Tire leak detection

Abstract

A computer-implemented method for detecting air leaks in a vehicle tyre (3), the method comprising: measuring a time series of air temperature values inside the vehicle tyre (3); measuring a time sequence of air pressure values inside the vehicle tyre (3); selecting a first group of air temperature values and a second group of air temperature values; selecting a group of air pressure values; deriving a first air temperature value based at least in part on the first group of air temperature values, and deriving 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 air temperature value or the derived second air temperature value, (ii) a temperature change between the derived first air temperature value and the derived second air temperature value, and (ii) a predetermined time constant; calculating an estimated standardized pressure based on the estimated air temperature value and the derived air pressure value; and determining whether a leak has occurred based at least in part on monitoring the estimated standardized pressure over time.

Description

Technical Field

[0001] The present invention relates to a computer-implemented method for detecting an air leak in a vehicle tire. The present invention also relates to a system for detecting an air leak in a vehicle tire and a corresponding computer program for detecting an air leak in a vehicle tire. Background Art

[0002] Being able to detect an air leak in a vehicle tire (which may be associated with a puncture) is important to the safety of the vehicle. An accurate measurement of the tire's air pressure can be obtained using sensors mounted inside the tire.

[0003] Sensors mounted inside a tire are often called tire-mounted sensors (TMS). TMS monitors parameters of the tire itself, such as tire air pressure and temperature, and extracts information about the tire's interaction with its surroundings, such as the road or vehicle.

[0004] Most fluctuations in measured tire air pressure are due to changes in the temperature of the tire and the air inside it. These fluctuations are undesirable when searching for a potential air loss event. Some current pressure loss detection methods normalize the air pressure in a vehicle's tire by taking a temperature reading along with the pressure reading and dividing the pressure reading by the temperature reading to eliminate temperature dependence. Summary of the Invention

[0005] According to a first aspect of the present invention, there is provided a computer-implemented method for detecting an air leak in a vehicle tire, the method comprising a measuring step and a processing step, the measuring step comprising:

[0006] measuring a time series of air temperature values inside a tire of the vehicle using an air temperature sensor and associating a timestamp with each air temperature value; and

[0007] measuring a time series of air pressure values inside a tire of the vehicle using an air pressure sensor and associating a timestamp with each air pressure value; and

[0008] The processing steps include:

[0009] selecting a first group of air temperature values from the time series of air temperature values, and

[0010] associating the first group with a first group timestamp, wherein the first group includes at least one measured air temperature value from the time series of air temperature values;

[0011] selecting a second group of air temperature values from the time series of air temperature values, and

[0012] associating the second group with a second group timestamp that is later than the first group timestamp, wherein the second group includes at least one measured air temperature value from the time series of air temperature values;

[0013] selecting a group of air pressure values from the time series of air pressure values and associating the group of air pressure values with a third group timestamp, the group of air pressure values comprising at least one measured air pressure value from the time series of air pressure values, wherein

[0014] The third group timestamp corresponds to the first group timestamp or the second group timestamp;

[0015] deriving a first air temperature value based at least in part on the first group of air temperature values, and deriving a second air temperature value based at least in part on the second group of air temperature values;

[0016] deriving an air pressure value based at least in part on the group of air pressure values;

[0017] calculating an estimated air temperature value at a time corresponding to the third group timestamp based at least in part on (i) the derived first air temperature value or the derived second air temperature value corresponding to the third group timestamp, (ii) a change in air temperature between the derived second air temperature value and the derived first air temperature value, and (iii) a predetermined time constant;

[0018] calculating an estimated normalized air pressure at a time corresponding to the third group timestamp based on the estimated air temperature value and the derived air pressure value;

[0019] A determination is made as to whether an air leak has occurred based at least in part on monitoring the estimated normalized air pressure over time.

[0020] While it is known to use air temperature readings to normalize tire air pressure readings, the inventors have recognized that temperature readings suffer from a time lag due to the tire, sensor, and sensor housing retaining heat, so the temperature reading from the air temperature sensor is typically slightly behind the actual current temperature of the air in the tire, which means that even normalized air pressure values still contain anomalies.

[0021] Those skilled in the art will appreciate that by normalizing the air pressure values using air temperature values that take into account how the air temperature changes and, therefore, how much of a time lag there may be, more accurate air pressure normalization can be achieved compared to normalization using raw air temperature values. This can produce a sequence of normalized air pressure values with less fluctuation, making it easier to detect true air pressure losses. This approach can be particularly advantageous in situations where the temperature of the air in the tire changes frequently and / or rapidly, such as at the beginning of a trip or during a trip where driving conditions may change, such as driving in stop-start traffic.

[0022] Those skilled in the art will readily appreciate that while a preferred application of the disclosed method is detecting air leaks in tires, the embodiments described in this disclosure are also suitable for detecting positive pressure changes (air pressure increases or inflation).

[0023] In an embodiment, monitoring the estimated normalized air pressure over time comprises repeating the method such that the measuring step and the processing step are performed continuously.

[0024] In an embodiment, each estimated air temperature value is calculated according to the following formula:

[0025]

[0026] Where T e (t3) is the estimated air temperature value at the third group timestamp, T d (t3) is the derived first air temperature value or the derived second air temperature value corresponding to the third group timestamp, τ is a predetermined time constant, Δt is the time difference between the first group timestamp and the second group timestamp, and ΔT d (t1, t2) is the change in air temperature between the derived second air temperature value and the derived first air temperature value with Δt.

[0027] 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, this method effectively estimates the amount of air (e.g., moles of air) inside the tire by accounting for the effects of real-time temperature fluctuations. This method may require very low resources, such as in terms of memory usage, CPU usage, and power (which may be particularly important when implementing the method in a TMS system).

[0028] In an embodiment, the predetermined time constant is related to the heat capacity of the air temperature sensor. Thus, the time delay of the air temperature sensor can be accounted for in the air temperature reading. It should be understood that during periods when the temperature of the air in the tire changes frequently, the temperature of the air temperature sensor itself will lag behind the actual temperature of the air, and the amount of this lag will depend on the heat capacity of the air temperature sensor (e.g., its body or housing). By calculating the estimated air temperature value based at least in part on a predetermined time constant related to the heat capacity of the particular air temperature sensor used to obtain the reading, the actual current temperature of the air in the tire can be more accurately estimated. It should be understood that the value of the predetermined time constant will vary for different air temperature sensor configurations (e.g., different housing sizes, shapes, materials, etc.).

[0029] In an 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 heat capacity of the air temperature sensor, the historical air temperature data must be collected using a sensor for which the time constant is predetermined. Because the time constant is not pressure sensor specific, the historical air pressure data need not be collected using the same air pressure sensor as used in the measuring step of the first aspect.

[0030] In an embodiment, the time constant is predetermined by:

[0031] obtaining historical air pressure and air temperature data collected using at least an air temperature sensor;

[0032] calculating a plurality of sequences of estimated normalized air pressure values using a plurality of different values of the time constant;

[0033] calculating, for each sequence of estimated normalized air pressure values, a difference between a maximum estimated normalized air pressure and a minimum estimated normalized air pressure; and

[0034] A time constant value that minimizes the difference is selected as the predetermined time constant.

[0035] In an embodiment, deriving a first air temperature value based at least in part on the first group of air temperature values comprises applying a low-pass filter to the first group of air temperature values. In an embodiment, deriving a second air temperature value based at least in part on the second group of air temperature values comprises applying a low-pass filter to the second group of air temperature values. In an embodiment, deriving an air pressure value based at least in part on the group of air pressure values comprises applying a low-pass filter to the group of air pressure values.

[0036] Deriving the air temperature value and / or the air pressure value by applying a low pass filter reduces out-of-band noise by removing small fluctuations in the data.

[0037] In an embodiment, the low-pass filter comprises a moving average, wherein a plurality of air pressure values and / or air temperature values falling within an averaging time window are averaged to produce a filtered air pressure value and / or a filtered air temperature value. It should be understood that the filtered air pressure value and / or filtered air temperature value will be the filtered (averaged) value associated with the group timestamp.

[0038] In an embodiment, a moving (eg, rolling) average window is used as a low-pass filter. The moving average window may have a length between 10 seconds and 1000 seconds, such as between 100 seconds and 500 seconds, such as 300 seconds.

[0039] In an embodiment, detecting an air leak includes comparing the estimated normalized air pressure to an air pressure threshold, and determining that an air leak exists if the estimated normalized air pressure is below the air pressure threshold.

[0040] 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 an air leak exists by:

[0041] calculating a difference between a maximum estimated normalized air pressure value and a minimum estimated normalized air pressure value in the time series of estimated normalized air pressure values;

[0042] comparing the difference to a difference threshold; and

[0043] If the difference is greater than the difference threshold, it is determined that an air leak exists.

[0044] 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 an air leak exists by:

[0045] averaging the estimated normalized air pressure values over a period of time to generate an air pressure reference value;

[0046] determining a difference between the estimated normalized air pressure value and an air pressure reference value; and

[0047] If the magnitude of the difference is greater than the difference threshold, then an air leak is determined to be present.

[0048] 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, wherein determining whether an air leak exists includes:

[0049] averaging the estimated normalized air pressure values over a plurality of subsequent time periods to generate a sequence of average air pressure values; and

[0050] If the average pressure value in the sequence tends to decrease, then an air leak is determined to be present.

[0051] In an embodiment, 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, wherein determining whether there is an air leak in any of the vehicle tires includes:

[0052] calculating an estimated normalized air pressure rate for each of a plurality of tires on the vehicle over a fixed length time window;

[0053] comparing the estimated normalized air pressure rate of the tire being assessed to one or more of the estimated normalized air pressure rates of other tires in the plurality of tires on the vehicle; and

[0054] Based on the comparison, it is determined whether the tire being evaluated has an abnormal estimated normalized air pressure ratio, which may be indicative of an air leak in the tire.

[0055] In an embodiment, the method includes indicating that an air leak has been detected in a vehicle tire. The indication can be made by any suitable means. In an embodiment, the method includes indicating to a user (e.g., a driver) via an audio / visual alarm, a dashboard alert, or an alert displayed on a driver's mobile phone application. In yet other embodiments, an indication that an air leak has been detected in a vehicle tire can be provided in an input to an onboard unit of the vehicle and or an advanced driver assistance system (ADAS) to take appropriate action to ensure the safety of the driver.

[0056] In an embodiment, the measurement steps and the processing steps are performed simultaneously, allowing air leak detection to be performed in real time. It should be understood that although the first set of measurement steps will need to be performed before the first set of processing steps (because processing can only be performed after the data has been collected), after that, the processing steps and the measurement steps will occur in parallel with each other. In other words, the processing steps are performed "on the go," rather than post-processing all the data. That is, all processing steps are performed at the sampling time, which allows for reduced memory usage and improved computing resources, as no separate post-processing steps are required after the sampling phase; such embodiments allow for faster determination of air leaks while optimizing the use of computing resources.

[0057] According to a second aspect of the present invention, there is provided a system for detecting an air leak in a vehicle tire, the system comprising:

[0058] an air temperature sensor and an air pressure sensor, the air temperature sensor and the air pressure sensor being mounted inside the tire and configured to perform the measuring step of the first aspect;

[0059] A memory configured to store a thermal capacity of the air temperature sensor

[0060] a predetermined time constant for the ratio; and

[0061] A processor configured to perform the processing steps of the first aspect.

[0062] In one embodiment, the predetermined time constant is related to the heat capacity of the air temperature sensor. Thus, the time delay of the air temperature sensor can be accounted for in the air temperature reading. It will be appreciated that during periods when the temperature of the air in the tire changes frequently, 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 heat capacity of the air temperature sensor (e.g., its body or housing). By calculating an estimated air temperature value based at least in part on a time constant related to the heat capacity of the particular air temperature sensor used to obtain the reading, a more accurate estimate of the actual current temperature of the air in the tire can be achieved. It will be appreciated that the value of the time constant will vary for different air temperature sensor configurations (e.g., different housing sizes, shapes, materials, etc.).

[0063] In an 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 heat capacity of the air temperature sensor, the historical air temperature data must be collected using the sensor for which the time constant is determined. Because the time constant is not pressure sensor specific, it is not necessary to use the same air pressure sensor as used in the measuring step of the first aspect to collect the historical air pressure data.

[0064] In an embodiment, the time constant is predetermined by:

[0065] obtaining historical air pressure and air temperature data collected using at least an air temperature sensor;

[0066] calculating a plurality of sequences of estimated normalized air pressure values using a plurality of different time constant values;

[0067] For each sequence of estimated normalized air pressure values, the maximum estimated normalized

[0068] the difference between the air pressure and the minimum estimated normalized air pressure; and

[0069] A time constant value that minimizes the difference is selected as the predetermined time constant.

[0070] In an embodiment, the system includes a tire mounted sensor (TMS) unit including an air temperature sensor and an air pressure sensor.

[0071] In an embodiment, the system includes a leak indication unit configured to output that an air leak has been detected in a tire of the vehicle.

[0072] In an embodiment, the TMS unit includes a microcontroller including a memory and a processor. Thus, the system can be implemented locally at the TMS unit mounted to the vehicle tire.

[0073] In an embodiment, the system includes a remote (e.g., cloud-based) server comprising a memory and a processor. The remote server may be arranged to receive (e.g., via wireless transmission) raw air temperature and pressure readings from TMS units mounted to vehicle tires. Multiple TMS units may report their readings to the same remote server, for example, as part of a fleet management system.

[0074] Of course, it should be understood that the term "server" as used herein means a computer or computing machine (e.g., a server device) connected to a network so that the server transmits data to other devices (e.g., computers or other machines) on the network and / or receives data from the other devices. Additionally or alternatively, the server can provide resources and / or services to other devices on the network. The network can be the Internet or some other suitable network. The server can be embodied in any suitable server type or server device, such as a file server, an application server, a communication server, a computing server, a web server, a proxy server, etc. The server can be a single computing device, or it can be a distributed system, that is, the server functionality can be divided across multiple computing devices. For example, the server can be a cloud-based server, that is, its functionality can be split "on demand" across many computers. In such an arrangement, server resources can be obtained from one or more data centers, which can be located in different physical locations.

[0075] Therefore, it should be understood that the processing steps described above and performed by the server can 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 processing steps can be performed by a single server that has access to all relevant information required.

[0076] 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 perform the measuring steps and to carry out the processing steps of the method of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] One or more non-limiting examples will now be described, by way of example only, and with reference to the accompanying drawings, in which:

[0078] Figure 1 is a schematic diagram of a system for detecting air leaks in vehicle tires mounted on a heavy goods vehicle according to an embodiment of the present invention;

[0079] Figure 2 is a schematic diagram of a TMS unit in a vehicle tire;

[0080] Figure 3 is a schematic diagram of a system for detecting air leaks in vehicle tires mounted on a heavy goods vehicle according to another embodiment of the present invention;

[0081] Figure 4 is a flow chart illustrating a computer-implemented method for detecting an air leak in a vehicle tire;

[0082] Figure 5 is a graph illustrating determination of a time constant;

[0083] Figure 6 is a graph showing a plurality of parameters related to tire air pressure plotted over time;

[0084] FIG7 a is another graph showing a plurality of parameters related to tire air pressure plotted over time;

[0085] FIG7 b is another graph showing multiple parameters related to tire air pressure plotted over time, illustrating an air leak event;

[0086] Figure 8 is another graph showing multiple parameters related to tire air pressure plotted over a shorter amount of time, illustrating a flat tire event.

[0087] Figure 1 FIG. 1 shows a system 1 for detecting an air leak 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 memory 12 and a processor 14 (e.g., Figure 2 The TMS unit 5 is configured to communicate with a gas leakage indication unit 10 in the vehicle, such as Figure 1 Indicated by the dotted communication lines.

[0088] Figure 2 The TMS unit 5 is shown in detail. 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.

[0089] Figure 3 FIG. 1 shows a system 100 for detecting an air leak in a vehicle tire 3 mounted on a vehicle 2 according to another embodiment. Figure 1 system, Figure 3 The system 100 includes a plurality of tire mounted sensor (TMS) units 5 each mounted within a tire 3 . Figure 3 The system 1 further includes a network communication device 6 disposed in the vehicle 2. In an embodiment, the network communication device 6 may be a dongle that is plugged into a port of the vehicle 2 (such as an OBD port, an FMS port, or other port). 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 in the vehicle 2. In this example, the TMS unit 5 communicates via The TMS unit 5 is connected to communicate with the network communication device 6. In another example, the TMS unit 5 sends data to the network communication device 6 using 433 MHz FSK modulation. It will be readily understood that any suitable form of short-range wireless communication or wired connection can be used. The network communication device 6 is networked and communicates with the remote server 7 via a wireless network connection (e.g., a cellular network). The remote server 7 is connected to the gas leak indication unit 10a in the cab of the vehicle 2 via the wireless network. In the illustrated embodiment, multiple gas leak indication units are provided, including the driver's mobile device 10b and the fleet manager's computer 10c.

[0090] It should be understood that Figure 1 In the system 1, since the TMS unit 5 includes the microcontroller 4, any processing is performed by the microcontroller 4, and therefore the system does not need to communicate with the remote server 7. However, in Figure 3 In the system 100, the TMS unit 5 communicates (via the communication device 6) with the remote server 7. Since the remote server 7 includes a memory 112 and a processor 114, the processing is performed at the remote server 7. Figure 2 The same TMS unit 5 shown can also be used in Figure 3 , but the microcontroller 14 may be omitted, not used for the processing steps described herein, or only partially used. Figure 3The TMS unit 5 of the system 100 may still have a microcontroller, and processing may be shared between the server 7 and the microcontroller 4. For example, the microcontroller 4 may perform edge processing.

[0091] Figure 1 and Figure 3 The system 1 is operable to perform the following described Figure 4 The method illustrated.

[0092] At steps 21 and 23, the air pressure sensor 18 and the air temperature sensor 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 can be set to, for example, 15 seconds. In an embodiment, the acquired pressure and temperature samples can be interpolated (resampled) to overcome possible problems caused by non-constant sampling time or missing some samples during transmission from the sensor.

[0093] Therefore, the output of steps 21 and 23 is the measured air temperature T m Time series and measured air pressure P m Time series.

[0094] At steps 25 and 27, the measured air temperature T m A first group and a second group of air temperature values are selected from the time series and associated with a first group timestamp and a second group timestamp, respectively. The second group timestamp is later than the first group timestamp. In an embodiment, the first group may contain only a single air temperature value, but of course, in other embodiments, the first group may include multiple air temperature values. In an embodiment, the second group may contain only a single air temperature value, but of course, in other embodiments, the second group may include multiple air temperature values.

[0095] At step 29, the measured air pressure P m A group of air pressure values is selected from the time series and associated with a third timestamp. The third timestamp corresponds to either the first group timestamp or the second group timestamp. In one embodiment, the group may contain only a single air pressure value, but of course, in other embodiments, the group may include multiple air pressure values.

[0096] At 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 a group includes only a single air temperature value, the deriving step simply involves taking the single value and its timestamp; however, in embodiments where a group includes multiple values, the deriving step involves processing the multiple values within the group to generate a single air temperature value for the entire group. The single air temperature value is then associated with the group timestamp.

[0097] Deriving a single air temperature value for each group of air temperature values can be performed using any known function suitable for those skilled in the art. In an exemplary embodiment, the deriving includes applying a filtering function, such as a low-pass filter or an averaging function (e.g., calculating a mean) across each group of air temperature values such that the derived first air temperature value and the derived second air temperature value are filtered values or average values, respectively.

[0098] At 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, whereas in embodiments where the group includes multiple values, the deriving step involves processing the multiple values within 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.

[0099] Deriving a single air pressure value based on a group of air pressure values can be performed using any known function suitable for those skilled in the art. In an exemplary embodiment, the deriving comprises applying a filtering function, such as a low-pass filter or an averaging function (e.g., calculating a mean) across the group of air pressure values such that the derived air pressure value is a filtered value or an average value.

[0100] In an exemplary embodiment, the derivation of the first air temperature value, the second air temperature value, and the air pressure value are all achieved by applying a rolling average window having a length of 300 seconds.

[0101] The rate at which the derived values are calculated can be decimated to reduce the computational requirements. In an exemplary embodiment, the data is decimated to a rate of 60 seconds. If a moving average window is used as the filter function, then in this embodiment the moving average will be considered to have a length n=5 (5×60s=300s).

[0102] The amount of filtering is obtained at sampling time, so the use of CPU resources is minimal, since no separate post-processing calculation stage is required. Memory resources are also optimized by combining sampling and filtering: for example, in the case where a moving average window of length n=5 is used as the filter function for the two groups of air temperature values and for the group of air pressure values, the memory required to perform the two moving averages is only related to 3n (i.e. 15) samples (i.e. 5 samples for pressure and 10 (5×2) samples for temperature).

[0103] At step 35, an estimated air temperature value is calculated using the derived first and second air temperature values and a predetermined time constant that is related to (e.g., proportional to) the heat capacity of the air temperature sensor embedded in the TMS unit 5. The mathematical description of these physical systems is typically a first-order differential equation:

[0104]

[0105] Where T e is the estimated actual air temperature, T m is the measured air temperature, and τ is a predetermined time constant (e.g., a time constant value predetermined to be proportional to the heat capacity of the thermometer embedded in the TMS unit 5). It will be readily appreciated that, where a derived (e.g., filtered / averaged) value of the air temperature is used, T m Replaced by T d (Derived air temperature).

[0106] According to Equation 1, a real-time numerical solution of the physical model of the air temperature sensor 16 is evaluated.

[0107] In the following about Figure 5 The calculation of the predetermined time constant τ is discussed below. The time constant τ is predetermined and can be stored in the memory 12 of the TMS unit 5.

[0108] By calculating T d Equation 1 is numerically solved by taking the discrete derivative of and using the predetermined time constant τ as an estimate of the system time constant τ of the air temperature sensor. Once τ has been previously estimated using available (e.g., historical, non-real-time) data, the real-time estimated air temperature T at a given time t3 corresponding to the third timestamp is e It can be approximated as:

[0109]

[0110] Where T e (t3) is the estimated air temperature at the third time stamp, T d(t3) is the derived air temperature at the third time stamp, τ is a predetermined time constant, Δt is the time difference between the first time stamp and the second time stamp, and ΔT d (t1, t2) is the derived air temperature change with Δt.

[0111] The third timestamp corresponds to the first group timestamp or the second group timestamp, wherein the second group timestamp is later than the first group timestamp. When the third timestamp corresponds to the first group timestamp, the quantity in equation 2 can be considered as a numerical approximation of the right derivative; when the third timestamp corresponds to the second group timestamp, the quantity in Equation 2 It can be considered as a numerical approximation of the left derivative: both derivatives converge to the same value within a small interval Δt, assuming that the function of temperature is differentiable in said interval.

[0112] Due to the characteristics of the sensor, only the air temperature is estimated instead of the air pressure. This is because the time lag of the measured pressure with respect to the actual instantaneous pressure is typically much shorter (e.g., less than 1 second). Therefore, the pressure time lag is practically negligible, and therefore, in the described embodiment, it is assumed that the derived (measured) pressure will be the same as the estimated air pressure value (P e =P d ). Of course, it will be appreciated that the pressure may also be estimated taking into account the characteristics of the air pressure sensor without departing from the disclosed invention.

[0113] At step 37 , the estimated normalized air pressure is calculated using the derived air pressure value and the estimated air temperature value according to Equation 3:

[0114]

[0115] Among them, P en is the estimated normalized air pressure, P d is the derived air pressure, T e is the estimated air temperature, and T0 is a constant.

[0116] P en is an estimate of the pressure measured continuously at a fixed temperature T0: the latter is an arbitrary constant that obtains quantities with the same pressure unit, usually called the reference temperature. In the exemplary embodiment, T0 = 300 Kelvin, which is associated with the average tire air temperature in Europe.

[0117] When considering the volume constant, based on the ideal gas law, P en The amount is also proportional to the molar amount of air contained within the vehicle tire to a good approximation. Therefore, it is an accurate indication of whether the air content in the vehicle tire is decreasing, which would indicate a puncture.

[0118] Finally, at step 39, the estimated normalized air pressure value is used to determine whether an air leak has occurred in the vehicle tire 3. Of course, it will be appreciated that steps 21-37 are repeated (e.g., continuously) such that a time series of estimated normalized air pressure values can be generated. Any suitable algorithm can be used to evaluate the estimated normalized air pressure over time to determine whether an air leak has occurred. Some exemplary algorithms are briefly discussed below, but it will be appreciated that the estimated normalized air pressure values obtained according to the described embodiments can be used in any suitable algorithm to determine whether an air leak has occurred.

[0119] Algorithm a)

[0120] The most basic check can be done by en This is done by taking the difference between the maximum and minimum values and comparing it with a suitable threshold:

[0121] ΔP en =max(P en )-min(P en )>P thr Equation 4

[0122] P thr It can be a predefined value or it can be a self-learned value. For example, in the case of a self-learned value, ΔP en (t0) can be evaluated in the first few minutes starting from some time t0, then P thr Can be created as ΔP en function of (t0).

[0123] When ΔP en >P thr When the current P en and the final average value (max(P en )+min(P en )) / 2 for comparison. If P en If P is greater than the average value, air filling can be detected. en If the value of ΔH is lower than the average value, an air leak can be detected. When the above inequality remains true for a certain amount of time, an air leak can be detected and signaled more reliably.

[0124] Algorithm b)

[0125] P can be evaluated within a set time period en The average value P of (t) ref , and the reference threshold can be created as P ref function.

[0126] When the relationship ΔP is satisfied en (t)=|P en (t)-P ref |>P thr When the current P en Is it greater than or less than P ref In the first case, the new P can be evaluated, for example, with the tire filled with air. ref In the latter case, there may be an air leak associated with an air leak (such as a puncture). When the above inequality remains true for a certain amount of time, an air leak can be more reliably detected and signaled.

[0127] Algorithm c)

[0128] At each moment t i The average value over time Δt can be considered an estimate of the average air content. The trend of the average air content over time can be considered an estimate of air leakage. If the average air content decreases over a period of time, an air leak may occur.

[0129] Algorithm d)

[0130] At each moment t i Evaluated within a fixed time Δt The trend can be used as an indicator of an air leak. If the minimum estimated air content decreases over a period of time, an air leak may be occurring.

[0131] Algorithm e)

[0132] P en Evaluations can be performed on multiple tires installed on the same vehicle over time. en An estimated normalized air pressure ratio is calculated for each tire on the vehicle over a fixed-length time window. By comparing the estimated normalized air pressure ratio of the tire being assessed with one or more of the estimated normalized air pressure ratios of other tires mounted on the vehicle, an abnormal ratio of the estimated normalized air pressure can be identified. Such an abnormal ratio can indicate an air leak in the tire being assessed.

[0133] refer to Figure 5 , the calculation of the predetermined time constant τ will now be explained. The predetermined time constant τ depends on the combined tire and sensor system configuration and only needs to be determined once for a specific system (tire+sensor) configuration.

[0134] Once determined, it can be used in Equation 2 to perform the equations Air temperature T e (t s ) in real time.

[0135] The predetermined time constant is calculated by applying an error (cost) function to a set of historical air pressure and air temperature data for which it is known that the amount of air in the tire has not changed (e.g., no air filling or air leaking). The error function is defined as:

[0136] ΔP en (τ)=max(P en (τ,t))-min(P en (τ,t)) Equation 5

[0137] where time t∈[T0,T1] spans a bounded time interval [T0,T1] which can range from hours to months.

[0138] The optimal value of τ is to make ΔP en Since the amount of air in the tire is known to have not changed during the collection of historical data, if air temperature fluctuations are accurately corrected (via the accurate value of τ applied by Equation 2), then ΔP en (τ) should be close to zero.

[0139] Figure 5 ΔP plotted against τ is shown en . Used to create Figure 5 The data for the graph was generated for three steering tires on a heavy goods vehicle over a 30-day period. It can be seen that for the specific sensor and tire system configuration for which this historical data was collected, the optimal value of τ was close to 350 seconds. This was then recorded in the memory 12 of the TMS unit 5 as a predetermined time constant unique to the tire and its TMS.

[0140] Through additional experiments, it has been shown that the τ estimation performed on a small dataset of three tires over 30 days has been shown to work well for a larger dataset consisting of more than 100 tires installed on several (>10) trucks operating for approximately one year under varying ambient temperature conditions.

[0141] Figure 6 Actual data of air pressure values for steering tires on a heavy goods vehicle measured over a 24 hour period, including some stops and restarts, is shown.

[0142] The top set of data points 51 are the raw air pressure readings. Large fluctuations can be seen in the raw air pressure readings 51.

[0143] A set of data points 53 roughly adjacent to the raw air pressure readings 51 are raw air temperature readings 53. As can be seen, fluctuations in the raw air pressure readings 51 follow fluctuations in the raw air temperature readings 53, but with a slight lag.

[0144] Also shown on the graph are two sets 55, 57 of normalized air pressure values. The set P of air pressure readings, labeled 55, is n The set of estimated normalized air pressure readings P, labeled 57, has been conventionally normalized using simply the raw air pressure readings 51 and the raw air temperature readings 53. en (“Physical Model Pressure”) has been calculated according to the physical model described in this disclosure (see Equation 3 above).

[0145] from Figure 6 It can be seen that the air pressure P estimated by the physical model en With ΔP en = 0.14 bar, while the normalized air pressure P n ΔP n =0.45 bar (ΔP en 3 times of the original air pressure P m In ΔP m =1.43 bar(ΔP en The results show that the α-to-β ratio of ...

[0146] P n Relative to P en The large oscillation is mainly due to the inability to pass P n The instantaneous air pressure value is well calculated. Any time the air pressure and air temperature change rapidly, the temperature lag (related to the thermal capacity of the specific tire and sensor system configuration) is n Outliers are generated in the data set. Figure 6 Such anomalies can be clearly seen in Figure 59. These anomalies can lead to false alarms, and air leaks may be detected when none exist. Alternatively, to reduce the chance of false alarms, the sensitivity of any leak detection algorithm must be reduced, but this in turn may result in slower leak detection than would otherwise be possible using a more sensitive algorithm. In contrast, because temperature lag is accounted for (as discussed above), the physically normalized air pressure remains much more stable. Thus, by accounting for temperature lag, the likelihood of false alarms can be reduced while still maintaining a sensitive leak detection algorithm.

[0147] Figures 7a and 7b show the Figure 6The same readings 51, 53, 55, and 57. Figure 7a plots data from a first HGV trip, which ran for 24 hours. Figure 7b plots data for a second trip, which also lasted 24 hours, but during which a slow air leak was present in the tire. Because the conventional normalized air pressure reading 55 is still affected by the abnormal signal spike, the leak cannot be detected as easily as using the estimated normalized air pressure reading 57 in Figure 7b.

[0148] Similarly, Figure 8 Shown with Figure 6 The same readings 51, 53, 55, 57 are plotted against data from a steering tire on a heavy goods vehicle that has been running for 2 hours. In the normalized pressure reading 55, the anomaly 60 caused by the pressure increase is followed by a downward trend over the next 40 minutes. If an algorithm designed to search for a downward trend in pressure uses this parameter P n (marked as 55), the false alarm can be identified as a gas leak event. Figure 8 As can be seen, the estimated normalized air pressure reading P en (labeled 57) remains much flatter and provides a stable signal, which will not trigger false alarms since there are no anomalies.

[0149] Therefore, from Figures 7a, 7b and Figure 8 The advantages associated with the present disclosure can be clearly understood.

Claims

1. A computer-implemented method for detecting air leaks in vehicle tires, the method comprising a measuring step and a processing step, the measuring step comprising: measuring a time series of air temperature values inside a tire of the vehicle using an air temperature sensor, and associating a timestamp with each air temperature value; as well as measuring a time series of air pressure values inside a tire of the vehicle using an air pressure sensor and associating a timestamp with each air pressure value; and The processing steps include: selecting a first group of air temperature values from the time series of air temperature values and associating the first group with a first group timestamp, wherein the first group includes 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 the second group with a second group timestamp that is later than the first group timestamp, wherein the second group includes 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 the group of air pressure values with a third group timestamp, the group of air pressure values comprising at least one measured air pressure value from the time series of air pressure values, wherein the third group timestamp corresponds to the first group timestamp or the second group timestamp; deriving a first air temperature value based at least in part on the first group of air temperature values, and deriving 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 timestamp based at least in part on (i) the derived first air temperature value or the derived second air temperature value corresponding to the third group timestamp, (ii) a change in air temperature between the derived second air temperature value and the derived first air temperature value, and (iii) a predetermined time constant; calculating an estimated normalized pressure at a time corresponding to the third group timestamp based on the estimated air temperature value and the derived air pressure value; A determination is made as to whether an air leak has occurred based at least in part on monitoring the estimated normalized pressure over time.

2. The method of claim 1 , wherein each estimated air temperature value is calculated according to the following formula: Where T e (t3) is the estimated air temperature value at the third group timestamp, T d (t3) is the derived first air temperature value or the derived second air temperature value corresponding to the third group timestamp, τ is the predetermined time constant, Δt is the time difference between the first group timestamp and the second group timestamp, and ΔT d (t1, t2) is the change in air temperature between the derived second air temperature value and the derived first air temperature value with Δt.

3. A method according to any preceding claim, wherein the predetermined time constant is related to the heat 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. A method according to any preceding claim, wherein the time constant is predetermined by: obtaining historical air pressure and air temperature data collected using at least the temperature sensor; calculating a plurality of sequences of estimated normalized air pressure values using a plurality of different time constant values; calculating, for each sequence of estimated normalized air pressure values, a difference between a maximum estimated normalized air pressure and a minimum estimated normalized air pressure; as well as The time constant value that minimizes the difference is selected as the predetermined time constant.

6. The method of any preceding claim, wherein deriving a first air temperature value based at least in part on the first group of air temperature values comprises applying a low pass filter to the first group of air temperature values; and / or wherein deriving a second air temperature value based at least in part on the second group of air temperature values comprises applying a low pass filter to the second group of air temperature values; And / or wherein deriving the air pressure value based at least in part on the group of air pressure values comprises applying a low pass filter to the group of air pressure values.

7. The method of claim 6, wherein the low-pass filter comprises a moving average, wherein a plurality of air pressure values and / or air temperature values falling within an averaging time window are averaged to produce a filtered air pressure value and / or a filtered air temperature value.

8. A method according to any preceding claim, wherein detecting an air leak comprises comparing the estimated normalised air pressure to an air pressure threshold, and determining that an air leak is present if the estimated normalised air pressure is below the air pressure threshold.

9. The method according to any one of claims 1 to 7, comprising: Calculating the estimated normalized air pressure at a plurality of times to generate a time series of estimated normalized air pressure values, wherein determining whether an air leak exists comprises: averaging the estimated normalized air pressure values over a plurality of subsequent time periods to generate a sequence of average air pressure values; and If the average pressure value in the sequence tends to decrease, then an air leak is determined to be present.

10. The method of any one of claims 1 to 7, wherein the vehicle tire is one of a plurality of vehicle tires mounted on a same vehicle, wherein the method comprises calculating a time series of estimated normalized air pressure values for each of the plurality of tires, wherein determining whether there is an air leak in any one of the vehicle tires comprises: An estimated normalized air pressure rate is calculated for each of the plurality of tires on the vehicle over a fixed-length time window: comparing the estimated normalized air pressure rate of the tire being assessed with one or more of the estimated normalized air pressure rates of other tires of the plurality of tires on the vehicle; as well as A determination is made based on the comparison whether the tire being assessed has an abnormal estimated normalized air pressure ratio, the abnormal estimated normalized air pressure ratio being indicative of an air leak in the tire.

11. The method according to any preceding claim, further comprising: An indication that an air leak has been detected in a tire of the vehicle.

12. A system for detecting an air leak in a vehicle tire, the system comprising: an air temperature sensor and an air pressure sensor, the air temperature sensor and the air pressure sensor being installed inside the tire and configured to perform the measuring step according to claim 1; a memory configured to store a predetermined time constant; as well as A processor configured to perform the processing steps of any preceding claim.

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

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

15. A computer program comprising instructions which, when executed by a computer, cause the computer to perform the measuring step according to claim 1 and to perform the processing step according to any one of claims 1 to 11.

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