Method for synchronizing tire-mounted sensors with road impact and measuring contact patch duration and amplitude

Abstract

A method is provided for measuring impact signals induced by acceleration data measured by an acceleration sensor mounted on a tire at a contact patch that contacts the road surface for each tire revolution over multiple tire revolutions while the tire is rolling on a road surface, the method includes acquiring acceleration data over multiple tire revolutions and processing the acceleration data with the sensor, the processing the acceleration data with the sensor includes processing the acceleration data to measure acceleration values ​​for each impact signal and to calculate an impact peak acceleration value (a_min), calculating a moving average of the impact peak acceleration values ​​(a_min) over the multiple tire revolutions, measuring a start time and an end time of each impact signal by comparing the acceleration values ​​of the acceleration data with a dynamic threshold adjusted according to the moving average of the impact peak acceleration values ​​(a_min), and generating a time-related parameter selected from one or more of the following from the measured start time and end time of each impact signal: a duration of the impact signal (t_patch), a period between two successive impact signals (t_rev), and a ratio of the duration (t_patch) and the period (t_rev). The method further includes transmitting the time-related parameters to an external server.

Description

[Technical Field]

[0001] The present invention relates to a sensor device that can be mounted on a vehicle tire, such as a tire-mounted sensor (TMS) mounted in the tire. In particular, the present invention relates to a method for processing data acquired at the contact patch by an acceleration sensor mounted on the tire. [Background technology]

[0002] Sensors mounted within a tire are generally referred to as tire-mounted sensors (TMS). TMS are used to monitor several parameters of the tire itself, such as tire pressure and tire temperature, as well as to extract information about the tire's interaction with its surroundings, such as the road or a vehicle. TMS typically include acceleration sensors that monitor wheel speed.

[0003] The TMS is typically locally powered using a battery and may have small local memory and CPU capacity. It will be appreciated that tire rotation occurs very frequently and therefore a large amount of data can be acquired by the acceleration sensor of the TMS. In order to conserve the battery (ideally so that the TMS battery lasts the life of the tire), the data acquisition and data analysis performed by the sensor must be optimized for battery life, memory, and CPU usage.

[0004] However, it will be appreciated that high performance data analysis with high accuracy and short processing times typically uses a lot of power and drains the battery.

[0005] One of the parameters that a TMS can measure is radial acceleration using an accelerometer. This data can be used to track the position of the TMS relative to the road, measure wheel angular velocity, and assess the load acting on the wheel or the remaining tread depth of the tire on which the TMS is installed. Summary of the Invention [Problem to be solved by the invention]

[0006] There is a need to improve the way TMS processes acquired data so as to maintain high accuracy in assessing features of interest in acceleration data while maintaining low battery usage. [Means for solving the problem]

[0007] According to a first aspect of the present invention, there is provided a method for measuring, over multiple revolutions of a tire rolling on a road surface, an impact signal induced in acceleration data measured by an acceleration sensor mounted on the tire at a contact patch that contacts the road surface for each revolution of the tire, the method comprising: acquiring acceleration data over multiple tire revolutions; and processing the acceleration data with a sensor. and processing the acceleration data with the sensor comprises: processing the acceleration data to measure an acceleration value for each impact signal and calculate an impact peak acceleration value (a_min); calculating a moving average (avg_a_min) of the impact peak acceleration values ​​over multiple tire revolutions; measuring the start and end times of each impact signal by comparing the acceleration values ​​of the acceleration data with a dynamic threshold that is adjusted according to a moving average (avg_a_min) of the impact peak acceleration values; generating, from the measured start and end times of each impulse signal, time-related parameters selected from one or more of the duration of the impulse signal (t_patch), the period between two successive impulse signals (t_rev), and the ratio of the duration (t_patch) to the period (t_rev); and the method further comprises transmitting the time-related parameters to an external server.

[0008] It will thus be appreciated that the present method uses a dynamic threshold to measure the start and end times of the impact signal, thereby generating t_patch, which is the difference between the start and end times of the impact signal; t_rev, which is the difference between the start / end of one impact signal and the start / end of the next impact signal; or the ratio of t_rev to t_patch. Because these are simple mathematical operations performed on the sensor, they consume less power than prior art methods that require more complex mathematical operations. This is particularly advantageous if the sensor performing the processing is battery-powered, since the battery preferably lasts for the lifetime of the tire in which the sensor is mounted. Because only time-related parameters are transmitted to the external server, battery consumption is further reduced since minimal data is transmitted, and memory is also saved because large amounts of acceleration data do not need to be stored by the sensor before transmission. In other words, the present method is suitable for execution on a processor associated with or embedded in the sensor, while retaining the advantage that real-time information (whether time-related parameters of the impact signal or other evaluated features of the acceleration data) is collected from the tire-mounted sensor.

[0009] The impact time (t_patch) can be calculated without any problems for both stationary (constant speed) and non-stationary (acceleration) conditions. Furthermore, the dynamic threshold allows the method according to the invention to be easily adapted to different conditions, such as different road conditions, vehicle conditions, load conditions, speed conditions, tire conditions, etc.

[0010] The transmitted data can be used to track the position of the sensor in the tire relative to the road or to measure wheel speed. An external server can perform further processing of the transmitted data to extract additional characteristics, such as the load on the tire, or wear characteristics, such as the remaining tread depth of the tire. It is not necessary to "train" the method using data from before the acceleration sensor was installed; tires that are pre-installed with sensors (provided, for example, by a firmware update) can also use the above method to extract useful information from the acceleration data. The above method can be implemented with sensors mounted on tires of passenger or commercial vehicles, such as trucks, buses, and SUVs.

[0011] The method provides acceleration data across multiple impact signals, and using the transmitted time-related parameters, vehicle speed estimation for each wheel revolution can be easily achieved.

[0012] The acceleration data can be processed to assess how impacts of the contact patch against the road change acceleration values ​​before and after each impact signal. In some embodiments, the method further includes processing the acceleration data to measure acceleration values ​​between impact signals and calculate an inter-impact peak acceleration value (a_max). Typically, a_max is located proximate to an impact signal in the acceleration data. In at least some embodiments, a_max is obtained every tire revolution and, unlike avg_a_min, is not obtained using a moving average.

[0013] In some embodiments, the method further includes adjusting the dynamic threshold in response to the inter-impact peak acceleration value (a_max). The dynamic threshold thus varies in response to two parameters: a_max and avg_a_min, where avg_a_min is a moving average and a_max is obtained for each tire revolution. Adjusting the dynamic threshold using these two parameters makes the dynamic threshold more sensitive to changes in acceleration data caused by changing conditions and ensures that the time-related parameters extracted using the dynamic threshold remain accurate even when the acceleration data changes.

[0014] In some embodiments, the dynamic threshold includes a first dynamic threshold used for measuring the start time of the impact signal and a second (preferably different) dynamic threshold used for measuring the end time of the impact signal. In various embodiments, the first and second dynamic thresholds each vary according to avg_a_min and a_max, but according to different functions, so that the first and second dynamic thresholds have different values. Since the impact signal may not be completely symmetric, it is advantageous to use two different thresholds to measure the start time and end time of the impact signal respectively. Thus, with the two thresholds, a more accurate method for obtaining the start time and end time of the impact signal is obtained.

[0015] In some embodiments, the first dynamic threshold is calculated as a function of the moving averages avg_a_min and a_max, and the function includes a coefficient x that biases the threshold towards avg_a_min. In some embodiments, the second dynamic threshold is calculated as a function of the moving averages avg_a_min and a_max, and the function includes a coefficient y (different from coefficient x) that biases the threshold towards a_max.

[0016] In some embodiments, the minimum dynamic threshold is calculated as a function of the moving averages avg_a_min, a_max, and coefficient x, where x is a fixed value satisfying 0 < x < 1. An example of the function used to obtain the first dynamic threshold is First dynamic threshold = (1 - x) × avg_a_min + x × a_max ≈ avg_a_min - (a_min - a_max) × x = avg_a_min - amplitude × x where avg_a_min is the moving average of the impact peak acceleration values and a_min is the impact peak acceleration value of the current impact signal. It will be understood that alternative threshold calculations may be used instead of the above functions. The above threshold calculation is very simple, so the required computing power is minimal, thus there is no significant battery consumption and the battery life is extended.

[0017] In some embodiments, the second dynamic threshold is calculated as a function of the moving averages avg_a_min, a_max, and a coefficient y, where y is a fixed value satisfying 0 < y < 1 and y ≠ x. For example, Second dynamic threshold = (1 - y) × avg_a_min + y × a_max ≈ avg_a_min - (a_min - a_max) × y = avg_a_min - amplitude × y where the amplitude is defined as a_min - a_max, i.e., the total displacement of the acceleration data of the impact signal. Similar to the minimum dynamic threshold, alternative calculations can be used.

[0018] Since the above threshold calculation is very simple, the required computing power is minimal, thus there is no significant battery consumption and the battery life is extended.

[0019] In some embodiments, the method further includes processing the acceleration data to measure the acceleration values between impact signals and calculating the average speed value between impact signals as the g-value. This g-value is equal to the centripetal acceleration of the sensor of an ideal tire (i.e., when there is no deformation of the tire on the road surface). The acceleration data can be converted to the g-value using a calibrated look-up table.

[0020] In some embodiments, the method further includes checking whether the generated time-related parameter is valid by comparing the value of g-value × (t_rev) 2 with an expected error value range. The expected error range can include two calibrated thresholds. If t_rev is within these two thresholds, it is considered valid; otherwise, it is considered invalid. Thus, if t_rev is not valid, the data obtained for that rotation of the tire can be discarded.

[0021] In some embodiments, the method further includes processing the acceleration data to measure an acceleration value when the tire is not moving, thereby determining a zero-g value for the tire, and processing the acceleration data for each impact signal to measure a zero offset in the acceleration value, calculated as the difference between a running average of peak acceleration values ​​(avg_a_min) and the zero-g value. The zero-g value is therefore equal to the background acceleration always present on the acceleration sensor, i.e., acceleration due to gravity. The zero-g value may also be selected as a starting value for determining avg_a_min before the acceleration sensor receives acceleration data or an impact signal. The zero-offset parameter may be transmitted to an external server, where it may be further processed to determine tire load and / or wear.

[0022] In some embodiments, the difference between the impact peak acceleration value (a_min) and the inter-impact peak acceleration value (a_max) is averaged over multiple tire revolutions to measure the contact patch amplitude (a_patch), which is then transmitted to an external server. The contact patch amplitude may therefore be equal to the maximum deviation of the acceleration data from the average g-value between impact signals. Because the difference between a_min and a_max is obtained for each impact signal at each tire revolution, averaging over multiple revolutions provides a more precise and accurate value of the contact patch amplitude, taking into account multiple tire revolutions. Furthermore, averaging over multiple revolutions reduces the frequency of data transmission, thereby reducing sensor memory and power consumption. The transmitted contact patch amplitude may be further used in the server, for example, for tire load / wear / speed calculations.

[0023] In some embodiments, the method further includes processing the acceleration data for each impact to determine the slope of at least one of the rising and falling edges of the impact signal. The time derivative of the acceleration data can be determined for each impact, and the maximum and minimum values ​​of this derivative can be determined to assess the slope. The slope of the impact signal before and after the impact peak can be used to estimate tire wear. For example, a larger slope may correspond to a worn tire, while a smaller slope may correspond to a newer tire.

[0024] In some embodiments, the method further includes transmitting amplitude-related parameters for each impact selected from one or more of an impact peak acceleration value (a_min), an inter-impact peak acceleration value (a_max), a slope, and a difference between a_min and a_max to an external server. Transmitting the amplitude-related parameter(s) instead of all acceleration data acquired by the sensor reduces the amount of data transmitted, thereby reducing transmission bandwidth and lowering battery consumption of the sensor. Memory is also saved because large amounts of acceleration data do not need to be stored by the sensor before transmission.

[0025] In some embodiments, the method further includes receiving the amplitude-related parameters at an external server and determining tire wear using the amplitude-related parameters. Methods for determining tire wear from amplitude-related parameters are known in the art. For example, U.S. Patent Application Publication No. 2021 / 0208029 (Bridgestone Corp.) describes a method for estimating the degree of tire wear from the magnitude of peaks appearing in a radial acceleration waveform obtained by differentiating radial acceleration detected by an acceleration sensor. The entire contents of U.S. Patent Application Publication No. 2021 / 0208029 are incorporated herein by reference.

[0026] In at least some embodiments, the amplitude-related parameters are used to determine or predict the remaining tread depth of a tire as an indicator of wear. As mentioned above, the slope can be used to determine tire wear because older tires have less remaining tread depth and are therefore less resistant to deformation on the surface they roll on. WO 2009 / 008502 (Bridgestone Corp.), the entire contents of which are hereby incorporated by reference, describes differentiating a detected acceleration waveform to assess the rate of tread deformation, which varies with the degree of tire wear. In addition to wear assessment, one or more amplitude-related parameters can be used to determine the load on the tire, the speed of the vehicle, or other information regarding the interaction of the tire with its surroundings, such as the road or a vehicle.

[0027] In at least some embodiments, the method includes transmitting at least one time-related parameter and at least one amplitude-related parameter to an external server. For example, U.S. Patent Application Publication No. 2021 / 0208029 (Bridgestone Corp.) describes a method for estimating the degree of tire wear using the slope (derivative of the radial acceleration value) and contact time ratio of an impact signal.

[0028] In some embodiments, the method further includes receiving the time-related parameters at an external server and using the time-related parameters to determine one or more of (i) the load, (ii) the vehicle center of gravity, and (iii) the tire rotational speed. For example, because the tire diameter is known, the tire rotational time, t_rev, can be used to determine the tire rotational speed. The ratio of t_rev to t_patch can be used to determine the vehicle center of gravity, because the ratio varies depending on the load on the vehicle and therefore the effect that each tire receives from that load.

[0029] In some embodiments, the method further comprises filtering the acceleration data from multiple tire revolutions using a moving average filter of length N (e.g., N=8 samples, e.g., N=16 samples) prior to the processing step of the present invention. This filter "smoothes" the acceleration data, as the acceleration data also has vibration noise, which can be minimized by filtering the acceleration data. This filtering also reduces the amount of data that can be stored at the sensor. Filtering the data reduces noise in the processed data, leading to more accurate calculations that can be used to determine tire characteristics.

[0030] In some embodiments, the acceleration data is stored in the sensor's memory before processing at the sensor, thus allowing the acceleration data to be processed less frequently and consuming less power than more frequent processing.

[0031] In some embodiments, the time-related and / or amplitude-related parameters are stored in a memory on the sensor before transmission. Again, reducing the frequency of data transmission reduces power consumption from the transmission, e.g., extending the life of a battery powering the sensor. For example, a transmitter used to transmit the time-related or amplitude-related parameters to an external server may be placed into a "sleep mode" after transmission to conserve power, only waking up occasionally to transmit stored data from memory.

[0032] In any of the above disclosed embodiments, the method is a computer-implemented method.

[0033] It will be appreciated that methods according to embodiments of the present invention may be implemented at least in part using firmware or software. Viewed from yet another aspect, the present invention also extends to a computer program product comprising computer-readable instructions executable to perform any or all of the methods described herein when, for example, executed on suitable data processing means, in particular a processor associated with or embedded in the acceleration sensor. Viewed from yet another aspect, the present invention also extends to a computer-readable storage medium storing firmware code which, when executed on a data processor, performs any of the methods described herein. Preferably, the data processor is associated with or embedded in the acceleration sensor. The storage medium may be a physical (or non-transitory) medium.

[0034] According to a second aspect of the present invention, there is provided a tire-mounted sensor system for measuring an impact signal over multiple revolutions of a tire rolling on a road surface, wherein the impact signal is induced by acceleration data measured by the tire-mounted sensor system mounted on the tire at a contact patch that contacts the road surface for each revolution of the tire, the tire-mounted sensor system comprising: a processor; a transmitter; and an acceleration sensor; The acceleration sensor acquires acceleration data over multiple tire revolutions, and the processor The acceleration data is processed to measure the acceleration value for each impact signal and calculate the impact peak acceleration value (a_min); Calculate the moving average (avg_a_min) of the impact peak acceleration values ​​over multiple tire rotations, measuring the start and end times of each impact signal by comparing the acceleration values ​​of the acceleration data with a dynamic threshold that is adjusted according to a moving average (avg_a_min) of the impact peak acceleration values; and generating, from the measured start and end times of each impulse signal, time-related parameters selected from one or more of: a duration of the impulse signal (t_patch), a period between two successive impulse signals (t_rev), and a ratio of the duration (t_patch) to the period (t_rev); The transmitter transmits the time-related parameters to an external server.

[0035] In one or more embodiments of the tire-mounted sensor system, the processor is configured to perform any of the method steps already described above.

[0036] One or more non-limiting examples will now be described, by way of example only, with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0037] [Figure 1] FIG. 2 is a schematic diagram of a tire on which an acceleration sensor is mounted. [Figure 2] 2 shows a diagram of acceleration data acquired by the sensor of FIG. 1 as a function of time, illustrating the shock signal induced in the acceleration sensor. [Figure 3] FIG. 1 is a schematic diagram of tire-mounted sensors and server components. [Figure 4] FIG. 1 is a block diagram illustrating steps taken by a processor to process data acquired by an acceleration sensor of the TMS. [Figure 5] FIG. 2 is a diagram of raw acceleration data acquired by the TMS of FIG. 1 as a function of time. [Figure 6] FIG. 5 is a diagram of the FSM used in FIG. 4 to determine t_start and t_end of the impulse signal. [Figure 7A] This is a graph of acceleration data for one revolution of a fully worn tire traveling at 30 km / h under full load. [Figure 7B] This is a graph of acceleration data for one revolution of a fully worn tire traveling at 60 km / h under full load. [Figure 8A] This is a graph of acceleration data for one revolution of a fully worn tire traveling at 60 km / h under full load. [Figure 8B] This is a graph of acceleration data for one revolution of a brand new tire with 100% remaining tread depth traveling at 60 km / h under full load. [Figure 9] FIG. 1 is a plot of acceleration data for an unloaded, fully worn tire over multiple tire revolutions. DETAILED DESCRIPTION OF THE INVENTION

[0038] FIG. 1 shows a tire mounted sensor (TMS) 2 with an acceleration sensor 4, such as an accelerometer, that is designed to monitor a property of a tire 6 when mounted within or on the inner surface of the tire 6.

[0039] When the TMS 2 is mounted inside the tire 6, it rotates together with the tire 6, and the tire 6 comes into contact with the ground 8, causing the tire 6 to deform at the portion of the tire 6 that is in contact with the ground 8. This is generally referred to as the "contact patch" of the tire 6.

[0040] The accelerometer 4 can be used to measure the radial acceleration of the tire 6. When the portion of the tire 6 on which the sensor module 2 is mounted comes into contact with the ground 8 (contact patch), an impact signal is introduced into the acceleration data generated by the accelerometer 4, as shown in FIG.

[0041] FIG. 2 shows an idealized view of acceleration data 10 acquired by a TMS as a function of time, illustrating an impact signal 12 induced in the acceleration data 10 over one revolution of a tire equipped with a TMS. The impact signal 12 has a peak acceleration value (a_min). Before and after (and adjacent to) the impact signal 12 are inter-impact peak acceleration values ​​(a_max), which are small peaks in the opposite direction to the impact signal 12. Except for a_max adjacent to the impact 12, the acceleration data has a constant value (g_value) between impact signals. The impact signal 12 has a rising edge from the inter-impact g_value to a_min, followed by a falling edge from a_min back to the g_value. In this example, the impact signal is shown as a downward peak in the acceleration data, although the direction of the y-axis representing the acceleration value could be reversed.

[0042] The amplitude of the impact signal 12 is defined as the difference between a_min and a_max, i.e., the maximum total deviation of the signal from the g_value. The duration of the impact signal (t_patch), which corresponds to the period when the TMS-containing portion of the tire is in contact with the road, is defined as the time between a first threshold level (t_start) on the rising edge of the impact signal 12 and a second threshold level (t_end) on the falling edge. The method for calculating these thresholds is described in more detail below with respect to subsequent figures. While only one impact signal is shown in Figure 2, it will be understood that there will typically be multiple tire revolutions, resulting in multiple impact signals 12. The time between one impact signal 12 and the next is the tire rotation time.

[0043] 3, a schematic diagram of the components of the tire-mounted sensor 2 of FIG. 1 is shown, along with the components of an external server 14 in communication with the TMS 2. The TMS 2 includes an acceleration sensor 4, such as an accelerometer, a processor 16, a transceiver 18, memory 20, and a battery 22. The external server 14 includes a processor 24, a transceiver 26, and memory 28.

[0044] The acceleration sensor 4 can be used to acquire radial acceleration data of the TMS 2. This data can then be passed to the transceiver 18 for transmission, passed to the processor 16 for processing and then transmitted by the transceiver 18, or passed to the memory 20 for storage and later processing or transmission by the processor 16 and transceiver 18.

[0045] The transceiver 18 of the TMS 2 may be, for example, a wireless transceiver configured to transmit acceleration data acquired by the acceleration sensor 4 to an external server 14. The transceiver 18 may also be configured to transmit acceleration data processed by the processor 16 to the external server 14. The server may then use this raw or processed data to determine tire / vehicle characteristics such as speed, load, tread depth, etc.

[0046] The TMS 2 may establish a "mobile" or telecommunications network connection with the server transceiver 26 of the external server 14 via a network service provider. The network connection may be established in a known manner using any number of communication standards, such as LTE (4G), GSM (2G and 3G), CDMA (2G and 3G), WAN, ISM band 433 MHz, BLE, 315 MHz, 433 MHz, FSK, 5G, etc.

[0047] The TMS 2 is powered by a battery 22 and the external server 14 is powered by an external power source (not shown). Alternatively, the TMS 2 may be powered by an energy harvesting device that obtains energy from the motion of the tires.

[0048] The processor 16 can be used to determine time and / or amplitude related parameters of the acceleration data collected by the acceleration sensor 4, such as the duration of the impact signal (t_patch), the period between two consecutive impact signals (t_rev), the ratio of the duration (t_patch) to the period (t_rev), the impact peak acceleration value (a_min), the inter-impact peak acceleration value (a_max), the slope of the rising and / or falling edges of the impact signal, and the difference between a_min and a_max (i.e., representing the contact patch amplitude (a_patch)).

[0049] These time and / or amplitude related parameters may then be transmitted to the server 14, which may perform further processing to determine tire / vehicle characteristics.

[0050] 4, there is shown a block diagram illustrating steps that may be taken by processor 15 to process data acquired by acceleration sensor 4 of TMS 2. The ability to perform processing steps immediately after acquisition of the data reduces the amount of data stored in memory 20 and provides information regarding the time of impact and other characteristics evaluated through processing more quickly than if processing were performed solely on external server 14.

[0051] The TMS 2, which includes the acceleration sensor 4, is mounted on the inner liner of the tire 6, and as the tire 6 begins to roll along the surface, the sensor 2 also begins to spin about the tire center. This spinning motion generates a centrifugal acceleration in the z-axis direction that can be measured using the acceleration sensor 4 in block 30, and this data is then converted into digital samples by an analog-to-digital conversion system in block 32. An ideal, undeformed tire would not deform and would simply measure this centrifugal acceleration as the tire rotates. However, as noted above, real tires deform at the portion of the tire that is in contact with the surface (the contact patch). This deformation can be seen in the data acquired by the sensor as an impact signal (shown in FIG. 2) when the portion of the tire 6 containing the sensor 2 comes into contact with the surface.

[0052] In block 34, the acceleration signal is filtered (see FIG. 5). The data acquired by the TMS2 during each revolution comprises acceleration data with added vibration noise. To improve the searchability and retrieval of the impact signal, this noise is filtered using a moving average filter of length N, where N is the number of samples used in the filter. For example, a sampling frequency of 4 kHz and a filter length of N=8 samples may be used. The moving average filter "smooths" the data over, for example, 8 samples, so that most of the vibration noise is removed with each tire revolution. To filter the data, a FIFO buffer of length N may be used so that only N samples and moving filter output values ​​need to be stored in memory 20. Therefore, this is the total amount of memory required for the operations of block 34.

[0053] The filtered data is then used in block 36. In block 36, the minimum value of the impact signal (a_min) and the maximum value of the inter-impact acceleration data (a_max) are determined from the filtered data (see FIG. 5). The amplitude (a_patch), i.e., a_min-a_max, is also calculated. The data can be evaluated during processing to find the maximum and minimum values ​​to use for a_max and a_min for each revolution. The output of block 36 is then input to block 38. Alternatively, the a_max and a_min values ​​for a revolution can be stored and input to blocks 38 and 40 for evaluation of the dynamic thresholds at the next revolution of the tire.

[0054] Block 38 evaluates a moving average of a_min (avg_a_min) over multiple tire revolutions. Starting from a set fixed reset value, the moving average avg_a_min is updated periodically or every tire revolution. The moving average avg_a_min is calculated as follows: avg_a_min=(old_a_min+a_min) / 2 where old_a_min is the moving average of the previous a_min and a_min is the value of a_min for one tire revolution.

[0055] One choice for the reset value is a zero g value, which is the acceleration value obtained from the acceleration data from Sensor 2 when the tire is not moving. If avg_a_min starts from a zero g value, it can be made to converge quickly.

[0056] The zero g value can further be used to determine a secondary zero offset parameter, which is defined as the distance between avg_a_min and the zero g value. Zero offset = avg_a_min - zero g The zero offset parameter can be used to estimate the load and wear and can therefore be sent to a server where the load and wear can be calculated. The zero offset parameter is affected by the sampling frequency of the analog-to-digital converter in block 32, the filtering of the signal in block 34, and the deformation of the tire itself.

[0057] A finite state machine (FSM) is implemented to determine the position of the sensor relative to the road surface and the timing of the impact signal in block 40. The FSM has three main states. SEARCH - Initial state, TMS is not in contact patch. CONTACT-PATCH-TMS is located in the contact patch. END - Final state, TMS is away from the contact patch. Determining these conditions is further described below with reference to FIGS.

[0058] In block 40, the timing of the impact signal is determined using a timer / clock. For example, a 400 kHz sampling timer can be used to obtain event timestamps (e.g., impact signal event timestamps). These timestamps are then defined in terms of the sampling clock count. Alternative methods of timing the impact signal can also be used. The start of the impact signal (t_start) occurs at the start of the CONTACT_APTCH state, and the end of the impact signal (t_end) occurs at the start of the END state. The generated timestamps are then used in block 42 to evaluate the reliability of the generated impact signal "event." When the FSM reaches the END state, the data is passed to block 42.

[0059] In block 42, the start time of the previous impact signal (t_start_old) is stored in memory 20. When the FSM generates the start time of the new impact signal (t_start_new) in block 40, the difference between these two timestamps is evaluated in block 42. Rotation time (t_rev) = t_start_new - t_start_old

[0060] Once t_rev is calculated, the processor 16 waits for a certain period after the END state is generated in block 40 to acquire data samples. This period is a fraction of t_rev and is a period during which the shock signal has ended and new samples can be obtained between shocks. This sample is obtained from the acceleration data between shocks and is subsequently converted to g-value using a calibrated look-up table. Since the g-value is proportional to the reciprocal of the square of t_rev, the error function is Error = g-value × (t_rev) 2 and can be calculated as such. This error value can be compared with two calibrated threshold values MAX_ERROR and MIN_ERROR. If MIN_ERROR < error < MAX_ERROR, t_rev is considered valid; otherwise, it is considered invalid. The valid or invalid output is subsequently transferred to block 44.

[0061] In block 44, if the t_rev value obtained in block 42 is valid, this block collects the output of block 46 (described later) along with the selection of the output from any of blocks 32 - 40. The selected data is subsequently sent to the memory 20 for storage or sent to the transceiver 18 for transmission to the external server 14.

[0062] As an example, the data can be sent using 433 MHz digital radio modulation or stored in a FRAM memory connected via an I2C bus. The data may also be sent using a BLE connection.

[0063] Alternatively, if the t_rev value obtained in block 42 is invalid, the data for that rotation is discarded.

[0064] Once the data has been stored / transmitted, a reset signal (shown as a dashed line in FIG. 4) is sent to block 40 to begin searching for a new impact signal indicative of a new contact patch.

[0065] Block 46 receives the outputs of blocks 32-40 and processor 16 performs a real-time evaluation of the acceleration data to extract features useful for inferring information about the tire and its interaction with the road surface and / or vehicle.

[0066] When block 46 receives the values ​​t_start and t_end from block 40, it sets the duration of the impulse signal (t_patch) to t_start=t_end-t_start The contact time ratio can be calculated as follows: ratio=t_patch / t_rev The derivative of the impact signal 12 is calculated and the maximum and minimum values ​​of this derivative can then be used to estimate the remaining tread depth of the tire.

[0067] If processor 16 has insufficient power to evaluate the data in block 46, the data received in block 46 from any of blocks 32-38 can be stored in temporary memory and evaluated after block 40. In such a case, the start of the FSM can be postponed until block 46 has completed the necessary calculations.

[0068] 5 shows a diagram of raw acceleration data 110 acquired by the TMS2 of FIG. 1 as a function of time. The shock signal 12 induced in the acceleration data 110 for one rotation of a tire equipped with the TMS2 is shown. It can be seen that, unlike the acceleration data 10 of FIG. 2, the raw acceleration data 110 of FIG. 5 has vibration noise.

[0069] 4, in block 34, the acceleration data 110 is filtered using a moving average filter of length N. This smooths the data, and the resulting smoothed acceleration data 110' is shown overlaid on the acceleration data 110. Because all acceleration data acquired by the TMS2 is smoothed, a smoothed impact signal 112' is also shown.

[0070] As previously described with reference to block 36 of Figure 4, the values ​​a_min, avg_a_min, and a_max are shown in Figure 5. It will be appreciated that a_min is the "peak" of the smoothed impact signal 112' and a_max is the "peak" of the smoothed acceleration data 110' between impact signals (inter-impact peaks).

[0071] The zero-g value is shown below the peak a_min. As mentioned above, zero-g is determined using TMS2 when the tire is not moving and is used as the starting value for determining avg_a_min. FIG. 5 shows how, before the impact signal 112', the running average avg_a_min is equal to the zero-g value. After the impact signal 112', a new a_min value is obtained, so the running average avg_a_min changes and has a different value than the zero-g value after the impact. Thus, it can be seen that avg_a_min converges to the a_min value over multiple tire revolutions.

[0072] The g_value is shown as the average inter-impulse value of the smoothed acceleration data 110' used to determine the validity of the rotational data acquired by the TMS2.

[0073] The impulse signal 12 has a rising edge from the g_value between impulses to the peak of the impulse signal at a_min, followed by a falling edge from a_min back to the g_value.

[0074] To trigger the FSM state change of FIG. 4 (block 40), the smoothed acceleration data 110’ is compared with two dynamic thresholds defined as functions of the moving average of avg_a_min, a_max, and fixed values x, y. As shown in FIG. 5, there are a first threshold and a second threshold, and the second threshold has a value closer to avg_a_min than the first threshold. Examples of the equations for these thresholds are described below. First threshold = (1 - x) × avg_a_min + x × a_max ≈ avg_a_min - (a_min - a_max) × x = avg_a_min - amplitude × x Second threshold = (1 - y) × avg_a_min + y × a_max ≈ avg_a_min - (a_min - a_max) × y = avg_a_min - amplitude × y Here, the amplitude is defined as a_min - a_max, i.e., the total displacement of the acceleration data 110’, and 0 < x, y < 1, and x < y.

[0075] Therefore, the dynamic first and second thresholds change based on the moving average avg_a_min and a_max. These values change due to factors such as the load on the tire and the rotational speed of the tire.

[0076] The t_start of the impact signal 112’ is defined when the acceleration data 110’ exceeds the first threshold, and the FSM changes its state from SEARCH to CONTACT - PATCH when the first threshold condition is satisfied. The t_end of the impact signal is defined when the acceleration data 110’ exceeds the second threshold, and the FSM changes its state from CONTACT_PATCH to END. The general operation of the FSM in block 40 is shown in FIG. 6.

[0077] 5 has arrows indicating where the start and end times of the impulse signal are defined using first and second thresholds. It will be appreciated that the first threshold is used to define the start (t_start) of the impulse signal 112' at the rising edge of the peak, and the second threshold is used to define the end (t_end) of the impulse signal 112' at the falling edge of the peak.

[0078] The zero offset value before the shock signal 112' is zero because avg_a_min is equal to zero g. However, after the shock signal 112', avg_a_min is no longer equal to zero g, so the zero offset value has a magnitude equal to the difference between avg_a_min and zero g.

[0079] Figure 6 is a diagram of the FSM used in block 40 to determine the t_start and t_end of the impact signal 112' from the dynamic first and second thresholds. It is clear from Figure 6 that the FSM continuously processes to determine the start and end of the CONTACT_PATCH state, with a new search beginning after determining the end of each impact signal 112'. This allows for the analysis of multiple impact signals over multiple revolutions, as the tires equipped with the TSM continue to spin as the vehicle moves.

[0080] Figures 7A and 7B show acceleration data for one revolution of a fully worn tire traveling under full load at two different speeds. Figure 7A shows acceleration data 210A for the tire of a vehicle traveling at 30 km / h, and Figure 7B shows acceleration data 210B for the tire of a vehicle traveling at 60 km / h. Similar to the data shown in Figure 5, the acceleration data 210A, 210B are filtered to produce smoothed data 210A', 210B'.

[0081] The acceleration data 210A' from a vehicle traveling at 30 km / h is flattened compared to the acceleration data 210B', and has a flatter, wider impact signal 212', resulting in a longer t_patch in Figure 7A than in Figure 7B. The a_max at 60 km / h in Figure 7B is much higher than the a_max at 30 km / h in Figure 7A.

[0082] In both Figures 7A and 7B, avg_a_min is the same, approximately equal to the zero-g value, and the zero offset is approximately zero. However, the first and second dynamic thresholds in Figure 7A differ from those in Figure 7B due to the difference in a_max at the two speeds. The first and second thresholds in Figure 7A are shown in dashed lines in Figure 7B, and the change in threshold is evident due to the increase in a_max caused by the faster tire rotation. As in Figure 7B, the faster the tire rotates, the shorter the impact signal 212B' will be because each part of the tire has a shorter period of contact with the road during high-speed rotation. Higher speeds also mean greater radial acceleration measured by TMS2 (because the tire must rotate faster), and therefore the value of a_max will be larger at higher speeds. Therefore, impact signal 212B' will have a larger amplitude (a_patch) than impact signal 212A'.

[0083] It is clear in both Figures 7A and 7B that the first and second dynamic thresholds change over time as a_max changes. Thus, as a_max increases, the first and second thresholds in both Figures 7A and 7B increase as well.

[0084] Figures 8A and 8B show acceleration data for one tire revolution traveling at 60 km / h under full load. Figure 8A shows acceleration data 310A for a vehicle tire with fully worn tires, while Figure 8B shows acceleration data 310B for a new tire (i.e., 100% tread depth remaining). Similar to the data shown in Figure 5, the acceleration data 310A, 310B are filtered to generate smoothed acceleration data 310A', 310B'.

[0085] The acceleration data 310A' and 310B' have similar shapes and high amplitude impact signals 312A', 312B'. The t_patch in Figure 8A is very similar to the t_patch in Figure 8B. Because the tires are traveling at the same speed and with the same load, the a_max is similar in both Figures 8A and 8B.

[0086] The raw acceleration data 310A is much noisier than the raw acceleration data 310B. This may be because a fully worn tire absorbs vibration noise less well than a new tire. Variable road conditions may also affect the degree of noise in the raw acceleration data.

[0087] 8A and 8B, the zero offset value is less than the zero g value because the value avg_a_min of the shock signal 312B' is lower than the avg_a_min of 312A' and is less than the zero g value. However, the avg_a_min in FIG. 8A is approximately equal to the zero g value so that the zero offset is approximately zero.

[0088] The first and second dynamic thresholds for both Figures 8A and 8B are very similar due to the similar values ​​of a_max and avg_a_min before the impact signals 312A', 312B'. However, it will be appreciated that after the impact in Figure 8B, the dynamic thresholds are lowered due to the lower avg_a_min for the new tire in Figure 8B, while the thresholds in Figure 8A remain unchanged after the impact.

[0089] The impact signal 312B' in Figure 8B has a different shape than the impact signal 312A' in Figure 8A. Therefore, to estimate tire wear, the slope in both Figures 8A and 8B can be evaluated by differentiating the acceleration data with respect to time. The derivative of signals 312A' and 312B' with respect to time can be calculated in block 46 of Figure 4, and the maximum and minimum values ​​of this derivative over multiple revolutions can be obtained, for example, using a moving average with a filter length of N=8.

[0090] FIG. 9 shows acceleration data 410 for an unloaded, fully worn tire over multiple tire revolutions. Similar to FIG. 5, the raw acceleration data 410 is smoothed using a moving average filter to produce smoothed acceleration data 410'. The tire speed may vary throughout the revolution shown in FIG. 9. avg_a_min and a_max vary throughout the revolution in FIG. 9, indicated by the change in high and low thresholds throughout the revolution. The adaptation of a_min over multiple tire revolutions to match the current state of the tire, such as speed, load, etc., is evident in FIG. 9.

[0091] At the start of the first rotation, avg_a_min is equal to the zero-g value. However, the a_min for the first rotation is higher than zero-g, so avg_a_min increases after the first rotation. Similarly, avg_a_min increases after the second and fifth rotations due to the a_min values ​​for each of these rotations. Therefore, it is clear that over multiple rotations, avg_a_min converges to a_min and the zero offset value increases.

[0092] All values ​​obtained from the acceleration data shown can be sent to a server for further analysis such as tire tread depth estimation, speed estimation, or load estimation.

[0093] While the present invention has been described in detail in connection with a limited number of embodiments, it should be readily understood that the present invention is not limited to such disclosed embodiments. For example, the effect of a high load on acceleration data compared to a lightly loaded vehicle is similar to the effect of a low speed on acceleration data compared to a high speed vehicle.

[0094] The present invention can be modified to incorporate any number of variations, alterations, substitutions, or equivalent arrangements not heretofore described, but which are commensurate with the scope of the present invention. Additionally, while various embodiments of the invention have been described, it is to be understood that aspects of the invention may include only some of the described embodiments. Accordingly, the invention is not to be deemed limited by the foregoing description, but is limited only by the appended claims.

Claims

1. A method for measuring an impact signal induced in acceleration data measured at a contact patch that contacts the road surface for each rotation of a tire by an acceleration sensor mounted on the tire over multiple rotations of the tire rolling on the road surface, comprising: acquiring the acceleration data over multiple revolutions of the tire; processing the acceleration data with the sensor; Including, The step of processing the acceleration data by the sensor comprises: processing the acceleration data to measure an acceleration value for each impact signal and calculate an impact peak acceleration value (a_min); calculating a running average (avg_a_min) of impact peak acceleration values ​​over the plurality of revolutions of the tire; measuring the start and end times of each impact signal by comparing the acceleration values ​​of the acceleration data with a dynamic threshold adjusted according to a moving average (avg_a_min) of the impact peak acceleration values; generating, from the measured start and end times of each impact signal, time-related parameters selected from one or more of the duration of the impact signal (t_patch), the period between two successive impact signals (t_rev), and the ratio of said duration (t_patch) to said period (t_rev); Including, The method further comprises transmitting the time-related parameters to an external server.

2. 2. The method of claim 1, further comprising the step of processing the acceleration data to measure acceleration values ​​between impact signals and to calculate a peak acceleration value between impacts (a_max).

3. 3. The method of claim 2, further comprising adjusting the dynamic threshold in response to the inter-impact peak acceleration value (a_max).

4. 3. The method of claim 2, wherein the dynamic thresholds include a first dynamic threshold used to measure the start time of the impact signal and a second dynamic threshold used to measure the end time of the impact signal.

5. 5. The method of claim 4, wherein the calculation of the first dynamic threshold uses a function of avg_a_min, a_max, and a coefficient x, where x is a fixed value satisfying 0<x<1.

6. 5. The method of claim 4, wherein the calculation of the second dynamic threshold uses a function of avg_a_min, a_max, and a coefficient y, where y is a fixed value satisfying 0<x<1 and y≠x.

7. 2. The method of claim 1, further comprising the step of processing the acceleration data to measure acceleration values ​​between impact signals and to calculate an average acceleration value between impact signals as a g-value.

8. 8. The method of claim 7, wherein g value x (t_rev) ２ The method further comprises the step of verifying whether the generated time period (t_rev) is valid by comparing the value of t_rev with an expected error value range.

9. 10. The method of claim 1, determining a zero g value for the tire by processing the acceleration data to determine an acceleration value when the tire is not moving; processing the acceleration data for each impact signal to measure a zero offset of the acceleration values ​​calculated as the difference between a running average (avg_a_min) of the peak acceleration values ​​and the zero g value; The method further comprises:

10. 3. The method of claim 2, wherein the difference between the impact peak acceleration value (a_min) and the inter-impact peak acceleration value (a_max) is averaged over multiple tire revolutions to measure a contact patch amplitude (a_patch), and the contact patch amplitude is transmitted to the external server.

11. 3. The method of claim 2, further comprising the step of processing the acceleration data for each impact to determine a slope of at least one of a rising edge and a falling edge of the impact signal.

12. 12. The method of claim 11, transmitting per-impact amplitude-related parameters selected from one or more of the impact peak acceleration value (a_min), the inter-impact peak acceleration value (a_max), the slope, and the difference between a_min and a_max to the external server; Optionally, receiving said amplitude-related parameters at said external server and using said amplitude-related parameters to determine tire wear. The method further comprises:

13. 2. The method of claim 1, further comprising receiving the time-related parameters at the external server and using the time-related parameters to determine one or more of: (i) load, (ii) vehicle center of gravity, and (iii) rotational speed of the tire.

14. A computer readable storage medium storing firmware code which, when executed on a data processor, performs the method of any one of claims 1 to 13.

15. 1. A tire-mounted sensor system that measures impact signals over multiple revolutions of a tire rolling on a road surface, comprising: The impact signal is induced by acceleration data measured by a tire-mounted sensor system mounted on the tire at a contact patch that contacts the road surface with each rotation of the tire; the tire-mounted sensor system includes a processor, a transmitter, and an acceleration sensor; the acceleration sensor acquires the acceleration data over multiple rotations of the tire; The processor: Processing the acceleration data to measure an acceleration value for each impact signal and calculate an impact peak acceleration value (a_min); calculating a running average (avg_a_min) of impact peak acceleration values ​​over the plurality of revolutions of the tire; measuring the start and end times of each impact signal by comparing the acceleration values ​​of the acceleration data with a dynamic threshold adjusted according to a moving average (avg_a_min) of the impact peak acceleration values; and generating, from the measured start and end times of each impact signal, time-related parameters selected from one or more of the duration of the impact signal (t_patch), the period between two successive impact signals (t_rev), and the ratio of said duration (t_patch) to said period (t_rev); The transmitter is a tire-mounted sensor system that transmits the time-related parameters to an external server.

Images