CN115795899B - New energy electric automobile howling noise evaluation method - Google Patents
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Abstract
The invention discloses a new energy electric automobile howling noise evaluation method, which comprises the following steps: 1) Tracking a motor rotating speed signal T1 (T) according to an in-car noise signal x (T) in the automobile acceleration process to obtain a noise power spectrum density matrix Pxx (f, rpm); drawing a colormap, and weighing A to obtain L1 (rpm); 2) Carrying out Kalman order filtering to obtain an in-vehicle order howling noise signal y (t) after filtering; 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 background noise of the order critical bandwidth, 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) Comparative Lstandard (rpm) and L1 (rpm). The invention can accurately evaluate the howling noise of the new energy automobile 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 automobile howling noise evaluation method.
Background
With the continuous development of new energy electric vehicles, market competition is more and more vigorous; compared with an internal combustion engine, the noise of the electric drive assembly of the new energy electric automobile mainly comprises high-frequency squeal order noise generated by electromagnetic force and gear meshing 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 vehicle development process of the electric drive automobile, the result generated by the mode often has larger deviation from the actual situation, the relation of frequency-noise-subjective feeling cannot be reflected, and the method has no guiding significance on the actual development work.
Disclosure of Invention
The invention aims to provide a new energy electric automobile howling noise evaluation method which can accurately evaluate the howling noise of the new energy electric automobile.
The technical scheme adopted by the invention is as follows:
a new energy electric automobile howling 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 transformation so as to obtain an order howling noise power spectrum density matrix Pxx (f, rpm); pxx (f, rpm) is a digital matrix representing the joint distribution of the electric drive assembly order howling noise energy in two dimensions of frequency f, motor speed rpm; wherein f is a discrete frequency point after the in-car noise signal x (T) tracking motor rotating speed signal T1 (T) is subjected to Fourier transformation 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 spectrum density matrix Pxx (f, rpm);
2) The method comprises the steps of performing order filtering processing on a noise signal x (T) in a vehicle and a motor rotating speed signal T1 (T) in the acceleration process of the vehicle based on a Kalman filter algorithm to obtain an in-vehicle order whistle noise signal y (T) after filtering;
3) The noise signal y (T) of the in-vehicle order after filtering tracks the motor rotating speed signal T1 (T) to carry out Fourier transformation, a filtered noise power spectrum density matrix Pyy (f, rpm) is obtained, and Pyy (f, rpm) represents the joint distribution of the noise energy in the vehicle in two dimensions of frequency f and motor rotating speed rpm;
4) According to the psychoacoustic critical band theory, using Pyy (f, rpm) in the step 3) to calculate the critical bandwidth noise energy corresponding to different center frequencies, and obtaining 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, carrying out A weighting on the order critical bandwidth background noise to obtain L2 (rpm), and carrying out corresponding noise amplitude adjustment according to an order range where the order is located to obtain an order noise evaluation standard line Lstandard (rpm);
6) The order noise evaluation criterion line Lstandard (rpm) is compared with the order noise L1 (rpm), and if the order noise L1 (rpm) exceeds the order noise evaluation criterion line Lstandard (rpm), the noise is considered to be perceived by the vehicle interior personnel, and the degree of perception is proportional to the noise excess.
In the step 2), the method for carrying out the order filtering processing on the noise signal x (T) and the motor rotating speed signal T1 (T) in the automobile in the acceleration process of the automobile based on the Kalman filter algorithm comprises the following steps: the order is selected using a kalman filter and the noise is attenuated using a 0.2 order bandwidth, preferably a-40 dB attenuation.
In step 4), the order critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm) is plotted in combination with the "Bark-f" relationship;
in step 4), the relation between Bark and frequency f is: when the frequency f is less than 500Hz, 1 bark=f/100, and the critical bandwidth is constant at 100Hz; when the frequency f > 500Hz, 1 bark=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 the frequency;
second, the order critical bandwidth background noise is extracted in a Pyy (Bark, rpm) matrix according to Bark versus frequency.
The formula for A-weighting the order critical bandwidth background noise in step 5) 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 0dB frequency weighting at 1kHz, with A being trimmed to the nearest 0.001dB 1 000=-2.000dB(A)。
In step 5), the order range is:
the low-order range of the motor and the speed reducer is less than 10;
the intermediate order range of the motor and the speed reducer is 10-25;
the high-order sub-range of the motor and the speed reducer is more than 25.
In step 5), the adjusted noise amplitude corresponding to the order range where the order is located is:
the amplitude of the adjusting noise corresponding to the low order of the motor and the speed reducer is +3dB;
the amplitude of the adjusting noise corresponding to the intermediate order of the motor and the speed reducer is +0 to-3 dB;
the amplitude of the high-order corresponding adjusting noise of the motor and the speed reducer is-3 dB.
In step 1), a colormap is plotted from a noise power spectral density matrix Pxx (f, rpm), and motor order noise L1 (rpm) is calculated with a weight a of 0.125 times the bandwidth as an evaluation target.
The invention has the beneficial effects that:
the new energy automobile howling noise can be accurately evaluated by comparing the order noise evaluation standard line Lstandard (rpm) with the order noise L1 (rpm), and the relation of frequency-noise-subjective feeling is truly reflected;
based on the psychoacoustic masking effect, a rotary mechanical order howling evaluation method is provided, so that order howling evaluation is objectified, and the influence of subjectivity of a person in subjective evaluation activities on an evaluation result is solved;
the amplitude adjustment strategy is provided for different order ranges, 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 flowchart of a new energy electric vehicle squeal noise evaluation method;
FIG. 2 is a graph of an in-vehicle noise power spectrum;
FIG. 3 is in-vehicle retarder order noise;
FIG. 4 is a graph of Kalman order filtering processing noise spectra;
FIG. 5 is a graph of background noise critical band noise spectra;
FIG. 6 is a graph comparing the noise of the retarder order to an evaluation criterion;
fig. 7 is a plot of center frequency versus critical width.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a new energy electric automobile howling noise evaluation method includes the following steps:
1) And (3) signal acquisition: the noise test of the whole vehicle is carried out on an outdoor test field under the condition of conforming to related regulations, and the vehicle condition is good; a microphone is arranged at the right ear of the driver; the vehicle accelerates according to the full throttle working condition, and the special acquisition equipment is used for recording a noise signal x (T) and a motor rotating speed signal T1 (T) in the acceleration process;
2) 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 transformation so as to obtain an order howling noise power spectrum density matrix Pxx (f, rpm); pxx (f, rpm) is a digital matrix representing the joint distribution of the electric drive assembly order howling noise energy in two dimensions of frequency f, motor speed rpm; wherein f is a discrete frequency point after the in-car noise signal x (T) tracking motor rotating speed signal T1 (T) is subjected to Fourier transformation in the automobile acceleration process, and rpm is a discrete rotating speed point; the usual colormap in the art is plotted against the noise power spectral density matrix Pxx (f, rpm), as shown in fig. 2; calculating motor order noise L1 (rpm) with 0.125 times order bandwidth, a weight as an evaluation object, as shown in fig. 3;
3) The method comprises the steps of performing order filtering processing on a noise signal x (T) in an automobile and a motor rotating speed signal T1 (T) in an automobile acceleration process based on a Kalman filter algorithm, setting a filter bandwidth parameter alpha%, wherein alpha=2, and obtaining an in-automobile order howling noise signal y (T);
the method for carrying out the order filtering processing on the noise signal x (T) and the motor rotating speed signal T1 (T) in the automobile in the acceleration process of the automobile based on the Kalman filter algorithm comprises the following steps: selecting the order by using a Kalman filter, and carrying out-40 dB attenuation on noise by adopting a 0.2-order bandwidth to obtain an in-vehicle order howling noise signal y (t) after filtering;
4) The post-filtering in-vehicle order howling noise signal y (T) tracks the motor rotating speed signal T1 (T) to perform Fourier transform to obtain a post-filtering noise power spectrum density matrix Pyy (f, rpm), as shown in FIG. 4, pyy (f, rpm) represents the joint distribution of in-vehicle noise energy in two dimensions of frequency f and motor rotating speed rpm;
5) According to the psychoacoustic critical band theory, using Pyy (f, rpm) in step 4), calculating the critical bandwidth noise energy corresponding to different center frequencies, as shown in fig. 7, to obtain a critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm), where 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, "Bark-f" (Bark versus frequency f) is: when the frequency f is less than 500Hz, 1 bark=f/100, and the critical bandwidth is constant at 100Hz; when the frequency f is greater than 500Hz, 1 bark=4log (f/100), the critical bandwidth increases with increasing center frequency, being 20% of the center frequency;
the critical bandwidth noise energies corresponding to the different center frequencies are shown in table 1.
TABLE 1
Critical band class | Center frequency (Hz) | Critical bandwidth (Hz) | Cut-off 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, carrying out A weighting on the order critical bandwidth background noise to obtain L2 (rpm), and carrying out corresponding noise amplitude adjustment according to an order range of the order to obtain an order noise evaluation standard line Lstandard (rpm), wherein the table 2 is shown in the specification;
the step of extracting the order critical bandwidth background noise in the Pyy (Bark, rpm) matrix 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 the frequency; secondly, extracting the order critical bandwidth background noise from a Pyy (Bark, rpm) matrix according to the Bark-frequency relationship;
the formula for A-weighting 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 0dB frequency weighting at 1kHz, with A being trimmed to 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 adjustment noise amplitude is +3dB;
the intermediate order range of the motor and the speed reducer is 10-25, and the corresponding adjusting noise amplitude is +0 to-3 dB;
the high-order sub-range of the motor and the speed reducer is more than 25, and the corresponding adjusting noise amplitude is-3 dB;
7) Comparing the order noise evaluation criterion line Lstandard (rpm) with 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 perceived by the in-vehicle person, and the degree of perception is proportional to the noise excess.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (2)
1. A new energy electric automobile howling noise evaluation method is characterized in that: 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 transformation to obtain a noise power spectrum density matrix Pxx (f, rpm); wherein f is a discrete frequency point after the in-car noise signal x (T) tracking motor rotating speed signal T1 (T) is subjected to Fourier transformation 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 spectrum density matrix Pxx (f, rpm);
2) The method comprises the steps of performing order filtering processing on a noise signal x (T) in a vehicle and a motor rotating speed signal T1 (T) in the acceleration process of the vehicle based on a Kalman filter algorithm to obtain an in-vehicle order whistle noise signal y (T) after filtering; the method for carrying out the order filtering processing on the noise signal x (T) and the motor rotating speed signal T1 (T) in the automobile in the acceleration process of the automobile based on the Kalman filter algorithm comprises the following steps: selecting the order by using a Kalman filter, and carrying out-40 dB attenuation on noise by adopting a 0.2-order bandwidth;
3) The post-filtering in-vehicle order howling noise signal y (T) tracks the motor rotating speed signal T1 (T) to carry out Fourier transformation to obtain a post-filtering noise power spectrum density matrix Pyy (f, rpm);
4) According to the psychoacoustic critical band theory, using Pyy (f, rpm) in the step 3) to calculate the critical bandwidth noise energy corresponding to different center frequencies, and obtaining a critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm), wherein Bark is equal to the width of one critical band; the specific method comprises the following steps:
drawing the order critical bandwidth background noise power spectral density matrix Pyy (Bark, rpm) in combination with a "Bark-f" relationship; wherein, the relation of Bark and frequency f is: when the frequency f is less than 500Hz, 1 bark=f/100, and the critical bandwidth is constant at 100Hz; when the frequency f is greater than 500Hz, 1 bark=4log (f/100), the critical bandwidth increases with increasing center frequency, being 20% of the center frequency;
5) Extracting the order critical bandwidth background noise from a Pyy (Bark, rpm) matrix, carrying out A weighting on the order critical bandwidth background noise to obtain L2 (rpm), and carrying out corresponding noise amplitude adjustment according to an order range of the order to obtain an order noise evaluation standard line Lstandard (rpm);
the step of extracting the order critical bandwidth background noise in the Pyy (Bark, rpm) matrix is as follows: 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 the frequency; secondly, extracting the order critical bandwidth background noise from a Pyy (Bark, rpm) matrix according to the Bark-frequency relationship;
the formula for A-weighting 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 0dB frequency weighting at 1kHz, with A being trimmed to the nearest 0.001dB 1 000=-2.000dB(A);
The order range is as follows: the low-order range of the motor and the speed reducer is less than 10; the intermediate order range of the motor and the speed reducer is 10-25; the high-order sub-range of the motor and the speed reducer is more than 25;
the adjusted noise amplitude corresponding to the order range where the order is located is: the amplitude of the adjusting noise corresponding to the low order of the motor and the speed reducer is +3dB; the amplitude of the adjusting noise corresponding to the intermediate order of the motor and the speed reducer is +0 to-3 dB; the amplitude of the adjusting noise corresponding to the high order of the motor and the speed reducer is-3 dB;
6) The order noise evaluation criterion line Lstandard (rpm) is compared with the order noise L1 (rpm), and if the order noise L1 (rpm) exceeds the order noise evaluation criterion line Lstandard (rpm), the noise is considered to be perceived by the vehicle interior personnel, and the degree of perception is proportional to the noise excess.
2. The new energy electric automobile squeal noise evaluation method according to claim 1, characterized in that: the colormap is plotted according to a noise power spectral density matrix Pxx (f, rpm), and motor order noise L1 (rpm) is calculated by taking the bandwidth of 0.125 times and the weight A as an evaluation object.
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基于阶次分析的汽车排气噪声运动感声品质设计方法研究;肖生浩;中国优秀硕士学位论文全文数据库,工程科技Ⅱ辑(2020年第07期);1-182 * |
基于阶次跟踪的变速箱啸叫噪声分析;栾文博;吴光强;徐炜卿;;振动与冲击(第11期);95-99 * |
基于阶次跟踪的某校车车内异常噪声试验研究;盛云,周宇,杜志良;农业装备与车辆工程;第52卷(第12期);23-27 * |
某纯电动汽车电机啸叫噪声优化;李彬;邓建交;牛文博;李守魁;李有意;;汽车科技(第03期);48-53 * |
汽车变速器齿轮啸叫噪声试验;王泽贵;裴质明;郝志勇;缪勇;周益;周观鹏;田文华;;浙江大学学报(工学版)(第07期);1307-1312 * |
纯电动汽车电机噪声测试与分析方法研究;严小俊;;上海汽车(第08期);6-9、19 * |
纯电动汽车路噪主动控制方法研究;孟豪;中国优秀硕士学位论文全文数据库,工程科技Ⅱ辑(2022年第01期);1-91 * |
纯电动汽车车内噪声主动控制技术研究;李鑫;中国优秀硕士学位论文全文数据库,工程科技Ⅱ辑(2022年第03期);1-106 * |
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