Device and method for calculating and / or monitoring the wear rate of a tire

Abstract

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

Description

Technical Field

[0001] The present disclosure generally relates to computer-implemented methods and apparatus for calculating and / or monitoring the wear rate of a tire. Background Art

[0002] The rate of tire wear is an important factor contributing to road safety. Replacing tires not early enough can lead to dangerous traffic situations or even accidents, which in turn can result in serious injuries or death and high risk of financial liability. Being able to replace tires at the right time is therefore not only a safety issue, but also an economic one. Missing the right point in time to replace a tire can lead to high costs due to potential accidents or damage, and replacing it too early can result in additional queue costs. Proper tire lifecycle management is also an ongoing issue, as replacing tires too early can lead to a waste of valuable resources.

[0003] Determining the right point in time to change tires is also an important success factor for vehicle logistics or fleet management. By being able to schedule vehicles for changing tires in a timely manner or in conjunction with the maintenance of other components of the vehicle, non-operating periods can be kept as short as possible, thereby achieving cost savings and increased reliability, especially considering commercial applications. For example, when changing tires, other components of the vehicle that have only a short remaining life or maintenance interval may also be replaced or overhauled. In particular, when managing a fleet including multiple long-haul trucks, properly monitoring the wear rate of the tires is important for, for example, deciding which of the available trucks is most suitable for a specific route.

[0004] Therefore, the ability to make an accurate prediction of the right time to change a tire is a key factor in making mobility and cargo transportation safer, greener, more reliable and more cost-effective.

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

[0006] EP 3800072A1 provides a technique for measuring the wear rate of a tire tread using the trend of a tire acceleration peak value. According to an embodiment of the 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 signal information from the signal receiver, and use the peak value of the acceleration in the longitudinal direction perpendicular to the axial direction of the tire in the acceleration signal from the inside of the tire to estimate the tread wear rate of the tire; a transmitter configured to receive analysis information as information related to the tread wear rate of the tire 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 a vehicle equipped with the tire. A Flexible Ring tire model as a mathematical model is used to verify the physical changes of the tire.

[0007] EP 3741589A1 discloses a tire wear estimator comprising a tire measurement system adapted to be mounted in or on the inner surface of a tire. The tire measurement system comprises a sensor adapted to sense a physical property of the tire and an acquisition system adapted to sample the signal of the sensor into a memory to acquire a disturbance in the sampled data caused by a contact patch of the tire when the sensor is mounted in or on the inner surface of the tire. The sampling rate of the acquisition system is high enough so that at least one oscillation indicative of the tread depth of the tire becomes detectable in the sampled data in and / or around the disturbance.

[0008] With the rise of IoT (Internet of Things) products, there are more robust and versatile tool chains available today than ever before. Based on IoT technology, methods involving tire-mounted sensors are able to obtain a list of tire in-use information and an algorithm that uses the tire-mounted sensor predictions as input to generate a more reliable and accurate tire wear estimation model that allows for precise predictions of remaining tread depth, remaining mileage, or remaining time before it is time to replace the tire. Summary of the invention

[0009] The above objectives are achieved by various computer-implemented methods and apparatuses for calculating and / or monitoring the wear rate of a tire disclosed herein.

[0010] According to a first aspect, the present disclosure provides a computer-implemented method for calculating the wear rate of a tire, the method comprising: providing an aggregation of residual tread depth assessments, i.e., RTD assessments, based at least in part on data obtained from at least one sensor of at least one tire attached to a vehicle; generating a mathematical model of the tire wear rate based at least in part on the aggregation of the provided RTD assessments; and calculating the tire wear rate based at least in part on the generated mathematical model of the tire wear rate.

[0011] According to an example of the first aspect, providing an aggregation of the RTD evaluations may also include: for each RTD evaluation in the aggregation of the provided RTD evaluations, performing a series of RTD evaluations based at least in part on data of at least one sensor attached to at least one tire of the vehicle over a preselected time span and / or mileage starting at a determined time point during operation of the vehicle and / or a determined mileage of at least one tire of the vehicle; removing one or more outliers in the series of RTD evaluations performed; and calculating an average of the series of RTD evaluations performed with the one or more outliers removed. The average may include any kind of mean, such as a Pythagorean mean, a median, a mode, a middle range, or a weighted mean, etc.

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

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

[0014] According to another example of the first aspect, selecting one of multiple pre-stored algorithms for generating the mathematical model of the tire wear rate based at least in part on an aggregation of provided RTD assessments also includes: providing the aggregation of the RTD assessments as input to multiple algorithms; running the multiple algorithms on the aggregation of provided RTD measurements; and selecting an algorithm from the multiple algorithms based at least in part on the execution of the multiple algorithms.

[0015] According to another example of the first aspect, the data obtained from at least one sensor attached to the at least one tire of the vehicle includes at least one of the following: radial acceleration, roll lateral acceleration, footprint estimation, inflation pressure, tire temperature, tire speed estimation.

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

[0017] According to another example of the first aspect, the telematics information of 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, type of road, load, tire inflation pressure, gear shifting, engine RPM, wheel speed, accelerator / brake pedal position, tire temperature, outside temperature, steering wheel angle.

[0018] According to another example of the first aspect, the tire wear rate includes at least one of: an estimated remaining tread depth of the tire, a remaining mileage of the tire, and a remaining time before replacing the tire according to a configured minimum tread depth.

[0019] In an 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 of the tire, a remaining mileage of the tire, and a remaining time before replacing the tire based on a configured minimum tread depth.

[0020] In another example of the first aspect, the control system is disposed in the vehicle.

[0021] In another example of the first aspect, the control system is disposed external to the vehicle.

[0022] According to another example of the first aspect, the provided aggregation of RTD measurements is stored in the vehicle or in the at least one tire of the vehicle.

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

[0024] According to a second aspect, the present disclosure provides an apparatus for calculating the wear rate of a tire, comprising: a component for providing an aggregate of a residual tread depth assessment, i.e., an RTD assessment, based at least in part on data obtained from at least one sensor of at least one tire attached to a vehicle; a component for generating a mathematical model of the tire wear rate based at least in part on the aggregate of the provided RTD assessment; and a component for calculating the tire wear rate based at least in part on the generated mathematical model of the tire wear rate.

[0025] In an example of the second aspect, the apparatus includes: components configured to perform any of the methods disclosed herein.

[0026] Other benefits and advantages of the present invention will become apparent upon careful reading of the detailed description and appropriate reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1a , Figure 1b and Figure 1c An example is illustrated of a comparison of the performance of a tire wear model that depends on vehicle dynamics and a corresponding model based at least in part on data obtained from at least one sensor attached to at least one tire of the vehicle in accordance with the present disclosure, in the case of a truck having tires with one or more lifted axles.

[0028] Figure 2 A block diagram of a first system for estimating a remaining tread depth of at least one tire of a vehicle according to the present disclosure is illustrated.

[0029] Figure 3 A block diagram illustrating a second system for estimating the remaining tread depth of at least one tire of a vehicle according to the present disclosure is shown. Figure 2 An expansion of the first system shown.

[0030] Figure 4 A block diagram of a third system for calculating a wear rate of a tire according to the present disclosure is illustrated, the third system being based on a first system for estimating a remaining tread depth of at least one tire of a vehicle (e.g., Figure 2 as shown) or a second system (e.g. Figure 3 shown).

[0031] Figure 5a A first aspect of an aggregation of providing residual tread depth assessments based at least in part on data obtained from at least one sensor of at least one tire attached to a vehicle is illustrated, which includes, for each RTD assessment in the provided RTD assessment aggregation, performing a series of RTD assessments based at least in part on data from at least one sensor of at least one tire attached to the vehicle.

[0032] Figure 5b A second aspect of providing an aggregated residual tread depth estimate based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle according to the present disclosure is illustrated, which includes removing one or more outliers in a series of RTD estimates performed.

[0033] Figure 5c A third aspect of providing an aggregated residual tread depth assessment based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle according to the present disclosure is illustrated, which includes calculating an average of a series of RTD assessments performed with one or more outliers removed.

[0034] Figure 6Exemplary pre-stored algorithms used in generating a model for calculating tire wear in accordance with the present disclosure are illustrated in the form of three graphs.

[0035] Figure 7a An exemplary data flow between a vehicle and a cloud according to the present disclosure is illustrated for possible provision of an aggregate of remaining tread depth assessments or reporting of calculated tire wear, including estimated remaining tread depth, remaining mileage of the tire, and / or remaining time before replacement based on a configured minimum tread depth.

[0036] Figure 7b An exemplary data flow between a vehicle, the cloud, and a user computing device according to the present disclosure is illustrated for possible provision of an aggregate of remaining tread depth assessments or possible reporting of calculated tire wear, including estimated remaining tread depth, remaining mileage of the tire, and / or remaining time before replacement based on a configured minimum tread depth.

[0037] Figure 7c An exemplary data flow between a vehicle, a cloud, and a control system according to the present disclosure is illustrated for possible provision of an aggregate of remaining tread depth assessments or possible reporting of calculated tire wear, including estimated remaining tread depth, remaining mileage of the tire, and / or remaining time before replacement based on a configured minimum tread depth.

[0038] Figure 8 An exemplary queue manager platform to which calculated tire wear rates may be reported according to the present disclosure is illustrated.

[0039] Figure 9a A computer-implemented method 900 for calculating a wear rate of a tire according to the present disclosure is illustrated.

[0040] Figure 9b Other aspects of providing aggregation of RTD evaluations according to the present disclosure are illustrated. DETAILED DESCRIPTION

[0041] The present disclosure provides a computer-implemented method and apparatus for calculating the wear rate of a tire.The computer-implemented method and apparatus according to the present disclosure provide numerous advantages.

[0042] Methods for estimating tire wear have been successfully developed based on vehicle dynamics information and statistical models, such as those sensed by sensors attached to the vehicle's tires. However, in some instances, the estimated tire wear may show deviations due to vehicle dynamic effects. Therefore, the resulting estimate may not be highly accurate in all cases.

[0043] To increase the accuracy of the estimated tire wear, an approach based on vehicle telematics information (i.e., the vehicle's odometer status) can be used. By relying on the vehicle odometer, the mileage of the tire can usually be estimated with very good accuracy. However, in some vehicle usage scenarios, such as for trucks (e.g., for lifting axles) or for vehicles that undergo several tire changes during the typical life of the tire (e.g., from summer tires to winter tires), this conventional approach introduces additional effort. This is mainly for three reasons:

[0044] First, in the case where the tire management system relies on vehicle telematics information, after any tire change, the new tire needs to be paired with the corresponding vehicle. In large fleets, this process requires additional effort from the fleet manager and is prone to errors, resulting in inconsistent fleet management data.

[0045] Secondly, in order to correctly predict the mileage and wear rate of trailer tires, a corresponding ad hoc monitoring system is required. For trucks, odometer data is only available for the tires of the tractor, not for the trailer. In the case where the trailer is disconnected from the tractor and / or connected to a different tractor without corresponding monitoring, the accuracy of the model used for tire wear estimation will be reduced.

[0046] Thirdly, in the case where the tire is mounted on a lifted axle, the corresponding method requires additional input data. In the case where information about the condition of the axle is not available to the model, the accuracy of the estimated kilometers performed by the corresponding tire and therefore the estimated wear rate will also be reduced.

[0047] The present invention discloses a computer-implemented method for calculating the wear state of a tire, which combines the simplicity of a first approach based on tire sensors with the increased accuracy of a second approach based on vehicle telematics information, while significantly reducing the additional effort associated with the second approach.

[0048] A computer-implemented method for calculating a tire wear rate includes providing an aggregation of residual tread depth (RTD) estimates based at least in part on data obtained from at least one sensor of at least one tire attached to a vehicle, generating a mathematical model of tire wear rate based at least in part on the provided aggregation of RTD estimates, and calculating the tire wear rate based at least in part on the generated mathematical model of tire wear rate.

[0049] Figure 1a , Figure 1b and Figure 1cThe difference in kilometers traveled by the vehicle itself and the tires that are part of the lifted axle is illustrated, and therefore illustrates a comparison between an approach based on vehicle telematics information without proper monitoring of the lifted axle of the truck and a method for calculating tire wear rate as disclosed herein. Figure 1a , Figure 1b and Figure 1c The respective horizontal axis shows the time (in hours) of a typical working day for a truck. Figure 1a , Figure 1b and Figure 1c The respective vertical axes show the estimated travel distance for a specific axle of the truck. Figure 1a , Figure 1b and Figure 1c The respective solid black lines indicate range estimates completed using conventional means. Figure 1a , Figure 1b and Figure 1c The respective black dashed lines show the estimated range based on the computer-implemented method described herein.

[0050] As from Figure 1a As can be seen, in the morning, the truck starts to drive towards the loading point and arrives at the loading point after about 50 km, during which distance the third axle is lifted, so no kilometer is completed according to the tires of the corresponding lifted axle. At the loading point, the trailer is loaded. The actual distance traveled by the tires on the lifted axle is 0 km. However, the corresponding truck has traveled about 50 km. When the truck is loaded, the lifted third axle will be lowered to the ground to distribute the load on all tires. In many cases, once the truck is loaded, it will go to one or more than one location for unloading.

[0051] Figure 1a The area indicated by ① in FIG. 1 identifies an empty vehicle going to a loading point. The empty vehicle travels 50 km with a lifted axle. Therefore, the odometer status increases by 50 km, while the kilometers of the lifted tires remain at zero.

[0052] exist Figure 1b In the example of , two different locations are identified. At the first location, which is reached around 12:00, the truck is only partially unloaded, and therefore the third axle remains down to the ground. At the second location, which is reached around 14:00, the truck is completely unloaded.

[0053] Figure 1bThe area indicated by ② in the figure marks the time when the vehicle makes its normal delivery after being loaded with the lifting axle pushed to the ground. The vehicle makes its first delivery at 12:00 and its second delivery at 14:00 (when it delivers the remaining cargo). Since the vehicle was first loaded, the odometer and the tires on the lifting axle have traveled 220 km.

[0054] like Figure 1c As shown, after the truck has been completely unloaded, the third axle is lifted again and the truck returned to its original position. In the case of using vehicle telematics information and simply estimating mileage based on the truck's odometer status, the vehicle telematics-based approach shows considerable disadvantages at this point. Compared to this conventional approach, the difference observed is 220 km traveled by the lifted tires (lower horizontal dashed line) versus 420 km traveled by the vehicle (upper horizontal dashed line). Therefore, in the absence of a monitoring system for the lifted axles of the trailer, using a vehicle telematics-based approach to estimate tire life will result in an estimated tire mileage that is much greater than the actual mileage.

[0055] Figure 1c The area indicated by ③ in the figure marks the time when the vehicle has completed all deliveries and is empty. The lifting axle is lifted and the vehicle runs 150 km back to the warehouse. The odometer increases by 150 km, but the tires on the lifting axle have only run 0 km during this time period. The total odometer is 420 km; the total kilometers run by the tires of the lifting axle is 220 km.

[0056] The present invention helps to avoid such errors and thus allows to more accurately determine the optimal time point for replacing the tire of a vehicle.Thus, the present invention makes the mobility of people and the transportation of goods safer, more environmentally friendly, more reliable and more cost-effective.

[0057] Remaining tread depth estimation based on tire mounted sensor (TMS) information

[0058] Figure 2 A block diagram of a first system for estimating a remaining tread depth of at least one tire of a vehicle is illustrated.

[0059] The system includes a tire mounted sensor (TMS) capable of acquiring a list of in-use information of the tire. The in-use information of the tire includes at least one of radial acceleration, roll lateral acceleration, footprint estimation, inflation pressure, tire temperature, and tire velocity estimation. The in-use information of the tire is provided by the TMS to a statistical model for estimating a remaining tread depth of the tire.

[0060] In addition, tire information including at least one of a tire manufacturer, a tire pattern, a tire specification, a tire size, a tire installation location, and tire retreading information is provided to the statistical model for estimating a remaining tread depth of the tire.

[0061] The remaining tread depth is estimated via a statistical model based at least in part on the in-use data of the tire and the tire information.

[0062] Figure 3 A flow chart of a second system for estimating the remaining tread depth of at least one tire of a vehicle is shown. The second system is an expanded version of the first system.

[0063] Additional information may be provided to the statistical model for estimating the residual tread depth. The additional information includes at least one of vehicle manufacturer, vehicle chassis, vehicle load, acceleration, velocity, GPS coordinates, type of road, engine load, gear shifting, engine RPM, wheel speed, throttle / brake pedal position, tire temperature, outside temperature, steering wheel angle, additional RTD in the operating measurement.

[0064] After estimating the remaining tread depth based at least in part on the additional information, the in-use data of the tire, and the tire information, the model output may be further validated based at least in part on the additional information.

[0065] Figure 4 A block diagram of a third system for calculating a wear rate of a tire based on a first system or a second system for estimating a residual tread depth of at least one tire of a vehicle is illustrated. An aggregation of residual tread depth estimates based at least in part on data obtained from at least one sensor attached to at least one tire of the vehicle is provided to an adaptive algorithm adapted to generate a mathematical model of tire wear rate based at least in part on the provided aggregation of RTD estimates. In some examples, the system additionally provides the adaptive algorithm with data obtained from at least one sensor attached to at least one tire of the vehicle. The adaptive algorithm calculates the tire wear rate or the expected remaining mileage of the life of the tire.

[0066] Figure 5a A first aspect of providing a residual tread depth assessment based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle is illustrated. Figure 5a On the horizontal axis, the mileage (km) is shown. On the vertical axis, the remaining tread depth is indicated. The upper left corner indicates the tire is new, i.e., the remaining tread depth is consistent with the technical data when the tire was sold and the mileage of the tire was zero. As the mileage increases, the remaining tread depth decreases. For example, for a new tire, the remaining tread depth should be about 13 mm.

[0067] A series of residual tread depth assessments is initiated at a determined point in time during use of the vehicle or at a determined mileage of the tire. Each residual tread depth assessment in the series of residual tread depth assessments is based at least in part on data from at least one sensor attached to at least one tire of the vehicle. The complete series of assessments is performed over a preselected time span and / or mileage starting at a determined point in time during operation of the vehicle and / or at a determined mileage of at least one tire of the vehicle.

[0068] For example, the determined mileage of at least one tire of the vehicle is approximately 0 km, i.e. the tire is new. The preselected mileage is 1000 kilometers. At 0 km, the first plurality of remaining tread depth evaluation series are initiated (see Figure 5a Each of the plurality of remaining tread depth evaluations is performed within a preselected mileage of 1000 km.

[0069] In addition to the first RTD evaluation series, Figure 5a Several additional RTD evaluation series are provided. Figure 5a Three additional RTD evaluation series with mileages of 5000km, 10000km and 15000km are highlighted (see Figure 5a ).

[0070] Figure 5b Another aspect of providing a residual tread depth estimate based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle is illustrated. Again, in Figure 5b On the horizontal axis, mileage (KM) is shown. On the vertical axis, the remaining tread depth is indicated. In order to reduce potential deviations due to vehicle dynamics effects within multiple RTD evaluation series, outliers in the RTD evaluation series performed are removed. For example, within each RTD evaluation series, a group of single RTD evaluations that show a higher variance than other RTD evaluations within the same series can be reduced to a single point. Removing outliers from each RTD evaluation series can be performed immediately after the RTD evaluation series has been completed, such as periodically within a preselected time span or preselected mileage of the tire, or when the evaluation in the series is needed as an input for further calculations and / or estimates.

[0071] Figure 5cA third aspect of providing an aggregated residual tread depth assessment based at least in part on data obtained from at least one sensor of at least one tire attached to a vehicle is illustrated. After removing outliers from a RTD assessment series, for each RTD assessment series, an average value of the RTD is calculated. The average value may include any kind of mean, such as a Pythagorean mean, median, mode, middle range, or weighted mean. The calculation of the average value may be performed immediately after the RTD assessment series has been completed, for example, periodically over a preselected time span or preselected mileage of a tire, or when a corresponding average value is needed as an input used for further calculations and / or estimations.

[0072] Figure 6 An aspect of generating a mathematical model of tire wear rate based at least in part on an aggregate of provided RTD measurements is illustrated, wherein one of a plurality of pre-stored algorithms is selected. Selecting one of the plurality of pre-stored algorithms may include providing the aggregate of RTD estimates as input to a plurality of algorithms, running the plurality of algorithms on the provided aggregate of RTD measurements, and selecting an algorithm from the plurality of algorithms based at least in part on running the plurality of algorithms.

[0073] As an example, Figure 6 Three different graphs for three different pre-stored algorithms are shown. For each of the three graphs, the remaining tread depth is shown on the vertical axis and the mileage (KM) of at least one tire read from the vehicle's odometer and / or the tire-mounted sensor is shown on the horizontal axis. Again, the upper left corner of each graph represents the tire in a new state, i.e., the remaining tread depth is consistent with the technical data when the tire was sold and the mileage of the tire was zero. As the mileage increases, the remaining tread depth decreases.

[0074] The filled black dots in each graph indicate the Figure 5a to Figure 5c The average values ​​of the RTD evaluation series are described. For example, in the first graph, three average values ​​of the RTD evaluation have been calculated, in the second graph, seven average values ​​of the RTD evaluation have been calculated, and in the third graph, seven average values ​​of the RTD evaluation have also been calculated.

[0075] The three algorithms illustrated by the three graphs may be run serially or at least partially in parallel and are adapted to find the best algorithm for generating a mathematical model of tire wear rate based at least partially on an aggregation of provided RTD measurements.

[0076] More generally, Figure 6The example shown in shows that after two or more aggregates of RTD estimates based at least in part on data obtained from at least one sensor of at least one tire attached to a vehicle have been provided, various pre-stored algorithms can be run, such as 3-point, linear regression, exponential regression, logarithmic regression, or neural network, etc. The algorithm of the pre-stored and run algorithms that is closest to the aggregate of the provided RTD estimates is selected for use in calculating tire wear rate based at least in part on the generated mathematical model of tire wear rate.

[0077] In one aspect, the pre-stored algorithms may be continuously modified, refined, or replaced / updated by the respective providers.

[0078] Aggregate or report tire wear rate providing residual tread depth assessment

[0079] Figure 7a , Figure 7b and Figure 7c Different ways of how the aggregation of RTD estimates can be provided or how the calculated tire wear rate can be reported are illustrated. The transmission of data such as the aggregation of RTD estimates or the calculated tire wear rate is illustrated by the black arrows. Alternatively or additionally, in the vehicle or in Figures 7a to 7c In the case where the calculation is performed at a component external to the vehicle not shown in the figure, the black arrows can illustrate the provision of an aggregate of the RTD estimate or the transmission of a calculated tire wear rate, or in other words, the output of the applied mathematical model. In any event, the dashed arrow indicates the provision of an aggregate of the RTD estimate or the reporting of a calculated tire wear rate or model output.

[0080] like Figure 7a As shown, in one aspect, a mathematical model of tire wear rate can be generated outside of a vehicle. Similarly, tire wear rate can be calculated outside of a vehicle (e.g., in a cloud computing environment). In this example, an aggregation of RTD assessments can be provided to a cloud computing device. Within the cloud computing device, generation of the model and calculation of tire wear can occur, and the calculated tire wear can be reported to at least the corresponding vehicle.

[0081] like Figure 7b and Figure 7c As shown, additionally or alternatively, the report may be sent to a user computing device (such as a smart phone, laptop, PC, or tablet) or a queue management platform.

[0082] In such Figure 7b and Figure 7cIn other examples shown, the tire wear rate may be calculated in the vehicle or in the tires of the vehicle. In this case, the calculated tire wear rate may be reported from the vehicle or the tires of the vehicle to at least one of the user computing device and the queue management platform. The corresponding queue management platform / software may be stored and run on a cloud computing device / server. In the corresponding queue management software, there may be a digital representation of each vehicle in a queue 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 has been calculated using one of the methods according to the present disclosure, the calculated tire wear rate may be reported to the queue management application.

[0083] In such Figure 8 Within the fleet management application shown, the calculated tire wear rate can be associated with a digital representation of the corresponding vehicle. This association can be made on a per-tire basis.

[0084] Thus, the application may be able to display the tire wear rate of each tire of the vehicle. In some aspects, the remaining time or mileage of each tire of the vehicle may be displayed. Based on the displayed information, detailed information about at least one of the calculated tire wear rate, remaining tread depth, remaining mileage, and remaining life of each tire of the vehicle may be provided to the user of the fleet management platform. If certain conditions are met, an alert message may be issued to, for example, the driver, the fleet manager, or any other user.

[0085] Computer-implemented method for calculating and / or monitoring the wear rate of a tire

[0086] Figure 9a A flow chart illustrating a computer-implemented method 900 for calculating and / or monitoring the wear rate of a tire is shown. The operations of the computer-implemented method may be performed by a computing device.

[0087] At 910, aggregation of a residual tread depth (RTD) assessment based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle is provided.

[0088] At 920 , a mathematical model of the aggregated tire wear rate based at least in part on the provided RTD estimates is generated.

[0089] At 930 , a tire wear rate is calculated based at least in part on the generated mathematical model of tire wear rate.

[0090] Figure 9b A flow chart further illustrating the aggregation of providing RTD evaluation is shown.

[0091] At 911, for each RTD assessment in the aggregate of provided RTD assessments, a series of RTD assessments are performed over a preselected time span and / or mileage starting at a determined point in time during operation of the vehicle and / or at a determined mileage of at least one tire of the vehicle and based at least in part on data from at least one sensor attached to at least one tire of the vehicle.

[0092] At 912, one or more outliers in the series of RTD evaluations performed are removed.

[0093] At 913, an average of the series of RTD evaluations performed is calculated with one or more outliers removed.

[0094] In another aspect, the provided aggregation of RTD evaluations includes two or more RTD evaluations.

[0095] In another aspect, generating a mathematical model of tire wear rate based at least in part on an aggregation of the provided RTD assessments further includes selecting one of a plurality of pre-stored algorithms based at least in part on the aggregation of the provided RTD assessments.

[0096] In another aspect, selecting an algorithm from a plurality of pre-stored algorithms for generating a mathematical model of tire wear rate based at least in part on an aggregation of provided RTD assessments also includes providing the aggregation of RTD assessments as input to a plurality of algorithms, running the plurality of algorithms on the aggregation of provided RTD measurements, and selecting an algorithm from the plurality of algorithms based at least in part on the running of the plurality of algorithms.

[0097] In another aspect, data obtained from at least one sensor attached to at least one tire of the vehicle includes at least one of radial acceleration, roll lateral acceleration, footprint estimate, inflation pressure, tire temperature, tire speed estimate.

[0098] In another aspect, the method further includes providing telematics information of the vehicle, and validating the output of the generated mathematical model of tire wear rate based at least in part on the provided telematics information of the vehicle.

[0099] In another aspect, the vehicle's telematics information includes at least one of: vehicle usage, tire pressure, tractor load, region, country, longitudinal acceleration, lateral acceleration, speed, GPS coordinates, odometer, road type, load, tire inflation pressure, gear shifting, engine RPM, wheel speed, accelerator / brake pedal position, tire temperature, outside temperature, steering wheel angle.

[0100] In another aspect, the tire wear rate includes at least one of an estimated remaining tread depth of the tire, a remaining mileage of the tire, and a remaining time before the tire is replaced based on a configured minimum tread depth.

[0101] In another aspect, the method includes reporting to a control system at least one of: a calculated tire wear rate, an estimated remaining tread depth of the tire, a remaining range of the tire, and a remaining time before the tire is replaced based on a configured minimum tread depth.

[0102] In another aspect, the control system is disposed in a vehicle.

[0103] In another aspect, the control system is disposed external to the vehicle.

[0104] In another aspect, the aggregation of the provided RTD measurements is stored in the vehicle or within at least one tire of the vehicle.

[0105] In another aspect, the aggregation of RTD measurements provided is stored external to the vehicle.

[0106] In another aspect, an apparatus for calculating a wear rate of a tire is provided. The apparatus includes means for providing an aggregated residual tread depth (RTD) estimate based at least in part on data obtained from at least one sensor of at least one tire attached to a vehicle, means for generating a mathematical model of the aggregated tire wear rate based at least in part on the provided RTD estimate, and means for calculating the tire wear rate based at least in part on the generated mathematical model of the tire wear rate.

[0107] In another aspect, the apparatus comprises components configured to perform any of the methods disclosed herein.

Claims

1. A computer-implemented method (900) for calculating a wear rate of a tire, the method comprising: providing (910) an aggregation of residual tread depth estimates, or RTD estimates, based at least in part on data obtained from at least one sensor attached to at least one tire of a vehicle; generating (920) a mathematical model of aggregated tire wear rate based at least in part on the provided RTD estimate; and The tire wear rate is calculated (930) based at least in part on the generated mathematical model of the tire wear rate.

2. The method according to claim 1, wherein: Providing (910) the aggregation of the RTD evaluation further comprises: for each RTD evaluation in the aggregate of provided RTD evaluations, performing (911) a series of RTD evaluations over a preselected time span and / or mileage starting at a determined point in time during operation of the vehicle and / or at a determined mileage of the at least one tire of the vehicle and based at least in part on data of the at least one sensor attached to the at least one tire of the vehicle; removing (912) one or more outliers in the series of RTD evaluations performed; and The average of the series of RTD evaluations performed is calculated (913) with the one or more outliers removed.

3. The method according to any one of claims 1 or 2, wherein: The provided aggregation of RTD evaluations includes two or more RTD evaluations.

4. The method according to claim 3, wherein: Generating the mathematical model of the tire wear rate based at least in part on the aggregation of the provided RTD estimates further comprises: One of a plurality of pre-stored algorithms is selected based at least in part on an aggregation of the provided RTD evaluations.

5. The method according to claim 4, wherein: Selecting one of a plurality of pre-stored algorithms for use in generating the mathematical model of tire wear rate based at least in part on an aggregation of the provided RTD estimates further comprises: providing an aggregation of said RTD evaluations as input to a plurality of algorithms; running the plurality of algorithms on an aggregation of the provided RTD measurements; and An algorithm from the plurality of algorithms is selected based at least in part on the execution of the plurality of algorithms.

6. The method according to any one of claims 1 to 5, wherein: The data obtained from at least one sensor attached to the at least one tire of the vehicle includes at least one of: radial acceleration, roll lateral acceleration, footprint estimation, inflation pressure, tire temperature, tire speed estimation.

7. The method according to any one of claims 1 to 6, further comprising: providing telematics information of said vehicle; as well as An output of the generated mathematical model of tire wear rate is validated based at least in part on the provided telematics information of the vehicle.

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

9. The method according to any one of claims 1 to 8, wherein: The tire wear rate includes at least one of: an estimated remaining tread depth of the tire, a remaining mileage of the tire, and a remaining time before the tire is replaced based on a configured minimum tread depth.

10. The method according to claim 9, further comprising: Reporting to a control system at least one of: a calculated tire wear rate, an estimated remaining tread depth of the tire, a remaining range of the tire, and a remaining time before replacing the tire based on the configured minimum tread depth.

11. The method according to claim 10, wherein: The control system is arranged in the vehicle.

12. The method according to claim 10, wherein: The control system is arranged external to the vehicle.

13. The method according to any one of claims 1 to 12, wherein: An aggregation of the provided RTD measurements is stored in the vehicle or within the at least one tire of the vehicle.

14. The method according to any one of claims 1 to 12, wherein: An aggregation of the provided RTD measurements is stored external to the vehicle.

15. A device for calculating the wear rate of a tire, comprising: means for providing an aggregation of a residual tread depth estimate, or RTD estimate, 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 of an aggregated tire wear rate based at least in part on the provided RTD estimate; as well as Means for calculating the tire wear rate based at least in part on the generated mathematical model of the tire wear rate.

16. The device according to claim 15, comprising: Component configured to carry out any of the methods claimed in claims 2 to 14.

Images