Method and computer program for modeling an interfering pulse in a vehicle electric system

• US 20050107926A1
• Filed: 09/02/2004
• Published: 05/19/2005
• Est. Priority Date: 09/06/2003
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Stiegler, Frank, Enders, Thorsten, Huck, Thorsten, Dostert, Klaus
• Granted Patent: US 7,424,326 B2
• Time in Patent Office: Days
• US Class Current: 701/1
• CPC Class Codes: B60R 16/023 for transmission of signals between vehicle parts or subsystems
• CPC Class Codes: G06F 2218/08 Feature extraction

Abstract

A method and a computer program for modeling an interfering pulse in a vehicle electric system are described. Previous approaches for synthesizing such interfering pulses have proven to be insufficiently realistic for the simulation of certain application cases. An interfering pulse is therefore modeled by random determination of the parameters of the parameter set representing the interfering pulse to be modeled on the basis of the density functions belonging to the parameters and by substituting the parameters thus obtained into a predetermined mathematical function. The interfering pulse modeled in this way is also a suitable starting point for modeling a pulse pattern, i.e., a random sequence of individually spaced interfering pulses and a pulse interferer scenario, i.e., a random sequence of individually spaced pulse patterns.

Claims

1. A method for modeling an interfering pulse in a vehicle electric system used at least one of for supplying power to various automotive components and for transmitting data between components, at least some of the components generating interfering pulses in the vehicle electric system during their operation, the method including:

empirically determining a plurality of interfering pulses;

simulating the individual previously determined interfering pulses via a mathematical function using a suitable parameter set, each parameter set containing a plurality of different parameters;

transforming individual parameters in all parameter sets into a value range suitable for modeling;

determining a density function for each transformed and non-transformed parameter via the plurality of its implementations contained in the parameter sets; and

modeling the interfering pulse by random determination of the parameters of a parameter set representing the interfering pulse to be modeled based on the density functions belonging to the parameters and by substituting the parameters thus obtained into the mathematical function.

2. The method of claim 1, wherein the mathematical function is as follows:

y_LS(t)=A₁·

expc₁|t−

t₁|+A₂·

exp−

c₂|t−

t₂|·

sin2π

f(t)·

t 

 

(1)

having parameters A₁, A₂, c₁, c₂, t₁, t₂, hereinafter also referred to as the first envelope curve parameters, and time-dependent parameter f(t) representing a time-dependent frequency vector.

3. The method of claim 1, wherein the mathematical function is as follows:

Y_Poly(t)=a₈t⁸+a₇t⁷+a₆t⁶+a₅t⁵+a₄t⁴+a₃t³+a₂t²+a₁t+a₀·

sin2π

f(t)·

t 

 

(2)

having parameters a₀ through a₈, hereinafter also referred to as the second envelope curve parameters, and time-dependent parameter f(t) representing a time-dependent frequency vector.

4. The method of claim 2, wherein the determination of the first or second envelope curve parameters of the suitable parameter set as part of the simulation of each individual interfering pulse includes:

providing a varied approximation of each individual previously empirically determined interfering pulse, each in the form of a plurality of approximating parameter sets; and

selecting a most suitable parameter set in the form of the best approximating parameter set via a decision criterion.

5. The method of claim 4, wherein the empirically determined interfering pulse is approximated as part of the varied approximation 42×

, 32×

via the short-time Fourier transformation having eight different window functions and four different time window lengths, 4×

via the autoregressive AR model having the orders 50, 100, 150 and 200, and 6×

via the Wigner-Ville transformation having six different distributions.

6. The method of claim 4, wherein:

the most suitable and optimized parameter set for simulating the previously empirically determined interfering pulse is selected accordingly using at least one selected cross-correlation coefficient as a decision criterion,

the selected cross-correlation coefficient is selected from a plurality of cross-correlation coefficients formed between the empirically determined interfering pulse and interfering pulses approximating it, and

each approximating interfering pulse is represented by one of the approximating parameter sets.

7. The method of claim 6, wherein the cross-correlation coefficient having the greatest value is selected as the decision criterion, and the approximating parameter set assigned to this cross-correlation coefficient is selected as the optimized parameter set.

8. The method of claim 6, further comprising:

preselecting “

n”

correlation coefficients having the “

n”

largest values from the plurality of formed correlation coefficients, each of the “

n”

correlation coefficients representing an approximating parameter set and thus representing an approximating interfering pulse; and

selecting the most suitable parameter set in the form of the one form the “

n”

preselected approximating parameter sets whose associated approximating interfering pulse which has the same number of zero crossings as the previously empirically determined interfering pulse to be simulated.

9. The method of claim 8, wherein performing the comparison of the number of zero crossings includes:

subdividing the empirically determined interfering pulse and the interfering pulse approximating it into individual partial ranges;

optimizing limits of the partial ranges with respect to a zero position error control to be performed subsequently;

performing the comparison for each individual partial range; and

subsequently forming the relative zero position error.

10. The method of claim 2, wherein the frequency-independent envelope curve parameter is determined using a least squares estimate.

11. The method of claim 3, wherein the frequency-independent envelope curve parameters are determined using polynomial interpolation.

12. The method of claim 1, further comprising:

modeling a pulse interferer pattern by executing the following steps;

determining a duration of the pulse interferer pattern;

determining a pulse interval assigned to the modeled interfering pulse; and

successively modeling additional interfering pulses and determining pulse intervals assigned individually to these interfering pulses until the pulse interferer pattern duration has been reached.

13. The method of claim 12, wherein the pulse interferer pattern duration is determined by statistically analyzing a density function derived from an empirically determined plurality of pulse interferer patterns.

14. The method of claim 12, wherein the pulse intervals are determined by statistically analyzing a density function derived from an empirically determined plurality of pulse interferer patterns.

15. The method of claim 12, wherein a pulse interferer scenario is modeled from a plurality of modeled pulse interferer patterns and pulse interferer pattern intervals individually assigned to these pulse interferer patterns.

16. A computer program executable on a processor, the computer program comprising:

computer program code for modeling an interfering pulse in a vehicle electric system used at least one of for supplying power to various automotive components and for transmitting data between components, at least some of the components generating interfering pulses in the vehicle electric system during their operation, by performing the following;

empirically determining a plurality of interfering pulses;

simulating the individual previously determined interfering pulses via a mathematical function using a suitable parameter set, each parameter set containing a plurality of different parameters;

transforming individual parameters in all parameter sets into a value range suitable for modeling;

determining a density function for each transformed and non-transformed parameter via the plurality of its implementations contained in the parameter sets; and

modeling the interfering pulse by random determination of the parameters of a parameter set representing the interfering pulse to be modeled based on the density functions belonging to the parameters and by substituting the parameters.

17. A data medium comprising:

a computer program executable on a processor, the computer program including;

computer program code for modeling an interfering pulse in a vehicle electric system used at least one of for supplying power to various automotive components and for transmitting data between components, at least some of the components generating interfering pulses in the vehicle electric system during their operation, by performing the following;

empirically determining a plurality of interfering pulses;

simulating the individual previously determined interfering pulses via a mathematical function using a suitable parameter set, each parameter set containing a plurality of different parameters;

transforming individual parameters in all parameter sets into a value range suitable for modeling;

determining a density function for each transformed and non-transformed parameter via the plurality of its implementations contained in the parameter sets; and

modeling the interfering pulse by random determination of the parameters of a parameter set representing the interfering pulse to be modeled based on the density functions belonging to the parameters and by substituting the parameters.