Dynamic tire load estimation using tire mounted footprint length sensors

Abstract

Various embodiments for calculating or estimating dynamic tire load from acceleration data are disclosed. A virtual print length may be calculated from acceleration data obtained from an inertial unit of the vehicle. Dynamic tire load or dynamic vehicle load data may be calculated from a virtual footprint length calculated from acceleration data obtained from the inertial unit.

Description

Technical Field

[0001] The present invention relates to a dynamic tire load estimation using a tire mounted footprint length sensor. Background Art

[0002] The footprint length of a tire is the length of the portion that is in contact with the road. Footprint length can be a good indicator of many characteristics of a tire, such as traction, handling, wear, fuel efficiency, comfort, performance, and load-carrying capacity. Footprint length will vary based on various factors of the tire, such as vehicle load, pressure, temperature, and the state of wear of the tire. However, calculating dynamic tire load can also be a useful metric to guide braking calculations, fuel consumption, vehicle stability control determinations, and other metrics and calculations. Summary of the invention

[0003] The present invention provides the following technical solutions:

[0004] 1. A method comprising:

[0005] receiving a plurality of footprint length measurements of the tire from a sensor;

[0006] receiving a plurality of acceleration data corresponding to a plurality of axes from an inertial unit;

[0007] filtering multiple blot length measurements to remove statistical outlier data samples;

[0008] generating a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; and

[0009] A virtual footprint length function is generated, from which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of footprint length measurements from the sensor.

[0010] 2. The method according to scheme 1 further comprises:

[0011] Acquire additional acceleration data from the inertial unit; and

[0012] Dynamic vehicle loads associated with the tire are calculated based on the additional acceleration data and the virtual footprint length of the tire.

[0013] 3. The method of claim 2, wherein additional acceleration data is captured after a parameter learning phase for generating the virtual footprint length parameters.

[0014] 4. The method according to claim 3, wherein the parameter learning phase is performed when the vehicle is started until the virtual footprint length parameter converges.

[0015] 5. The method of claim 1 , wherein the virtual footprint length of the tire is calculated at a frequency substantially the same as a frequency at which the inertial unit reports acceleration data.

[0016] 6. The method according to Scheme 1, wherein the acceleration data includes lateral axis acceleration data and longitudinal axis acceleration data.

[0017] 7. The method according to claim 1, wherein a recursive least squares parameter estimation process is used to generate the virtual footprint length parameter to generate the virtual footprint length function.

[0018] 8. The method of claim 1, wherein the virtual footprint length function relates acceleration data from the inertial unit to the footprint length of the tire.

[0019] 9. A system comprising:

[0020] a computing device comprising a processor and a memory; and

[0021] The machine-readable instructions stored in the memory, when executed by the processor, cause the computing device to at least:

[0022] receiving a plurality of footprint length measurements of the tire from a sensor;

[0023] receiving a plurality of acceleration data corresponding to a plurality of axes from an inertial unit;

[0024] filtering multiple blot length measurements to remove statistical outlier data samples;

[0025] generating a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; and

[0026] A virtual footprint length function is generated, from which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of footprint length measurements from the sensor.

[0027] 10. The system of claim 9, wherein the machine-readable instructions further cause the computing device to at least:

[0028] acquiring additional acceleration data from the inertial unit; and

[0029] Dynamic vehicle loads associated with the tire are calculated based on the additional acceleration data and the virtual footprint length of the tire.

[0030] 11. The system of claim 10, wherein additional acceleration data is captured after a parameter learning phase to generate the virtual footprint length parameters.

[0031] 12. The system according to claim 11, wherein the parameter learning phase is performed when the vehicle is started until the virtual footprint length parameter converges.

[0032] 13. The system of claim 9, wherein the virtual footprint length of the tire is calculated at a frequency substantially the same as the frequency at which the inertial unit reports acceleration data.

[0033] 14. A system according to Option 9, wherein the acceleration data includes lateral axis acceleration data and longitudinal axis acceleration data.

[0034] 15. The system of claim 9, wherein the virtual footprint length parameter is generated using a recursive least squares parameter estimation process to generate the virtual footprint length function.

[0035] 16. A non-transitory computer-readable medium comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least:

[0036] receiving a plurality of footprint length measurements of the tire from a sensor;

[0037] receiving a plurality of acceleration data corresponding to a plurality of axes from an inertial unit;

[0038] filtering multiple blot length measurements to remove statistical outlier data samples;

[0039] generating a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; and

[0040] A virtual footprint length function is generated, from which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of footprint length measurements from the sensor.

[0041] 17. The non-transitory computer readable medium of claim 16, wherein the machine readable instructions further cause the computing device to at least:

[0042] acquiring additional acceleration data from the inertial unit; and

[0043] Dynamic vehicle loads associated with the tire are calculated based on the additional acceleration data and the virtual footprint length of the tire.

[0044] 18. The non-transitory computer readable medium of claim 17, wherein additional acceleration data is captured after a parameter learning phase to generate the virtual footprint length parameter.

[0045] 19. The non-transitory computer readable medium of claim 18, wherein the parameter learning phase is performed when the vehicle is started until the virtual footprint length parameter converges.

[0046] 20. The non-transitory computer-readable medium of claim 16, wherein the acceleration data comprises lateral axis acceleration data and longitudinal axis acceleration data. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Many aspects of the present disclosure may be better understood with reference to the following drawings. The components in the drawings are not necessarily drawn to scale, but emphasis is placed on clearly illustrating the principles of the present disclosure. In addition, in the drawings, the same reference numerals designate corresponding parts throughout the several views.

[0048] Figure 1A is a schematic diagram of a front perspective view of a tire according to various embodiments of the present disclosure, wherein a portion of the tire is shown cut away to illustrate sensors within the tire.

[0049] Figure 1B is a schematic diagram of a vehicle showing the axes along which the inertial unit can acquire acceleration data.

[0050] Figure 2 is a diagram of a network environment according to various embodiments of the present disclosure.

[0051] Figure 3 It is a diagram showing various embodiments according to the present disclosure. Figure 2 A flow diagram of an example of functionality implemented partially by an application program executed in a computing environment within a network environment.

[0052] Figure 4 It is a diagram showing various embodiments according to the present disclosure. Figure 2 A flow diagram of an example of functionality implemented partially by an application program executed in a computing environment within a network environment. DETAILED DESCRIPTION

[0053] Various methods for calculating dynamic wheel loads on vehicle wheels are disclosed. The term "dynamic wheel load" is the wheel load on each wheel and is dependent on the current or real-time condition of the vehicle. The dynamic wheel loads may be calculated based on various parameters collected about the real-time state of the vehicle. For example, the sprung mass, unsprung mass, the respective distances of the vehicle's center of gravity from the front and rear axles, the yaw moment of inertia, the vehicle's wheelbase, the height of the center of gravity, the height of the roll center, the height of the unsprung mass, and other parameters may be used to calculate the dynamic loads in real-time. However, many vehicles are not equipped with a sensor suite that provides a way to collect the above data in real-time to calculate the real-time dynamic loads on each tire of the vehicle.

[0054] However, many vehicles are equipped with tire pressure monitoring system (TPMS) sensors that can collect tire pressure data at regular intervals. In addition, many vehicles are also equipped with inertial units that can collect vehicle acceleration data, such as longitudinal acceleration and lateral acceleration data. The acceleration data can be generated and provided on the vehicle's controller area network bus (CAN bus) so that other systems that communicate with the CAN bus can access the data. In addition, in some examples, the TPMS sensors on each wheel of the vehicle can provide periodic tire pressure data to other systems. In some examples, the TPMS sensors can wirelessly communicate with other systems in the vehicle to provide tire pressure data. In other examples, the TPMS sensors can provide tire pressure data to the vehicle's CAN bus.

[0055] According to an example of the present disclosure, the tire footprint length can be calculated based on the tire pressure data and the acceleration data. Subsequently, the dynamic load on each wheel can be calculated based on the tire footprint length. The footprint length refers to the length of the portion of the tire that contacts the road surface (called the contact patch). Footprint length measurement can be important for tires because it is related to contact patch and traction, handling, wear and tire life, fuel efficiency, comfort, performance and load-bearing capacity. However, the footprint length may vary depending on various factors of the tire, such as vehicle load, tire inflation pressure, temperature and / or other tire factors. In some cases, an increase in inflation pressure will reduce the footprint length. Conversely, a reduction in inflation pressure will increase the footprint length.

[0056] Additionally, in some cases, an increase in load or weight can also increase the footprint length. Conversely, a decrease in load or weight can also decrease the footprint length. When these factors change within a tire configured for an optimal footprint length, the result is that the tire will operate at a suboptimal footprint length. To address this, the tire may have to compensate to achieve the optimal desired footprint length, or other vehicle systems may need to account for the suboptimal footprint length.

[0057] Various methods have been used to calculate the compensated footprint length. However, many methods of calculating the compensated footprint length previously required knowledge of the vehicle's load state or at least an estimate of the vehicle's load because the pressure sensitivity of the footprint length varies with load. For example, previous models may first use the footprint length to calculate an indicator of the vehicle's load state. This load indicator can then be used in a classification model along with the inflation pressure to predict the vehicle's load state. When the pressure sensitivity for each load state remains relatively constant, we can calculate the compensated footprint length. The compensated footprint length can then be used to calculate the dynamic load on each wheel of the vehicle. The footprint length calculation can be based on tire pressure data received from a TPMS sensor. However, TPMS sensors often only report tire pressure data periodically, and dynamic load data is only useful to other vehicle systems if it is provided in essentially real time. In many vehicle systems, inertial units can provide acceleration data in near real time, for example at a frequency of 100 Hz.

[0058] Therefore, examples of the present disclosure can calculate a real-time virtual footprint length based on real-time acceleration data. Based on the calculated real-time virtual footprint length, examples of the present disclosure can then estimate dynamic load data for each wheel of the vehicle.

[0059] Once the dynamic load calculation is complete, the dynamic load data can be provided to other vehicle systems, such as the steering system, vehicle stability control system, braking system or other vehicle management systems, which can improve their respective performance by incorporating the real-time dynamic load calculation.

[0060] In the following discussion, a general description of the system and its components will be provided, followed by a discussion of its operation. Although the following discussion provides illustrative examples of the operation of the various components of the present disclosure, the use of the following illustrative examples does not exclude other implementations consistent with the principles disclosed by the following illustrative examples.

[0061] like Figure 1A , an example of a tire 100 mentioned in various embodiments of the present disclosure is shown. The tire 100 can include a multilayer structure made of various materials. Each tire 100 includes a pair of sidewalls extending to a circumferential tread 103, which engages the ground during vehicle operation. When mounted on a wheel 106, the tire 100 can be configured to maintain gas in an internal cavity between an inner wall transverse to the sidewall, an inner liner attached transversely to the circumferential tread 103, and an inner cavity of the wheel 106. The internal cavity can maintain a varying amount of pressure based on at least various configurations of the tire, such as tire size (e.g., depth, radius, circumference, etc.), tire material, and environmental conditions (e.g., ambient temperature, etc.).

[0062] The tire 100 may include a sensor 109. The sensor 109 may be fixed to an inner liner (e.g., Figure 1A ), the inner wall of the tire 100, or at least one of the interior of the wheel 106. In some embodiments, the sensor 109 can be fixed using an adhesive. In some embodiments, the sensor 109 can be fixed by being embedded in the structure to which the sensor 109 is fixed (e.g., the tire wall, the wheel 106, etc.).

[0063] Sensor 109 may be used to measure footprint length, inflation pressure, and temperature, as well as various other measurements. Sensor 109 may measure footprint length using metric units (e.g., millimeters, centimeters, etc.) or imperial units (e.g., inches, feet, etc.). Sensor 109 may measure inflation pressure in various unit types, such as pounds per square inch (PSI), Pascals (Pa) or kilopascals (kPa), bars, atmospheres (ATM), and kilograms per square centimeter (kg / cm 2 ). The sensor 109 may measure temperature in various unit scales, such as Celsius, Fahrenheit, Kelvin, or other temperature scales. In various embodiments, the sensor 109 may be a tire pressure monitoring system (TPMS) sensor.

[0064] like Figure 1B As shown, an example of a vehicle 113 mentioned in various embodiments of the present disclosure is shown. The vehicle 113 may be equipped with one or more tires 100. The tires 100 may each include a TPMS sensor that reports footprint length data and / or tire pressure data. In addition, the inertial unit of the vehicle 113 may report acceleration data detected along various axes. For example, the inertial unit of the vehicle 113 may report acceleration data detected along the longitudinal axis 135 and the transverse axis 131. By utilizing the acceleration data and the footprint length data reported by the sensor 109 in each tire 100 of the vehicle 113, the example of the present disclosure can calculate the dynamic load data occurring on each wheel of the vehicle 113.

[0065] refer to Figure 2 , shows a network environment 200 according to various embodiments. The network environment 200 may include a computing environment 203, one or more tires 100, and an inertial unit 101, which may communicate data with each other via a network 206. In one example, the network environment 200 may include a vehicle 113, where multiple computing devices communicate via a vehicle CAN bus or other wired or wireless network.

[0066] The network 206 may include a vehicle CAN bus, a wide area network (WAN), a local area network (LAN), a personal area network (PAN), or a combination thereof. These networks may include wired or wireless components or a combination thereof. Wired networks may include Ethernet, cable networks, fiber optic networks, and telephone networks, such as dial-up, digital subscriber line (DSL), and integrated services digital network (ISDN) networks. Wireless networks may include cellular networks, satellite networks, Institute of Electrical and Electronics Engineers (IEEE) 802.11 wireless networks (i.e., wireless networks). ), Networks, microwave transmission networks, and other networks that rely on radio broadcasting. Network 206 may also include a combination of two or more networks 206. Examples of network 206 may include the Internet, an intranet, an extranet, a virtual private network (VPN), and similar networks.

[0067] The inertial unit 101 may provide lateral acceleration data and longitudinal acceleration data detected within the vehicle. The acceleration data may be provided by one or more accelerometers capable of detecting acceleration data. The inertial unit 101 may publish or otherwise provide acceleration data along one or more axes, such as a lateral axis 131 and a longitudinal axis 135 of the vehicle 113.

[0068] The computing environment 203 may include one or more computing devices including processors, memory and / or network interfaces. For example, a computing device may be configured to perform calculations on behalf of other computing devices or applications. As another example, such a computing device may host and / or provide content to other computing devices in response to content requests. As another example, such a computing device may be a central computing device installed in a vehicle. In addition, the computing environment 203 may use multiple computing devices, which may be arranged in one or more server groups or computer groups or other arrangements. Such computing devices may be located in a single installation or may be distributed between many different geographical locations. For example, the computing environment 203 may include multiple computing devices, which together may include managed computing resources, grid computing resources, or any other distributed computing arrangements. In some cases, the computing environment 203 may correspond to elastic computing resources, in which the capacity of the allocated processing, network, storage, or other computing-related resources may vary over time.

[0069] Various applications or other functions may be executed in computing environment 203. Components executing on computing environment 203 include dynamic load calculation application 209, as well as other applications, services, processes, systems, engines, or functions not discussed in detail herein.

[0070] Dynamic load calculation application 209 may perform various actions. In various embodiments, dynamic load calculation application 209 may calculate dynamic loads on one or more wheels or tires of a vehicle based on data received from sensors 109 in respective tires 100 and inertial unit 101 of vehicle 113. In some examples, dynamic load calculation application 209 may receive uncompensated footprint length data from sensors 109. In some examples, dynamic load calculation application 209 may convert the uncompensated footprint length data into compensated footprint length data.

[0071] Dynamic load calculation application 209 may generate one or more functions based on observed footprint length data obtained from sensor 109 of tire 100. A first function may include a function by which real-time footprint length data or virtual footprint length data may be calculated based on observed acceleration data received from inertial unit 101 of vehicle 113. A second function may include a function by which dynamic load data may be calculated from virtual footprint data, which may also be calculated based on observed acceleration data obtained from inertial unit 101 of vehicle 113.

[0072] The first function may be generated by the dynamic load calculation application 209 and may include a function customized based on the conditions of the vehicle 113. Temperature, tire pressure, suspension parameters, tire condition, and other variables may affect the footprint length of each tire 100 of the vehicle 113. Therefore, the dynamic load calculation application 209 may enter a parameter learning phase, which may be performed at startup of the vehicle 113 or during initial operation of the vehicle 113 (e.g., the first few minutes of operation after the vehicle is started).

[0073] The parameter learning phase may generate constants that may be added and / or multiplied with the acceleration data obtained from the inertial unit 101 along the transverse axis 131 and / or the longitudinal axis 135. These constants may form a function from which the virtual footprint length data may be calculated based on the real-time acceleration data obtained from the inertial unit 101. The parameter learning phase and the function generated by the dynamic load calculation application 209 will be in Figure 3 This is described in further detail in the discussion of .

[0074] Additionally, the dynamic load calculation application 209 can calculate dynamic vehicle load data at each wheel using the virtual footprint length data, which can be calculated in real time or at the same frequency that the inertial unit 101 provides acceleration data. The dynamic vehicle load data can be calculated in real time or at the same frequency that the inertial unit 101 provides acceleration data along the lateral axis 131 and the longitudinal axis 135 of the vehicle 113.

[0075] Various data are stored in a data store 212 accessible to the computing environment 203. The data store 212 may represent a plurality of data stores 212, which may include relational or non-relational databases, such as object-oriented databases, hierarchical databases, hash tables, or similar key-value data stores, as well as other data storage applications or data structures. In addition, combinations of these databases, data storage applications, and / or data structures may be used together to provide a single logical data store. The data stored in the data store 212 is associated with the operation of various applications or functional entities described below. The data may include tire label information 215, pressure measurements 218, footprint measurements 221, acceleration data 222, virtual footprint length parameters 223, dynamic vehicle load data 225, and possibly other data.

[0076] The tire label information 215 may indicate information related to a specified tire 100. For example, the tire label information 215 may include tire size, which is often expressed as tire width, aspect ratio, and diameter (e.g., "P215 / 60R16," etc.). In another example, the tire label information 215 may include load capacity, spare tire information, tire type, speed rating, and various other information. In various embodiments, the tire label information 215 may include one or more recommended tire pressures, such as a target pressure 224, a maximum tire pressure, and a minimum tire pressure. The target pressure 224 may indicate the optimal tire inflation pressure for the tire under standard conditions. The target pressure 224 may be measured in various unit types, such as pounds per square inch (PSI), Pascals (Pa) or kilopascals (kPa), bars, atmospheric pressure (ATM), and kilograms per square centimeter (kg / cm 2 ).

[0077] The pressure measurement 218 may represent the inflation pressure of the tire 100 received from the sensor 109 of the tire 100 over a period of time. The pressure measurement 218 may be measured in various unit types, such as pounds per square inch (PSI), Pascals (Pa) or kilopascals (kPa), bars, atmospheres (ATM), and kilograms per square centimeter (kg / cm 2 When pressure measurements 218 are received from sensors 109 of tire 100, dynamic load calculation application 209 may store pressure measurements 218. Each pressure measurement 218 may be stored in association with the time at which pressure measurement 218 was made and the tire 100 to which pressure measurement 218 corresponds.

[0078] Footprint measurements 221 may represent the length of a footprint of tire 100 received from sensor 109 over a period of time. Footprint measurements 221 may be measured in various unit types, such as metric units (e.g., millimeters, centimeters, etc.) or imperial units (e.g., inches, feet, etc.). In at least some embodiments, footprint measurements 221 may be measured along centerline 115 of footprint 112 produced by tire 100. Footprint measurements 221 may be stored by dynamic load calculation application 209 as they are received from sensor 109 of tire 100. Each footprint measurement 221 may be stored with a timestamp corresponding to the time when footprint measurement 221 was acquired. Each footprint measurement 221 may also be stored in association with the tire 100 to which footprint measurement 221 corresponds. In many examples, sensors 109 associated with tire 100 may only provide footprint length measurements periodically, such as once every few seconds. Therefore, generating dynamic vehicle load data based on such measurements is not useful to other systems in the vehicle 113 because the frequency with which the sensors 109 provide data is so low.

[0079] The acceleration data 222 may represent acceleration data obtained by the inertial unit 101. The acceleration data may be obtained from the inertial unit 101 in real time, or at a frequency at which the inertial unit 101 can provide acceleration data, such as at a clock frequency of a CAN bus. The acceleration data may represent accelerations detected by the inertial unit 101 along the transverse axis 131 and the longitudinal axis 135.

[0080] VFL parameters 223 represent parameters from which a function may be formed for calculating virtual footprint length data from acceleration data 222 reported by inertial unit 101. As described above, dynamic load calculation application 209 may generate a function during a parameter learning phase that relates acceleration data 222 to virtual footprint length data, which may be calculated in real time or at the same frequency at which acceleration data 222 is reported by inertial unit 101.

[0081] Dynamic vehicle load data 225 represents dynamic vehicle load data that may be calculated based on virtual footprint length data calculated based on virtual footprint length parameters 223. In some examples, dynamic vehicle load data 225 may be associated with each wheel or tire 100 of vehicle 113. Dynamic vehicle load data 225 may also be associated with a timestamp corresponding to the corresponding acceleration data 222 reported by inertial unit 101 for generating the virtual footprint length data.

[0082] Next reference Figure 3 , shows a flowchart that provides an example of the operation of a portion of the dynamic load calculation application 209. Figure 3The flowchart of provides only an example of many different types of functional arrangements that may be used to implement the operation of the illustrated portion of the dynamic load calculation application 209. As an alternative, Figure 3 The flowchart of FIG. 200 may be viewed as an example of elements of a method implemented within the network environment 200 . Figure 3 It is shown how the dynamic load calculation application 209 may determine a virtual footprint length parameter 223 from which a virtual footprint length and subsequent dynamic vehicle load data 225 may be calculated.

[0083] Beginning at block 303, the dynamic load calculation application 209 may receive a footprint length measurement from the sensor 109. In some examples, a footprint length measurement may be received from each sensor 109 corresponding to each tire 100 of the vehicle. The sensor 109 may measure the footprint length of the tire 100 using metric units (e.g., millimeters, centimeters, etc.) or imperial units (e.g., inches, feet, etc.). The sensor 109 may send the footprint length of the tire 100 to the dynamic load calculation application 209, for which the dynamic load calculation application 209 may receive the footprint length of the tire as an uncompensated footprint length. The dynamic load calculation application 209 may store the uncompensated footprint length as a footprint measurement 221 in the data storage 212.

[0084] At block 309, the dynamic load calculation application 209 may receive acceleration data 222 from the inertial unit 101. The acceleration data 222 may be associated with the lateral axis 131 and the longitudinal axis 135 of the vehicle and reported by the inertial unit 101 to the CAN bus so that other vehicle systems may access the acceleration data 222.

[0085] At block 312 , dynamic load calculation application 209 may filter acceleration data 222 and footprint measurements 221 to remove outlier data. Outlier data may represent data samples that are statistical outliers compared to the remaining data obtained from sensors 109 and inertial unit 101 of tire 100.

[0086] At block 315, the dynamic load calculation application 209 may generate VFL parameters from which a function may be formed to calculate virtual footprint length data based on the acceleration data 222 obtained from the inertial unit 101. In one example, a recursive least squares parameter estimation process may be performed to relate the footprint length to the acceleration data 222. For example, an equation such as Equation 1 may be generated.

[0087] FPL_MM=p00+p10*AX_G+p01*AY_G

[0088] (Equation 1)

[0089] In the above example, FPL_MM may represent footprint measurements 221 received from sensor 109. AX_G represents acceleration data 222 for lateral axis 131 of vehicle 113 at the same time stamp as footprint measurements 221. AY_G represents acceleration data 222 for longitudinal axis 135 of vehicle 113 at the same time stamp as footprint measurements 221. P00, P10, and P01 represent virtual footprint length parameters 223 from which a function may be formed to calculate the virtual footprint length from acceleration data 222. A recursive least squares parameter estimation process may be run on footprint measurements 221 and acceleration data 222 characterizing the vehicle until virtual footprint length parameters 223 converge.

[0090] Thus, at block 318, the function defining the virtual footprint length data may be represented by Equation 2:

[0091] VFPL=p00+p10*AX_G+p01*AY_G

[0092] (Equation 2)

[0093] In Equation 2 above, VFPL represents virtual footprint length data and may be calculated using acceleration data 222 obtained from inertial unit 101 in real time or at a higher frequency than sensor 109 provides footprint measurements 221. Thereafter, the process may proceed to completion.

[0094] Figure 3 The process shown in can be performed at vehicle startup or periodically to generate virtual footprint length parameters 223. The process can also be performed when dynamic load calculation application 209 detects a tire change or tire inflation or deflation event. In addition, the process can be performed for each tire 100 of vehicle 113 so that virtual footprint length parameters 223 and corresponding VFL functions can be generated for each tire 100 of vehicle 113. In some examples, sensor 109 can also stop transmission of footprint length data once the parameter learning phase has been completed and once virtual footprint length parameters 223 are determined by dynamic load calculation application 209.

[0095] Next reference Figure 4 , shows a flowchart that provides an example of the operation of a portion of the dynamic load calculation application 209. Figure 4 The flowchart of provides only an example of many different types of functional arrangements that may be used to implement the operation of the illustrated portion of the dynamic load calculation application 209. As an alternative, Figure 4 The flowchart of FIG. 200 may be viewed as an example of elements of a method implemented within the network environment 200 . Figure 4It shows how dynamic load calculation application 209 may calculate dynamic vehicle load data 225 from acceleration data 222 obtained from inertial unit 101 .

[0096] First, at block 406, the dynamic load calculation application 209 may obtain or generate a VFL function corresponding to the tire 100 of the vehicle 113. The VFL function may be based on Figure 3 The virtual footprint length parameter 223 calculated during the parameter learning phase described in the discussion is generated.

[0097] At block 409, the dynamic load calculation application 209 may acquire acceleration data 222 from the inertial unit 101 in real time or at a frequency at which the inertial unit 101 reports acceleration data 222 to the CAN bus or network 206. The acceleration data 222 acquired at block 409 may be additional acceleration data acquired after the parameter learning phase has been completed.

[0098] At step 412, the dynamic load calculation application 209 may calculate dynamic vehicle load data 225 based on the virtual footprint length data that may be calculated from the acceleration data 222. The dynamic load calculation application 209 may store the dynamic vehicle load data 225 in the data storage 212 or provide the dynamic vehicle load data 225 to other vehicle systems, such as a braking system or stability control system of the vehicle 113. In one example, the dynamic vehicle load data 225 may be published on a CAN bus of the vehicle 113 so that other vehicle systems may utilize the dynamic vehicle load data 225.

[0099] Figure 4 The illustrated process may be performed for each wheel or tire 100 of the vehicle 113 , so that dynamic vehicle load data 225 may be obtained for each tire 100 of the vehicle 113 .

[0100] Many of the software components discussed above are stored in the memory of each computing device and are executable by the processor of each computing device. In this regard, the term "executable" refers to a program file in a form that can ultimately be run by a processor. An example of an executable program can be a compiler that can be converted into a machine code in a format that can be loaded into a random access portion of a memory and run by a processor; a source code that can be expressed in an appropriate format, such as an object code that can be loaded into a random access portion of a memory and executed by a processor; or a source code that can be interpreted by another executable program to generate instructions in a random access portion of a memory for execution by a processor. The executable program can be stored in any part or component of a memory, including a random access memory (RAM), a read-only memory (ROM), a hard disk, a solid-state drive, a universal serial bus (USB) flash drive, a memory card, an optical disk (such as a compact disk (CD) or a digital versatile disk (DVD), a floppy disk, a tape, or other storage component.

[0101] Memory includes volatile and non-volatile memory and data storage components. Volatile components are components that do not retain data values ​​when power is off. Non-volatile components are components that retain data when power is off. Therefore, memory can include random access memory (RAM), read-only memory (ROM), hard disk drive, solid state drive, USB flash drive, memory card accessed by a memory card reader, floppy disk accessed by an associated floppy disk drive, optical disk accessed by an optical drive, magnetic tape accessed by an appropriate tape drive, or other memory components, or any combination of two or more of these memory components. In addition, RAM can include static random access memory (SRAM), dynamic random access memory (DRAM) or magnetic random access memory (MRAM) and other such devices. ROM can include programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or other similar memory devices.

[0102] Although the applications and systems described herein can be implemented as software or code executed by general hardware as described above, as an alternative, they can also be implemented in dedicated hardware or a combination of software / general hardware and dedicated hardware. If implemented in dedicated hardware, each can be implemented as a circuit or state machine using any one or combination of a variety of technologies. These technologies may include, but are not limited to, discrete logic circuits with logic gates, application-specific integrated circuits (ASICs) with appropriate logic gates, field programmable gate arrays (FPGAs), or other components, etc., for implementing various logical functions when one or more data signals are applied. These technologies are generally well known to those skilled in the art, so they are not described in detail herein.

[0103] The flow chart shows the functions and operations of the implementation of each part of each embodiment of the present disclosure. If implemented in software form, each block can represent a module, segment or code portion including program instructions to implement a specified logical function. The program instruction can be implemented in the form of source code or machine code, the source code includes human-readable statements written in a programming language, and the machine code includes digital instructions that can be recognized by a suitable execution system (such as a processor in a computer system). Machine code can be converted from source code through various processes. For example, a compiler can be used to generate machine code from source code before executing the corresponding application program. As another example, machine code can be generated from source code while using an interpreter to execute. Other methods can also be used. If implemented in hardware form, each block can represent a circuit or multiple interconnected circuits to implement a specified logical function.

[0104] Although the flowchart shows a specific execution order, it should be understood that the execution order may be different from the depicted order. For example, the execution order of two or more blocks may be disrupted relative to the shown order. In addition, two or more blocks displayed in succession may be executed simultaneously or partially simultaneously. In addition, in some embodiments, one or more blocks shown in the flowchart may be skipped or omitted. In addition, any number of counters, state variables, warning signals or messages may be added to the logic flow described herein for purposes of enhancing practicality, statistics, performance measurement, or providing troubleshooting assistance, etc. It should be understood that all such changes are within the scope of the present disclosure.

[0105] In addition, any logic or application including software or code described herein may be embodied in any non-transitory computer-readable medium for use by or in connection with an instruction execution system (e.g., a processor in a computer system or other system). In this sense, logic may include statements, including instructions and declarations that can be obtained from a computer-readable medium and executed by an instruction execution system. In the context of the present disclosure, a "computer-readable medium" may be any medium that can contain, store, or maintain the logic or application described herein for use by or in connection with an instruction execution system. In addition, a collection of distributed computer-readable media located on multiple computing devices (e.g., a storage area network or a distributed or clustered file system or database) may also be collectively considered a single non-transitory computer-readable medium.

[0106] Computer readable media may include any of a number of physical media, such as magnetic, optical, or semiconductor media. More specific examples of suitable computer readable media include, but are not limited to, magnetic tape, magnetic floppy disk, magnetic hard disk, memory card, solid state drive, USB flash drive, or optical disk. In addition, the computer readable medium may be a random access memory (RAM), including static random access memory (SRAM) and dynamic random access memory (DRAM), or a magnetic random access memory (MRAM). In addition, the computer readable medium may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other types of storage devices.

[0107] In addition, any logic or application described herein can be implemented and constructed in a variety of ways. For example, the one or more applications can be implemented as modules or components of a single application. In addition, one or more applications described herein can be executed in a shared or separate computing device or a combination thereof. For example, multiple applications described herein can be executed in the same computing device, or in multiple computing devices in the same computing environment 203.

[0108] Unless expressly stated otherwise, disjunctive language such as the phrase "at least one of X, Y, or Z" should be understood as generally used to indicate that an item, term, etc. may be X, Y, or Z, or any combination thereof (e.g., X; Y; Z; X or Y; X or Z; Y or Z; X, Y, or Z; etc.), depending on the context. Thus, such disjunctive language is generally not intended to, and should not, imply that certain embodiments require that at least one of X, at least one of Y, or at least one of Z each be present.

[0109] It should be emphasized that the above-mentioned embodiments of the present disclosure are merely examples of possible implementations for a clear understanding of the principles of the present disclosure. Many changes and modifications may be made to the above-mentioned embodiments without substantially departing from the spirit and principles of the present disclosure. All such modifications and changes are intended to be included herein within the scope of the present disclosure and are protected by the following claims.

[0110] The following clauses describe various embodiments of the present disclosure.While the following clauses describe some embodiments of the present disclosure, other embodiments of the present disclosure are also set forth above.

[0111] Item 1 - A method comprising: receiving a plurality of footprint length measurements of a tire from a sensor; receiving a plurality of acceleration data corresponding to a plurality of axes from an inertial unit; filtering the footprint length data to remove statistical outlier data samples; generating a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; and generating a virtual footprint length function by which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of the footprint length measurements from the sensor.

[0112] Clause 2—The method of clause 1, further comprising: acquiring additional acceleration data from the inertial unit; and calculating a dynamic vehicle load associated with the tire based on the additional acceleration data and a virtual footprint length of the tire.

[0113] Clause 3—The method of clause 2, wherein additional acceleration data is captured after a parameter learning phase to generate the virtual footprint length parameter.

[0114] Clause 4—The method of clause 3, wherein a parameter learning phase is performed at vehicle startup until the virtual footprint length parameter converges.

[0115] Clause 5—The method of any of clauses 1-4, wherein the virtual footprint length of the tire is calculated at a frequency substantially the same as the frequency at which the inertial unit reports acceleration data.

[0116] Clause 6—A method according to any of clauses 1 to 5, wherein the acceleration data comprises lateral axis acceleration data and longitudinal axis acceleration data.

[0117] Clause 7—The method of any of clauses 1-6, wherein the virtual footprint length parameter is generated using a recursive least squares parameter estimation procedure to generate the virtual footprint length function.

[0118] Clause 8—A method according to any of clauses 1-7, wherein the virtual footprint length function relates acceleration data from the inertial unit to the footprint length of the tire.

[0119] Clause 9 - A system comprising: a computing device including a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: receive a plurality of footprint length measurements of a tire from a sensor; receive a plurality of acceleration data corresponding to a plurality of axes from an inertial unit; filter the footprint length data to remove statistical outlier data samples; generate a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; and generate a virtual footprint length function by which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of the footprint length measurements from the sensor.

[0120] Clause 10 - The system of clause 9, wherein the machine-readable instructions further cause the computing device to at least: obtain additional acceleration data from the inertial unit; and calculate a dynamic vehicle load associated with the tire based on the additional acceleration data and a virtual footprint length of the tire.

[0121] Clause 11 - The system of clause 10, wherein additional acceleration data is captured after a parameter learning phase to generate the virtual footprint length parameter.

[0122] Clause 12-A system according to clause 11, wherein a parameter learning phase is performed at vehicle startup until the virtual footprint length parameter converges.

[0123] Clause 13—A system according to any of clauses 9-13, wherein the virtual footprint length of the tire is calculated at a frequency substantially the same as the frequency at which the inertial unit reports acceleration data.

[0124] Clause 14—A system according to any of clauses 9-14, wherein the acceleration data comprises lateral axis acceleration data and longitudinal axis acceleration data.

[0125] Clause 15 - The system of any of clauses 9-15, wherein the virtual footprint length parameter is generated using a recursive least squares parameter estimation process to generate the virtual footprint length function.

[0126] Clause 16 - A non-transitory computer-readable medium comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least: receive a plurality of footprint length measurements of a tire from a sensor; receive a plurality of acceleration data corresponding to a plurality of axes from an inertial unit; filter the footprint length data to remove statistical outlier data samples; generate a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; and generate a virtual footprint length function by which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of the footprint length measurements from the sensor.

[0127] Clause 17 - The non-transitory computer-readable medium of clause 16, wherein the machine-readable instructions further cause the computing device to at least: obtain additional acceleration data from the inertial unit; and calculate a dynamic vehicle load associated with the tire based on the additional acceleration data and a virtual footprint length of the tire.

[0128] Clause 18 - The non-transitory computer-readable medium of any of clauses 16-18, wherein additional acceleration data is captured after a parameter learning phase of generating the virtual footprint length parameter.

[0129] Clause 19 - The non-transitory computer readable medium of any of clauses 16-18, wherein the parameter learning phase is performed at vehicle startup until the virtual footprint length parameter converges.

[0130] Clause 20 - The non-transitory computer-readable medium of any of clauses 16-19, wherein the acceleration data comprises lateral axis acceleration data and longitudinal axis acceleration data.

Claims

1. A method comprising: receiving a plurality of footprint length measurements of the tire from a sensor; receiving a plurality of acceleration data corresponding to a plurality of axes from an inertial unit; filtering multiple blot length measurements to remove statistical outlier data samples; generating a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; as well as A virtual footprint length function is generated, from which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of footprint length measurements from the sensor.

2. The method according to claim 1, further comprising: Get additional acceleration data from the inertial unit; as well as Dynamic vehicle loads associated with the tire are calculated based on the additional acceleration data and the virtual footprint length of the tire.

3. The method of claim 2, wherein additional acceleration data is captured after a parameter learning phase to generate the virtual footprint length parameters. 4 . The method according to claim 3 , wherein the parameter learning phase is performed when the vehicle is started until the virtual footprint length parameter converges.

5. The method according to claim 1, wherein: The virtual footprint length of the tire is calculated at substantially the same frequency as the inertial unit reports acceleration data. The method of claim 1 , wherein the acceleration data comprises lateral axis acceleration data and longitudinal axis acceleration data.

7. The method of claim 1, wherein the virtual footprint length parameters are generated using a recursive least squares parameter estimation process to generate the virtual footprint length function.

8. The method of claim 1, wherein the virtual footprint length function relates acceleration data from the inertial unit to a footprint length of the tire.

9. A system comprising: A computing device including a processor and a memory; as well as The machine-readable instructions stored in the memory, when executed by the processor, cause the computing device to at least: receiving a plurality of footprint length measurements of the tire from a sensor; receiving a plurality of acceleration data corresponding to a plurality of axes from an inertial unit; filtering multiple blot length measurements to remove statistical outlier data samples; generating a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; as well as A virtual footprint length function is generated, from which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of footprint length measurements from the sensor.

10. A non-transitory computer-readable medium comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least: receiving a plurality of footprint length measurements of the tire from a sensor; receiving a plurality of acceleration data corresponding to a plurality of axes from an inertial unit; filtering multiple blot length measurements to remove statistical outlier data samples; generating a virtual footprint length parameter based on the plurality of footprint length measurements and the acceleration data; as well as A virtual footprint length function is generated, from which a virtual footprint length of the tire can be calculated based on the acceleration data, the virtual footprint length of the tire being calculated in the absence of footprint length measurements from the sensor.

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