Tire load monitoring

Abstract

1. A computer-implemented method (100) for monitoring a current load (201) on a tire (3) mounted on a vehicle (2), the method (100) including: obtaining (e.g., by a server device (7)) sensor readings (23) from one or more tire monitoring system (TMS) sensors (5) mounted on the tire (5); accessing (e.g., by the server device (7)) characteristic information (25 a) related to the tire (5); selecting (e.g., by the server device (7)) an appropriate one of a plurality of computer models (15) based on the characteristic information (25 a); and using (e.g., by the server device (7)) the selected computer model (15) to estimate a current tire load value (201) based on the sensor readings (23).

Description

Technical Field

[0001] The present invention relates to a method for monitoring the current load of a tire mounted on a vehicle. The present invention further relates to a system for monitoring the current load applied to a tire.

Background Art

[0002] Overloading of a tire may lead to potential failure accompanied by accident risk; meanwhile, loading with incorrect inflation pressure increases tire distortion, reduces the durability of the tire casing, and may cause irregular wear. Overloading of a vehicle may also result in driving instability and violation of laws.

[0003] Sensors mounted inside a tire are generally referred to as tire monitoring system (TMS) sensors. TMS sensors are used to monitor several parameters of the tire itself, such as inflation pressure, internal air temperature and tire rotation time, as well as to extract information related to the interaction between the tire and its surrounding environment (e.g., roads and vehicles).

[0004] Current methods for monitoring tire load use telematics information such as vehicle speed, acceleration, engine RPM, engine load, and shock absorber data. Such methods can be inaccurate, may require collection of a considerable amount of data, and can mean that accurate load estimation calculations may take a considerable amount of time.

[0005] It is known to use an acceleration sensor arranged in a tire to measure the amount of deflection when the tire comes into contact with the ground (the so-called "contact patch").

[0006] There still remains a need for a rapid and reliable load assessment that can be used to monitor tire usage conditions.

Summary of the Invention

[0007] According to a first aspect of the present invention, a computer implementation method for monitoring the current load on a tire mounted on a vehicle is provided, and this method is: This involves obtaining sensor readings from one or more tire monitoring system (TMS) sensors attached to the tires (for example, by a server device), Accessing characteristic information about tires (for example, by a server device), Based on characteristic information, select the appropriate one from among several computer models (for example, by server device), This includes using a selected computer model (for example, by a server device) to estimate the current tire load value based on sensor readings.

[0008] As used herein, the term “current” means real-time or near real-time, and therefore monitoring the current load will be understood to mean that the load is estimated in real time or with a slight delay. Similarly, the current sensor reading is the sensor reading taken at or near the time the load estimation is performed.

[0009] Those skilled in the art will understand that by selecting an appropriate (e.g., adjusted) model based on characteristic information, the current sensor readings can be used more effectively to estimate the current tire load. Since multiple different models are prepared and the appropriate model is selected based on tire characteristic information, the model itself does not need to consider tire characteristic information as a variable. Therefore, the selected model can be simpler and, consequently, less computationally demanding. This has the additional advantage that the model can be run on equipment with less processing power and / or the model can be run more quickly. Clearly, quickly estimating the current tire load is important, especially when the tire is overloaded, as this can lead to dangerous tire failure. Furthermore, reducing the number of variables within each model leads to a more accurate estimation of the current tire load.

[0010] In some embodiments, the characteristic information relating to the tire includes one or more of the following: tire manufacturer, tread pattern, tire specifications, tire compound, tire stiffness, tire size, tire mounting position, and retread information.

[0011] In some preferred embodiments, the multiple models include separate models specifically optimized for known tire products from a given manufacturer. By providing separate models for specific tire products, the complexity of each model is significantly reduced, and thus the models can be used to calculate the current tire load more quickly and accurately, while having fewer computational requirements.

[0012] In some embodiments, the method includes accessing characteristic information about the vehicle. When the characteristic information about the vehicle is accessed, one suitable model among several may be selected based on both the characteristic information about the tire and the characteristic information about the vehicle.

[0013] In some embodiments, the characteristic information relating to the vehicle includes one or more of the following: the vehicle manufacturer, the vehicle chassis type, the vehicle's usage, the number of axles, the product type, the number of tires, and the number of tires mounted on each axle.

[0014] Such vehicle characteristic information can contribute to the accuracy of load estimation. Vehicle usage can be defined as a road type driving profile, which may include any information that can be derived from the amount of driving performed on each road type. For example, a road type driving profile may include a breakdown of each road type and the distance driven on each road type. It may also optionally include the average time driven on each road type for a particular vehicle or vehicle type. Furthermore, it may optionally include a percentage based on distance and / or time driven on each road type. For example, it may be known that 60% of a vehicle's total distance is driven on highways, 20% on suburban roads, and 10% on urban roads.

[0015] A product type can be defined as a specific vehicle product from a given manufacturer.

[0016] In some embodiments, the multiple models include separate models that are specifically optimized for known tire products from a given manufacturer and adapted for known vehicle products from a given manufacturer.

[0017] In some embodiments, each of the multiple models is a fitting algorithm developed by studying the correlation between each parameter from sensor readings and the tire load of a tire (and optionally the vehicle as well) having specific characteristic information.

[0018] In some embodiments, the method includes obtaining current telematics information from the vehicle. In such embodiments, the telematics information may include one or more of the following: GPS position, vehicle speed, vehicle lateral / longitudinal acceleration, road type, engine load, gear shift, engine RPM, wheel speed, throttle / brake pedal position, tire temperature, external temperature, and / or steering angle.

[0019] In some embodiments, the method includes detecting a favorable load measurement state using current telematics information and obtaining TMS sensor readings when the favorable load measurement state is detected. One or more of the following can be used to detect the favorable load measurement state: engine load information, throttle input, brake input, steering input, lateral vehicle acceleration, and longitudinal vehicle acceleration. The load on the tires changes as a result of the dynamic movement of the vehicle; for example, the front tire load increases under braking. By detecting a favorable load measurement state, the possibility of abnormal load estimation can be reduced.

[0020] In some embodiments, the sensor readings include one or more of radial acceleration, expansion pressure, and internal air temperature. For example, one or more tire monitoring system (TMS) sensors attached to a tire may include an acceleration sensor and / or a pressure sensor and / or a temperature sensor.

[0021] It is now understood that radial acceleration values ​​can be particularly useful for load estimation because they provide an accurate measure of tire rolling dynamics. This approach can be used in addition to, or separately from, more traditional sensor readings of pressure and / or temperature. Thus, in some embodiments, the method further includes: obtaining sensor readings including radial acceleration values; analyzing the radial acceleration values ​​to calculate one or more tire parameters selected from tire contact patch length, tire wear estimate, tire rotation time (t_rev), automatic mounting position determination, and tire mileage estimate; and using the selected model to estimate the current tire load value based on one or more of the sensor readings and tire parameters. Tire contact patch length may be defined as the total time (t_patch) that a single portion of the tire remains in contact with the ground, or as the contact patch ratio, which is the ratio between the total time (t_patch) that a single portion of the tire remains in contact with the ground and the total tire rotation time (t_rev). Automatic mounting position determination may be defined as determining the position of the tire on the axle / chassis (e.g., left side, front axle).

[0022] In some embodiments, the method includes providing the user with feedback relating to the current tire load value. In some embodiments, the feedback is provided in real time.

[0023] In some embodiments, the feedback includes one or more of the following: a notification that the tire load index has been exceeded; a notification that the tires are running at an incorrect inflation pressure for the current load; a notification that the vehicle has exceeded its maximum load capacity; and a notification that the vehicle has an unbalanced weight distribution across its tires. Early notification of load problems may enable a user, who may be a driver or fleet manager, to take steps to correct the problem and reduce the likelihood of failures such as tire punctures.

[0024] In some embodiments, the model is used to detect the current tire load based on sensor readings and telematics information. By utilizing both TMS sensor readings and telematics information, the amount of data input to the model increases, which can lead to more accurate load estimation.

[0025] In some preferred embodiments, sensor readings include inflation pressure and radial acceleration values, the radial acceleration value being analyzed (e.g., by a TMS sensor) to provide information regarding the tire contact patch length and tire rotation time (t_rev), and characteristic information regarding the tire includes the tire compound or tire stiffness and tire size. Radial acceleration and inflation pressure are raw sensor readings, while footprint length and tire rotation time (t_rev) can be calculated using raw values ​​measured for radial acceleration. In embodiments, rotation time may be measured directly. In embodiments, rotation time (t_rev) (whether measured directly or calculated using radial acceleration values) can be used with the tire radius to calculate the tangential velocity of the tire. Inflation pressure may be a denormalized pressure reading or a normalized pressure reading, the normalized pressure reading being the internal air pressure divided by the internal temperature.

[0026] In an embodiment, the method comprises estimating the tire contact patch length and the tire rotation time (t_rev) by evaluating peaks appearing in a waveform obtained by differentiating a time-series waveform of tire radial acceleration detected by an acceleration sensor attached to the tire. Such a technique is known from International Publication No. 2020071249 A1, which is incorporated herein by reference.

[0027] In some embodiments, the tangential tire velocity v is

[0028] [Formula] calculated using radial acceleration values in accordance with , wherein a is the radial acceleration and r is the tire radius.

[0029] In other embodiments, the tangential tire velocity v is

[0030] [Formula] calculated using the tire rotation time (t_rev) by , wherein r is the tire radius and t_rev is the tire rotation time. In other embodiments, the sensor reading may comprise a reading from a wheel speed sensor. This means wheel speed, and thus tangential tire velocity, can be measured directly.

[0031] In some embodiments, the characteristic information about the tire comprises the tire compound or tire stiffness, and the tire size.

[0032] In some embodiments, the method comprises suggesting an action to a user, and optionally the action is load rebalancing and / or pressure adjustment.

[0033] In some embodiments, the current tire load value is calculated only once during the vehicle's journey. Since the vehicle is unlikely to be loaded or unloaded while in motion, the tire load is unlikely to change significantly during the journey. Reducing load estimation to once per journey can save energy, which is particularly advantageous when using battery-powered TMS sensors.

[0034] In some embodiments, the method includes detecting the start of driving using TMS data and / or telematics data from one or more tire monitoring system (TMS) sensors, and estimating the current tire load value in response to the detection of the start of driving.

[0035] In some embodiments, the start of travel is detected by determining that a non-zero radial acceleration reading is present in the TMS data after an inactive period.

[0036] In this embodiment, the length of the inactivity period is compared to a threshold, and if the time exceeds the threshold, a new start of driving is determined.

[0037] By comparing the relevant period with a threshold, it is possible to detect, for example, the possibility of an incorrect start of driving after a vehicle has briefly stopped at a signal and then started moving again.

[0038] In a preferred set of embodiments, a time history set of the vehicle's GPS location is collected from telematics data, and a start of driving is determined based on the GPS location changing after a period of stationary activity. In the embodiment, the time the vehicle was stationary is compared to a threshold, and if the time exceeds the threshold, a new start of driving is determined.

[0039] By comparing the time the vehicle was stationary with a threshold, it is possible to detect the possibility of an incorrect start of travel, for example, after the vehicle briefly stopped at a signal and then started moving again.

[0040] In other embodiments, the start of driving may be detected by identifying an "ignition on" signal from telematics data.

[0041] In the embodiment, after the start of driving is detected, the method includes periodically acquiring data on a predetermined number of tire rotations over a certain period of time after the start of driving is detected. Data acquisition can be started as soon as the start of driving is detected, or immediately after the start of driving is detected, for example, immediately after the optimal load estimation state is detected after the start of driving is detected.

[0042] In some embodiments, the current tire load value is calculated at fixed time intervals or fixed travel distance intervals. This can reduce energy consumption by reducing the number of load estimations performed. Therefore, the method described herein may be repeated at fixed time intervals or fixed travel distance intervals.

[0043] In some embodiments, the method includes storing an estimated current tire load value. The estimated current tire load value may be stored in a server, a user output device, or another device.

[0044] In some embodiments, the method includes collecting estimated current tire load values ​​for a given tire over a period of time (e.g., the lifespan of a tire fitted to a particular vehicle).

[0045] In some embodiments, the method includes outputting tire usage data that shows the current load on the tire as a function of time over a period of time. Such output may allow users, such as fleet managers or tire manufacturers, to determine whether the tire was routinely overloaded and therefore understand how this affected the tire's lifespan.

[0046] In some embodiments, each of the multiple models is pre-trained on historical data acquired for tires and / or vehicles having characteristic information. This means that each model is optimized to correlate the characteristic information with observed measurements of tire load (e.g., linearly and / or non-linearly). Such modeling may be based on, for example, linear regression, random forests, support vector regressors, neural networks, or any combination thereof.

[0047] According to a second aspect of the present invention, Applying the method described herein (according to any embodiment of the first aspect) to each tire mounted on the vehicle, Accessing information about the arrangement of tires on each axle of a vehicle (e.g., vehicle characteristic information), A computer implementation method is provided for calculating the total vehicle load or the load per axle, thereby monitoring the current load on the vehicle.

[0048] It will be understood that this embodiment may, for example, include (and preferably include) one or more (e.g., all) of the preferred and optional features disclosed herein that relate to other embodiments and models of the present invention, where applicable.

[0049] According to a third aspect of the present invention, a system for monitoring the current load on a tire is provided, configured to perform the method according to the first aspect, wherein the system One or more TMS sensors are mounted on the tire and configured to acquire sensor readings, A processor configured to process sensor readings, Includes a memory configured to store sensor readings and / or parameters based on sensor readings.

[0050] It will be understood that this embodiment may, for example, include (and preferably include) one or more (e.g., all) of the preferred and optional features disclosed herein that relate to other embodiments and models of the present invention, where applicable.

[0051] In some embodiments, the processor is embodied, for example, as a tire-mounted processor in the same tire-mounted device as one or more TMS sensors. In some embodiments, the processor is included in a vehicle-mounted receiver for sensor readings, such as a suitable network communication device (e.g., a dongle). In some embodiments, the processor is embodied as a server located remotely from the vehicle, such as a cloud server. In some embodiments, the sensor readings are processed (e.g., analyzed) by one or more of these processors.

[0052] In some embodiments, the system includes one or more user output devices for displaying estimated tire load values ​​and / or notifications and / or suggested actions to the user. The above description applies equally to such systems.

[0053] In some embodiments, at least one of the tire monitoring system (TMS) sensors mounted on the tire is an accelerometer, preferably a radial accelerometer. The sensor reading may include a radial acceleration reading (e.g., a waveform) as the radial acceleration value.

[0054] In some embodiments, at least one of the tire monitoring system (TMS) sensors attached to the tire is an air pressure sensor, preferably mounted inside the tire to measure internal air pressure.

[0055] In some embodiments, at least one of the tire monitoring system (TMS) sensors attached to the tire is preferably an air temperature sensor attached inside the tire to measure the internal air temperature.

[0056] In some embodiments, each sensor (or a group of sensors) is mounted on the inner surface of the tire, for example, attached to the surface of the inner liner facing the tread, or mounted on the inner surface of the tire on the sidewall side. However, the sensors do not necessarily have to be mounted on the inner surface of the tire; for example, some or all of the sensors may be embedded inside the tire.

[0057] In at least some embodiments, one or more tire monitoring system (TMS) sensors mounted on a tire include a transmitter for transmitting sensor readings, optionally a processor configured to preprocess the sensor readings, and a battery for supplying power to the transmitter at regular detection intervals (e.g., 10 seconds, 30 seconds, 60 seconds, etc.).

[0058] Naturally, the term “server” as used herein will be understood to mean a computer or machine (e.g., a server device) connected to a network to send and / or receive data from other devices on that network (e.g., computers or other machines). Additionally or alternatively, a server may provide resources and / or services to other devices on the network. The network may be the Internet or any other suitable network. A server may be embodied in any suitable server type or server device, e.g., a file server, application server, communications server, computing server, web server, proxy server, etc. A server may be a single computing device or a distributed system, meaning that the server’s functions may be divided across multiple computing devices. For example, a server may be a cloud-based server, meaning that its functions may be divided across many computers on an “on-demand” basis. In such a configuration, server resources may be obtained from one or more data centers that may be located in different physical locations.

[0059] Therefore, it will be understood that the processes described above, executed by a server, can be executed by a single computing device, i.e., a single server, or by multiple separate computing devices, i.e., multiple servers. For example, the entire process may be executed by a single server that has access to all the relevant information required. [Brief explanation of the drawing]

[0060] One or more non-exclusive examples are illustrated, for illustrative purposes only, with reference to the attached drawings. [Figure 1] This diagram shows a schematic representation of a load monitoring system installed in a vehicle used for transporting heavy loads. [Figure 2] This is a schematic diagram of the load monitoring system. [Figure 3] This is a flowchart illustrating a load monitoring method. [Figure 4] This is a 3D plot showing the correlation between pressure, load, and contact surface. [Figure 5] This flowchart shows a more detailed part of the method described in Figure 3. [Figure 6] This flowchart shows an alternative method to the one shown in Figure 5. [Figure 7] A scatter plot of the predicted load (using the model according to the embodiment of the present invention) against the true load is shown. [Figure 8] This is a histogram showing the distribution of the variable "True Load - Predicted Load". [Figure 9] This shows the output provided to the user. [Modes for carrying out the invention]

[0061] It will be understood that the features and method steps shown by dashed lines in the drawings are optional and may be freely omitted in embodiments of the present invention.

[0062] Figure 1 shows a system 1 that monitors the load applied to multiple vehicle tires 3 mounted on a vehicle 2. System 1 comprises multiple tire monitoring system (TMS) sensors 5, each mounted inside a tire 3, and a network communication device 6 provided in the vehicle 2. In an embodiment, the network communication device 6 may be a dongle plugged into a port on the vehicle 2, such as an OBD port, FMS port, or other. In an alternative embodiment, the network communication device 6 may be a permanently installed transceiver box. The TMS sensors 5 are configured to communicate with a remote server 7 via the network communication device 6 in the vehicle 2. The remote server 7 may comprise an online database / cloud / platform. In this example, the TMS sensors 5 communicate with the network communication device 6 via a Bluetooth® connection, but it will be understood that any suitable form of short-range wireless communication or wired connection may be used. The network communication device 6 is networked and communicates with the remote server 7 via a wireless network connection (e.g., a cellular network). The remote server 7 is connected to a user output device 10 via the wireless network. In the illustrated embodiment, a plurality of user output devices 10 are provided, namely a user terminal 10a located inside the cabin of the vehicle 2, a user mobile device 10b that can be carried by the user, and a fleet manager's computer 10c.

[0063] Figure 2 is a schematic diagram showing the input to Model 15 implemented by the remote server 7. First, characteristic information 25, which includes characteristic information about the tire 25a and optionally characteristic information about the vehicle 25b, is received from memory 11. In this embodiment, memory 11 may be implemented in the remote server 7 itself. Alternatively, memory may be contained in the TMS sensor 5, the network communication device 6, or a further server (not shown) operably connected to the remote server 7. Since the model is built and trained to be used with a specific tire product, characteristic information 25a is used to select Model 15 from a plurality of Models 14. This will be described later in relation to Figure 3. The selected Model 15 receives input data. TMS information 23 is obtained from a plurality of TMS sensors 5 attached to the tire 3 of the vehicle 2 and input to the selected Model 15. In this embodiment, telematics information 21 is received from the vehicle 2 and optionally input to the selected Model 15 as indicated by the dashed arrows in Figure 2. By inputting telematics information 21 and TMS information 23 into the selected model 15, the accuracy of the model 15 can be improved. This telematics information 21 can be acquired by sensors on the vehicle 2 using any known method.

[0064] The operation of System 1 will be explained with reference to the flowchart in Figure 3, which shows the load monitoring method 100.

[0065] Steps 101-107 outline the process for monitoring the load on a single tire 3 of vehicle 2. In step 101, sensor readings are obtained from TMS sensors 5 attached to the tire. Each sensor 5 acquires data for n tire rotations every m seconds at the start of each run for several minutes. In an embodiment, each sensor 5 may acquire data for 10 tire rotations every 15 seconds for 5 minutes after the start of the run. The sensor readings 23 include radial acceleration, inflation pressure, estimated contact patch length, and tire rotation time (t_rev) / tangential tire speed. Radial acceleration readings take two forms. Firstly, raw radial acceleration readings. These are used to generate waveforms (acceleration plotted against time) from which the contact patch length can be estimated. Secondly, the mean radial acceleration is calculated considering only the acceleration readings obtained outside the contact patch. This mean radial acceleration can be used to estimate the tangential tire speed. These estimates are not directly measured, but their calculation is performed by the processor of the TMS sensor 5 itself, as is known, and is therefore still considered to be the sensor readings received from the TMS sensor. The contact patch length is estimated by evaluating the peak appearing in the waveform obtained by differentiating the time-series waveform of the tire radial acceleration detected by an accelerometer attached to the tire. Such a technique is known from International Publication No. 2020071249(A1), which is incorporated herein by reference. The tire contact patch length is estimated as the ratio of contact time to rotation time. On the other hand, the tangential velocity of the tire can be calculated according to Equation 1,

[0066]

number

[0067] In the formula, v is the tangential tire velocity, a is the mean radial acceleration outside the contact surface, and r is the tire radius. In other embodiments, the tangential tire velocity v is,

[0068]

number

[0069] In step 103, characteristic information 25 is accessed from memory 11. The characteristic information includes either or both of vehicle characteristic information 25b and tire characteristic information 25a. Tire characteristic information 25a may include one or more of the following: tire manufacturer, tread pattern, specifications, size, mounting location, retread information, and additional remaining tread depth (RTD) measured during tire inspection. In a preferred embodiment, tire characteristic information 25a may correspond to a specific tire product from a specific manufacturer. Vehicle characteristic information 25b may include one or more of the following: vehicle manufacturer, chassis (number of tires / axles), vehicle usage, and tractor load.

[0070] In step 105, one of several pre-trained models 14 is selected based on characteristic information 25. Model 15 includes an algorithm pre-trained on historical data. This historical data includes data from three sources. • Indoor testing rig ○ A drum or similar device capable of simulating tire behavior under different speeds, pressures, and load conditions. • Outdoor testing ○ Test the vehicle in a controlled environment such as a test ground or public road, under specific tire and vehicle conditions. ○Load variations can be introduced during outdoor testing by measuring the vehicle's weight or by using a specialized vehicle capable of applying variable tire load / force. • Test vehicle ○A vehicle on a public road in an uncontrolled state where the condition of the vehicle / tires may be unknown or only partially known.

[0071] Since vehicle characteristic information 25b and tire characteristic information 25a can significantly impact tire load monitoring, separate models are provided for different vehicle and / or tire characteristics. This is because a model capable of handling all vehicle and tire characteristics is likely to have limited computational requirements. In a preferred embodiment, for each tire product, a separate model 15 is constructed based on the characteristic information 25a related to that tire. These models 15 are vehicle-independent and therefore work with any vehicle on which the tire product can be mounted. This improves the accuracy of the models and reduces the complexity of the models 15. As a result, while constructing many separate models may be more demanding than constructing a single model, simpler models are more suitable for implementation on devices with limited processing power, such as the TMS sensor device 5 or the network communication device 6 in the vehicle 2. In a preferred embodiment, different tire compounds and stiffnesses, as well as tire sizes, are used for each tire product as the tire characteristic information 25a that forms the basis for model selection.

[0072] In step 107, TMS information 23 is input into the selected model 15. Model 15 correlates the input TMS information with characteristic information to determine the load on tire 3. This correlation may be linear or nonlinear. The algorithm used to create model 15 is linear regression, random forest, support vector regressor, neural network, or an ensemble of all or a subset of the above. During the training phase of model 15, historical data is analyzed to study the correlation between each parameter from the TMS data 23 and the tire load for each specific tire product. From this correlation, a fitted model 15 can be created for that particular tire product.

[0073] Figure 4 shows a 3D scatter plot created using historical (training) data. The plots show the load plotted against pressure and ground surface ratio. This particular training data includes measurements taken at three different pressures and therefore contains three different lines within the data. However, it is clear that there is a correlation between these three variables, and therefore, based on the plotted training data, a best-fitting plane can be generated using any known correlation algorithm. This best-fitting plane represents Model 15, which can be used to estimate the load when the ground surface and pressure are known.

[0074] Model 15 is optimized using a cross-validation procedure with multiple folds. The historical data is split into multiple sub-datasets (folds), and different combinations of model variation and historical data are sequentially tested to determine which model variation is best for solving the problem. Thus, Model 15 is optimized around the best model variation.

[0075] Steps 101-107 in Figure 3 relate to load estimation itself, but Figure 3 also includes other steps related to evaluating when to perform load estimation. Step 111 detects the start of a run. The method for detecting the start of a run will be described in more detail in relation to Figures 5 and 6. The objective of the present invention is to monitor the current tire load and to provide feedback to the user about the current tire / vehicle load state. However, since most TMS sensors operate on battery power, it is advantageous to minimize the number of TMS transmissions in order to reduce data costs and maximize the battery life of the TMS sensors. A possible solution to achieve these two objectives is to reduce the number of load detection calculations to once per vehicle run in order to detect possible weight changes. This solution operates on the assumption that the load is unlikely to change during a run because the most likely cause of a load change is the addition, removal, or movement of cargo that requires vehicle 2 to stop. The start of a new run can be detected by implementing the start of a run detection procedure in step 111. Since a new run has started, the load condition may have changed between runs, and therefore, in response to the detection of the start of a run, it is necessary to detect the updated tire load. As shown in Figure 3, once the start of a run is detected, method 100 can proceed directly to step 101 to begin load estimation, but alternatively, method 100 can proceed to step 113, as described below.

[0076] In some embodiments, the number of TMS transmissions can be controlled by performing data measurements during travel at fixed time / distance intervals. Such embodiments may be implemented separately from or in combination with the detection of the start of travel.

[0077] The load on the vehicle tires 3 is affected by the dynamic state that changes during driving due to weight transfer. For example, when vehicle 2 is braking, the load on the front tires 3 of vehicle 2 increases, and the load on the rear tires 3 of vehicle 2 decreases. Similarly, when vehicle 2 is accelerating, the load on the rear tires 3 of vehicle 2 increases, and the load on the front tires 3 of vehicle 2 decreases. The same applies to lateral acceleration. When vehicle 2 is turning, the outer tires 3 of the turn experience a greater load than the inner tires 3 of the turn. Clearly, the load calculated based on TMS sensor readings acquired during such load transfer events does not have high accuracy, and therefore, in order to improve the accuracy of the load monitoring method 100, it is advantageous to detect a favorable state for load monitoring evaluation. This can be done by acquiring telematics data in step 113 and analyzing the telematics data in step 115 to determine whether the current dynamic state is favorable for load monitoring evaluation.

[0078] Telematics data 21 is acquired by sensors within the vehicle 2 and collected by a network communication device 6 connected to the vehicle 2's OBD, FMS, or similar port. In the illustrated embodiment, a single network communication device 6 is provided to communicate with all of the TMS sensors 5, but it will be understood that there may be multiple network communication devices 6. For example, in large vehicles, multiple network communication devices 6 may be necessary if the range of the radio signals from the TMS sensors 5 is not sufficient for all TMS sensors 5 to be within range of a single network communication device 6. Telematics information 21 may include one or more of the following: acceleration, velocity, GPS coordinates, road type, engine load, gear shift, engine RPM, wheel speed, throttle / brake, pedal position, tire temperature, ambient temperature, and steering angle.

[0079] For example, the longitudinal and lateral acceleration of the vehicle may be compared to thresholds, and if the vehicle's acceleration is lower than these thresholds, it is determined that the condition is favorable for load monitoring evaluation. The best dynamic condition for load monitoring evaluation may exist when the vehicle is stationary or traveling in a straight line at a constant speed on a highway, etc. If, in step 115, the condition is determined to be favorable for load monitoring estimation, method 100 proceeds to step 101. If the condition is unfavorable, method 100 returns to step 113, and updated telematics data 21 is obtained.

[0080] In the illustrated embodiment, the network communication device 6 streams telematics data 21 to the remote server 7.

[0081] Steps 111, 113, and 115 are not inseparably linked, and it will be understood that the method according to the present invention can detect the start of driving without analyzing the dynamic state, and similarly, can analyze the dynamic state without first detecting the start of driving.

[0082] In embodiments, the dynamic state may be evaluated in ways that do not require the use of telematics data 21. For example, in embodiments, TMS information 23 may be used to determine a favorable state. In such embodiments, relatively constant values ​​of radial wheel acceleration readings may indicate a constant speed, and therefore a favorable state. Alternatively, multiple load monitoring estimates can be performed and then analyzed. High fluctuations in estimated load over a short period may indicate that the state was unfavorable, and therefore such results should be ignored.

[0083] Method 100, described in relation to steps 101-103, estimates the current load on a single tire. In embodiments, it may be useful to calculate the load on the entire vehicle, as vehicle overload can lead to driving instability and / or legal violations.

[0084] In step 117, steps 101-107 are repeated for each tire of vehicle 2, and the current load estimate for each tire 3 is obtained. Although Figure 3 is formatted as a flowchart, it will be understood that steps 101-107 are performed simultaneously for each tire 3. Since the load values ​​are intended to indicate the current load, the load value evaluations for each tire 3 should be time-aligned. Each tire 3 is equipped with at least one TMS sensor 5. Each TMS sensor 5 communicates with the same network communication device 6.

[0085] In step 119, the current total vehicle load is easily calculated by summing the individual tire load values ​​of each tire 3 on vehicle 2. To perform this sum, it is necessary to know the configuration of the vehicle, in particular the number of axles and the number of tires 3. If the number of tires 3 per axle is known, the load per axle can also be calculated in the same way according to Equation 2.

[0086]

number

[0087] During the ceremony, w i is the load on each individual tire, and N is the number of axles or tires on the vehicle, which depends on whether the calculation is for total axle load or total vehicle load.

[0088] The vehicle configuration is included in the vehicle characteristic information 25b obtained from memory 11, as explained in relation to Figure 2.

[0089] In step 109, the load value is transmitted to the output device 10 for display to the user. As described in relation to Figure 1, the output device 10 may be a user terminal 10a in the vehicle, a user's mobile device 10b, and / or a fleet manager's computer 10c. Since the method 100 of the present invention is designed to monitor the current tire load, it is advantageous that feedback is provided in real time. In a simpler embodiment, only the raw load value (tire load value or total vehicle load value or both) may be displayed to the user, but in embodiments, the load value may be analyzed and specific warnings may be provided to the user. For example, the user may be notified when the tire load index is exceeded, or when the tires are running at an incorrect inflation pressure for the current load, or when the vehicle exceeds its maximum load capacity, or when the vehicle is characterized by an unbalanced weight distribution (left / right and / or between axles). This analysis may be performed in the TMS sensor 5, the vehicle 2, the network communication device 6, the remote server 7, or the output device 10. Specifically, the analysis may be performed by an application installed on the user's mobile device 10b.

[0090] Figure 5 shows a flowchart illustrating how to detect the start of driving in step 111 of method 100 of Figure 3. In step 121, a time history set of the GPS position of vehicle 2 is collected from telematics information 21. However, it will be understood that the GPS information can come from any suitable source. The GPS information can come from a GPS sensor integrated into vehicle 2, or the network communication device 6 may have a GPS sensor, or a separate GPS sensor may be provided in vehicle 2. In step 123, the GPS coordinate number resolution is reduced to a certain spatial precision. This reduction can be performed by truncation, rounding, or similar methods. In embodiments, the resolution may be 100m.

[0091] In step 125, a time instance in which the GPS position changes is identified, and the time difference between that time instance and the minimum time of the previous GPS coordinates is calculated. The minimum time of the previous GPS coordinates is the first time when those GPS coordinates were recorded, and therefore, the time difference between the first instance in which the GPS coordinates were recorded and the instance in which the GPS coordinates change is equal to the amount of time that vehicle 2 was stationary. In step 127, the time difference, i.e., the time that vehicle 2 was stationary, is compared to a predetermined threshold, and if the time exceeds the threshold, a new driving event is triggered in step 139. If the time is shorter than the threshold, the process is resumed.

[0092] The threshold is set as a compromise between the need to reduce the likelihood of new driving events being falsely triggered and the need to ensure that new driving events are not missed. For example, if the threshold is set too low, it may lead to a new driving event being triggered after vehicle 2 has stopped at a traffic light or in traffic. On the other hand, the threshold should not be set so high that it may potentially miss stops long enough for the vehicle load state to change, such as a stop at a depot where cargo has been added to vehicle 2. In this embodiment, a given threshold is set to 10 minutes.

[0093] In step 123, the accuracy of the spatial coordinates is reduced. This is to reduce the possibility of false start of a run. For example, if the vehicle is moving around the depot, the GPS coordinates will change, but a new run has not yet started. By reducing the spatial accuracy of the GPS coordinates to, for example, 100m, a new run is triggered only when the vehicle's position has changed significantly, as that significant movement indicates the start of a potential new run.

[0094] Figure 6 shows a flowchart illustrating an alternative method for detecting the start of a new run using TMS information 23. In step 131, sensor readings are obtained from the TMS sensor 5 mounted on the tire 3. These readings may include radial acceleration values. In step 133, an inactive period is identified, which may be a period in which the radial acceleration value is equal to zero. In step 135, a time instance in which the sensor readings change is identified, and the time difference from the start of the inactive period is calculated. This allows for determining the length of time the vehicle was stationary, in the same manner as described above with respect to the method in Figure 4. In step 137, the time difference is compared to a predetermined threshold, and if it exceeds the threshold, a new run event is triggered. If the difference is less than the threshold, the method resumes. The threshold is set according to the same criteria as described in relation to the method in Figure 5.

[0095] Figure 7 shows a scatter plot of the true load against the load predicted using the TMS load prediction algorithm according to an embodiment of the present invention. The model used was specifically optimized for Bridgestone's Duravis Drive tire product. As is clearly visible from the plot, for all three pressures tested, the data points are on or near the Y=X line, indicating that the predictions made by the model are accurate.

[0096] Furthermore, Figure 8 shows a histogram illustrating that the distribution of true load-predicted load values ​​is concentrated around the bin corresponding to the lower values ​​(meaning that the true load and predicted load are similar in value).

[0097] Figure 9 shows the output screen 200 displayed to the user on the output device 10. The current tire load 201 for each tire 3 is shown. The output screen 200 also displays the axle load 203 for each axle, the total vehicle load 205, and the payload 207 which is equal to the total vehicle load 205 minus the weight of the vehicle itself. In the illustrated embodiment, the output screen 200 also displays other information such as the current vehicle speed 209 and the current vehicle coordinates 211.

[0098] In the described embodiment, processing steps such as the implementation of Model 15 are performed on the remote server 7. However, it is assumed that some or all of the processing steps of Method 100 may be performed on the edge by the network communication device 6, by the vehicle 2 itself, or by the TMS sensor 5. The driving start detection 111 may similarly be implemented directly on the edge, on the network communication device 6, or on the vehicle 2, or, in embodiments that do not require telematics data, it may be implemented directly on the TMS sensor firmware.

[0099] In the embodiment described above, a single current load value is calculated. It is conceivable that this method may be configured to detect multiple load values ​​during operation and combine these values ​​using a moving average, a total measurement average, or other appropriate aggregation function. Such embodiments can further improve the accuracy of load detection.

Claims

1. A computer implementation method for monitoring the current load on a tire mounted on a vehicle, wherein the method is: The process involves obtaining sensor readings from one or more tire monitoring system (TMS) sensors attached to the aforementioned tire, Accessing characteristic information regarding the aforementioned tire, Based on the aforementioned characteristic information, the appropriate computer model is selected from among multiple computer models, wherein the appropriate computer model is a computer model that has been adjusted to match the aforementioned characteristic information. A computer implementation method comprising using the selected, adjusted computer model to estimate the current tire load value based on the sensor readings.

2. The method according to claim 1, wherein the characteristic information relating to the tire includes one or more of the following: tire manufacturer, tread pattern, tire specifications, tire compound, tire stiffness, tire size, tire mounting position, and retread information.

3. The method according to claim 1 or 2, wherein the plurality of computer models include separate computer models that are specifically optimized for known tire products from a given manufacturer.

4. The method according to claim 1, further comprising accessing characteristic information relating to the said vehicle.

5. The method according to claim 4, wherein the characteristic information relating to the vehicle includes one or more of the following: the vehicle manufacturer, the chassis type of the vehicle, the usage status of the vehicle, the number of axles, the product type, the number of tires, and the number of tires mounted on each axle.

6. The method according to claim 1, comprising obtaining current telematics information from the vehicle, wherein the telematics information optionally includes one or more of the following: GPS position, vehicle speed, vehicle lateral / longitudinal acceleration, road type, engine load, gear shift, engine RPM, wheel speed, throttle / brake pedal position, tire temperature, external temperature, and / or steering wheel angle.

7. The method according to claim 6, comprising detecting a preferred load measurement state using the current telematics information and acquiring the sensor reading when the preferred load measurement state is detected.

8. The method according to claim 1, wherein the sensor reading includes one or more of radial acceleration, expansion pressure, and internal air temperature.

9. To obtain sensor readings including radial acceleration values, The radial acceleration value is analyzed to calculate one or more tire parameters selected from tire contact patch length, tire wear estimation, tire rotation time, automatic mounting position identification, and tire mileage estimation. The method according to claim 1, further comprising estimating the current tire load value based on the sensor readings and one or more of the tire parameters using the selected, adjusted computer model.

10. The method according to claim 1, comprising providing the user with real-time feedback relating to the current tire load value, wherein the feedback optionally includes one or more of the following: a notification that the tire load index is exceeded; a notification that the tire is running at an incorrect inflation pressure for the current load; a notification that the vehicle is exceeding its maximum load capacity; and a notification that the vehicle has an unbalanced weight distribution across its tires.

11. The method according to claim 1, wherein the current tire load value is calculated only once during the driving of the vehicle.

12. The method according to claim 11, comprising detecting the start of driving using data from one or more tire monitoring system (TMS) sensors and / or telematics data, and estimating the current tire load value in response to the detection of the start of driving.

13. The method according to claim 1, wherein the current tire load value is calculated at regular time intervals or at regular distance intervals.

14. The method according to claim 1, wherein each of the plurality of computer models is pre-trained on historical data acquired for tires having the same characteristic information.

15. A system for monitoring the current load on a tire, configured to perform the method described in claim 1, the system is One or more TMS sensors are mounted on the tire and configured to acquire sensor readings, A processor configured to process the sensor readings, A system including a memory configured to store the sensor readings and / or parameters based on the sensor readings.

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