Tire pressure estimation

• US 7,263,458 B2
• Filed: 07/07/2003
• Issued: 08/28/2007
• Est. Priority Date: 07/07/2003
• Status: Active Grant

• Current Assignee: Nira Dynamics AB
• Original Assignee: Nira Dynamics AB
• Inventors: Hall, Peter, Lindskog, Peter, Gustavsson, Fredrik, Forssell, Urban, Gustavsson, Tony
• Primary Examiner(s): COSIMANO, EDWARD R
• Application Number:
  US10/563,524
• Publication Number:
  US 20070061100A1
• Time in Patent Office: 1,513 Days
• Field of Search: 731/46, 731/462, 731/463, 731/464, 731/465, 731/468, 340/438, 340/442, 340/446, 340/447, 340/448, 340/449, 702/138, 702/140, 702/141, 702/142, 702/145
• US Class Current: 702/138
• CPC Class Codes: B60C 23/061 by monitoring wheel speed measuring distance traversed on the ground by vehicles G01C22/00
• CPC Class Codes: B60C 23/062 Frequency spectrum analysis of wheel speed signals, e.g. using Fourier transformation

Abstract

A method of estimating the occurrence of a specific tire pressure deviation between actual and nominal pressure values for one or a plurality of wheels (i) is provided. One or more wheel radius analysis measurement values (ΔR) are subsequently obtained from a wheel radius analysis component (104), wherein the wheel radius analysis measurement values (ΔR) are related to single wheel radius values (Δr_i) of which each is indicative of the wheel radius of a particular wheel (i). One or more wheel vibration data values (Δƒ_i) are subsequently obtained from a wheel vibration analysis component (102), wherein each of the wheel vibration data values (Δƒ_i) is indicative of a vibration phenomena in the time dependent behavior of the rotational velocity of a particular wheel (i). One or more tire pressure output values (η_i, Δp_i) are calculated on the basis of both the wheel radius analysis measurement values (ΔR) and the wheel vibration data values (Δƒ_i) wherein each tire pressure output value (η_i, Δp_i) is indicative of the specific tire pressure deviation for a particular wheel (i).

Claims

1. A method for estimating the occurrence of a specific tire pressure deviation between actual and nominal pressure values for one or a plurality of wheels (i), comprising the following steps:

a) subsequently obtaining one or more wheel radius analysis measurement values (Δ

R) from a wheel radius analysis component (104), wherein the wheel radius analysis measurement values (Δ

R) are related to single wheel radius values (Δ

r_i) of which each is indicative of the wheel radius of a particular wheel (i);

b) subsequently obtaining one or more wheel vibration data values (Δ

ƒ

_i) from a wheel vibration analysis component (102), wherein each of the wheel vibration data values (Δ

ƒ

_i) is indicative of a vibration phenomena in the time dependent behavior of the rotational velocity of a particular wheel (i); and

c) calculating one or more tire pressure output values (η

_i,Δ

p_i) on the basis of both the wheel radius analysis measurement values (Δ

R) and the wheel vibration data values (Δ

ƒ

_i) wherein each tire pressure output value (η

_i,Δ

p_i) is indicative of the specific tire pressure deviation for a particular wheel (i).

2. The method of claim 1, wherein the calculation of the tire pressure output value (η

_i) for each wheel (i) comprises the following;

calculating a first probability value (P_i^ƒ

) from the wheel vibration data value (Δ

ƒ

_i) which is indicative of the statistical significance of the deviation of the wheel vibration data value (Δ

ƒ

_i) from a nominal wheel vibration value;

calculating a second probability value (P_i^r) from the wheel radius analysis measurement values (Δ

R) which is indicative of the statistical significance of the deviation of the single wheel radius values (Δ

r_i) from a nominal wheel radius value; and

calculating the tire pressure output value (η

_i) from the first and second probability values (P_i^ƒ

,P_i^r).

3. The method of claim 2, wherein the first and second probability values (P_i^ƒ

,P_i^r) are cumulative probability distribution function values; and

the tire pressure output value (η

_i) is based on the product of the first and second cumulative probability distribution function values (P_i^ƒ

,P_i^r).

4. The method of claim 3, wherein the first and second cumulative probability distribution function values (P_i^ƒ

,P_i^r) are Gaussian cumulative probability distribution function values; and

the calculation of the first and second probability values (P_i^ƒ

,P_i^r) is further based on a first and a second standard deviation parameter (σ

_ƒ

,σ

_r), respectively.

5. The method of claim 4, wherein the product of the first and second cumulative probability distribution function value (P_i^ƒ

,P_i^r) is further multiplied with a weight factor (W_i<sup>ƒ

r</sup>), which is calculated on the basis of the wheel vibration data value (Δ

ƒ

_i), the wheel radius analysis measurement values (Δ

R) or the single wheel radius values (Δ

r_i), and of standard deviation parameters (σ

_ƒ

,σ

_r).

6. The method of claim 5, wherein the weight factor w_i^ƒ

r is calculated as follows;

$W_i^{\mathrm{fr}}=\exp <br><br>\left({\mathrm{\sigma <br><br>}}_1<br><br><br><br>\frac{\mathrm{\Delta <br><br>}<br><br><br><br>f_i}{{\mathrm{\sigma <br><br>}}_f}-\frac{\mathrm{\Delta <br><br>}<br><br><br><br>r_i}{{\mathrm{\sigma <br><br>}}_r}<br><br>\right)·<br><br>\exp <br><br>\left({\mathrm{\sigma <br><br>}}_2<br><br><br><br>\frac{\mathrm{\Delta <br><br>}<br><br><br><br>f_i<br><br>\mathrm{\Delta <br><br>}<br><br><br><br>r_i}{{\mathrm{\delta <br><br>}}_f<br><br>{\mathrm{\sigma <br><br>}}_r}<br><br>\right),$

wherein Δ

ƒ

_i is the wheel vibration data value, Δ

r_i is the single wheel radius value, σ

_ƒ

and σ

_r are standard deviation parameters, and σ

₁ and σ

₂ are tuning parameters.

7. The method of claim 5, wherein the weight factor W_i^ƒ

r is calculated as follows;

$W_i^{\mathrm{fr}}=\exp <br><br>\left(\mathrm{\sigma <br><br>}<br><br><br><br>\frac{\mathrm{\Delta <br><br>}<br><br><br><br>f_i<br><br>\mathrm{\Delta <br><br>}<br><br><br><br>r_i}{{\mathrm{\sigma <br><br>}}_f<br><br>{\mathrm{\sigma <br><br>}}_r}<br><br>\right),$

wherein Δ

ƒ

_i is the wheel vibration data value, Δ

r_i is the single wheel radius value, σ

_ƒ

and σ

_r are standard deviation parameters, and σ

is a tuning parameters.

8. The method of claim 1, wherein the calculation of the tire pressure output value (Δ

p_i) is based on a model assuming a linear relationship between on the one hand the wheel vibration data and the wheel radius analysis measurement values (Δ

<sub>ƒ

i</sub>,Δ

R) and on the other hand the tire pressure output value (Δ

p_i).

9. The method of claim 8, wherein the tire pressure output value (Δ

p_i) is calculated from the wheel vibration data and the wheel radius analysis measurement values (Δ

ƒ

_i,Δ

R) by a Least Mean Square method (516).

10. The method of claim 8, wherein the pressure deviation value (Δ

p_i) is calculated from the wheel vibration data and the wheel radius analysis measurement values (Δ

ƒ

_i,Δ

R) by an adaptive filter (716).

11. The method of claim 10, wherein the adaptive filter (716) is a Kalman filter.

12. The method of claim 1, wherein the wheel radius analysis measurement values (Δ

R) are transformed to modified wheel radius values (Δ

{tilde over (R)}) which are less sensitive to load changes on the plurality of wheels (i).

13. The method of claim 1, which comprises the following steps:

calculating a load balance value (l) on the basis of the wheel vibration data and the wheel radius analysis measurement values (Δ

ƒ

_i,Δ

R) which is indicative of a load balance on the plurality of wheels (i);

calculating load balance corrected wheel radius analysis measurement values (Δ

{tilde over (R)}) on the basis of the wheel radius analysis measurement values (Δ

R) and the estimated load balance value (l).

14. The method of claim 13, wherein the calculation of the load balance value (l) is based on a model assuming a linear relationship between on the one hand the wheel vibration data and the wheel radius analysis measurement values (Δ

ƒ

_i,Δ

R) and on the other hand the tire pressure output values (Δ

p_i) and the load balance value (l).

15. The method of claim 14, wherein the load balance value (l) is calculated from the wheel vibration data and the wheel radius analysis measurement values (Δ

ƒ

_i,Δ

R) by a Least Mean Square method (104).

16. The method of claim 14, wherein the load balance value (l) is calculated from the wheel vibration data and the wheel radius analysis measurement values (Δ

ƒ

_i,Δ

R) by an adaptive filter (1014).

17. The method of claim 16, wherein the adaptive filter (1014) is a Kalman filter.

18. The method of claim 1, which comprises the following steps:

collecting data value pairs consisting of a single wheel radius data value (Δ

r_i) and a wheel vibration data value (Δ

ƒ

_i) during test drives,

defining an area which comprises the collected data value pairs, and

calculating the tire pressure output value by testing whether an actual data value pair obtained during normal drives lies within the defined area or not.

19. The method of claim 1, wherein the tire pressure output value is calculated on the basis of a χ

²-test from the single wheel radius data values (Δ

r_i) and the wheel vibration data values (Δ

ƒ

_i).

20. The method of claim 1, wherein the calculation of the tire pressure output value (Δ

p_i) is based on a model assuming a linear relationship between on the one hand the wheel vibration data and the wheel radius analysis measurement values (Δ

ƒ

_i,Δ

R) and on the other hand the tire pressure output value (Δ

p_i) and a load balance value (l), wherein the load balance value (l) is treated as a random variable and the tire pressure output value (Δ

p_i) is calculated by a Least Square method from the model.

21. The method of claim 1, wherein the calculation of the tire pressure output value (Δ

p_i) is based on a specific function relating the tire pressure output value (Δ

p_i) with the wheel vibration data values (Δ

ƒ

_i), the wheel radius analysis measurement values (Δ

R) and further parameters, wherein the further parameters are determined during test drives by a Least Square method on the basis of the specific function, obtained tire pressure output values (Δ

p_i), obtained wheel vibration data values (Δ

ƒ

_i) and corresponding tire pressure values.

22. The method of claim 21, wherein the specific function is a series expansion in the wheel vibration data values (Δ

ƒ

_i) and the wheel radius analysis measurement values (Δ

R).

23. The method of claim 21, wherein the series expansion is established by a neural network or a radial basis function network.

24. The method of claim 1, wherein each of the wheel radius analysis measurement values either corresponds to a single wheel radius value or to a linear combination of single wheel radius values.

25. The method of claim 1, wherein the vibration phenomena comprises spectral properties in the time dependent behavior of the rotational velocity of a particular wheel (i).

26. A system for estimating the occurrence of a specific tire pressure deviation between actual and nominal pressure values for one or a plurality of wheels (i), comprising:

a) a first component for subsequently obtaining one or more wheel radius analysis measurement values (Δ

R) from a wheel radius analysis component (104), wherein the wheel radius analysis measurement values (Δ

R) are related to single wheel radius values (Δ

r_i) of which each is indicative of the wheel radius of a particular wheel (i);

b) a second component for subsequently obtaining one or more wheel vibration data values (Δ

ƒ

_i) from a wheel vibration analysis component (102), wherein each of the wheel vibration data values (Δ

ƒ

_i) is indicative of a vibration phenomena in the time dependent behavior of the rotational velocity of a particular wheel (i); and

c) a third component for calculating one or more tire pressure output values (η

_i,Δ

p_i) on the basis of both the wheel radius analysis measurement values (Δ

R) and the wheel vibration data values (Δ

ƒ

_i) wherein each tire pressure output value (η

_i,Δ

p_i) is indicative of the specific tire pressure deviation for a particular wheel (i).

27. A computer program product including program code for carrying out a digital signal processing method, when executed on a computer system, for estimating the occurrence of a specific tire pressure deviation between actual and nominal pressure values for one or a plurality of wheels (i), comprising the following steps:

a) subsequently obtaining one or more wheel radius analysis measurement values (Δ

R) from a wheel radius analysis component (104), wherein the wheel radius analysis measurement values (Δ

R) are related to single wheel radius values (Δ

r_i) of which each is indicative of the wheel radius of a particular wheel (i);

b) subsequently obtaining one or more wheel vibration data values (Δ

ƒ

_i) from a wheel vibration analysis component (102), wherein each of the wheel vibration data values (Δ

ƒ

_i) is indicative of a vibration phenomena in the time dependent behavior of the rotational velocity of a particular wheel (i); and

c) calculating one or more tire pressure output values (η

_i,Δ

p_i) on the basis of both the wheel radius analysis measurement values (Δ

R) and the wheel vibration data values (Δ

ƒ

_i) wherein each tire pressure output value (η

_i,Δ

p_i) is indicative of the specific tire pressure deviation for a particular wheel (i).