CN115795899A - New energy electric vehicle squeaking noise evaluation method - Google Patents
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Abstract
The invention discloses a new energy electric vehicle squeaking noise evaluation method, which comprises the following steps: 1) Tracking a motor rotating speed signal T1 (T) according to an in-vehicle noise signal x (T) in an automobile acceleration process to obtain a noise power spectral density matrix Pxx (f, rpm); drawing colormap, and weighting A to obtain L1 (rpm); 2) Performing Kalman order filtering to obtain a filtered in-vehicle order howling noise signal y (t); 3) Obtaining a filtered noise power spectral density matrix Pyy (f, rpm); 4) Performing critical band calculation to obtain a critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm); 5) Extracting the order critical bandwidth background noise, performing A weighting to obtain L2 (rpm), and performing corresponding noise amplitude adjustment according to the order range of the order to obtain an order noise evaluation standard line Lstandard (rpm); 6) Comparison was made between Lstandard (rpm) and L1 (rpm). The method can accurately evaluate the new energy automobile squeaking noise and truly reflect the relation of frequency-noise-subjective feeling.
Description
Technical Field
The invention relates to an evaluation method, in particular to a new energy electric vehicle squeaking noise evaluation method.
Background
With the continuous development of new energy electric automobiles, the market competition is more intense; compared with an internal combustion engine, the noise of the electric drive assembly of the new energy electric automobile is mainly composed of high-frequency howling order noise generated by the engagement of electromagnetic force and gears and high-frequency umbrella-shaped noise generated by IGBT pulse width modulation. At present, engineering experience is still adopted to evaluate noise in the whole development process of an electrically driven automobile, the result generated by the method often has larger deviation with the actual situation, the relation of frequency-noise-subjective feeling cannot be reflected, and the method has no guiding significance to the actual development work.
Disclosure of Invention
The invention aims to provide a new energy electric vehicle howling noise evaluation method which can accurately evaluate the new energy electric vehicle howling noise.
The technical scheme adopted by the invention is as follows:
a new energy electric vehicle squeaking noise evaluation method comprises the following steps:
1) In the acceleration process of the automobile, an in-automobile noise signal x (T) tracks a motor rotating speed signal T1 (T) to carry out Fourier transform to obtain an order howling noise power spectrum density matrix Pxx (f, rpm); pxx (f, rpm) is a digital matrix and represents the joint distribution of the order howling noise energy of the electric drive assembly on two dimensions of frequency f and motor rotating speed rpm; wherein f is a discrete frequency point after an in-car noise signal x (T) tracks a motor rotating speed signal T1 (T) to carry out Fourier transform in the automobile acceleration process, and rpm is a discrete rotating speed point; obtaining motor order noise L1 (rpm) as an evaluation object according to the noise power spectral density matrix Pxx (f, rpm);
2) Based on a Kalman filter algorithm, carrying out the order filtering processing on a noise signal x (T) in the automobile and a motor rotating speed signal T1 (T) in the automobile acceleration process to obtain a filtered automobile interior order howling noise signal y (T);
3) Carrying out Fourier transform on a filtered vehicle interior order squeaking noise signal y (T) tracking a motor rotating speed signal T1 (T) to obtain a filtered noise power spectrum density matrix Pyy (f, rpm), wherein the Pyy (f, rpm) represents the combined distribution of vehicle interior noise energy on two dimensions of frequency f and motor rotating speed rpm;
4) Calculating critical bandwidth noise energy corresponding to different central frequencies by using Pyy (f, rpm) in the step 3) according to a psychoacoustic critical band theory to obtain a critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm), wherein Bark is equal to the width of one critical band;
5) Extracting the order critical bandwidth background noise from a Pyy (Bark, rpm) matrix, performing A weighting on the order critical bandwidth background noise to obtain L2 (rpm), and performing corresponding noise amplitude adjustment according to an order range of the order to obtain an order noise evaluation standard line Lstandard (rpm);
6) The order noise evaluation standard line Lstandard (rpm) and the order noise L1 (rpm) are compared, and if the order noise L1 (rpm) exceeds the order noise evaluation standard line Lstandard (rpm), it is considered that the noise is perceivable by the vehicle interior, and the perception degree is proportional to the noise excess amount.
In the step 2), the method for carrying out the filtering processing of the order on the noise signal x (T) and the motor rotating speed signal T1 (T) in the automobile acceleration process based on the Kalman filter algorithm comprises the following steps: and selecting the order by using a Kalman filter, and attenuating the noise by adopting a bandwidth of 0.2 order, preferably-40 dB attenuation.
In the step 4), a background noise power spectral density matrix Pyy (Bark, rpm) of the order critical bandwidth is drawn by combining a Bark-f relation;
in step 4), the relationship between Bark and frequency f is: when the frequency f is less than 500Hz, 1Bark = f/100, and the critical bandwidth is constant at 100Hz; when the frequency f > 500Hz, 1bark =4log (f/100), the critical bandwidth increases with increasing center frequency, about 20% of the center frequency.
The step of extracting the order critical bandwidth background noise in Pyy (Bark, rpm) matrix in step 5) is:
firstly, determining the order analysis rotating speed range, and acquiring a corresponding frequency range according to the proportional relation between the order and the rotating speed and frequency;
next, the order critical bandwidth background noise is extracted in a Pyy (Bark, rpm) matrix according to the Bark versus frequency relationship.
The formula for weighting A to the order critical bandwidth background noise in the step 5) is as follows:
wherein, f 1 ＝20.60Hz，f 2 ＝107.7Hz，f 3 ＝737.9Hz，f 4 ＝12194Hz，A 1 000 is a normalized constant expressed in decibels, corresponding to the gain required to provide a frequency weighting of 0dB at 1kHz, with A being the nearest 0.001dB 1 000＝-2.000dB(A)。
In step 5), the order range is as follows:
the low-order range of the motor and the reducer is less than 10;
the intermediate order range of the motor and the reducer is 10-25;
the high-order range of the motor and the speed reducer is more than 25.
In step 5), the adjustment noise amplitude corresponding to the order range of the order is:
the noise amplitude of the motor and the low-order corresponding to the speed reducer is adjusted to be +3dB;
the noise amplitude of the motor and the middle order of the reducer is correspondingly adjusted to be +0 to-3 dB;
the noise amplitude of the motor and the high-order corresponding adjustment noise of the speed reducer is-3 dB.
In the step 1), colormap is drawn according to a noise power spectral density matrix Pxx (f, rpm), and motor order noise L1 (rpm) is calculated by taking 0.125-time bandwidth and A weight as an evaluation object.
The invention has the beneficial effects that:
the order noise evaluation standard line Lstandard (rpm) is compared with the order noise L1 (rpm), so that the new energy automobile squeaking noise can be accurately evaluated, and the relationship of frequency-noise-subjective feeling is truly reflected;
based on the psychoacoustic masking effect, a rotating machinery order howling evaluation method is provided, so that order howling evaluation is objective, and the influence of human subjectivity on an evaluation result in subjective evaluation activities is solved;
and the amplitude adjustment strategy aiming at different order ranges is provided, so that the evaluation is more accurate.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a new energy electric vehicle howling noise evaluation method;
FIG. 2 is a graph of the power spectrum of noise in a vehicle;
FIG. 3 is an in-vehicle retarder order noise;
FIG. 4 is a Kalman order filter processing noise spectrum diagram;
FIG. 5 is a plot of the background noise critical band noise spectrum;
FIG. 6 is a graph comparing the reduction gear order noise to an evaluation criterion curve;
fig. 7 is a graph of center frequency versus critical width.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a new energy electric vehicle howling noise evaluation method includes the following steps:
1) Signal acquisition: the whole vehicle noise test is carried out in an outdoor test field which accords with relevant regulation conditions, so that the vehicle condition is good; disposing a microphone at a right ear of a driver; the vehicle is accelerated to run according to the working condition of a full accelerator, and a noise signal x (T) and a motor rotating speed signal T1 (T) in the accelerating process are recorded by using professional acquisition equipment;
2) In the automobile acceleration process, an in-automobile noise signal x (T) tracks a motor rotating speed signal T1 (T) to carry out Fourier transform to obtain an order howling noise power spectrum density matrix Pxx (f, rpm); pxx (f, rpm) is a digital matrix and represents the joint distribution of the order howling noise energy of the electric drive assembly on two dimensions of frequency f and motor rotating speed rpm; wherein f is a discrete frequency point of an in-car noise signal x (T) tracking a motor rotating speed signal T1 (T) to perform Fourier transform in the automobile acceleration process, and rpm is a discrete rotating speed point; plotting a colormap, which is commonly used in the art, from the noise power spectral density matrix Pxx (f, rpm), as shown in fig. 2; calculating the motor order noise L1 (rpm) with 0.125 times order bandwidth and A weight as an evaluation object, as shown in FIG. 3;
3) Based on a Kalman filter algorithm, carrying out filtering processing of the order on a noise signal x (T) in the automobile and a motor rotating speed signal T1 (T) in the automobile acceleration process, setting a filter bandwidth parameter alpha%, wherein alpha =2, and obtaining an order squeaking noise signal y (T) in the automobile;
the method for carrying out the filtering processing of the order on the noise signal x (T) and the motor rotating speed signal T1 (T) in the automobile in the acceleration process based on the Kalman filter algorithm comprises the following steps: selecting the order by using a Kalman filter, and performing-40 dB attenuation on noise by adopting 0.2-order bandwidth to obtain a filtered in-vehicle order howling noise signal y (t);
4) After filtering, the vehicle interior order squeaking noise signal y (T) tracks the motor rotating speed signal T1 (T) to perform Fourier transform, and a filtered noise power spectral density matrix Pyy (f, rpm) is obtained, as shown in FIG. 4, the Pyy (f, rpm) represents the combined distribution of vehicle interior noise energy on two dimensions of frequency f and motor rotating speed rpm;
5) Calculating critical bandwidth noise energy corresponding to different central frequencies by using Pyy (f, rpm) in the step 4) according to psychoacoustic critical band theory, and obtaining a critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm) as shown in fig. 7, wherein Bark is equal to the width of one critical band; the order critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm) is plotted in conjunction with the "Bark-f" relationship, as shown in fig. 5;
wherein the relationship between "Bark-f" (Bark and frequency f) is as follows: when the frequency f is less than 500Hz, 1bark = f/100, the critical bandwidth is constant at 100Hz; when the frequency f is more than 500Hz, 1bark =4log (f/100), the critical bandwidth increases with the increase of the central frequency and is 20% of the central frequency;
the critical bandwidth noise energies for the different center frequencies are shown in table 1.
TABLE 1
| Critical band stage | Center frequency (Hz) | Critical bandwidth (Hz) | Cutoff frequency (Hz) |
| 20 | |||
| 1 | 50 | 80 | 100 |
| 2 | 150 | 100 | 200 |
| 3 | 250 | 100 | 300 |
| 4 | 350 | 100 | 400 |
| 5 | 450 | 110 | 510 |
| 6 | 570 | 120 | 630 |
| 7 | 700 | 140 | 770 |
| 8 | 840 | 150 | 920 |
| 9 | 1000 | 160 | 1080 |
| 10 | 1170 | 190 | 1270 |
| 11 | 1370 | 210 | 1480 |
| 12 | 1600 | 240 | 1720 |
| 13 | 1850 | 280 | 2000 |
| 14 | 2150 | 320 | 2320 |
| 15 | 2500 | 380 | 2700 |
| 16 | 2900 | 450 | 3150 |
| 17 | 3400 | 550 | 3700 |
| 18 | 4000 | 700 | 4400 |
| 19 | 4800 | 900 | 5300 |
| 20 | 5800 | 1100 | 6400 |
| 21 | 7000 | 1300 | 7700 |
| 22 | 8500 | 1800 | 9500 |
| 23 | 10500 | 2500 | 12000 |
| 24 | 13500 | 3500 | 15500 |
6) Extracting the order critical bandwidth background noise from a Pyy (Bark, rpm) matrix, performing A weighting on the order critical bandwidth background noise to obtain L2 (rpm), and performing corresponding noise amplitude adjustment according to an order range of the order to obtain an order noise evaluation standard line Lstandard (rpm), see Table 2;
the steps of extracting the order critical bandwidth background noise in Pyy (Bark, rpm) matrix are: firstly, determining the order analysis rotating speed range, and acquiring a corresponding frequency range according to the proportional relation between the order and the rotating speed and frequency; secondly, extracting the order critical bandwidth background noise in a Pyy (Bark, rpm) matrix according to the relation between the Bark and the frequency;
the formula for weighting A to the order critical bandwidth background noise is:
wherein, f 1 ＝20.60Hz，f 2 ＝107.7Hz，f 3 ＝737.9Hz，f 4 ＝12194Hz，A 1 000 is a normalized constant expressed in decibels, corresponding to the gain required to provide a frequency weighting of 0dB at 1kHz, with A being the nearest 0.001dB 1 000＝-2.000dB(A)；
The order range and the corresponding adjusted noise amplitude are:
the low-order range of the motor and the speed reducer is less than 10, and the corresponding noise amplitude is adjusted to be +3dB;
the intermediate order range of the motor and the reducer is 10-25, and the corresponding noise amplitude is adjusted to be + 0-3 dB;
the high-order range of the motor and the reducer is more than 25, and the corresponding noise amplitude is adjusted to be-3 dB;
7) Comparing the order noise evaluation standard line Lstandard (rpm) and the order noise L1 (rpm), as shown in fig. 6; if the order noise L1 (rpm) exceeds the order noise evaluation criterion line Lstandard (rpm), it is considered that the noise is perceivable by the vehicle interior, and the perception degree is proportional to the noise excess amount.
It will be appreciated that modifications and variations are possible to those skilled in the art in light of the above teachings, and it is intended to cover all such modifications and variations as fall within the scope of the appended claims.
Claims (10)
1. A new energy electric vehicle squeaking noise evaluation method is characterized by comprising the following steps: the method comprises the following steps:
1) In the acceleration process of the automobile, an in-automobile noise signal x (T) tracks a motor rotating speed signal T1 (T) to carry out Fourier transform to obtain a noise power spectral density matrix Pxx (f, rpm); wherein f is a discrete frequency point after an in-car noise signal x (T) tracks a motor rotating speed signal T1 (T) to carry out Fourier transform in the automobile acceleration process, and rpm is a discrete rotating speed point; obtaining motor order noise L1 (rpm) as an evaluation object according to a noise power spectral density matrix Pxx (f, rpm);
2) Based on a Kalman filter algorithm, carrying out the order filtering processing on a noise signal x (T) in the automobile and a motor rotating speed signal T1 (T) in the automobile acceleration process to obtain a filtered automobile interior order howling noise signal y (T);
3) Carrying out Fourier transform on the filtered vehicle interior order squeaking noise signal y (T) tracking the motor rotating speed signal T1 (T) to obtain a filtered noise power spectral density matrix Pyy (f, rpm);
4) Calculating critical bandwidth noise energy corresponding to different central frequencies by using Pyy (f, rpm) in the step 3) according to a psychoacoustic critical band theory to obtain a critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm), wherein Bark is equal to the width of one critical band;
5) Extracting the order critical bandwidth background noise from a Pyy (Bark, rpm) matrix, performing A weighting on the order critical bandwidth background noise to obtain L2 (rpm), and performing corresponding noise amplitude adjustment according to an order range of the order to obtain an order noise evaluation standard line Lstandard (rpm);
6) The order noise evaluation standard line Lstandard (rpm) and the order noise L1 (rpm) are compared, and if the order noise L1 (rpm) exceeds the order noise evaluation standard line Lstandard (rpm), it is considered that the noise is perceivable by the vehicle interior, and the perception degree is proportional to the noise excess amount.
2. The new energy electric vehicle howling noise evaluation method according to claim 1, characterized in that: in the step 2), the method for carrying out the filtering processing of the order on the noise signal x (T) and the motor rotating speed signal T1 (T) in the automobile acceleration process based on the Kalman filter algorithm comprises the following steps: and selecting the order by using a Kalman filter, and attenuating the noise by adopting a 0.2-order bandwidth.
3. The new energy electric vehicle squeal noise evaluation method according to claim 2, characterized in that: and selecting the order by using a Kalman filter, and performing-40 dB attenuation on the noise by adopting the bandwidth of 0.2 order.
4. The new energy electric vehicle squeaking noise evaluation method as claimed in claim 1, characterized in that: in step 4), the method for calculating critical bandwidth noise energy corresponding to different center frequencies by using Pyy (f, rpm) in step 3) to obtain the critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm) comprises the following steps: the order critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm) is plotted in conjunction with the "Bark-f" relationship.
5. The new energy electric vehicle squeal noise evaluation method according to claim 4, characterized by comprising the following steps: bark is related to frequency f by: when the frequency f is less than 500Hz, 1bark = f/100, the critical bandwidth is constant at 100Hz; when the frequency f is more than 500Hz, 1bark =4log (f/100), the critical bandwidth increases with the increase of the central frequency, being 20% of the central frequency.
6. The new energy electric vehicle squeaking noise evaluation method as claimed in claim 1, characterized in that: the step of extracting the order critical bandwidth background noise in Pyy (Bark, rpm) matrix in step 5) is:
firstly, determining the order analysis rotating speed range, and acquiring a corresponding frequency range according to the proportional relation between the order and the rotating speed and frequency;
next, the order critical bandwidth background noise is extracted in a Pyy (Bark, rpm) matrix according to the Bark versus frequency relationship.
7. The new energy electric vehicle squeaking noise evaluation method as claimed in claim 1, characterized in that: the formula for weighting A on the order critical bandwidth background noise in the step 5) is as follows:
wherein f is 1 ＝20.60Hz，f 2 ＝107.7Hz，f 3 ＝737.9Hz，f 4 ＝12194Hz，A 1 000 is a normalized constant expressed in decibels, corresponding to the gain required to provide a frequency weighting of 0dB at 1kHz, with A being the nearest 0.001dB 1 000＝-2.000dB(A)。
8. The new energy electric vehicle howling noise evaluation method according to claim 1, characterized in that: the order range is as follows:
the low-order range of the motor and the reducer is less than 10;
the intermediate order range of the motor and the reducer is 10-25;
the high-order range of the motor and the reducer is more than 25.
9. The new energy electric vehicle squeal noise evaluation method according to claim 8, characterized in that: the adjusted noise amplitude corresponding to the order range in which the order is located is:
the noise amplitude of the motor and the low-order corresponding to the speed reducer is adjusted to be +3dB;
the noise amplitude of the motor and the middle order of the reducer is correspondingly adjusted to be +0 to-3 dB;
the noise amplitude of the motor and the reducer is adjusted to-3 dB in high order.
10. The new energy electric vehicle squeaking noise evaluation method as claimed in claim 1, characterized in that: colormap is drawn according to a noise power spectral density matrix Pxx (f, rpm), and motor order noise L1 (rpm) is calculated by taking a 0.125-time bandwidth and A weight as an evaluation object.
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