pre-stored algorithms for calculating a tire wear rate includes the steps of executing a plurality of algorithms for calculating tire wear, and selecting an algorithm from the plurality of algorithms that produces a tire wear calculation that is closest to a tire wear value obtained in at least one operational measurement.

[0033] According to yet another example of the third aspect, the step of calculating the tire wear rate further includes a step of calculating the tire wear rate according to a self-tuning model, the self-tuning model being tuned based on data of at least one operational measurement of at least one tire of the vehicle.

[0034] According to yet another example of the third aspect, the data-driven mathematical tire wear model can be trained based on a plurality of calculated tire wear rates, the plurality of calculated tire wear rates being based on data acquired from a plurality of vehicles, the acquired data including technical data of at least one tire of each of the vehicles, technical data of each of the vehicles, and telematics information of each of the vehicles. Thus, according to this aspect, the data-driven mathematical tire wear model is continuously adapted according to data provided by the plurality of vehicles.

[0035] According to yet another / fourth aspect, the present invention provides a fourth computer-implemented method for calculating and / or monitoring a tire wear rate of a vehicle, the method comprising the steps of transmitting technical data of at least one tire of the vehicle, transmitting the vehicle technical data, transmitting vehicle telematics information, and obtaining a calculated tire wear rate based at least in part on the transmitted technical data of the at least one tire of the vehicle, the transmitted vehicle technical data, and the transmitted vehicle telematics information, the calculated tire wear rate being calculated according to a data-driven mathematical tire wear model.

[0036] In one example of the fourth aspect, the computer-implemented method for calculating and / or monitoring a tire wear rate of a vehicle further includes transmitting data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle, and the calculated tire wear rate is further calculated based at least in part on the at least one in-operation measurement of the at least one characteristic of the at least one tire of the vehicle.

[0037] According to yet another example of the fourth aspect, a computer-implemented method for calculating and / or monitoring tire wear rate of a vehicle may include estimating a remaining tread depth and / or a remaining mileage of the tire and / or a remaining time until replacement according to a set minimum tread depth based on the calculated tire wear rate.

[0038] According to yet another example of the fourth aspect, a computer-implemented method for calculating and / or monitoring tire wear rate of a vehicle may include reporting to a control system at least one of the calculated tire wear rate, an estimated remaining tread depth, remaining mileage of the tire, and remaining time until replacement according to a set minimum tread depth.

[0039] In one example of the fourth aspect, the control system may be located in a vehicle.

[0040] In yet another example of the fourth aspect, the control system is located external to the vehicle and enables collection from a plurality of vehicles of at least calculated tire wear rates, estimated remaining tread depths, remaining mileage of the tires, and remaining time until replacement according to a set minimum tread depth.

[0041] According to yet another example of the fourth aspect, the technical data for at least one tire of the vehicle includes at least one of a tire manufacturer, a tire model, a tire pattern, a tire specification, a tire size, a tire mounting position, retread information, and a tire batch number.

[0042] According to yet another example of the fourth aspect, the computer-implemented method for calculating and / or monitoring tire wear rate of a vehicle as described above, comprising performing an in-operation measurement of at least one characteristic of at least one tire of the vehicle, further comprises measuring the remaining tread depth during operation, and preferably relating the measured remaining tread depth to an odometer wear rate of the vehicle.

[0043] According to yet another example of the fourth aspect, the vehicle information from the vehicle includes at least one of vehicle manufacturer, vehicle chassis, vehicle usage, tractor load, region, country, longitudinal acceleration, lateral acceleration, speed, GPS coordinates, odometer, road type, load, tire pressure, gear shifting, engine RPM, wheel speed, throttle / brake pedal position, tire temperature, outside air temperature, and steering wheel angle.

[0044] In a further fifth aspect, a first apparatus is provided comprising: means for acquiring technical data of at least one tire of a vehicle; means for acquiring the technical data of the vehicle; means for acquiring data of at least one measurement of at least one characteristic of the at least one tire of the vehicle; and means for calculating a tire wear rate based at least in part on the acquired technical data of the at least one tire of the vehicle, the acquired vehicle technical data, and the acquired data of the at least one measurement of the at least one characteristic of the at least one tire of the vehicle in accordance with a self-tuning mathematical tire wear model.

[0045] In a further sixth aspect, there is provided a second apparatus comprising: means for transmitting technical data of at least one tire of a vehicle; means for transmitting the vehicle technical data; means for transmitting data of at least one measurement of at least one characteristic of the at least one tire of the vehicle; and means for obtaining a calculated tire wear rate based at least in part on the transmitted technical data of the at least one tire of the vehicle, the transmitted vehicle technical data, and the transmitted data of the at least one measurement of the at least one characteristic of the at least one tire of the vehicle, wherein the calculated tire wear rate is calculated according to a self-tuning mathematical tire wear model.

[0046] In a further seventh aspect, a third apparatus is provided comprising: means for acquiring technical data of at least one tire of a vehicle; means for acquiring technical data of the vehicle; means for acquiring vehicle telematics information; and means for calculating a tire wear rate based at least in part on the acquired technical data of the at least one tire of the vehicle, the acquired vehicle technical data, and the acquired vehicle telematics information of the vehicle according to a data-driven mathematical tire wear model.

[0047] In a further eighth aspect, a fourth apparatus is provided comprising: means for transmitting technical data of at least one tire of a vehicle; means for transmitting the vehicle technical data; means for transmitting vehicle telematics information; and means for obtaining a calculated tire wear rate based at least in part on the transmitted technical data of the at least one tire of the vehicle, the transmitted vehicle technical data, and the transmitted vehicle telematics information of the vehicle, wherein the calculated tire wear rate is calculated according to a data driven mathematical tire wear model.

[0048] Still other benefits and advantages of the present invention will become apparent upon perusal of the detailed description with appropriate reference to the accompanying drawings. [Brief description of the drawings]

[0049] [Figure 1]FIG. 1 illustrates an example calculated tire wear chart for tires of a vehicle according to a self-tuning tire wear model based on a combination of tire information for at least one tire of the vehicle, technical information for the vehicle, and at least one in-operation measurement of tire wear in accordance with the present disclosure. [Diagram 2] Figure 2a illustrates an exemplary pre-stored algorithm in the form of three charts used during the process for accurately calculating tire wear according to the present disclosure, and Figure 2b illustrates the functional aspects of a self-tuning mathematical model for increasing the accuracy of the calculated tire wear rate according to the present disclosure in the form of another exemplary tire wear chart for a vehicle. [Diagram 3] 3a shows yet another chart of exemplary tire wear for a tire of a vehicle for a data-driven tire wear model based on a combination of tire information for at least one tire of the vehicle, vehicle technical information, and vehicle telematics information in accordance with the present disclosure. FIG. 3b shows yet another chart of exemplary tire wear for a tire of a vehicle for a data-driven tire wear model based on a combination of data of tire information for at least one tire of the vehicle, vehicle technical information, vehicle telematics information, and at least one in-operation measurement of remaining tread depth in accordance with the present disclosure. [Figure 4] Figure 4a shows various exemplary scenarios including loading conditions and road types that are the basis for the development of a data-driven mathematical tire wear model according to the present disclosure. Figure 4b shows exemplary values ​​determined for maximum distance traveled per mm of truck tread wear (km / mm) for various road types and loading conditions according to the present disclosure. Figure 4c shows the relative difference between exemplary values ​​determined for maximum distance traveled per mm of truck tread wear (km / mm) for various road types and loading conditions according to the present disclosure. [Diagram 5]FIG 5A illustrates an example data flow between a vehicle and a cloud for calculating tire wear and reporting that may be performed on the calculated tire wear, including estimated remaining tread depth, remaining mileage of the tire, and / or remaining time until replacement according to a set minimum tread depth, according to the present disclosure. FIG 5B illustrates an example data flow between a vehicle, a cloud, and a user computing device for calculating tire wear and reporting that may be performed on the calculated tire wear, including estimated remaining tread depth, remaining mileage of the tire, and / or remaining time until replacement according to a set minimum tread depth, according to the present disclosure. FIG 5C illustrates an example data flow between a vehicle, a cloud, and a control system for calculating tire wear and reporting that may be performed on the calculated tire wear, including estimated remaining tread depth, remaining mileage of the tire, and / or remaining time until replacement according to a set minimum tread depth, according to the present disclosure. [Figure 6] 1 illustrates an example fleet manager platform that may receive reports of calculated tire wear rates in accordance with the present disclosure. [Figure 7] 7 shows a flow chart illustrating a computer-implemented method 700 for calculating and / or monitoring tire wear rates of a vehicle in accordance with the present disclosure. [Figure 8] 8 shows a flow chart illustrating a computer-implemented method 800 for calculating and / or monitoring tire wear rates of a vehicle in accordance with the present disclosure. [Figure 9] 9 shows a flow chart illustrating a computer-implemented method 900 for calculating and / or monitoring tire wear rates of a vehicle in accordance with the present disclosure. [Figure 10] 1 shows a flow chart illustrating a computer-implemented method 1000 for calculating and / or monitoring tire wear rates of a vehicle in accordance with the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0050] The present disclosure provides a computer-implemented method and apparatus for calculating and / or monitoring the wear rate of tires of a vehicle. The computer-implemented method and apparatus according to the present disclosure provide many advantages. The present invention allows for the determination of the optimal time to change tires of a vehicle, thus making the mobility of people and the transportation of goods safer, greener, more reliable and more economical.

[0051] Self-tuning mathematical tire wear models versus purely statistical models FIG. 1 shows a chart illustrating an exemplary tire wear profile for a vehicle tire. Along the horizontal axis, the mileage of the tire is shown. Along the vertical axis, tire characteristics such as remaining tread depth, RTD, etc. are shown. In the upper left corner, the tire is in a new condition, i.e. in this example, the tire has a remaining tread depth of 13 mm and a mileage of 0 km. A remaining tread depth of 3 mm indicates the end of life of the tire in this example. The dashed and dotted lines represent the estimated tire wear calculated by a pure statistical model based on tire parameters such as tire size, tire position, etc. In this example, it is shown that the average tire mileage calculated by using a pure statistical model is about 180,000 kilometers (shown by the dashed and dotted line), with a deviation of about 80,000 kilometers in both directions, so that the statistical mileage ranges from 100,000 kilometers to 260,000 kilometers (shown by the thin dashed line). It can therefore be seen that the exact time when the minimum tread depth is reached cannot be assessed with any precision.

[0052] Fig. 1 further shows that a measurement of the remaining tread depth at a point in time and taking this measurement into account when calculating the tire wear rate can improve upon the conventional purely statistical method. In the illustrated example, an in-service measurement is performed at about 110,000 kilometers (shown by the solid triangle). The in-service measurement indicates that the tire wear is below average, in other words that the current tire wear rate (in this case the remaining tread depth) is above average.

[0053] In order to assess when the tire reaches its end of life, i.e. a remaining tread depth of 3 mm, according to the present disclosure, a self-tuning mathematical tire wear model is applied. The self-tuning mathematical tire wear model is provided with the tire's characteristics measured at a certain mileage (here, the RTD measured at 110,000 kilometers) and further input data on the vehicle's tire's technical data and the vehicle's technical data. With these data, the self-tuning mathematical model calculates the future expected tire wear, for example until the tire reaches its end of life (for example until a certain minimum RDT, for example 3 mm is reached). It is thus possible to calculate how the tire's remaining tread depth develops with respect to the mileage. The calculated remaining tread depth of the tire is shown by a solid black line, and its deviation is shown by a dashed double-dot line. As can be seen, the average mileage of the tire in this example is about 200,000 kilometers, with only a deviation of about 40,000 kilometers in both directions, so that the statistical mileage is improved to a range of 160,000 kilometers to 240,000 kilometers. Thus, the deviation range has been reduced by 50%, allowing a more accurate assessment of the exact point in time when the minimum tread depth is reached.

[0054] The input data regarding technical data of the vehicle's tires may include one or more of: tire manufacturer, tire pattern, tire specification, tire size, tire mounting location, retread information, country, or region.

[0055] The input data regarding the technical data of the vehicle may include one or more of the vehicle manufacturer, the vehicle chassis, the vehicle usage, and / or the tractor load.

[0056] Additionally, the self-tuning mathematical model uses as input data from at least one in-service measurement of at least one characteristic of the tire, such as the actual remaining tread depth of the tire and the mileage reading from an odometer.

[0057] By executing a computer-implemented method for calculating and / or monitoring a tire wear rate of a vehicle, technical data of at least one tire of the vehicle and data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle are obtained, and a tire wear rate is calculated according to a self-tuning mathematical model based on the obtained technical data of the at least one tire of the vehicle and the obtained data of at least one in-operation measurement of at least one characteristic of the at least one tire of the vehicle.

[0058] The above steps can be repeated with further in-service measurements after the tire has undergone further mileage (as described in more detail below). Typically, each iteration of in-service measurements and new calculations of tire wear rates provides greater precision as to when a tire will reach end of life, and therefore greater predictability for, for example, truck route planning.

[0059] 2a illustrates an example embodiment of a self-tuning mathematical model that a computer-implemented method for calculating and / or monitoring tire wear rate of a vehicle may follow to calculate tire wear and / or tire wear rate. In one embodiment, the calculation of tire wear rate may be performed after obtaining data of at least one in-operation measurement of a tire characteristic, such as remaining tread depth combined with respective mileage readings from the vehicle's odometer.

[0060] In one embodiment, the calculation of the tire wear rate may include the execution of multiple pre-stored algorithms. Figure 2a shows three different charts for three different pre-stored algorithms. For each of the three graphs, the vertical axis shows the remaining tread depth (RTD) and the horizontal axis shows the mileage (KM) of at least one tire as read from the vehicle's odometer. The top left corner of each graph represents the state of the remaining tread depth according to the technical data at the time of sale of the tire when the tire is new, i.e., the tire has 0 mileage. As the mileage increases, the remaining tread depth decreases.

[0061] Each solid circle in the graphs represents a measurement of remaining tread depth associated with the vehicle's odometer status. For example, in the first graph, two previous in-service measurements have been taken, in the second graph, six previous in-service measurements have been taken, and in the third graph, six previous in-service measurements have been taken. Each open circle in the graphs represents a calculated RTD for a particular mileage for which further in-service measurements have been scheduled based on previous measurements (solid circles) and the applied algorithm. Each cross (X) in the graphs represents the most recent in-service measurement of RTD at a particular mileage. For ease of illustration, a cross (X) is shown over the open circle in each of the graphs.

[0062] The three graphs show three algorithms that can be run in parallel and are suitable for finding the best algorithm for prediction. In this example, the first graph shows a significant offset between the calculated RTD (open circle) and the RTD measurement (cross). Therefore, the application of the "three points" algorithm, where the last two measurements are used for the calculation of the future RTD, is inaccurate in this case. The second graph, showing the linear regression model, shows a small offset between the calculated RTD (open circle) and the RTD measurement (cross). Therefore, in this case, this algorithm may be preferable to the first algorithm for the calculation of the future RTD, taking the current measurement as a further measurement point for the calculation of the future RTD. Finally, the third graph, showing the exponential regression model, shows an even smaller offset between the calculated RTD (open circle) and the RTD measurement (cross). Therefore, in this case, this may be the most preferable algorithm for the calculation of the future RTD. Again, the current measurement (open circle) is taken as a further measurement point for the calculation of the future RTD.

[0063] More generally, the example shown in Figure 2a illustrates that various pre-stored algorithms may be executed after obtaining data from at least one measurement of remaining tread depth associated with the vehicle's odometer status, such as three-point, linear regression, exponential regression, logarithmic regression, or neural network. The pre-stored and executed algorithm that produces a calculated tire wear rate (e.g., RTD) for a particular mileage that is closest to the measured tire wear rate (e.g., RTD) at that particular mileage is selected for calculation of the tire wear rate over the life of the tire.

[0064] Algorithms can utilize all available in-service measurements of tire characteristics and, in combination with other input information provided to the model, such as tire manufacturer, tire pattern, tire specification, tire size, tire mounting position, retread information, vehicle manufacturer, vehicle chassis, vehicle usage, tractor load, country, or region, to estimate which pre-stored algorithm best describes each characteristic of the tire in service. In one embodiment, curve fitting techniques can be applied. Each tire characteristic is then extrapolated using the algorithm that produces the best fit result for each value of one or more in-service measurements. Based on this extrapolation, the remaining life of the tire can be determined. The end-of-life value of the tire can be defined as 3 mm tread depth or any other value defined by the user.

[0065] In one aspect, the pre-stored algorithms of the self-tuning mathematical models may be continually modified, improved, or replaced / updated by respective providers.

[0066] Fig. 2b shows a graph illustrating yet another aspect of the self-tuning mathematical model. On the horizontal axis, the mileage of the vehicle's tires in km and three tests performed at a particular odometer state are shown. On the vertical axis, the remaining tread depth is shown in the form of a discrete value for a new tire ("New Tire"). Furthermore, three remaining tread depth states ("RTD1", "RTD2", "RTD3") when the three tests are performed, and another discrete value of the remaining tread depth at the end of the tire's life (TWI, Tread Wear Indicator) are shown.

[0067] When a tire is new, the self-tuning model can be fed tire and vehicle data at 1, and a trend in tire wear and remaining tread depth can be calculated (shown by dashed line A). A first inspection is scheduled at a particular mileage. At this mileage, the self-tuning model has calculated a particular RTD (shown by 2). An actual in-service measurement of remaining tread depth performed at this particular mileage results in a lower value than that calculated. This value is shown as "RTD1" at 3. As a result, as shown by the downward arrow, the self-tuning model adjusts its prediction and the calculated tire wear is greater than originally predicted.

[0068] Thus, if the calculated value of a tire characteristic, e.g. "RTD1", deviates from the actual measured value of each tire characteristic, the mathematical model self-corrects, i.e., the mathematical model is tuned. Tuning the mathematical model involves making the calculated value of the tire characteristic equal to the actual measured value of that characteristic at the mileage at which the measurement is made. Tuning the model may further include fitting the model to the most recent actual measured value of each tire characteristic and at least one previous measured value of each tire characteristic, if available.

[0069] The above procedure can be repeated for each additional in-service measurement of each tire property, thus the second and third test and measurements "RTD2", "RTD3", respectively. These values ​​are also designated by odd numbers 5 and 7 (and 9 for TWI). Furthermore, the calculated values ​​are designated by even numbers, here 4, 6, and 8. This allows for continuous self-tuning of the model. The more measurements performed, the more accurate the model and the smaller the calculation error.

[0070] In one aspect, the self-tuning mathematical model for calculating tire wear rate may be continuously modified / improved by adaptation of big data methods and / or machine learning techniques. For example, the self-tuning mathematical tire wear model may be trained based on a plurality of calculated tire wear rates, the plurality of calculated tire wear rates being based on data acquired from a plurality of vehicles, the acquired data including technical data of at least one tire of each of the vehicles, technical data of each of the vehicles, and data of at least one in-operation measurement of at least one characteristic of the at least one tire of each of the vehicles.

[0071] Over time, a lot of data can be acquired, representing a myriad of driving scenarios involving many different vehicles under a variety of different traffic conditions. Each data can be used to continuously train a self-tuning mathematical method for calculating tire wear rates, thus generating added value. Based on all the available data, machine learning techniques can determine the wear pattern for a particular scenario.

[0072] In another aspect, the self-tuning mathematical model can be manually modified, for example, if a particular tire product is found to behave differently than previously expected under certain scenarios. The large number of inputs to the model can even limit the modification to a particular batch of tire models from a particular manufacturing facility. For example, a particular batch of tire models manufactured at a particular facility may be found to perform differently than other batches from other facilities. In such a case, the behavior of a single batch of tires can be taken into account by the model.

[0073] Another important factor influencing tire wear is the country in which the vehicle with the monitored tires is driven. Some countries may have flat and wide roads, while others may have narrow and curvy roads in relation to mountain ranges. Another country-specific factor influencing tire wear is the climate, which may vary, for example, whether a particular country has long summers and warm winters or cold winters and short summers.

[0074] Additionally, other factors such as humidity or dryness can affect tire wear.

[0075] Tire wear may also vary depending on the quality of infrastructure that a country has to offer. Some countries may invest heavily in road infrastructure, while others may invest less. In the latter, lack of maintenance may result in damage to the road surface, which may further contribute to a shorter tire lifespan. Another factor that may affect tire lifespan may be the quality of driver education in each country. Yet another factor to consider may be the different driving rules in different countries. Some countries, such as the Netherlands, have very strict speed limits even on highways, while other countries, such as Germany, have no speed limits on highways. This may result in very different stresses on tires depending on the country.

[0076] Furthermore, it must be taken into account that the boundary conditions in different countries may not be very uniform. Therefore, regions may also be used to calculate the tire wear as accurately as possible. In many countries, the quality of infrastructure in large economic centers may differ from the countryside. Additionally or alternatively, some areas of a country may be flat, while others may be mountainous. In either case, tire wear patterns may vary greatly. Therefore, regional data may be considered more precise than national data.

[0077] Data-driven mathematical modeling of tire wear with and without in-motion measurements Figures 3a and 3b show other charts of an exemplary calculated tire wear profile for a vehicle tire. Corresponding to Figure 1, along the horizontal axis the mileage of the tire is shown. Along the vertical axis the remaining tread depth is shown. In the upper left corner the tire is in a new condition, i.e. (in this example) 0 km mileage and (in this example) 13 mm remaining tread depth. When the remaining tread depth reaches a value of (in this example) 3 mm, the tire reaches the end of its life.

[0078] The dashed lines represent the range of tire mileage calculated according to a purely statistical model. The two sets of dashed and long-dotted lines represent the predicted range of tire mileage according to a data-driven mathematical model taking into account the vehicle's telematics information. In particular, the vehicle's telematics information may include data on the vehicle's load (e.g., empty or loaded) and the vehicle's usage (regional or long-distance scenario).

[0079] The vehicle's telematics information allows the tire wear rate to be calculated even more accurately. For example, tire wear may differ if the vehicle is used for short distances with heavy traffic or mainly for long distance trips where especially trucks often have a constant speed for long periods of time. Furthermore, the measured tire pressure can be taken into account as a further factor contributing to tire wear. If the pressure is for example too low (i.e. deviating from the recommended tire pressure), the contact area of ​​the tire with the road increases. As a result, tire wear is accelerated. Information about the measured tire pressure is even more important in connection with the tractor load of the vehicle. As is known, the recommended tire pressure varies depending on the load of the vehicle. Thus, if the vehicle is heavily loaded but the measured tire pressure is within the range of the recommended tire pressure for an unloaded vehicle, tire wear will still increase.

[0080] Generally, i.e. without considering tire pressure, load affects tire wear, so a heavily loaded vehicle will experience more tire wear than an unloaded vehicle.

[0081] The predicted remaining mileage of tires varies greatly with the vehicle load and use case. When the vehicle is loaded and used in a local scenario, the remaining mileage is much shorter than when the vehicle is empty and used in a long distance scenario. Taking into account the vehicle's telematics information, the remaining mileage prediction accuracy can be obviously improved.

[0082] For example, if the vehicle is used in a long distance scenario in an unladen state (see dashed and dotted lines with wide dots), the average mileage of the tires is approximately 235,000 kilometers (see dashed and dotted lines with wide dots) with a deviation of approximately 20,000 kilometers in both directions, resulting in a statistical mileage in the range of 215,000 kilometers to 255,000 kilometers (see dashed and dotted lines with wide dots).

[0083] Similarly, when the vehicle is loaded and used in a regional scenario (see dash-dotted and double-dashed lines with small dots), the average mileage of the tires is 120,000 kilometers (see dash-dotted line with small dots) with a deviation of about 20,000 kilometers in both directions, so the statistical mileage ranges from 100,000 kilometers to 140,000 kilometers (see dash-dotted line with small dots).

[0084] As a result, the exact point in time at which the minimum tread depth is reached or the distance traveled can be more accurately assessed, since the deviation range is 75% smaller compared to the state of the art (e.g. purely statistical models) and 50% smaller compared to using a self-tuning mathematical model that does not take into account the vehicle's telematics information as shown in Figure 1.

[0085] Vehicle telematics information may include at least one of vehicle usage, tire pressure, tractor load, longitudinal acceleration, lateral acceleration, speed, GPS coordinates, odometer, road type, load, tire pressure, gear shifting, engine RPM, wheel speed, throttle / brake pedal position, tire temperature, outside air temperature, and steering wheel angle.

[0086] As shown in FIG. 3b, in some embodiments, the advantages of the data-driven mathematical model can be combined with an in-operation measurement corresponding to the in-operation measurement as described for the self-tuning mathematical model. For example, the second computer-implemented method can include a step of obtaining data from at least one in-operation measurement. In this case, the calculated tire wear based on the data-driven model can be improved by the mechanism of the self-tuning model as described in relation to FIG. 2a and FIG. 2b. When an in-operation measurement of at least one characteristic of the tire, such as the remaining tread depth, is performed, the measured value of the remaining tread depth is compared with the calculated value of the tread depth, i.e., this measurement is used to correct the data-driven mathematical model. Considering that the process of performing the in-operation measurement and fitting the model corresponds to the measurement and fitting in the case of the self-tuning model, for the sake of simplicity, these features will not be described more extensively than above. However, it is understood that these features described above can also be applied here.

[0087] As can be seen in Figure 3b, when the vehicle is empty and used in a long distance scenario (see dashed and dotted lines with wide dots), the average mileage of the tires is about 240,000 kilometers (see dashed and dotted lines with wide dots) with a deviation of about 10,000 kilometers in both directions, so the statistical mileage is in the range of 230,000 kilometers to 250,000 kilometers (see dashed and dotted lines with wide dots).

[0088] When the vehicle is loaded and used in a regional scenario (see dash-dotted and double-dashed lines with small dots), the average mileage of the tyres is 160,000 km (see dash-dotted line with small dots) with a deviation of about 10,000 km in both directions, so the statistical mileage ranges from 150,000 km to 170,000 km (see dash-dotted line with small dots).

[0089] As a result, the deviation range is approximately 90% smaller compared to the state of the art, allowing a more accurate assessment of the exact time when the minimum tread depth is reached or the distance traveled. Furthermore, the deviation range is 75% smaller compared to the method using a self-tuning mathematical model without taking into account the vehicle's telematics information as shown in Figure 1. Finally, by combining the advantages of the second computer-implemented method including a data-driven mathematical model with the advantages of obtaining data from at least one operational measurement, the deviation range can be further reduced by 50% compared to when a data-driven mathematical model is used without additional measurement data.

[0090] Development of a data-driven mathematical tire wear model Figure 4a shows different conditions that are the basis for the development of a data-driven mathematical tire wear model. Vehicle telematics information such as speed, acceleration, gear shifting, engine RPM, wheel speed, brake / throttle pedal position, tire pressure, tire temperature, outside air temperature, or steering wheel angle can be taken into account to play an important role in predicting tire wear more accurately.

[0091] A series of experiments was performed on multiple tires to correlate each vehicle's telematics with tire wear, focusing on the main drivers of tire wear, including, for example, load, acceleration, and temperature. Load conditions were defined as minimum and maximum loads so that fully loaded trips were studied for acceleration. Similarly, curved and highway routes were defined to maximize the difference in lateral and longitudinal acceleration.

[0092] In a series of experiments, multiple trucks were driven side-by-side on two different types of roads, i.e., curved road and highway, in summer and winter, with different loading conditions for the trucks. At regular intervals, tread wear measurements based on 3D scanning were performed to define the 360° profile of the tire. Based on the measurements, a relationship between tire wear per kilometer and vehicle telematics information was extracted.

[0093] The maximum distance traveled until the tire reaches the end of its life was determined for each vehicle load scenario i.e. unladen and loaded condition on different road types i.e. highway and curvy road as shown in Figure 4b. Based on the maximum distance traveled until the tire reaches the end of its life on different road types, the distance traveled per mm of tire tread wear was determined. This value is also expressed in kilometers per mm (KPM). The table shown in Figure 4b gives the KPM for different axles of the truck for each road condition.

[0094] Figure 4c shows the percentages of the results from Figure 4b. For example, as can be seen here, in the case of a curved road, the KPM of the steered axle of an empty truck is expected to be 80% of the KPM of an empty truck driving on the highway. For the driving axle on the curved road, the KPM value of the loaded truck is only 37% compared to the empty truck on the highway.

[0095] The resulting general formula for remaining tread depth can be expressed as follows: Remaining tread depth = F(mileage, tire technical data, vehicle technical data, A x ,A y , Load, IP} Here, A x is the longitudinal acceleration, A y is the lateral acceleration and IP is the tire pressure.

[0096] Among other features, the data-driven mathematical tire wear model provided by the present invention also estimates the wear energy due to longitudinal acceleration. The wear energy is calculated by the longitudinal force (F x ) and slip (S x ), and slip can be calculated based on the wheel speed (V x ) and vehicle speed (V v ) δWE=FxdSx=Fx(Vx-Vv)dt Each slip ratio (SR) can be expressed as follows:

[0097]

number

[0098] Then, the equation for wear energy is: δWE = FxVv SR dt It becomes.

[0099] When the slip ratio (SR) is small, the longitudinal force (F x ) and braking slip ratio (B p It is possible to find a linear correlation with SR. F x =ZBpSR

[0100] Using this relationship, it is possible to express the wear energy as follows:

[0101]

number

[0102] where m is the mass of each tire on the vehicle. The above formula represents the wear energy for a specific time interval. By integrating the above formula over a certain period of time, the total wear energy due to longitudinal forces can be calculated as follows:

[0103]

number

[0104] B p can vary according to the wear stage, it is possible to divide the tires into different wear stages and recalculate the wear energy accordingly.

[0105] A similar calculation can be performed for the wear energy due to lateral force (index y), taking into account that at small slip angles there is a linear correlation between lateral force and slip angle α.

[0106]

number

[0107] From the data-driven experiments, the longitudinal and lateral acceleration residuals for different road and load scenarios are determined. In a further step, weighting factors (a1, b1, etc.) that can be used to scale the general equation for tire wear energy to obtain a data-driven wear rate based on load and acceleration are derived from the residuals as follows:

[0108]

number

[0109] In the above formula, m tire is the mass acting on each tire, V v is the vehicle speed, a y is the lateral acceleration, a xneg is the deceleration (negative acceleration), a xposvisc is the acceleration used to increase speed (positive acceleration), taking into account the component due to air resistance.

[0110] Calculating and reporting tire wear rates Figures 5A, 5B and 5C show different ways in which the calculated tire wear rate can be reported. The transmission of data such as tire technical data, vehicle technical data and vehicle telematics information is indicated by black arrows. If the calculation is performed in the vehicle or in a component external to the vehicle that is not shown in Figure 5, the black arrows may alternatively or additionally indicate the transmission of the calculated tire wear rate, in other words the output of the applied mathematical model. In any case, the dotted arrows indicate the reporting of the calculated tire wear rate or the model output.

[0111] As shown in Figure 5A, in one embodiment, the tire wear rate may be calculated external to the vehicle, for example, on a cloud computing device. In this example, technical data of at least one tire of the vehicle, technical data of the vehicle, and telematics information of the vehicle may be transmitted to the cloud computing device. Calculations of tire wear may be performed within the cloud computing device, and the calculated tire wear may be reported to at least the vehicle.

[0112] Additionally or alternatively, the reports may be sent to a user computing device, such as a smart phone, notebook, PC, or tablet, or to a fleet management platform, as shown in FIGS. 5B and 5C.

[0113] 5B and 5C, the tire wear rate is calculated at the vehicle, in which case the calculated tire wear can be reported from the vehicle to a user computing device or a fleet management platform.

[0114] FIG. 6 shows a screenshot of an example fleet management application based on a computer-implemented method for calculating and / or monitoring the wear rate of tires of a vehicle according to the present disclosure. The fleet management platform / software may be stored and executed on a cloud computing device / server. For each fleet management software, there may be a digital representation of each vehicle of a fleet of at least one vehicle. The digital representation of the vehicle may include technical data of the vehicle such as brand, vehicle model, number of axles, tires used, etc. Once the tire wear rate of a vehicle is calculated using one of the methods according to the present disclosure, the calculated tire wear rate may be reported to a fleet management application. Within the fleet management application, the calculated tire wear rate may be associated with the digital representation of the vehicle. This association may be performed on a tire-by-tire basis.

[0115] As a result, the application may be able to display the tire wear rate for each tire of the vehicle. In some aspects, the remaining time or mileage for each tire of the vehicle may be displayed. Based on the displayed information, a user of the fleet management platform may be provided with detailed information regarding at least one of the calculated tire wear rate, remaining tread depth, remaining mileage, and remaining life of each tire of the vehicle. If certain conditions are met, a warning message may be issued, for example, to the driver, fleet manager, or any other user.

[0116] COMPUTER-IMPLEMENTED METHOD FOR CALCULATING AND / OR MONITORING TIRE WEAR RATE - Patent application 7 shows a flow chart illustrating a computer-implemented method 900 for calculating and / or monitoring tire wear rates for a vehicle. The operations of the computer-implemented method 700 may be implemented by a computing device.

[0117] At 710, technical data for at least one tire of the vehicle is obtained.

[0118] At 720, vehicle technical data is obtained.

[0119] At 730, data is obtained for at least one measurement of at least one characteristic of at least one tire of the vehicle.

[0120] At 740, a tire wear rate based at least in part on the obtained technical data of the at least one tire of the vehicle and the obtained data of the at least one measurement of the at least one characteristic of the at least one tire of the vehicle is calculated according to a self-tuning mathematical tire wear model.

[0121] In another aspect, calculating the tire wear rate may include selecting one of a plurality of pre-stored algorithms for calculating the tire wear rate after performing at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle.

[0122] In yet another aspect, selecting one of a plurality of pre-stored algorithms for calculating a tire wear rate after performing at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle in accordance with the self-tuning mathematical tire wear model may include executing a plurality of algorithms for calculating tire wear, and selecting one of the plurality of pre-stored algorithms that produces a tire wear calculation that is closest to the tire wear obtained in the at least one in-operation measurement.

[0123] In yet another aspect, the self-tuning model is tuned based on data from at least one in-operation measurement of at least one tire of the vehicle.

[0124] In yet another aspect, the self-tuning mathematical tire wear model is trained based on a plurality of calculated tire wear rates, the plurality of calculated tire wear rates being based on data acquired from a plurality of vehicles, the acquired data including technical data of at least one tire of each of the vehicles, technical data of each of the vehicles, and telematics information of each of the vehicles.

[0125] In yet another aspect, the remaining tread depth and / or remaining mileage of the tire and / or remaining time until replacement according to a set minimum tread depth is estimated based on the calculated tire wear rate.

[0126] In yet another aspect, at least one of the calculated tire wear rate, the estimated remaining tread depth, the remaining mileage of the tire, and the remaining time until replacement according to a set minimum tread depth is reported to the control system.

[0127] In yet another aspect, the control system is located in a vehicle.

[0128] In another aspect, the control system is located external to the vehicle and enables collection from a plurality of vehicles of at least calculated tire wear rates, estimated remaining tread depths, remaining mileage of the tires, and remaining time until replacement according to a set minimum tread depth.

[0129] 8 shows a flow chart illustrating a computer-implemented method 800 for calculating and / or monitoring tire wear rates for a vehicle. The operations of the computer-implemented method 800 may be implemented by a computing device.

[0130] At 810, technical data for at least one tire of the vehicle is transmitted.

[0131] At 820, vehicle technical data is transmitted.

[0132] At 830, data of at least one measurement of at least one characteristic of at least one tire of the vehicle is transmitted.

[0133] At 840, a calculated tire wear rate is obtained that is calculated according to a self-tuning mathematical tire wear model and is based in part on the transmitted technical data of at least one tire of the vehicle and the obtained vehicle telematics information.

[0134] In yet another aspect, the remaining tread depth and / or remaining mileage of the tire and / or remaining time until replacement according to a set minimum tread depth is estimated based on the calculated tire wear rate.

[0135] In yet another aspect, at least one of the calculated tire wear rate, the estimated remaining tread depth, the remaining mileage of the tire, and the remaining time until replacement according to a set minimum tread depth is reported to the control system.

[0136] In yet another aspect, the control system is located in a vehicle.

[0137] In another aspect, the control system is located external to the vehicle and enables collection from a plurality of vehicles of at least calculated tire wear rates, estimated remaining tread depths, remaining mileage of the tires, and remaining time until replacement according to a set minimum tread depth.

[0138] 9 shows a flow chart illustrating a computer-implemented method 900 for calculating and / or monitoring tire wear rates for a vehicle. The operations of the computer-implemented method 900 may be implemented by a computing device.

[0139] At 910, technical data for at least one tire of the vehicle is obtained.

[0140] At 920, vehicle technical data is obtained.

[0141] At 930, vehicle telematics information is obtained.

[0142] At 940, a tire wear rate based at least in part on the obtained technical data of at least one tire of the vehicle and the obtained vehicle telematics information is calculated according to a data-driven mathematical tire wear model.

[0143] In yet another aspect, data from at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle can be obtained, and calculating the tire wear rate further includes calculating the tire wear rate based at least in part on the obtained data of the at least one in-operation measurement of the at least one characteristic of the at least one tire of the vehicle.

[0144] In another aspect, calculating a tire wear rate based at least in part on data obtained from at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle may include selecting one of a plurality of pre-stored algorithms for calculating the tire wear rate after performing the at least one in-operation measurement of the at least one characteristic of the at least one tire of the vehicle.

[0145] In yet another aspect, selecting one of a plurality of pre-stored algorithms for calculating a tire wear rate after performing at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle in accordance with the self-tuning mathematical tire wear model may include executing a plurality of algorithms for calculating tire wear, and selecting one of the plurality of pre-stored algorithms that produces a tire wear calculation that is closest to the tire wear obtained in the at least one in-operation measurement.

[0146] In yet another aspect, calculating the tire wear rate further includes calculating the tire wear rate according to a self-tuning model, the self-tuning model being tuned based on data of at least one in-operation measurement of at least one tire of the vehicle.

[0147] In yet another aspect, the data-driven mathematical tire wear model is trained based on a plurality of calculated tire wear rates, the plurality of calculated tire wear rates being based on data acquired from a plurality of vehicles, the acquired data including technical data of at least one tire of each of the vehicles, technical data of each of the vehicles, and telematics information of each of the vehicles.

[0148] In yet another aspect, the remaining tread depth and / or remaining mileage of the tire and / or remaining time until replacement according to a set minimum tread depth is estimated based on the calculated tire wear rate.

[0149] In yet another aspect, at least one of the calculated tire wear rate, the estimated remaining tread depth, the remaining mileage of the tire, and the remaining time until replacement according to a set minimum tread depth is reported to the control system.

[0150] In yet another aspect, the control system is located in a vehicle.

[0151] In another aspect, the control system is located external to the vehicle and enables collection from a plurality of vehicles of at least calculated tire wear rates, estimated remaining tread depths, remaining mileage of the tires, and remaining time until replacement according to a set minimum tread depth.

[0152] 10 shows a flow chart illustrating a computer-implemented method 1000 for calculating and / or monitoring tire wear rates of a vehicle. The operations of the computer-implemented method 1000 may be implemented by a computing device.

[0153] At 1010, technical data for at least one tire of the vehicle is transmitted.

[0154] At 1020, vehicle technical data is transmitted.

[0155] At 1030, vehicle telematics information is transmitted.

[0156] At 1040, a calculated tire wear rate is obtained that is calculated according to a data-driven mathematical tire wear model and is based at least in part on the transmitted technical data of at least one tire of the vehicle and the obtained vehicle telematics information.

[0157] In yet another aspect, data from at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle can be transmitted, and the calculated tire wear rate is further calculated based at least in part on the at least one in-operation measurement of the at least one characteristic of the at least one tire of the vehicle.

[0158] In yet another aspect, the remaining tread depth and / or remaining mileage of the tire and / or remaining time until replacement according to a set minimum tread depth is estimated based on the calculated tire wear rate.

[0159] In yet another aspect, at least one of the calculated tire wear rate, the estimated remaining tread depth, the remaining mileage of the tire, and the remaining time until replacement according to a set minimum tread depth is reported to the control system.

[0160] In yet another aspect, the control system is located in a vehicle.

[0161] In another aspect, the control system is located external to the vehicle and enables collection from a plurality of vehicles of at least calculated tire wear rates, estimated remaining tread depths, remaining mileage of the tires, and remaining time until replacement according to a set minimum tread depth.

Claims

1. A computer-implemented method (700) for calculating the tire wear rate of a vehicle, comprising: obtaining (710) technical data of at least one tire of the vehicle; obtaining (720) technical data of the vehicle; obtaining (730) data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle; calculating (740) a tire wear rate based at least in part on the obtained technical data of at least one tire of the vehicle, the obtained technical data of the vehicle, and the obtained data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle according to a self-tuning mathematical tire wear model; and a method comprising the steps above.

2. The method according to claim 1, wherein the step of calculating the tire wear rate further comprises: selecting one of a plurality of pre-stored algorithms for calculating the tire wear rate. and a method further comprising the step above.

3. The method according to claim 2, wherein the step of selecting one of a plurality of pre-stored algorithms for calculating the tire wear rate comprises: executing a plurality of pre-stored algorithms for calculating the tire wear rate; selecting an algorithm that gives a calculated value of the tire wear rate closest to the tire wear rate based on the obtained data of at least one in-operation measurement among the plurality of algorithms. and a method comprising the steps above.

4. The method according to claim 1, wherein the self-tuning mathematical tire wear model is tuned based on data of at least one in-operation measurement of at least one tire of the vehicle.

5. The method according to claim 1, wherein the self-tuning mathematical tire wear model is tuned based on a plurality of calculated tire wear rates, the plurality of calculated tire wear rates are based on data obtained from a plurality of vehicles, and the obtained data includes technical data of at least one tire of each vehicle, technical data of each vehicle, and data of at least one in-operation measurement of at least one characteristic of at least one tire of each vehicle.

6. A computer-implemented method (800) for calculating the tire wear rate of a vehicle, comprising: A step (810) of transmitting technical data of at least one tire of a vehicle A step (820) of transmitting technical data of the vehicle A step (830) of transmitting data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle A step (840) of obtaining a calculated tire wear rate based at least in part on the transmitted technical data of at least one tire of the vehicle, the transmitted technical data of the vehicle, and the transmitted data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle, wherein the calculated tire wear rate is calculated according to a self-tuning mathematical tire wear model A method including the above steps. **Claim 7** In the method according to claim 6, A step of estimating the remaining time until replacement according to the remaining tread depth and / or the remaining driving distance of the tire and / or the set minimum tread depth based on the calculated tire wear rate A method further including the above step. **Claim 8** In the method according to claim 7, A step of reporting at least one of the calculated tire wear rate, the estimated remaining tread depth, the remaining driving distance of the tire, and the remaining time until replacement according to the set minimum tread depth to a control system A method further including the above step. **Claim 9** In the method according to claim 8, the control system is arranged in the vehicle. **Claim 10** In the method according to claim 8, the control system is arranged outside the vehicle and enables collecting at least the calculated tire wear rate, the estimated remaining tread depth, the remaining driving distance of the tire, and the remaining time until replacement according to the set minimum tread depth from a plurality of vehicles. **Claim 11** In the method according to claim 1, the technical data of at least one tire of the vehicle includes at least one of a tire manufacturer, a tire model, a tire pattern, tire specifications, a tire size, a tire mounting position, retread information, and a batch number of the tire. **Claim 12** In the method according to claim 1, the step of performing an in-operation measurement of at least one characteristic of at least one tire of the vehicle includes a step of measuring the remaining tread depth during operation and preferably a step of associating the measured remaining tread depth with the state of the vehicle's odometer. **Claim 13** An apparatus for monitoring the tire wear rate of a vehicle, the method comprising: means for obtaining technical data of at least one tire of the vehicle; means for obtaining technical data of the vehicle; means for obtaining data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle; means for calculating a tire wear rate based at least in part on the technical data of at least one tire of the vehicle obtained, the technical data of the vehicle obtained, and the data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle obtained, according to a self-tuning mathematical tire wear model An apparatus comprising the above.

14. An apparatus for monitoring the tire wear rate of a vehicle, the method comprising: means for transmitting technical data of at least one tire of the vehicle; means for transmitting technical data of the vehicle; means for transmitting telematics information of the vehicle; means for obtaining a calculated tire wear rate based at least in part on the transmitted technical data of at least one tire of the vehicle, the transmitted technical data of the vehicle, and the data of at least one in-operation measurement of at least one characteristic of at least one tire of the vehicle, the calculated tire wear rate being calculated according to a self-tuning mathematical tire wear model An apparatus comprising the above.

15. The apparatus according to claim 13, configured to execute any one of the methods according to any one of claims 2 to 5.

16. The apparatus according to claim 14, configured to execute any one of the methods according to any one of claims 7 to 12.

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