Tire Air Loss Detection

Abstract

deriving a first air temperature value based at least in part on the first group of air temperature values ​​and a second air temperature value based at least in part on the second group of air temperature values; deriving an air pressure value based at least in part on the group of air pressure values; calculating an estimated air temperature value based at least in part on (i) the derived first or second air temperature value, (ii) a change in temperature between the derived first and second air temperature values, and (ii) a predetermined time constant; calculating an estimated normalized pressure based on the estimated air temperature value and the derived air pressure value; and determining whether air loss has occurred based at least in part on monitoring the estimated normalized pressure over time.

Description

[Technical Field]

[0001] The present invention relates to a computer-implemented method for detecting air loss in a vehicle tire. The present invention further relates to a system for detecting air loss in a vehicle tire and a corresponding computer program for detecting air loss in a vehicle tire.

[0002] Being able to detect air loss in a vehicle tire, which may be associated with a puncture, is important to vehicle safety. Accurate measurements of tire pressure can be obtained using sensors mounted inside the tire.

[0003] Sensors mounted inside the tire are commonly called tire monitoring system (TMS) sensors. TMS are used to monitor several parameters of the tire itself, such as tire pressure and tire air temperature, as well as to extract information about the interaction of the tire with its surrounding environment, such as the road or vehicles.

[0004] The majority of variations in measured tire pressure are due to changes in the temperature of the tire and the air inside it. These variations are undesirable when looking for potential air loss events. Some current pressure loss detection methods normalize the air pressure in a vehicle's tires by taking a temperature reading along with the pressure reading and dividing the pressure reading by the temperature reading to remove the temperature dependency. Summary of the Invention

[0005] According to a first aspect of the present invention, there is provided a computer-implemented method for detecting air loss in a vehicle tire, the method comprising a measuring step and a processing step, the measuring step comprising: measuring a time series of air temperature values ​​inside a vehicle tire using an air temperature sensor and associating a timestamp with each air temperature value; and measuring a time series of air pressure values ​​within a vehicle tire using an air pressure sensor and associating a timestamp with each air pressure value; and The processing steps are: selecting a first group of air temperature values ​​from the time series of air temperature values ​​and associating therewith a first group timestamp, the first group including at least one measured air temperature value from the time series of air temperature values; selecting a second group of air temperature values ​​from the time series of air temperature values ​​and associating therewith second group timestamps, the second group timestamps being later than the first group timestamps, the second group including at least one measured air temperature value from the time series of air temperature values; selecting a group of air pressure values ​​from the time series of air pressure values ​​and associating therewith a third group of timestamps, the group of air pressure values ​​including at least one measured air pressure value from the time series of air pressure values, the third group of timestamps corresponding to either the first group of timestamps or the second group of timestamps; deriving a first air temperature value based at least in part on the first group of air temperature values ​​and a second air temperature value based at least in part on the second group of air temperature values; deriving an air pressure value based at least in part on the group of air pressure values; calculating an estimated air temperature value at a time corresponding to the third group of timestamps based at least in part on (i) the derived first or second air temperature value corresponding to the third group of timestamps, (ii) a change in air temperature between the derived second air temperature value and the derived first air temperature value, and (ii) a predetermined time constant; calculating an estimated normalized air pressure at a time corresponding to the third group of time stamps based on the estimated air temperature value and the derived air pressure value; and determining whether an air loss has occurred based at least in part on monitoring the estimated normalized air pressure over time.

[0006] While it is known to use air temperature readings to normalize tire pressure readings, the inventors have recognized that temperature readings are subject to a time lag as the tire, sensor, and sensor casing retain heat, and therefore the temperature reading from an air temperature sensor often lags slightly behind the actual current temperature of the air in the tire, meaning that even the normalized air pressure value will still contain anomalies.

[0007] Those skilled in the art will appreciate that by normalizing the pressure values ​​using air temperature values ​​that take into account how air temperature changes and therefore what the time lag may be, a more accurate pressure normalization can be achieved compared to normalization using raw air temperature values. This can result in a series of normalized pressure values ​​with less variation, making it easier to detect true pressure loss. This method can be particularly advantageous in situations where the temperature of the air in the tire changes frequently and / or quickly, such as at the start of a trip or during a trip where driving conditions may change, such as driving in stop-start traffic.

[0008] Although a preferred application of the disclosed method is the detection of air loss in a tire, one skilled in the art will readily appreciate that embodiments of the present disclosure are also suitable for detecting positive pressure changes (air gain or inflation).

[0009] In an embodiment, monitoring the estimated normalized air pressure over time includes repeating the method such that the measuring and processing steps are performed continuously.

[0010] In an embodiment, each estimated air temperature value is calculated according to:

[0011]

number

[0012] The inventors have recognized that by calculating the estimated air temperature based not only on changes in air temperature but also on a predetermined time constant, the present method effectively estimates the amount of air (e.g., moles of air) inside the tire by considering the effects of real-time temperature fluctuations. This method may require very low resources, for example, in terms of memory occupation, CPU usage, and power (which is particularly important when implementing the method in a TMS system).

[0013] In an embodiment, the predetermined time constant is related to the thermal capacity of the air temperature sensor. Thus, the latency of the air temperature sensor can be taken into account in the air temperature reading. It will be appreciated that during periods when the temperature of the air inside the tire is frequently changing, the temperature of the air temperature sensor itself will lag behind the actual temperature of the air, and the magnitude of this lag will depend on the thermal capacity of the air temperature sensor (e.g., its body or casing). By calculating an estimated air temperature value based at least in part on the predetermined time constant related to the thermal capacity of the particular air temperature sensor used to obtain the reading, a more accurate estimation of the actual current temperature of the air inside the tire can be achieved. It will be appreciated that the value of the predetermined time constant will vary for different air temperature sensor configurations (e.g., different casing sizes, shapes, materials, etc.).

[0014] In one embodiment, the predetermined time constant is calculated using historical air pressure and air temperature data collected using at least an air temperature sensor. Because the time constant is related to the thermal capacity of the air temperature sensor, the historical air temperature data must be collected using a sensor with a predetermined time constant. Because the time constant is not inherent to the pressure sensor, it is not necessary to collect the historical air pressure data using the same air pressure sensor as used in the measurement process of the first aspect.

[0015] In an implementation, the time constant is: obtaining historical air pressure and air temperature data collected using at least an air temperature sensor; calculating a plurality of series of estimated normalized air pressure values ​​using a plurality of different values ​​of the time constant; For each series of estimated normalized air pressure values, calculating the difference between the maximum estimated normalized air pressure and the minimum estimated normalized air pressure; and The predetermined time constant is determined in advance by selecting a time constant value that minimizes the difference.

[0016] In embodiments, deriving a first air temperature value based at least in part on the first group of air temperature values ​​includes applying a low pass filter to the first group of air temperature values. In embodiments, deriving a second air temperature value based at least in part on the second group of air temperature values ​​includes applying a low pass filter to the second group of air temperature values. In embodiments, deriving an air pressure value based at least in part on the group of air pressure values ​​includes applying a low pass filter to the group of air pressure values.

[0017] Deriving the air temperature and / or air pressure values ​​by applying a low pass filter reduces out of band noise by removing small fluctuations in the data.

[0018] In an embodiment, the low pass filter comprises a moving average, and multiple air pressure and / or air temperature values ​​that fall within an averaging time window are averaged to produce a filtered air pressure and / or filtered air temperature value. It will be appreciated that it is the filtered (averaged) value that is associated with the group timestamp.

[0019] In an embodiment, a moving (e.g., rolling) averaging window is used as the low-pass filter. The moving averaging window may have a length between 10 seconds and 1000 seconds, such as between 100 seconds and 500 seconds, for example 300 seconds.

[0020] In an embodiment, detecting air loss includes comparing the estimated normalized air pressure to an air pressure threshold, and determining that there is air loss if the estimated normalized air pressure is lower than the air pressure threshold.

[0021] In an embodiment, the method includes calculating the estimated normalized air pressure at multiple times to generate a time series of estimated normalized air pressure values; Calculating the difference between the maximum estimated normalized air pressure value and the minimum estimated normalized air pressure value in the time series of estimated normalized air pressure values; comparing the difference to a difference threshold; and determining that an air loss exists if the difference is greater than a difference threshold; and determining whether an air loss exists by:

[0022] In an embodiment, the method includes calculating the estimated normalized air pressure at multiple times to generate a time series of estimated normalized air pressure values; averaging the estimated normalized air pressure values ​​over a period of time to generate an air pressure reference value; Determining the difference between the estimated normalized air pressure value and the air pressure reference value; and Determining that air loss exists if the magnitude of the difference is greater than a difference threshold, and thus determining whether air loss exists.

[0023] In an embodiment, the method includes calculating an estimated normalized air pressure at a plurality of times to generate a time series of estimated normalized air pressure values, and determining whether air loss exists comprises: averaging the estimated normalized air pressure values ​​within a plurality of subsequent time periods to generate a series of average air pressure values; and determining that an air loss exists if the series of average pressure values ​​is trending downward.

[0024] In an embodiment, the vehicle tire is one of a plurality of vehicle tires mounted on the same vehicle, and the method includes calculating a time series of estimated normalized air pressure values ​​for each of the plurality of tires, and determining whether air loss exists in any one of the vehicle tires includes: Calculating an estimated normalized inflation rate over a fixed length time window for each of a plurality of tires of a vehicle; comparing the estimated normalized inflation pressure rating of the tire being evaluated to one or more of the estimated normalized inflation pressure ratings of other tires in the plurality of tires on the vehicle; and and determining from the comparison whether the tire being evaluated has an abnormal estimated normalized inflation rate that may indicate air loss in the tire.

[0025] In embodiments, the method includes indicating that air loss in a vehicle tire has been detected. This indication can be provided by any suitable means. In embodiments, the method includes indicating to a user (e.g., a driver) via an audio / visual alarm, a dashboard alert, or an alert displayed on the driver's mobile phone app. In yet other embodiments, the indication that air loss in a vehicle tire has been detected can be provided in an input to a vehicle on-board unit and / or an advanced driver assistance system (ADAS) for appropriate action to ensure driver safety.

[0026] In embodiments, the measurement and processing steps are performed simultaneously so that air loss detection occurs in real time. It will be understood that the first group of measurement steps needs to be performed before the first group of processing steps (as processing can only be performed after data has been collected), but that thereafter the processing and measurement steps occur in parallel with one another. In other words, the processing steps are performed "on the fly" rather than processing all of the data later. That is, all processing steps are performed at the time of sampling, which allows for reduced memory usage and improved computational resources since a separate post-processing step is not required after the sampling phase; such embodiments allow for faster determination of air loss while optimizing the use of computational resources.

[0027] According to a second aspect of the present disclosure, there is provided a system for detecting air loss in a vehicle tire, the system comprising: an air temperature sensor and an air pressure sensor mounted inside the tire and configured to perform the measurement step of the first aspect; a memory configured to store a predetermined time constant proportional to the thermal capacity of the air temperature sensor; and a processor configured to perform the processing steps of the first aspect.

[0028] In an embodiment, the predetermined time constant is related to the thermal capacity of the air temperature sensor. Thus, the latency of the air temperature sensor can be taken into account in the air temperature reading. It will be appreciated that during periods of frequent changes in the temperature of the air within the tire, the temperature of the air temperature sensor itself will lag behind the temperature of the air, and the magnitude of this lag will depend on the thermal capacity of the air temperature sensor (e.g., its body or casing). By calculating an estimated air temperature value based at least in part on the time constant related to the thermal capacity of the particular air temperature sensor used to take the reading, a more accurate estimate of the actual current temperature of the air within the tire can be obtained. It will be appreciated that the value of the time constant will vary for different air temperature sensor configurations (e.g., different casing sizes, shapes, materials, etc.).

[0029] In one embodiment, the predetermined time constant is calculated using historical air pressure and air temperature data collected using at least an air temperature sensor. Because the predetermined time constant is related to the thermal capacity of the air temperature sensor, the historical air temperature data must be collected using the sensor for which the time constant has been determined. Because the time constant is not inherent to the pressure sensor, it is not necessary to collect the historical air pressure data using the same air pressure sensor as used in the measurement process of the first aspect.

[0030] In an implementation, the time constant is: obtaining historical air pressure and air temperature data collected using at least an air temperature sensor; calculating a plurality of series of estimated normalized air pressure values ​​using a plurality of different time constant values; For each series of estimated normalized air pressure values, calculating the difference between the maximum estimated normalized air pressure and the minimum estimated normalized air pressure; and The predetermined time constant is determined in advance by selecting a time constant value that minimizes the difference.

[0031] In an embodiment, the system comprises a tire mounted sensor (TMS) unit that includes an air temperature sensor and an air pressure sensor.

[0032] In an embodiment, the system comprises an air loss indication unit configured to output that air loss in a vehicle tire has been detected.

[0033] In an embodiment, the TMS unit includes a microcontroller including a memory and a processor, so the system can be implemented locally in a TMS unit mounted on a vehicle tire.

[0034] In an embodiment, the system includes a remote (e.g., cloud-based) server including a memory and a processor. The remote server may be configured to receive raw air temperature and pressure readings (e.g., via wireless transmission) from TMS units mounted on vehicle tires. Multiple TMS units may report their readings to the same remote server, for example, as part of a fleet management system.

[0035] It will be understood, of course, that the term “server,” as used herein, refers to a computer or computing machine (e.g., a server device) connected to a network such that the server transmits and / or receives data from other devices (e.g., computers or other machines) on the network. Additionally or alternatively, the server may provide resources and / or services to other devices on the network. The network may be the Internet or any other suitable network. The server may be embodied in any suitable server type or device, e.g., a file server, an application server, a communication server, a computing server, a web server, a proxy server, etc. The server may be a single computing device or may be a distributed system, i.e., the functionality of the server may be divided across multiple computing devices. For example, the server may be a cloud-based server, i.e., its functionality may be divided across many computers “on demand.” In such a configuration, server resources may be obtained from one or more data centers, which may be located in different physical locations.

[0036] It will therefore be appreciated that the processing steps described above performed by a server may be performed by a single computing device, i.e., a single server, or by multiple separate computing devices, i.e., multiple servers. For example, all of the processing steps may be performed by a single server that has access to all the relevant information needed.

[0037] According to a third aspect of the present invention there is provided a computer program comprising instructions which, when executed by a computer, cause the computer to carry out the measuring steps and to carry out the processing steps of the method of the first aspect. [Brief explanation of the drawings]

[0038] One or more non-limiting examples will now be described, by way of example only, with reference to the accompanying drawings. [Figure 1] 1 is a schematic diagram of a system for detecting air loss in a vehicle tire installed on a heavy goods vehicle, according to an embodiment of the present invention; [Figure 2] 1 is a schematic diagram of a TMS unit in a vehicle tire. [Figure 3] 4 is a schematic diagram of a system for detecting air loss in a vehicle tire installed on a heavy goods vehicle according to another embodiment of the present invention. [Figure 4] 1 is a flowchart illustrating a computer-implemented method for detecting air loss in a vehicle tire. [Figure 5] 10 is a graph showing the determination of a time constant. [Figure 6] 1 is a graph showing multiple parameters related to tire pressure plotted over time. [Figure 7a] 10 is another graph showing multiple parameters related to tire pressure plotted over time. [Figure 7b] 10 is another graph showing multiple parameters related to tire pressure plotted over time, illustrating an air loss event. [Figure 8] 10 is another graph showing multiple parameters related to tire pressure plotted over a shorter period of time, illustrating an air loss event;

[0039] 1 illustrates a system 1 for detecting an air loss event in a vehicle tire 3 mounted on a vehicle 2, according to an embodiment. The system 1 includes a plurality of tire mounted sensor (TMS) units 5, each mounted within a tire 3. The TMS units 5 each include a microcontroller 4 having a memory 12 and a processor 14 (as seen in FIG. 2). The TMS units 5 are configured to communicate with an air loss indication unit 10 within the vehicle, as shown by the dashed communication line in FIG. 1.

[0040] 2 shows in detail the TMS unit 5. The TMS unit comprises a microcontroller 4, which in turn comprises a memory 12 and a processor 14. The TMS unit 5 comprises two sensors: an air temperature sensor 16 and an air pressure sensor 18.

[0041] FIG. 3 illustrates a system 100 for detecting an air loss event in a vehicle tire 3 mounted on a vehicle 2, according to another embodiment. Similar to the system of FIG. 1, the system 100 of FIG. 3 includes multiple tire-mounted sensor (TMS) units 5, each mounted within a tire 3. The system 1 of FIG. 3 also includes a network communication device 6 provided on the vehicle 2. In an embodiment, the network communication device 6 may be a dongle that plugs into a port on the vehicle 2, such as an OBD port, an FMS port, or the like. In an alternative embodiment, the network communication device 6 may be a permanently installed transceiver box. The TMS unit 5 is configured to communicate with a remote server 7 having a processor 114 and a memory 112 via the communication device 6 within the vehicle 2. In this example, the TMS unit 5 communicates with the network communication device 6 via a Bluetooth® connection. In another example, the TMS unit 5 transmits data to the network communication device 6 at 433 MHz FSK modulation. It will be readily 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 via a wireless network connection (e.g., a cellular network) with a remote server 7. The remote server 7 is connected via the wireless network to an air loss indication unit 10a inside the passenger compartment of the vehicle 2. In the illustrated embodiment, multiple air loss indication units are provided, further including a driver's mobile device 10b and a fleet manager's computer 10c.

[0042] It will be appreciated that in system 1 of FIG. 1 , because TMS unit 5 includes microcontroller 4, any processing is performed by microcontroller 4, and therefore the system does not need to communicate with remote server 7. However, in system 100 of FIG. 3 , TMS unit 5 communicates with remote server 7 (via communication device 6). Because remote server 7 includes memory 112 and processor 114, processing is performed on remote server 7. The same TMS unit 5 shown in FIG. 2 may also be used in the system of FIG. 3 , although microcontroller 14 may be omitted and may not be used or may only be partially used for the processing steps described herein. In an embodiment, TMS unit 5 of system 100 according to FIG. 3 may still have a microcontroller, and processing may be shared between server 7 and microcontroller 4. For example, microcontroller 4 may perform edge processing.

[0043] Both the systems 1 of Figures 1 and 3 are operable to carry out the method illustrated in Figure 4, which is described below.

[0044] In steps 21 and 23, the air pressure and air temperature sensors 18, 16 in the TMS unit 5 are sampled and a timestamp is associated with each sampled air temperature value and each sampled air pressure value. In an embodiment, the sampling time is constant and may be set to, for example, 15 seconds. In an embodiment, the acquired pressure and temperature samples may be interpolated (resampling) to overcome problems that may arise from non-constant sampling times or from the loss of some samples during transmission from the sensors.

[0045] Therefore, the output of steps 21 and 23 is the measured air temperature T m Time series and measured air pressure P m It is a chronological order.

[0046] In steps 25 and 27, the measured air temperature T mFirst and second groups of air temperature values ​​are selected from the time series and associated with first and second group timestamps, respectively. The second group timestamp is later than the first group timestamp. In embodiments, the first group may include only a single air temperature value, although it should be understood that in other embodiments, the first group may include multiple air temperature values. In embodiments, the second group may include only a single air temperature value, although it should be understood that in other embodiments, the second group may include multiple air temperature values.

[0047] In step 29, the group of air pressure values ​​is divided into the measured air pressure P m A timestamp is selected from the time series and associated with a third timestamp, which corresponds to either a timestamp in the first group or a timestamp in the second group. In embodiments, the group may include only a single air pressure value, although of course, in other embodiments, the group may include multiple air pressure values.

[0048] In step 31, a first air temperature value is derived based on the first group, and a second air temperature value is derived based on the second group. In embodiments where the group includes only a single air temperature value, the deriving step simply involves taking the single value and its timestamp, while in embodiments where the group includes multiple values, the derivation involves processing the multiple values ​​in the group to generate a single air temperature value for the entire group. The single air temperature value is then associated with the group's timestamp.

[0049] The derivation of a single air temperature value for each group of air temperature values ​​may be performed using any known function suitable to one skilled in the art. In an exemplary embodiment, the derivation includes applying a filtering function, such as a low pass filter or an averaging function (e.g., calculating an average), over each group of air temperature values ​​such that the derived first air temperature value and the derived second air temperature value are filtered or average values, respectively.

[0050] In step 33, an air pressure value is derived based on the group of air pressure values. In embodiments where the group includes only a single air pressure value, the deriving step simply involves taking the single value and its timestamp, while in embodiments where the group includes multiple values, the derivation involves processing the multiple values ​​in the group to generate a single air pressure value for the entire group. The single air pressure value is associated with a third group timestamp that is the same as either the first group timestamp or the second group timestamp.

[0051] The derivation of a single air pressure value based on a group of air pressure values ​​may be performed using any known function suitable to one skilled in the art. In an exemplary embodiment, the derivation includes applying a filtering function, such as a low pass filter or an averaging function (e.g., calculating an average), over the group of air pressure values ​​so that the derived air pressure value is a filtered or averaged value.

[0052] In an exemplary embodiment, the derivation of the first air temperature value, the second air temperature value, and the air pressure value are all accomplished by applying a rolling averaging window having a length of 300 seconds.

[0053] The rate at which derived values ​​are calculated may be decimated to reduce computational requirements. In an exemplary embodiment, the data is decimated to a 60 second rate. If a moving averaging window is used as the filtering function, the moving average is considered to have a length n=5 (5×60 s=300 s) in this embodiment.

[0054] Since the filtered quantities are obtained at the sampling time and therefore no separate post-processing calculation stage is required, CPU resource usage is minimal. Memory resources are also optimized by the combined use of sampling and filtering. For example, if a moving averaging window of length n=5 is used as the filtering function for both the group of air temperature values ​​and the group of air pressure values, the memory required to perform the two moving averages is only associated with 3n (i.e., 15) samples, i.e., 5 for pressure and 10 (5×2) samples for temperature.

[0055] In step 35, an estimated air temperature value is calculated using the derived first and second air temperature values ​​and a predetermined time constant related to (e.g., directly proportional to) the thermal capacity of the air temperature sensor incorporated in the TMS unit 5. The mathematical description of these physical systems is generally a first-order differential equation.

[0056]

number

[0057] According to Equation 1, a real-time numerical solution of the physical model of the air temperature sensor 16 is evaluated.

[0058] The calculation of the predetermined time constant τ is described below in connection with Figure 5. The time constant τ may be predetermined and stored in the memory 12 of the TMS unit 5.

[0059] Equation 1 is the discrete derivative T of the given time constant τ as an estimate of the system time constant τ of the air temperature sensor. d If τ is previously estimated using available (e.g., historical, not real-time) data, then the real-time estimated air temperature T at a given time t corresponding to the third timestamp can be calculated using e can be approximated by:

[0060] formula 2

number

[0061] The third timestamp corresponds to either a timestamp in the first group or a timestamp in the second group, and the timestamp in the second group is later than the timestamp in the first group. When the third timestamp corresponds to a timestamp in the first group, the quantity

[0062]

number

[0063]

number

[0064] Taking into account the characteristics of the sensor, only the air temperature is estimated, not the air pressure. This is because the time lag of the measured pressure relative to the actual instantaneous pressure is generally much shorter (e.g., less than 1 second). Therefore, the pressure time lag can be practically ignored, and therefore, in the described embodiment, the derived (measured) pressure is assumed to be the same as the estimated air pressure value (P e =P d ) Of course, it will be appreciated that the pressure can be estimated taking into account the characteristics of the air pressure sensor without departing from the disclosed invention.

[0065] In step 37, an estimated normalized air pressure is calculated according to Equation 3 using the derived air pressure value and the estimated air temperature value.

[0066]

number

[0067] P en is an estimate of the pressure measured consistently at a fixed temperature T0, the latter being an arbitrary constant commonly called the reference temperature, necessary to obtain a quantity with the same units of pressure. In an exemplary embodiment, T0=300 Kelvin, which corresponds to the average tire air temperature in Europe.

[0068] P en The volume is also proportional to a good approximation to the amount of moles of air contained within the vehicle tire based on the gas perfection law when taking into account the volume constant, and therefore accurately represents whether the vehicle tire is low in air, which would indicate a puncture.

[0069] Finally, in step 39, the estimated normalized air pressure value is used to determine whether air loss has occurred in the vehicle tire 3. Of course, it will be understood that steps 21-37 may be repeated (e.g., continuously) such that a time series of estimated normalized air pressure values ​​may be generated. Any suitable algorithm may be used to evaluate the estimated normalized air pressure over time to determine whether air loss has occurred. Some example algorithms are briefly described below, but it should be understood that the estimated normalized air pressure values ​​obtained according to the described implementations may be used in any suitable algorithm to determine whether air loss has occurred.

[0070] Algorithm a) The most basic check is P enThis can be done by comparing the difference between the maximum and minimum values ​​obtained by, with an appropriate threshold: ΔP en =max(P en )-min(P en )>P thr formula 4

[0071] P thr may be a predefined value or a self-learning value. For example, in the case of a self-learning value, ΔP en (t0) can be evaluated in the first few minutes starting at some time t0, and P thr And ΔP en It can be created as a function of (t0).

[0072] ΔP en >P thr In the case of en The final average value (max(P en )+min(P en A further check can be made to compare with )) / 2. en If P is greater than the average value, an air filling can be detected. en If the above inequality remains true for a period of time, an air leak can be more reliably detected and signaled.

[0073] Algorithm b) P en (t) average value P ref can be evaluated over a set period of time, and the reference threshold is P ref It can be created as a function of

[0074] In this case, ΔP en (t)=|P en (t)-P ref |>P thr current P en P ref A further check can be made to verify whether it is greater than or less than. In the first case, the new P refThe value can be evaluated, for example, in the case of tire inflation. In the latter case, there is a high probability of air loss associated with a leak, such as a puncture. If the above inequality remains true for a certain period of time, air leaks can be more reliably detected and signaled.

[0075] Algorithm c) At each moment t i Average value of time Δt

[0076]

number

[0077] Algorithm d) At each moment t i The trend is evaluated over a period of time Δt.

[0078]

number

[0079] Algorithm e) P en can be evaluated over time for multiple tires installed on the same vehicle. The estimated normalized inflation rate is P en For each tire on the vehicle, the estimated normalized inflation pressure may be calculated over a fixed-length time window using the . By comparing the rate of estimated normalized inflation pressure of the tire being evaluated with one or more of the estimated normalized inflation pressure rates of other tires mounted on the vehicle, an abnormal rate of estimated normalized inflation pressure may be identified. Such an abnormal rate may indicate an air leak in the tire being evaluated.

[0080] Calculation of the predetermined time constant τ will now be described with reference to Figure 5. The predetermined time constant τ depends on the combined tire and sensor system configuration and only needs to be determined once for a particular system (tire + sensor) configuration.

[0081] Once determined, it is s Air temperature at t e (t s ) can be used in Equation 2 to perform real-time estimation of

[0082] The predetermined time constant is calculated by applying an error (cost) function to a set of historical air pressure and temperature data where the amount of air in the tire is known not to have changed (e.g., there have been no inflations or leaks). The error function is defined as follows: ΔP en (τ)=max(P en (τ,t))-min(P en (τ,t)) Equation 5 A time t∈[T0,T1] spans over a defined time interval [T0,T1], which can range from a few hours to a few months.

[0083] The optimal value of τ is ΔP en (τ) is minimized. Since the amount of air in the tire is known to have not changed over the course of historical data collection, if air temperature fluctuations are accurately corrected (via a precise value of τ applied via Equation 2), then ΔP en (τ) should be close to 0.

[0084] Figure 5 shows the ΔP en 5 shows τ plotted against τ. The data used to create the plot in FIG. 5 was generated over a 30 day period for three steering tires on a heavy goods vehicle. For the particular sensor and tire system configuration for which this historical data was collected, it can be seen that the optimal value for τ is close to 350 seconds. This is recorded in memory 12 of TMS unit 5 as a predetermined time constant specific to the tire and its TMS.

[0085] Through additional experiments, it is shown that the τ estimation performed on a small dataset of three tires over a 30-day period proves to perform well on a larger dataset consisting of over 100 tires mounted on several (>10) trucks running for approximately one year in a variety of ambient temperature conditions.

[0086] FIG. 6 shows actual data of steering tire pressure values ​​on a heavy goods vehicle measured over a 24 hour period including stops and restarts.

[0087] The top group of data points 51 are raw air pressure readings. Raw air pressure readings 51 can show large variations.

[0088] The group of data points 53 running approximately adjacent to the raw air pressure reading 51 are the raw air temperature readings 53. It can be seen that the fluctuations in the raw air pressure readings 51 follow the fluctuations in the raw air temperature readings 53, but with a slight lag.

[0089] Two sets of normalized air pressure values ​​55, 57 are also shown on the plot. The set of air pressure readings P labeled as 55 n are conventionally normalized simply using the raw air pressure readings 51 and the raw air temperature readings 53. A set of estimated normalized air pressure readings P labeled 57 en ("Physical Model Pressure") is calculated according to the physical model described in this disclosure (see Equation 3 above).

[0090] From Figure 6, the air pressure P evaluated by the physical model en is ΔP en = 0.14 bar, but with a conventional normalized air pressure P n is ΔP n = 0.45 bar (approximately 3 times ΔP en ) and the raw air pressure P m is ΔP m = 1.43 bar (approximately 10 times ΔPen ) can be seen to vary in the range

[0091] P en Larger vibrations for P n is mainly P n This is due to transient pressure values ​​that are not fully accounted for by P. Whenever the pressure and temperature change rapidly, the temperature time lag (related to the thermal capacity of the particular tire and sensor system configuration) n This generates outliers in the dataset. Such anomalies 59 can be clearly seen in Figure 6. These anomalies can lead to false positives, where an air leak is detected when one is not present. Alternatively, to reduce the likelihood of a false positive, the sensitivity of any air loss detection algorithm must be reduced, which may result in slower air loss detection than would be possible with a more sensitive algorithm. In contrast, the physical normalized air pressure remains much more stable because it takes into account the temperature time lag (as discussed above). Therefore, by accounting for the temperature time lag, the likelihood of a false positive can be reduced while still maintaining a highly sensitive air loss detection algorithm.

[0092] Figures 7a and 7b show the same readings 51, 53, 55, and 57 as Figure 6. Figure 7a plots data from a heavy goods vehicle traveling during a first 24-hour trip. Figure 7b plots data for a second trip, also lasting 24 hours, but during the second trip there is a slow leak of air into the tire. As before, normalized pressure reading 55 is still subject to abnormal signal spikes, so the leak cannot be detected as easily as it can be detected using estimated normalized pressure reading 57 of Figure 7b.

[0093] Similarly, Figure 8 shows the same readings 51, 53, 55, and 57 as in Figure 6 plotted for data from a steering tire on a heavy goods vehicle traveling for two hours. Conventionally, in the normalized pressure reading 55, an anomaly 60 resulting from an increase in pressure is followed by a downward trend over the next 40 minutes. This parameter P n If the pressure reading (labeled 55) had been used by an algorithm designed to look for downward trending pressures, a false positive could be identified here as an air loss event. However, in Figure 8, the estimated normalized air pressure reading P en It can be seen that (labeled 57) remains much flatter, providing a stable signal and not triggering a false positive due to the absence of anomalies.

[0094] Thus, from Figures 7a, 7b and 8, the advantages associated with the present disclosure can be clearly understood.

Claims

1. 1. A computer-implemented method for detecting air loss in a vehicle tire, the method comprising: a measuring step; and a processing step, the measuring step comprising: measuring a time series of air temperature values ​​inside said vehicle tires using an air temperature sensor and associating a timestamp with each air temperature value; and measuring a time series of air pressure values ​​within said vehicle tires using an air pressure sensor and associating a timestamp with each air pressure value; and The processing step comprises: selecting a first group of air temperature values ​​from the time series of air temperature values ​​and associating therewith a first group timestamp, the first group including at least one measured air temperature value from the time series of air temperature values; selecting a second group of air temperature values ​​from the time series of air temperature values ​​and associating therewith a second group of timestamps, the second group of timestamps being later than the first group of timestamps, the second group including at least one measured air temperature value from the time series of air temperature values; selecting a group of air pressure values ​​from the time series of air pressure values ​​and associating therewith a third group of timestamps, the group of air pressure values ​​including at least one measured air pressure value from the time series of air pressure values, the third group of timestamps corresponding to either the first group of timestamps or the second group of timestamps; deriving a first air temperature value based at least in part on the first group of air temperature values ​​and a second air temperature value based at least in part on the second group of air temperature values; deriving an air pressure value based at least in part on said group of air pressure values; calculating an estimated air temperature value at a time corresponding to the third group of timestamps based at least in part on: i) the derived first or second air temperature value corresponding to the third group of timestamps; ii) a change in air temperature between the derived second air temperature value and the derived first air temperature value; and ii) a predetermined time constant; calculating an estimated normalized pressure at a time corresponding to the third group of timestamps based on the estimated air temperature value and the derived air pressure value; determining whether an air loss has occurred based at least in part on monitoring the estimated normalized pressure over time.

2. Each estimated air temperature value is calculated according to: [Equation 1] Here, T ｅ (t ３ ) is the estimated air temperature value at the third group of timestamps, and T ｄ (t ３ ) is the derived first or second air temperature value corresponding to the third group of timestamps, τ is the predetermined time constant, Δt is the time difference between the first group of timestamps and the second group of timestamps, and ΔT ｄ (t １ , t ２ 2. The method of claim 1, wherein Δt is the change in air temperature between the derived second air temperature value and the derived first air temperature value over Δt.

3. The method according to any one of claims 1 to 2, wherein the predetermined time constant is related to the thermal capacity of the temperature sensor.

4. The method of claim 3 , wherein the predetermined time constant is calculated using historical air pressure and air temperature data collected using at least the temperature sensor.

5. The time constant is obtaining historical air pressure and air temperature data collected using at least said temperature sensor; calculating a plurality of series of estimated normalized air pressure values ​​using a plurality of different time constant values; calculating, for each series of estimated normalized air pressure values, the difference between the maximum estimated normalized air pressure and the minimum estimated normalized air pressure; and The method according to any one of claims 1 to 4, wherein the predetermined time constant is predetermined by selecting a time constant value that minimizes the difference.

6. Deriving a first air temperature value based at least in part on the first set of air temperature values ​​includes applying a low pass filter to the first set of air temperature values; and / or deriving a second air temperature value based at least in part on the second set of air temperature values ​​includes applying a low pass filter to the second set of air temperature values; and / or wherein deriving an air pressure value based at least in part on said group of air pressure values ​​comprises applying a low pass filter to said group of air pressure values.

7. 7. The method of claim 6, wherein the low pass filter includes a moving average, and wherein a plurality of air pressure and / or air temperature values ​​that fall within an averaging time window are averaged to generate a filtered air pressure and / or filtered air temperature value.

8. 8. The method of claim 1, wherein detecting air loss comprises comparing the estimated normalized air pressure to an air pressure threshold, and determining that there is air loss if the estimated normalized air pressure is lower than the air pressure threshold.

9. The method according to any one of claims 1 to 7, determining whether an air loss exists includes calculating the estimated normalized air pressure at a plurality of times to generate a time series of estimated normalized air pressure values; averaging the estimated normalized air pressure values ​​within a plurality of subsequent time periods to generate a series of average air pressure values; and determining that an air loss exists if the series of the average pressure values ​​is trending downward.

10. The vehicle tire is one of a plurality of vehicle tires mounted on a same vehicle, and the method includes calculating a time series of estimated normalized air pressure values ​​for each of the plurality of tires, and determining whether air loss exists in any one of the vehicle tires includes: Calculating an estimated normalized inflation rate over a fixed length time window for each of the plurality of tires of the vehicle; comparing the estimated normalized inflation pressure rate of the tire being evaluated to one or more of the estimated normalized inflation pressure rates of other tires in the plurality of tires on the vehicle; and and determining from said comparison whether the tire being evaluated has an abnormal estimated normalized inflation pressure rate that may be indicative of air loss in the tire.

11. The method of any one of claims 1 to 10, further comprising indicating that air loss in the vehicle tire has been detected.

12. 1. A system for detecting air loss in a vehicle tire, the system comprising: an air temperature sensor and an air pressure sensor mounted inside the tire and configured to perform the measurement process of claim 1; a memory configured to store a predetermined time constant; A processor configured to perform the processing steps of any one of claims 1 to 11.

13. The system of claim 12 further comprising a tire mounted sensor (TMS) unit including the air temperature sensor and the air pressure sensor.

14. 14. A system according to claim 12 or 13, comprising an air loss indication unit configured to output that air loss in the vehicle tyre has been detected.

15. A computer program comprising instructions that, when executed by a computer, cause the computer to carry out the measurement step according to claim 1 and the processing step according to any one of claims 1 to 11.

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