CN108827452A - A kind of noise rating method of permanent-magnet synchronous hub motor - Google Patents
A kind of noise rating method of permanent-magnet synchronous hub motor Download PDFInfo
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Abstract
The present invention discloses a kind of noise rating method of permanent-magnet synchronous hub motor for electric vehicle, acquire the noise signal of motor model machine, obtain total loudness, roughness, tone color, sharpness, this five parameters,acoustics of noise fluctuations intensity, tester is selected to play identical noise signal multidigit, every bit test person gives a mark to the Annoyance degree of noise signal, obtain the subjective annoyance score of model machine, using five parameters,acoustics as the independent variable of supporting vector machine model, using subjective annoyance score as dependent variable training pattern, obtain the Annoyance degree Score on Prediction model based on support vector machines；It acquires noise signal and processing of the motor to be evaluated in five kinds of operating conditions to obtain five parameters,acoustics and be input to model, the subjective annoyance predicted scores and calculates weighted average；The present invention comprehensively considers the actual operating mode of motor, and the evaluation finally provided more meets actual permanent-magnet synchronous hub motor noise condition.
Description
Technical Field
The invention relates to a permanent magnet synchronous hub motor for an electric automobile, in particular to a noise evaluation method of the motor, which evaluates the noise of the permanent magnet synchronous hub motor according with the subjective feeling of people,
background
The permanent magnet synchronous hub motor is an important driving type of an electric automobile due to short transmission chain, high efficiency, excellent performance and high safety, however, the noise of the permanent magnet synchronous hub motor becomes a main noise source of the electric automobile, and the permanent magnet synchronous hub motor has many reasons, including wide speed regulation range, large load, harmonic current, rotor eccentricity and the like.
The existing noise evaluation method adopts sound power level and sound pressure level to evaluate noise, the sound power level or the sound pressure level only can reflect the comprehensive effect of the noise under the whole frequency band, and different noises under the same sound power or sound pressure cannot be evaluated, so that the evaluation of the noise by using the sound power or the sound pressure alone is incomplete, the acoustic characteristics of a lot of noises cannot be comprehensively reflected, the evaluation index is single and simple, and various characteristics of the noise cannot be comprehensively reflected. And because the perception of the human auditory system to the abnormal noise is influenced by various factors such as physiology, psychology and the like, the influence of the noise on the subjective feeling of people is still difficult to evaluate by using one or more acoustic parameters, and the subjective scoring of each motor is also influenced by time and subjective factors, so that the unified evaluation of the scale of a large number of permanent magnet synchronous hub motors cannot be carried out.
At present, more and more acoustic parameters such as loudness, sharpness, roughness, fluctuation intensity and the like are introduced into noise evaluation of the motor, however, the objective evaluation indexes still cannot fully and intuitively reflect all characteristics of noise.
Disclosure of Invention
The invention aims to solve the problems of the existing motor noise evaluation method, and provides a noise evaluation method of a permanent magnet synchronous hub motor for an electric vehicle, which comprehensively considers objective acoustic parameters of motor noise and human subjective feeling and relatively comprehensively evaluates the motor noise.
The invention discloses a noise evaluation method of a permanent magnet synchronous hub motor, which is realized by the following technical scheme: the method comprises the following steps:
(1) collecting a noise signal of a motor prototype, and processing the noise signal to obtain five acoustic parameters of the prototype, such as total loudness, roughness, timbre, sharpness and noise fluctuation intensity;
(2) playing the noise signals of the same prototype for a plurality of selected testers, and scoring the annoyance degree of the noise signals by each tester to obtain the subjective annoyance degree score of the prototype;
(3) taking the total loudness, roughness, timbre, sharpness and noise fluctuation intensity of the prototype as independent variables of a support vector machine model, and taking the subjective annoyance degree score of the prototype as a dependent variable training model to obtain an annoyance degree score prediction model based on the support vector machine;
(4) collecting noise signals of a motor to be evaluated under five working conditions of starting, accelerating, low-speed cruising, high-speed cruising and braking and decelerating, and processing the noise signals to obtain the total loudness, roughness, timbre, sharpness and noise fluctuation intensity of the noise signals of the motor to be evaluated under the five working conditions;
(5) inputting the total loudness, roughness, timbre, sharpness and noise fluctuation intensity in the step (4) into the annoyance degree score prediction model based on the support vector machine in the step (3) to obtain a subjective annoyance degree score predicted by the motor to be evaluated;
(6) and calculating the weighted average of the five subjective annoyance degree prediction scores, namely the comprehensive score of the motor noise to be evaluated.
The invention has the beneficial effects that:
1. a plurality of objective acoustic parameters for evaluating noise are obtained through collection and analysis of a noise source, the physical characteristics of the noise in the running process of the permanent magnet synchronous hub motor and the difference of subjective evaluation of people on the noise are comprehensively considered, and more comprehensive objective acoustic parameters for evaluating the noise of the hub motor are provided.
2. The method comprises the steps of calculating acoustic parameters of noise signals of the permanent magnet synchronous hub motor by utilizing noise psychology, establishing a prediction model based on the annoyance degree score of a Support Vector Machine (SVM), comprehensively considering the proportion of various operating conditions of the permanent magnet synchronous motor in actual use, grading subjective annoyance degree of noise by collecting the noise signals under various operating conditions, and then carrying out weighted average on the grades of all the operating conditions to obtain a comprehensive grade for evaluating the annoyance degree of the noise. Meanwhile, the actual operation condition of the motor is comprehensively considered in the evaluation process, and finally given evaluation also better conforms to the actual noise condition of the permanent magnet synchronous hub motor.
Drawings
FIG. 1 is a flow chart for building a Support Vector Machine (SVM) based annoyance score prediction model;
FIG. 2 is a schematic diagram of noise signal acquisition of a permanent magnet synchronous hub motor;
fig. 3 is a noise evaluation flow chart of the permanent magnet synchronous hub motor to be evaluated.
Detailed Description
As shown in FIG. 1, the invention firstly collects the noise signal of a prototype of the permanent magnet synchronous hub motor. Comprehensive noise signal acquisition is carried out on a prototype of a permanent magnet synchronous hub motor, as shown in fig. 2, an electric vehicle driven by the permanent magnet synchronous hub motor is placed in the middle of a sound-proof chamber, and the influence of external noise is avoided. Five noise collection positions are set, namely a stator surface M1 of the in-wheel motor, M2 at the position 1M on the right side of the in-wheel motor, M at the position 1M in front of the in-wheel motor, M4 at the position 1M behind the in-wheel motor and M5 at the middle position of a driving shaft. Microphones are respectively arranged at five positions to collect motor noise signals, noise time domain signals are collected, and the noise time domain signals collected by the microphones are input to a computer outside a soundproof room.
When the noise time domain signal is collected, the range of the main operation working condition rotating speed N of the hub motor is simulated to be 0-1000r/min and the range of the load T is simulated to be 0-100 N.m by combining the speed range of the hub motor driving the electric automobile. Respectively collecting noise time domain signals under 50 working conditions, wherein the rotating speed n is respectively set to be 10, 20, 50, 100, 200, 400, 600, 700, 800 and 1000 r/min; the loads T are set at 20, 40, 60, 80, 100, respectively. The rotating speed n and the load T are arranged and combined to set 50 working conditions, and all the operating conditions of the hub motor are basically covered. Noise time domain signals of five positions under 50 working conditions are collected in the sound insulation chamber of FIG. 2, 250 groups of noise time domain signals are collected in total, the 250 groups of noise time domain signals are input into a computer, and the computer stores the signals in wav format files for analysis.
As shown in fig. 1, the computer performs acoustic parameter calculation on the saved noise time domain signal. Firstly, each noise time domain signal is processed with discrete Fourier transform (FFT), and the noise time domain signal is transformed intoA noise frequency domain signal. The signal amplitude of the noise frequency domain signal at the frequency f is yf. And then, respectively calculating five acoustic parameters of the total loudness N, the roughness R, the timbre T, the sharpness S and the noise fluctuation intensity F of the model machine noise. The method comprises the following specific steps:
1. calculating the total loudness N: only the frequency domain signals of which the frequency range is 20Hz to 15000Hz are subjected to calculation processing in consideration of the range of the noise signals actually received by the human ear. The signal in the frequency domain of 20Hz to 15000Hz is divided into 15 frequency bands. The invention is divided into 15 frequency bands: 20-110Hz, 110-200Hz, 200-400Hz, 00-800Hz, 800-1300Hz, 1300-1900Hz, 1900-2500Hz, 2500-3200Hz, 3200-4000Hz, 4000-000Hz, 5000-6500Hz, 6500-8000Hz, 8000-10000Hz, 10000-12000Hz, 12000-15000 Hz. Sound pressure y to noise frequency domain signal in i-th frequency bandfCalculating geometric mean value to obtain mean sound pressure PiThen, the sound pressure L of each frequency is obtained by calculationiThe calculation formula is as follows:
i is the number of frequency bands, and i sequentially takes 15 corresponding frequency bands from 1 to 15; f. ofimin、fimaxRespectively the lowest frequency and the highest frequency of the ith frequency band; p0As reference sound pressure, P0＝2×10-5Pa。
By sound pressure L per frequencyiCalculating to obtain critical frequency band characteristic loudness N of ith frequency bandi：
In the formula, α is a differentiation factor, and is generally 0.85.
Finally, the specific loudness N of all frequency bandsiThe total loudness N is obtained by integration:
2. calculating the roughness R: firstly, according to each section of noise frequency domain signal, determining sound pressure y in ith frequency bandfMaximum value ymax(i) And minimum value ymin(i) (ii) a Then, the difference Δ L of the excitation levels of the frequency bands is calculatedE(i)：
Then, based on the difference Δ LE(i) Calculating to obtain roughness R:
wherein f ismodIs the modulation frequency.
3. Calculating the tone color T: timbre T is calculated according to
Wherein f isimin、fimaxRespectively the lowest frequency and the highest frequency of the ith frequency band; y isimidThe signal amplitude corresponding to the median frequency of the ith section of frequency.
4. Calculating sharpness S: the sharpness S is calculated according to:
wherein g (i) is a weight function of the sharpness of the noise in each frequency bin,k2for sharpness factor, k is taken2＝0.1。
5. Calculating the noise fluctuation intensity F:
according to the sound pressure y of each frequency bandfMaximum value y ofmax(i) And minimum value ymin(i) The sound pressure variation amount Δ l (i) is calculated:
ΔL(i)＝ymax(i)-ymin(i)，
then, the noise fluctuation intensity F is calculated according to the following formula:
wherein f ismodIs the modulation frequency.
The computer stores and obtains five acoustic parameters of the total loudness N, the roughness R, the tone T, the sharpness S and the noise fluctuation intensity F of the prototype signal.
Then, a plurality of testers are selected as the main subject of the subjective evaluation of noise, for example, 20 testers are selected as the main subject of the subjective evaluation of noise in the invention, the testers are normal hearing persons, wherein 10 people are male and 10 people are female. In a room with 30 db background noise, the temperature in the room is 23 degrees and the humidity is 50%. And sequentially playing the same 250 groups of noise time domain signals stored before the computer by adopting a closed earphone, wherein the playing time of each group of noise time domain signals is 15 s.
The noise is classified into 20 levels according to the dysphoric ability, and the noise is more easily irritated when the level is higher. In the 1 to 20 point arrangement, the higher the score, the more annoying the noise is. Each tester scored the annoyance of 250 sets of noisy time-domain signals. Thus, each group of noise time domain signals obtains 20 scores, the 20 scores of each group are averaged to obtain the subjective annoyance degree score G of 250 groups of noise time domain signals, and then the subjective annoyance degree score G corresponding to the 250 noise time domain signals is input into the computer.
A Support Vector Machine (SVM) -based model is established in a computer, and the total loudness N, the roughness R, the timbre T, the sharpness S and the noise fluctuation intensity F are used as independent variables of the model, and the subjective annoyance degree score G is used as a dependent variable training model. Firstly, carrying out normalization preprocessing on total loudness N, roughness R, timbre T, sharpness S, noise fluctuation intensity F and subjective annoyance fraction G in a computer to obtain 250 groups of training data. And (4) adopting 125 groups of data to train the model, and verifying the accuracy of the model by using the other 125 groups of data to obtain the annoyance degree score prediction model based on the support vector machine. The model can be applied to all permanent magnet synchronous hub motors to be evaluated.
And finally, placing the permanent magnet synchronous hub motor to be evaluated in a sound insulation chamber, and collecting the noise signal of the permanent magnet synchronous hub motor, wherein the collection method is the same as that of the prototype. Considering the practical application scene of the hub motor for the electric automobile, the noise collection of the motor to be evaluated covers five working conditions, namely a first starting working condition and the speed of 0-10 km/h; the second acceleration condition is that the speed is 10-30 km/h; a third low-speed cruising condition with a speed of 60km/h, a fourth high-speed cruising condition with a speed of 90km/h and a fifth braking deceleration condition. In the environment shown in fig. 2, five working conditions of the motor to be evaluated are simulated, noise signal samples are collected at the same time, the sampling frequency is 44100Hz, each working condition comprises five noise signal samples, and the noise signal samples are stored in a computer in a wav format file. The computer carries out acoustic parameter analysis on the noise signal to obtain five groups of acoustic parameters of each working condition of the permanent magnet synchronous hub motor to be evaluated: total loudness N, roughness R, timbre T, sharpness S, noise fluctuation intensity F.
Inputting five groups of total loudness N, roughness R, tone T, sharpness S and noise fluctuation intensity F of each working condition into a support vector machine-based annoyance degree score prediction model to obtain predictionSubjective annoyance score G. Then, the subjective annoyance degree score G under each working condition is averaged to obtain five subjective annoyance degree prediction scoresWherein m is the number of the working condition. Then, subjective annoyance degree prediction scoring is carried out on the five working conditionsCarrying out comprehensive weighted average to obtain a weighted average value, and finally obtaining a comprehensive score G of the noise of the hub motor*I.e. the composite score G of the motor noise to be evaluated*：
The subjective annoyance degree prediction scores of the motor to be evaluated under a first starting working condition, a second accelerating working condition, a third low-speed cruising working condition, a fourth high-speed cruising working condition and a fifth braking and decelerating working condition are respectively obtained.
Claims (5)
1. A noise evaluation method of a permanent magnet synchronous hub motor is characterized by comprising the following steps:
(1) collecting a noise signal of a motor prototype, and processing the noise signal to obtain five acoustic parameters of the prototype, such as total loudness, roughness, timbre, sharpness and noise fluctuation intensity;
(2) playing the noise signals of the same prototype for a plurality of selected testers, and scoring the annoyance degree of the noise signals by each tester to obtain the subjective annoyance degree score of the prototype;
(3) taking the total loudness, roughness, timbre, sharpness and noise fluctuation intensity of the prototype as independent variables of a support vector machine model, and taking the subjective annoyance degree score of the prototype as a dependent variable training model to obtain an annoyance degree score prediction model based on the support vector machine;
(4) collecting noise signals of a motor to be evaluated under five working conditions of starting, accelerating, low-speed cruising, high-speed cruising and braking and decelerating, and processing the noise signals to obtain the total loudness, roughness, timbre, sharpness and noise fluctuation intensity of the noise signals of the motor to be evaluated under the five working conditions;
(5) inputting the total loudness, roughness, timbre, sharpness and noise fluctuation intensity in the step (4) into the annoyance degree score prediction model based on the support vector machine in the step (3) to obtain a subjective annoyance degree score predicted by the motor to be evaluated;
(6) and calculating the weighted average of the five subjective annoyance degree prediction scores, namely the comprehensive score of the motor noise to be evaluated.
2. The noise evaluation method of the permanent magnet synchronous hub motor according to claim 1, wherein the noise evaluation method comprises the following steps: in the step (1), the collected noise signals of the motor prototype are time domain signals, each time domain signal is subjected to discrete Fourier transform to obtain frequency domain signals, only the frequency domain signals with the frequency range of 20Hz to 15000Hz are processed, and the frequency domain signals with the frequency range of 20Hz to 15000Hz are divided into 15 frequency bands.
3. The noise evaluation method of the permanent magnet synchronous hub motor according to claim 2, wherein the noise evaluation method comprises the following steps: total loudnessRoughness ofTimbreSharpness degreeIntensity of noise fluctuation
Wherein,yfis the sound pressure y in the i-th frequency bandf；fimin、fimaxThe lowest frequency and the highest frequency of the ith frequency band are respectively; p0＝2×10-5Paα is 0.85;ymax(i)、ymin(i) respectively, the sound pressure y in the ith frequency bandfMaximum and minimum values of; f. ofmodIs the modulation frequency;k2＝0.1；ΔL(i)＝ymax(i)-ymin(i)。
4. the noise evaluation method of the permanent magnet synchronous hub motor according to claim 1, wherein the noise evaluation method comprises the following steps: in the step (6), the weighted average of the five subjective annoyance degree prediction scoresThe subjective annoyance degree prediction scores of the motor to be evaluated under the starting working condition, the accelerating working condition, the low-speed cruising working condition, the high-speed cruising working condition and the braking and decelerating working condition are respectively obtained.
5. The noise evaluation method of the permanent magnet synchronous hub motor according to claim 1, wherein the noise evaluation method comprises the following steps: in the step (1), when the noise signal of the motor prototype is collected, the rotating speed range of the motor is simulated to be 0-1000r/min, and the load range is simulated to be 0-100 N.m.
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Application publication date: 20181116 Assignee: Honeycomb drive system (Jiangsu) Co.,Ltd. Assignor: JIANGSU University Contract record no.: X2023320000069 Denomination of invention: A Noise Evaluation Method of Permanent Magnet Synchronous Hub Motor Granted publication date: 20200731 License type: Exclusive License Record date: 20230116 |