Tire Stiffness Estimation System

Abstract

A tire longitudinal stiffness estimation system includes an electronic communication system disposed on a vehicle. Sensors are disposed on the vehicle, communicate with the electronic communication system, and a processor is accessible through the electronic communication system. The sensors measure parameters associated with the vehicle and transmit data of the parameters to the processor. A mu slip curve generator receives the parameters to generate a mu slip curve in real time from the data. An extraction module extracts raw data from a linear portion of the mu slip curve. A denoising module denoises the raw data from the mu slip curve by determining a vector of the raw data, an orientation of the vector, and an orientation of the vector. The denoising module generates denoised data, and a stiffness calculator receives the denoised data and generates an estimation of the longitudinal stiffness for the tire.

Description

Technical Field

[0001] The present invention generally relates to tire monitoring and estimation systems. More particularly, the present invention relates to systems for predicting certain tire characteristics. Specifically, the present invention relates to a system for real-time estimation of tire stiffness. Background Art

[0002] As is known in the art, a vehicle is supported by multiple tires. During operation of the vehicle, the stiffness of each tire affects the performance and characteristics of the tire. For example, the longitudinal stiffness of the tire (which is the stiffness of the tire in its longitudinal or travel direction) can be used to distinguish between different road surface conditions and / or different wear states of the tire. In addition, the longitudinal stiffness can be used to improve the operation of vehicle control systems such as adaptive cruise control (ACC), antilock braking system (ABS), electronic stability program (ESP), acceleration slip regulation (ASR), etc.

[0003] Due to the usefulness of the tire longitudinal stiffness, it is desirable to generate an accurate estimate of the longitudinal stiffness. In the prior art, systems for providing such estimates have been developed. However, in order to achieve an accurate longitudinal stiffness estimate, such prior art systems have been complex and often use data from multiple sources. For example, data from the vehicle, from the tire, and from a remote data server can be used.

[0004] Using such complex systems and data from such a variety of sources can be undesirably difficult to implement. In addition, such complex systems require a relatively large amount of computational load. When a relatively large amount of computational load is involved, such systems may not be able to be executed on a vehicle-mounted processor, thereby undesirably requiring additional sources such as cloud computing and undesirably taking a relatively large amount of time to generate a real-time estimate.

[0005] Accordingly, there is a need in the art for a system for real-time estimation of tire longitudinal stiffness that provides an accurate estimate based on data from a limited source and has a low computational load. Summary of the Invention

[0006] In accordance with one aspect of an exemplary embodiment of the present invention, a longitudinal stiffness estimation system for supporting at least one tire of a vehicle is provided. The system includes: an electronic communication system disposed on the vehicle; and at least one sensor disposed on the vehicle and in electronic communication with the electronic communication system. A processor is accessible through the electronic communication system. The sensor measures selected parameters associated with the vehicle and transmits data of the selected parameters to the processor through the electronic communication system. A mu slip curve generator communicates with the processor, receives the selected parameters, and generates a mu slip curve in real time from the transmitted data. An extraction module communicates with the processor and extracts raw data from a linear portion of the mu slip curve. A denoising module communicates with the processor and denoises the raw data from the mu slip curve by determining a vector of the raw data, an orientation of the vector, and an orientation of the vector. The denoising module generates denoised data, and a stiffness calculator receives the denoised data and generates an estimation of the longitudinal stiffness of the tire. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The present invention will be described by way of example and with reference to the accompanying drawings, in which:

[0008] Figure 1 is a perspective view of a tire and a vehicle employed in conjunction with the longitudinal stiffness estimation system of the present invention;

[0009] Figure 2 is a flow chart showing an exemplary embodiment of the longitudinal stiffness estimation system of the present invention;

[0010] Figure 3 is Figure 2 a graphical representation of a part of the longitudinal stiffness estimation system shown in;

[0011] Figure 4 is Figure 2 a graphical representation of another part of the longitudinal stiffness estimation system shown in;

[0012] Figure 5 is Figure 2 a graphical representation of another part of the longitudinal stiffness estimation system shown in;

[0013] Figure 6 is Figure 2 a graphical representation of another part of the longitudinal stiffness estimation system shown in;

[0014] Figure 7 is Figure 2 a graphical representation of another part of the longitudinal stiffness estimation system shown in;

[0015] Figure 8is a graphical representation of a road condition monitor of a longitudinal stiffness estimation system as shown in Figure 2 ; and

[0016] Figure 9 is a graphical representation of a tire wear monitor of a longitudinal stiffness estimation system as shown in Figure 2 .

[0017] Throughout the drawings, like numerals refer to like parts.

[0018] Definition

[0019] "ANN" or "artificial neural network" is an adaptive tool for non-linear statistical data modeling that changes its structure during a learning phase based on external or internal information flowing through the network. An ANN neural network is a non-linear statistical data modeling tool that is used to model complex relationships between inputs and outputs or to discover patterns in data.

[0020] "Axial" and "axially" mean a line or direction parallel to the axis of rotation of the tire.

[0021] "CAN" is an abbreviation for Controller Area Network and is used in conjunction with a CAN bus, which is an electronic communication system in a vehicle.

[0022] "Circumferential" means a line or direction extending along the perimeter of the circumferential tread surface of the tire perpendicular to the axial direction.

[0023] "Cloud computing" means computer processing involving computing power and / or data storage distributed across multiple data centers, typically facilitated by access and communication using the Internet.

[0024] "Inner" means the side of the tire that is closest to the vehicle when the tire is mounted on a wheel and the wheel is mounted on a vehicle.

[0025] "Kalman filter" is a set of mathematical equations that implement a predictor-corrector type estimator that is optimal in the sense that it minimizes the estimated error covariance when certain assumed conditions are met.

[0026] "Lateral" means the axial direction.

[0027] "Luenberger observer" is a state observer or estimation model. A "state observer" is a system that provides an estimate of the internal state of a given physical system based on measurements of the inputs and outputs of the physical system. It is typically computer-implemented and forms the basis for many practical applications.

[0028] "MSE" is an abbreviation for mean square error, which is the error between the measured signal and the estimated signal that the Kalman filter minimizes.

[0029] "Outer side" means the side of the tire that is furthest from the vehicle when the tire is mounted on a wheel and the wheel is mounted on a vehicle.

[0030] "Radial" and "radially" mean in a direction radially towards or away from the axis of rotation of the tire.

[0031] "TPMS" means tire pressure monitoring system. DETAILED DESCRIPTION

[0032] Exemplary embodiments of the longitudinal stiffness estimation system of the present invention are indicated at 10 in Figures 1 - 9 As shown in Figure 1 the system estimates the longitudinal stiffness of the tire 12 that supports the vehicle 14. Although the vehicle 14 is depicted as a passenger car, the present invention will not be so limited. The principles of the present invention find application in other vehicle classes, such as commercial trucks, where the vehicle may be supported by more or fewer tires.

[0033] Each tire 12 has a conventional structure and is mounted on a wheel 16. Each tire 12 includes a pair of sidewalls 18 that extend to a circumferential tread 20. Each tire 12 may be equipped with a sensor or transducer 24, which may be a tire pressure monitoring (TPMS) module or sensor, and detects tire parameters such as the pressure within the tire cavity 20 and the tire temperature. The sensor 24 is preferably fixed to the inner liner 22 of the tire 12 by suitable means, such as an adhesive.

[0034] The tire 12 includes a longitudinal stiffness, which is its stiffness in the longitudinal or travel direction. Turning to Figure 2 the longitudinal stiffness estimation system 10 calculates the longitudinal stiffness of the tire 12 by providing a longitudinal stiffness estimate 52. Aspects of the longitudinal stiffness estimation system 10 are preferably executed on a processor 26, which is accessible via an electronic communication system on the vehicle, such as a CAN bus system 28, which enables central communication between multiple vehicle sensors. The processor 26 may be a local processor mounted on the vehicle 14 or may be a remote processor, such as a cloud computing processor.

[0035] The longitudinal stiffness estimation system 10 preferably provides longitudinal stiffness estimates 52 for each tire 12 mounted on the drive wheels 16 of a vehicle 14. For example, in a front-wheel drive vehicle 14, the system 10 generates stiffness estimates 52 for each of the front tires 12. For convenience purposes, the system 10 is described with respect to one tire 12, it being understood that the estimates 52 are preferably provided for each tire 12 mounted on the drive wheels 16 of the vehicle 14.

[0036] The longitudinal stiffness estimation system 10 receives as input certain parameters measured by sensors mounted on the vehicle 14 and in electronic communication with the vehicle CAN bus system 28. Specifically, the CAN bus 28 electronically transmits the longitudinal acceleration (A x or a x ) 30 of the vehicle 14, the wheel speed 32, the throttle or accelerator pedal position 34, the brake pedal position 36, and the vehicle reference speed 38 to an acceleration module 40 and a mu (µ) slip curve generator 42. The vehicle reference speed 38 can be obtained from a global positioning system (GPS) or other reliable source of the vehicle reference speed.

[0037] In the acceleration module 40, the throttle pedal position 34 and the brake pedal position 36 are used to confirm that the vehicle 14 is accelerating. For example, if the throttle pedal position 34 is below a predetermined throttle threshold, or if the brake pedal position 36 is above a predetermined brake threshold, the system 10 determines that the vehicle 14 is not accelerating. When the vehicle 14 is not accelerating, the system 10 does not proceed to the mu (µ) slip curve generator 42. If the throttle pedal position 34 is greater than the predetermined throttle threshold and / or the brake pedal position 36 is below the predetermined brake threshold, the system 10 determines that the vehicle 14 is accelerating and proceeds to the mu (µ) slip curve generator 42.

[0038] Additionally with respect to Figure 3 , the mu (µ) slip curve generator 42 generates a mu (µ) slip curve 44 in real time. On the vertical axis, the mu (µ) slip curve 44 plots the frictional force between the tire 12 and the surface on which the tire is traveling, which frictional force is represented by the coefficient of friction mu (µ). The mu (µ) slip curve generator 42 uses the longitudinal acceleration 30 of the vehicle 14 to approximate the coefficient of friction µ during vehicle acceleration. On the horizontal axis, the mu (µ) slip curve 44 plots the tire slip 46, which is the relative movement between the tire 12 and the surface on which the tire is traveling. The mu (µ) slip curve generator 42 uses the wheel speed 32 and the vehicle reference speed 38 to calculate the slip 46:

[0039] Slip 46, percentage (%) =

[0040] In this way, the µ-slip curve generator 42 of the longitudinal stiffness estimation system 10 uses input signals from the vehicle CAN bus system 28 to generate the µ-slip curve 44 in real time.

[0041] The slope 48 of the linear portion 50 of the µ-slip curve 44 corresponds to the longitudinal stiffness of the tire 12. As will be described in more detail below, the longitudinal stiffness estimation system 10 extracts the longitudinal stiffness of the tire 12 and provides the longitudinal stiffness estimate 52 in an accurate manner.

[0042] Reference Figure 2 and 4 For, the extraction module 54 extracts the raw data 56 from the linear portion 50 of the µ-slip curve 44. The raw data 56 includes signal noise, i.e., unwanted modifications in the data that occur during capture, storage, transmission, and / or processing. Therefore, the raw data 56 must be denoised or cleaned to improve its accuracy. It is to be understood that, for the purpose of illustrating the principles of the present invention, Figures 4 - 9 the data shown in

[0043] Now turning to Figure 2 and Figure 5 For, the denoising module 58 denoises or cleans the raw data 56. The denoising module 58 preferably applies principal component analysis (PCA) to determine patterns from the raw data 56 for predictive analysis. For example, PCA identifies principal components or eigenvectors that are characteristic vectors of a linear transformation of the raw data 56. In this way, the pattern visualized or represented by the vector 60 is determined from the raw data 56.

[0044] The PCA of the denoising module 58 predicts the orientation 62 of the vector 60 corresponding to the data variance rather than predicting each value of the raw data 56. The first principal component of the raw data is used to determine the orientation of the raw data 56 and the vector 60. In addition to the orientation 62, another parameter called the direction of the vector 60 is also determined, which is indicated by theta (θ). The direction θ enables an accurate fit of the vector 60 that covers the variance of the data to be obtained. The direction θ is the angle between the horizontal line 64 extending from the origin of µ and the orientation 62 of the vector 60.

[0045] For accuracy, the direction θ must reflect the correct alignment of the vector 60 with the data. Since the direction θ is initially an unknown value, a density module 66 is employed to determine the direction. In the density module 66, the determination of the optimal value of the direction θ is driven by the data.

[0046] Regarding Figure 2 and 6, in the density module 66, the data is transformed to polar coordinates 68, and a density contrast plot 72 oriented towards θ is generated. The density is indicated by rho (ρ). Since the data is symmetric, positive values can be used for simplicity. Additionally, since the data is not continuous, the top ten percent (10%) of the standard deviation of the data is used to select the data range 70. The density ρ of the data range 70 is calculated based on the median of the data:

[0047]

[0048] The median value towards θ is used to determine the density center of the data range 70, which corresponds to the optimal value of the orientation.

[0049] Additionally regarding Figure 7 , once the orientation θ is determined, the data range 70 is transformed back to the μ contrast slip 46 plot, and the orientation 62 of the vector 60 is calculated using polar coordinates. Specifically, the coefficient of the second polar axis PC2 is divided by the coefficient of the first polar axis PC1 to determine the orientation 62. In this way, the orientation 62 of the vector 60 and the orientation θ are determined, thereby denoising the data 74.

[0050] Once the data has been denoised 74 by determining the orientation 62 of the vector 60 and the orientation θ, the stiffness calculator 76 determines the slope of the vector. The slope of the vector 60 is the longitudinal stiffness estimate 52 for the tire 12. Preferably, the longitudinal stiffness estimate 52 is transmitted via the CAN bus system 28 to other vehicle control systems for use in such systems and / or for determining certain conditions of the tire 12.

[0051] For example, going to Figure 8 , the longitudinal stiffness estimate 52 can be used in the road condition monitor 106 to monitor the condition of the road surface on which the tire 12 is traveling by differentiating between different road surface conditions 88. The stiffness 52 of the tire 12 exhibits rapid time-varying characteristics on different road surface conditions 88. More particularly, in the non-temperature-compensated plot 78, each tire stiffness estimate 52 is different for summer tires 80, all-season tires 82, and winter tires 84 on dry surfaces 90, wet surfaces 92, snow-covered surfaces 94, and ice-covered surfaces 96, respectively.

[0052] Because tire stiffness is sensitive to temperature, it is important to correct the tire stiffness estimate 52 for the effect of temperature, as shown in the temperature compensation plot 86. From the temperature compensation plot 86, it can be seen that temperature compensation can exaggerate the differences in the stiffness estimate 52, especially for the summer tire 80 and the all-season tire 82. In addition, it can be seen that the stiffness estimate 52 for the winter tire 84 generally exhibits a lower dependence on the type of road surface 88, while the stiffness estimates for the summer tire 80 and the all-season tire 82 are higher on the icy surface 96 than on the snow-covered surface 94. Based on this information, the longitudinal stiffness estimate 52 can therefore be used by the road surface monitor 106 to distinguish between the dry road surface 90, the wet road surface 92, the snow-covered road surface 94, and the icy road surface 96.

[0053] Regarding Figure 9 , the longitudinal stiffness estimate 52 can be used in the wear state monitor 108 to monitor the wear state of the tire 12 by distinguishing between different wear states. The stiffness 52 of the tire 12 exhibits a slow time-varying characteristic with wear. More particularly, in the μ-contrast slip plot 98 for the worn tire 100, the stiffness estimate 52 is at least thirty percent (30%) higher than the stiffness estimate in the μ-contrast slip plot 102 for the worn tire 104. Based on this information, the longitudinal stiffness estimate 52 can therefore be used by the wear state monitor 108 to distinguish between different wear states of the tire 12.

[0054] In this way, the longitudinal stiffness estimation system 10 of the present invention provides the stiffness estimate 52 for the tire 12 in real time based on input signals from standard vehicle systems such as the CAN bus system 28. Therefore, the longitudinal stiffness estimation system 10 of the present invention provides an accurate stiffness estimate 52 based on a minimum number of data sources. In addition, the use of the denoising module 58 described above in the longitudinal stiffness estimation system 10 involves a low computational load and can therefore be executed on the vehicle-based processor 26, as opposed to prior art systems that involve a high computational load and must be executed remotely.

[0055] The present invention also includes a method for estimating the longitudinal stiffness of the tire 12. The method includes the steps presented above and shown in Figures 1 to 9 . The longitudinal stiffness estimation system 10 and the accompanying method of the present invention can be referred to as the Nouri technology.

[0056] It is to be understood that the structure and method of the stiffness estimation system 10 described above can be altered or rearranged, or components or steps known to those skilled in the art can be omitted or added, without affecting the overall concept or operation of the present invention. For example, although the system 10 has been described above using the acceleration condition of the vehicle 14, the system can be applied to cruise and braking conditions without affecting the overall concept or operation of the present invention.

[0057] Example Embodiment

[0058] Example 1 includes a longitudinal stiffness estimation system for at least one tire supporting a vehicle, the longitudinal stiffness estimation system comprising: an electronic communication system disposed on the vehicle; at least one sensor disposed on the vehicle and in electronic communication with the electronic communication system; a processor accessible via the electronic communication system; the at least one sensor measuring a selected parameter associated with the vehicle and transmitting data of the selected parameter to the processor via the electronic communication system; a mu-slip curve generator in communication with the processor and receiving the selected parameter, and generating a mu-slip curve in real time from the transmitted data; an extraction module in communication with the processor and extracting raw data from a linear portion of the mu-slip curve; a denoising module in communication with the processor and denoising the raw data from the mu-slip curve by determining a pattern represented by a vector, an orientation of the vector, and an orientation of the vector from the raw data, wherein the denoising module generates denoised data; and a stiffness calculator receiving the denoised data and generating a longitudinal stiffness estimate for the at least one tire.

[0059] Example 2 includes the longitudinal stiffness estimation system of Example 1, wherein the selected parameter includes at least one of a longitudinal acceleration of the vehicle, a wheel speed, and a vehicle reference speed.

[0060] Example 3 includes the longitudinal stiffness estimation system of Example 2, wherein the mu-slip curve generator receives the longitudinal acceleration of the vehicle to approximate the mu in the mu-slip curve as the coefficient of friction between the tire and the surface on which it travels.

[0061] Example 4 includes the longitudinal stiffness estimation system of Example 2, wherein the mu-slip curve generator receives the wheel speed and the vehicle reference speed to calculate the slip in the mu-slip curve.

[0062] Example 5 includes the longitudinal stiffness estimation system of Example 1, further comprising an acceleration module that receives a throttle position and a brake pedal position to determine whether the vehicle is accelerating.

[0063] Example 6 includes the longitudinal stiffness estimation system described in Example 1, wherein the mode is determined as the eigenvector of the original data.

[0064] Example 7 includes the longitudinal stiffness estimation system described in Example 1, wherein the orientation of the vector is the angle between the horizontal line extending from the origin of mu in the mu slip curve and the orientation of the vector.

[0065] Example 8 includes the longitudinal stiffness estimation system described in Example 1, wherein the denoising module includes a density module to determine the orientation of the vector, and the density module transforms the original data into polar coordinates and generates a density contrast orientation plot.

[0066] Example 9 includes the longitudinal stiffness estimation system described in Example 8, wherein the density module determines the selected data range in the density contrast orientation plot and calculates the density of the selected data range based on the median of the selected data range.

[0067] Example 10 includes the longitudinal stiffness estimation system described in Example 9, wherein the density module determines the density center of the selected data range, and the density center corresponds to the optimal value of the orientation of the vector.

[0068] Example 11 includes the longitudinal stiffness estimation system described in Example 1, wherein the denoising module uses polar coordinates to determine the orientation of the vector, and the coefficient of the second polar axis is divided by the coefficient of the first polar axis to determine the orientation of the vector.

[0069] Example 12 includes the longitudinal stiffness estimation system described in Example 1, wherein the stiffness calculator determines the slope of the vector to generate a longitudinal stiffness estimate.

[0070] Example 13 includes the longitudinal stiffness estimation system described in Example 1, wherein the processor is installed on the vehicle.

[0071] Example 14 includes the longitudinal stiffness estimation system described in Example 1, wherein the processor is a cloud computing processor.

[0072] Example 15 includes the longitudinal stiffness estimation system described in Example 1, wherein the system provides a longitudinal stiffness estimate for each tire installed on the drive wheels of the vehicle.

[0073] Example 16 includes the longitudinal stiffness estimation system described in Example 1, wherein the longitudinal stiffness estimate is transmitted to the vehicle control system through the electronic communication system.

[0074] Example 17 includes the longitudinal stiffness estimation system described in Example 1, further including a road surface condition monitor, wherein the road surface condition monitor receives a plurality of longitudinal stiffness estimations, and based on the time-varying characteristics of the longitudinal stiffness estimations, the road surface condition monitor differentiates between at least two of a dry road surface, a wet road surface, a snow-covered road surface, and an icy road surface.

[0075] Example 18 includes the longitudinal stiffness estimation system described in Example 17, wherein the road surface condition monitor includes a temperature correction for each longitudinal stiffness estimation.

[0076] Example 19 includes the longitudinal stiffness estimation system described in Example 1, further including a wear state monitor, wherein the wear state monitor receives a plurality of longitudinal stiffness estimations, and based on the time-varying characteristics of the longitudinal stiffness estimations, the wear state monitor differentiates between different wear states of the tire.

[0077] Example 20 includes the longitudinal stiffness estimation system described in Example 19, wherein the wear state monitor differentiates between different wear states of the tire based on a worn tire stiffness estimation that is higher than a new tire stiffness estimation.

[0078] The present invention has been described with reference to the preferred embodiments. Others will envision potential modifications and alterations upon reading and understanding this specification. It is to be understood that all such modifications and alterations are included within the scope of the present invention as set forth in the appended claims or their equivalents.

Claims

1. A longitudinal stiffness estimation system for at least one tire supporting a vehicle, the longitudinal stiffness estimation system comprising: An electronic communication system disposed on the vehicle; At least one sensor disposed on the vehicle and in electronic communication with the electronic communication system; A processor accessible via the electronic communication system; The at least one sensor measures a selected parameter associated with the vehicle and transmits data of the selected parameter to the processor via the electronic communication system; A mu-slip curve generator in communication with the processor and receiving the selected parameter, and generating a mu-slip curve in real time from the transmitted data; An extraction module in communication with the processor and extracting raw data from the linear portion of the mu-slip curve; A denoising module in communication with the processor and denoising the raw data from the mu-slip curve by determining a pattern represented by a vector, an orientation of the vector, and an orientation of the vector, wherein the denoising module generates denoised data; And A stiffness calculator that receives the denoised data and generates a longitudinal stiffness estimate for the at least one tire.

2. The longitudinal stiffness estimation system according to claim 1, wherein the selected parameter includes at least one of a longitudinal acceleration of the vehicle, a wheel speed, and a vehicle reference speed.

3. The longitudinal stiffness estimation system according to claim 2, wherein the mu-slip curve generator receives the longitudinal acceleration of the vehicle to approximate mu in the mu-slip curve as a coefficient of friction between the tire and the surface on which it travels.

4. The longitudinal stiffness estimation system according to claim 2, wherein the mu-slip curve generator receives the wheel speed and the vehicle reference speed to calculate the slip in the mu-slip curve.

5. The longitudinal stiffness estimation system according to claim 1, further comprising an acceleration module that receives a throttle position and a brake pedal position to determine whether the vehicle is accelerating.

6. The longitudinal stiffness estimation system according to claim 1, wherein the pattern is determined as an eigenvector of the raw data.

7. The longitudinal stiffness estimation system according to claim 1, wherein the orientation of the vector is an angle between a horizontal line extending from the origin of mu in the mu-slip curve and the orientation of the vector.

8. The longitudinal stiffness estimation system according to claim 1, wherein the denoising module includes a density module to determine the orientation of the vector, and the density module transforms the raw data into polar coordinates and generates a density contrast orientation plot.

9. The longitudinal stiffness estimation system according to claim 8, wherein the density module determines a selected data range in the density contrast orientation plot and calculates the density of the selected data range based on the median of the selected data range.

10. The longitudinal stiffness estimation system according to claim 9, wherein the density module determines the density center of the selected data range, and the density center corresponds to the optimal value of the orientation of the vector.

11. The longitudinal stiffness estimation system according to claim 1, wherein the denoising module uses polar coordinates to determine the orientation of the vector, and the coefficient of the second polar axis is divided by the coefficient of the first polar axis to determine the orientation of the vector.

12. The longitudinal stiffness estimation system according to claim 1, wherein the stiffness calculator ascertains the slope of the vector to generate a longitudinal stiffness estimate.

13. The longitudinal stiffness estimation system according to claim 1, wherein the processor is mounted on the vehicle.

14. The longitudinal stiffness estimation system according to claim 1, wherein the processor is a cloud computing processor.

15. The longitudinal stiffness estimation system according to claim 1, wherein the system provides a longitudinal stiffness estimate for each tire mounted on the drive wheels of the vehicle.

16. The longitudinal stiffness estimation system according to claim 1, wherein the longitudinal stiffness estimate is transmitted to the vehicle control system through the electronic communication system.

17. The longitudinal stiffness estimation system according to claim 1, further comprising a road surface condition monitor, wherein the road surface condition monitor receives a plurality of longitudinal stiffness estimates, and based on the time-varying characteristics of the longitudinal stiffness estimates, the road surface condition monitor differentiates between at least two of dry road surface, wet road surface, snow-covered road surface, and ice-covered road surface.

18. The longitudinal stiffness estimation system according to claim 17, wherein the road surface condition monitor includes a temperature correction for each longitudinal stiffness estimate.

19. The longitudinal stiffness estimation system according to claim 1, further comprising a wear state monitor, wherein the wear state monitor receives a plurality of longitudinal stiffness estimates, and based on the time-varying characteristics of the longitudinal stiffness estimates, the wear state monitor differentiates between different wear states of the tire.

20. The longitudinal stiffness estimation system according to claim 19, wherein the wear state monitor differentiates between different wear states of the tire based on the fact that the wear tire stiffness estimate is higher than the new tire stiffness estimate.

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