Electric drive assembly sound quality evaluation method and device, electronic equipment and storage medium

Abstract

The invention relates to an electric drive assembly sound quality evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of a noise signal, obtaining a preprocessing signal, processing an initial filtering noise signal based on a preset human ear hearing model processing strategy, and obtaining a final noise signal, and carrying out nonlinear processing on the final noise signal to obtain the intensity of the final noise signal, calculating tone loudness by combining a preset autocorrelation function, further determining characteristic tones, calculating an average value of the characteristic tones in a time domain to obtain tone scheduling, and finally obtaining a sound quality evaluation result of the electric drive assembly. Therefore, the problems that the sound quality of the whole vehicle is poor, the driving experience and brand attraction of the vehicle user are affected and the market competitiveness of the new energy vehicle is not favorably improved due to the fact that the sound quality evaluation result of the electric drive assembly of the new energy vehicle is not matched with the subjective feeling of the user in the prior art are solved, the subjective feeling of human ears is simulated, and the user experience is improved. The NVH level of the new energy vehicle is improved, and the driving experience of a user is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, electronic device and storage medium for evaluating the acoustic quality of an electric drive assembly. Background Technology

[0002] Currently, compared to internal combustion engine vehicles, new energy vehicles lack the masking effect of engine noise, have significantly different powertrain architectures, and exhibit significant changes in in-vehicle acoustic characteristics. Among these, the electric drive assembly, as a core component, has a complex system structure. Under the coupling effect of multiple excitation sources such as electromagnetic, mechanical, and aerodynamics, various noise components superimpose, resulting in significant spectral overlap. Its radiated noise specifically manifests as high-frequency discrete characteristics, obvious harmonic orders, and prominent howling phenomena. These high-frequency single-frequency components directly affect the in-vehicle acoustic environment.

[0003] However, there are few evaluation methods for electric drive assemblies in related technologies. The evaluation mainly relies on the single parameter of sound pressure level. This method ignores the subjective perception of the noise of the electric drive assembly by the human ear, resulting in the evaluation results not matching the user's subjective perception. This leads to poor sound quality of the whole vehicle, affecting the driving experience of vehicle users and the attractiveness of the product brand, and is not conducive to improving the competitiveness of new energy vehicles in the market. This needs to be addressed urgently. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for evaluating the acoustic quality of electric drive assemblies. This addresses the problem that existing evaluation methods for electric drive assemblies in new energy vehicles are limited, primarily relying on a single parameter, sound pressure level. This approach ignores the subjective perception of noise from the electric drive assembly, leading to evaluation results that fail to match user experience, resulting in poor overall vehicle acoustic quality. This negatively impacts the user's driving experience, product brand appeal, and the competitiveness of new energy vehicles in the market. The proposed method simulates the subjective perception of the human ear, improving the noise, vibration, and harshness (NVH) levels of new energy vehicles and enhancing the user's driving experience.

[0005] The first aspect of this application provides a method for evaluating the acoustic quality of an electric drive assembly, including the following steps: Acquire the noise signal of the electric drive assembly; The noise signal is preprocessed to obtain a preprocessed signal. Based on the preset human hearing model processing strategy, the initial filtered noise signal is processed to obtain the final noise signal. The final noise signal is then subjected to nonlinear processing, and the intensity of the noise signal is obtained based on the nonlinear processing result. Based on a preset autocorrelation function, the pitch loudness of the noise signal is calculated according to the intensity of the noise signal, and the characteristic pitch of the noise signal is determined according to the pitch loudness of the noise signal. The average value of the characteristic pitch of the noise signal is calculated in the time domain to obtain the pitch of the noise signal, and the sound quality evaluation result of the electric drive assembly is obtained according to the pitch of the noise signal.

[0006] Optionally, in some embodiments, the initial filtered noise signal is processed based on a preset processing strategy to obtain the final noise signal, including: Based on a preset filter function, the preprocessed signal is subjected to human auditory filtering to obtain an initial filtered noise signal; Based on a preset auditory filter, the initial filtered noise signal is filtered to obtain a new noise signal; The noise signal is divided into data and rectified by half-wave to obtain the noise signal after half-wave rectification. The mean of the noise signal after half-wave rectification is calculated to obtain the final noise signal.

[0007] Optionally, in some embodiments, the preset auditory filter is: ; in, This is a preset auditory filter; Extract the real part of the filter; It is a low-pass filter; The center frequency of the filter; For time; For low-pass filtering; For key frequency band scaling, It is the imaginary unit.

[0008] Optionally, in some embodiments, the final noise signal is: ; in, This is the final noise signal; The bandwidth lengths corresponding to different critical band ratio scales; This is the noise signal after half-wave rectification; The critical band ratio scale; For the sample index within the block; It is a half-wave rectifier function; For time blocks.

[0009] Optionally, in some embodiments, the preset filter function is: ; in, It is a second-order filter. For filter functions, The filter order; For frequency.

[0010] Optionally, in some embodiments, determining the characteristic pitch of the noise signal based on the pitch loudness of the noise signal includes: Determine the weighting function for pitch loudness; The characteristic pitch of the noise signal is obtained by multiplying the weighting function and the pitch loudness of the noise signal.

[0011] Optionally, in some embodiments, the preset autocorrelation function is: ; in, This is a preset autocorrelation function; The bandwidth lengths corresponding to different critical band ratio scales; For delay The cross-correlation between the subsequent signal and the original signal; The signal is after rectification; For delay The following signal; For key frequency band scaling; This represents the number of delayed samples; This is the index for samples within the block.

[0012] A second aspect of this application provides an acoustic quality evaluation device for an electric drive assembly, comprising: The acquisition module is used to acquire the noise signal of the electric drive assembly; The processing module is used to preprocess the noise signal to obtain a preprocessed signal, and based on the preset human hearing model processing strategy, process the initial filtered noise signal to obtain the final noise signal, perform nonlinear processing on the final noise signal, and obtain the intensity of the noise signal based on the nonlinear processing result. The evaluation module is used to calculate the pitch loudness of the noise signal based on the intensity of the noise signal according to the preset autocorrelation function, determine the characteristic pitch of the noise signal according to the pitch loudness of the noise signal, calculate the average value of the characteristic pitch of the noise signal in the time domain to obtain the pitch of the noise signal, and obtain the sound quality evaluation result of the electric drive assembly according to the pitch of the noise signal.

[0013] Optionally, in some embodiments, the processing module is specifically used for: Based on a preset filter function, the preprocessed signal is subjected to human auditory filtering to obtain an initial filtered noise signal; Based on a preset auditory filter, the initial filtered noise signal is filtered to obtain a new noise signal; The noise signal is divided into data and rectified by half-wave to obtain the noise signal after half-wave rectification. The mean of the noise signal after half-wave rectification is calculated to obtain the final noise signal.

[0014] Optionally, in some embodiments, the preset auditory filter is: ; in, This is a preset auditory filter; Extract the real part of the filter; It is a low-pass filter; The center frequency of the filter; For time; For low-pass filtering; For key frequency band scaling, It is the imaginary unit.

[0015] Optionally, in some embodiments, the final noise signal is: ; in, This is the final noise signal; The bandwidth lengths corresponding to different critical band ratio scales; This is the noise signal after half-wave rectification; The critical band ratio scale; For the sample index within the block; It is a half-wave rectifier function; For time blocks.

[0016] Optionally, in some embodiments, the preset filter function is: ; in, It is a second-order filter. For filter functions, The filter order; For frequency.

[0017] Optionally, in some embodiments, the evaluation module is specifically used for: Determine the weighting function for pitch loudness; The characteristic pitch of the noise signal is obtained by multiplying the weighting function and the pitch loudness of the noise signal.

[0018] Optionally, in some embodiments, the preset autocorrelation function is: ; in, This is a preset autocorrelation function; The bandwidth lengths corresponding to different critical band ratio scales; For delay The cross-correlation between the subsequent signal and the original signal; The signal is after rectification; For delay The following signal; For key frequency band scaling; This represents the number of delayed samples; This is the index for samples within the block.

[0019] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the acoustic quality evaluation method for an electric drive assembly as described in the first aspect embodiment.

[0020] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the acoustic quality evaluation method for an electric drive assembly as described in the first aspect embodiment.

[0021] This application focuses on the electric drive assembly of new energy vehicles. Addressing the difficulty in evaluating high-frequency noise in the electric drive assembly, it proposes a human hearing model. By constructing this model, the objective parameter of noise signal tone scheduling is used to evaluate the sound quality of the electric drive assembly, thereby simulating subjective human auditory perception. This model effectively simulates human subjective auditory experience. Furthermore, this application also proposes a method for evaluating the sound quality of the electric drive assembly. This addresses the problem that existing technologies use relatively simple evaluation parameters for the sound quality of electric drive assemblies, which significantly differ from user subjective perception. By using objective parameters to describe subjective perception, this method further improves the NVH level of new energy vehicles and enhances the user's driving experience.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for evaluating the acoustic quality of an electric drive assembly according to an embodiment of this application; Figure 2 This is a front view schematic diagram of a noise measurement point arrangement according to an embodiment of this application; Figure 3 This is a flowchart illustrating a method for calculating the pitch loudness of a noise signal according to an embodiment of this application; Figure 4This is a flowchart of a noise signal tone scheduling calculation method according to an embodiment of this application; Figure 5 This is a block diagram of an acoustic quality evaluation device for an electric drive assembly provided according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0025] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for evaluating the acoustic quality of an electric drive assembly according to embodiments of this application. Addressing the limitations of existing methods for evaluating electric drive assemblies in the background section, which primarily rely on sound pressure level as a single parameter, this approach neglects the subjective perception of noise from the electric drive assembly. This results in evaluation results that fail to match user experience, leading to poor overall vehicle acoustic quality. This negatively impacts the user's driving experience, brand appeal, and the competitiveness of new energy vehicles in the market. This application provides a method for evaluating the acoustic quality of an electric drive assembly. The method involves preprocessing a noise signal to obtain a preprocessed signal, processing the initial filtered noise signal based on a preset human hearing model to obtain a final noise signal, performing nonlinear processing on the final noise signal to obtain its intensity, calculating pitch loudness using a preset autocorrelation function, determining the characteristic pitch, calculating the average value of the characteristic pitch in the time domain to obtain the tone, and finally obtaining the acoustic quality evaluation result of the electric drive assembly. This addresses the problem that there are few evaluation methods for electric drive assemblies in related technologies, which mainly rely on a single parameter, sound pressure level. This method ignores the subjective perception of noise from the electric drive assembly by the human ear, resulting in evaluation results that cannot match the user's subjective experience. This leads to poor overall vehicle sound quality, affecting the user's driving experience and the product brand's appeal, and hindering the competitiveness of new energy vehicles in the market. This method simulates the subjective perception of the human ear, improves the NVH level of new energy vehicles, and enhances the user's driving experience.

[0026] Specifically, Figure 1 A flowchart of the acoustic quality evaluation method for an electric drive assembly provided in the embodiments of this application.

[0027] like Figure 1 As shown, the method for evaluating the acoustic quality of this electric drive assembly includes the following steps: In step S101, the noise signal of the electric drive assembly is acquired.

[0028] Among them, the noise signal of the electric drive assembly is the sound-related physical signal generated by the electric drive assembly during operation.

[0029] Specifically, the embodiments of this application must be carried out on a test bench in a semi-anechoic chamber, and the acoustic environment must comply with the GB / T 6882 standard; the ambient temperature should be controlled within the range of (25±2)℃, the relative humidity should be maintained within the range of 45%RH–75%RH, and the atmospheric pressure should be maintained within the range of 86kPa–106kPa.

[0030] Furthermore, the acoustic measurements in this application embodiment must use a sound level meter or equivalent system conforming to Class I or Type 1 requirements in GB / T 3785.1, with a frequency response covering 20Hz–20kHz; and the microphone must be calibrated with a Class 1 acoustic calibrator (conforming to GB / T 15173) before and after each measurement, with data having a deviation exceeding 0.5dB invalid; vibration measurements should use a triaxial accelerometer with a frequency response of not less than 10kHz and a mass of not more than 8g; the accuracy of the rotational speed measurement equipment should be better than 2%; the data acquisition system must have a 24-bit A / D conversion resolution and a sampling rate of not less than 51.2kHz to support sound pressure, vibration, rotational speed, and spectrum analysis; the test power supply must provide a stable DC voltage, and the voltage regulation error should not exceed ±2.5V (≤250V) or ±1% (>250V), and must be compatible with the power and voltage range of the motor system; the dynamometer system should be able to accurately control the rotational speed and torque, and its performance parameters must meet the operating requirements of the tested electric drive system.

[0031] Furthermore, the installation method in this embodiment should be the same as in normal use, preferably using the original vehicle mounting system and drive shaft. If the original vehicle mounting system cannot be used, a special rubber block can be used instead, but the installation posture should be consistent with the original vehicle installation state. The drive shaft should preferably be installed according to the vehicle's overall installation angle; if it cannot be installed according to the vehicle's overall installation angle, it should be installed horizontally. In addition, if the drive motor and inverter are integrated, they can be installed directly in their original state; if the drive motor and inverter are separate, the inverter needs to undergo acoustic treatment or be moved away from the measured motor to avoid affecting noise measurement.

[0032] Furthermore, the embodiments of this application should not generate abnormal vibrations or noise during operation. If auxiliary equipment connected to the electric drive system or the dynamometer's auxiliary equipment affects noise measurement, it should be acoustically isolated or installed outside the test environment, such as a regulated power supply or cooling fan. In addition, five microphones are used for noise measurement points, positioned 1 meter away from the outer contour of the electric drive system and pointing towards the geometric center of the outer contour. Specific measurement point location requirements are as follows... Figure 2 As shown, Figure 2This is a front view schematic diagram of a noise measurement point arrangement provided in one embodiment of this application. Figure 2 As shown, Figure 2 The core component is the microphone 201. The vibration measurement points in this embodiment use the vehicle coordinate system: X-direction—the front-to-back direction of the vehicle, with rearward being positive; Y-direction—the left-to-right direction of the vehicle, with rightward being positive; Z-direction—the vertical direction of the vehicle, with upward being positive. Furthermore, this embodiment places triaxial acceleration sensors at points of interest in various components, such as the drive motor housing, reducer housing, and inverter housing. The sensors should be mounted on a rigid structural surface, close to the excitation source, and the vibration transmission path to the excitation source should be free of vibration damping or isolation devices. Therefore, based on the above testing method, this embodiment can obtain the noise signal of the electric drive assembly.

[0033] In step S102, the noise signal is preprocessed to obtain a preprocessed signal, and the initial filtered noise signal is processed based on a preset human hearing model processing strategy to obtain a final noise signal. The final noise signal is then subjected to nonlinear processing, and the intensity of the noise signal is obtained based on the nonlinear processing result.

[0034] In some embodiments, the initial filtered noise signal is processed to obtain the final noise signal based on a preset processing strategy, including: performing human auditory filtering on the preprocessed signal based on a preset filter function to obtain the initial filtered noise signal; filtering the initial filtered noise signal based on a preset auditory filter to obtain a new noise signal; performing data division and half-wave rectification on the noise signal to obtain the half-wave rectified noise signal, and calculating the mean of the half-wave rectified noise signal to obtain the final noise signal.

[0035] Among them, the preprocessed signal is the intermediate signal obtained after preprocessing the noise signal; the preset human ear hearing model processing strategy is a model that simulates the human ear's perception characteristics of sounds of different frequencies and intensities; the initial filtered noise signal is the noise signal obtained after preliminary filtering; the final noise signal is the final noise data obtained after the initial filtered noise signal is processed by the human ear hearing model; the intensity of the noise signal is an index that quantifies the strength of the noise; the preset filter function is a pre-set mathematical formula for signal filtering; the preset auditory filter is an auditory filter group composed of overlapping asymmetric filters; the new noise signal is the updated noise signal generated after filtering and other processing; and the noise signal after half-wave rectification is the output signal after half-wave rectification.

[0036] Specifically, in this embodiment, the noise signal from the electric drive assembly bench obtained above can be preprocessed, mainly by padding the data with zeros at both ends to facilitate subsequent data processing. After preprocessing the noise signal, this embodiment further applies auditory filtering to the preprocessed signal, mainly including the head-related transfer function of the outer ear and the transfer function of the middle or inner ear. The preset filter function is used to describe the complex filtering effects of the head, auricle, and trunk; the middle or inner ear transfer function is used to describe the nonlinearity of human hearing. Based on this filter, the equal loudness curves of 20-90 phon are mainly simulated. The preset filter function is: ; in, It is a second-order filter. For filter functions, The filter order; For frequency.

[0037] Furthermore, after the aforementioned auditory filtering, the embodiments of this application can process noise signals. Through a preset auditory filter, key bandwidths and frequency conversions related to inner ear frequencies are achieved. When sound travels along the basilar membrane in the inner ear, it mediates the firing of auditory hair cells. The non-constant ratio of bandwidth to frequency in the preset auditory filter results in high frequency resolution in the low-frequency band and high time resolution in the high-frequency band, with the product of time and frequency resolution being extremely small at all frequencies—this endows humans with the ability to recognize short-term low-frequency events. The preset auditory filter is as follows: ; in, This is a preset auditory filter; Extract the real part of the filter; It is a low-pass filter; The center frequency of the filter; For time; For low-pass filtering; For key frequency band scaling, It is the imaginary unit.

[0038] Meanwhile, embodiments of this application can perform data segmentation and half-wave rectification on the noise signal to obtain a half-wave rectified noise signal, and then calculate the mean of the half-wave rectified noise signal to obtain the final noise signal. The final noise signal is: ; in, This is the final noise signal; The bandwidth lengths corresponding to different critical band ratio scales; This is the noise signal after half-wave rectification; The critical band ratio scale; For the sample index within the block; It is a half-wave rectifier function; For time blocks.

[0039] Furthermore, embodiments of this application can perform nonlinear processing on the final noise signal, converting the final noise signal into a nonlinear relationship with a specific loudness, and obtain the intensity of the noise signal based on the nonlinear processing result.

[0040] In step S103, based on a preset autocorrelation function, the pitch loudness of the noise signal is calculated according to the intensity of the noise signal, and the characteristic pitch of the noise signal is determined according to the pitch loudness of the noise signal. The average value of the characteristic pitch of the noise signal is calculated in the time domain to obtain the pitch of the noise signal, and the sound quality evaluation result of the electric drive assembly is obtained according to the pitch of the noise signal.

[0041] In some embodiments, determining the characteristic pitch of a noise signal based on its pitch loudness includes: determining a weighting function for the pitch loudness; and obtaining the characteristic pitch of the noise signal by multiplying the weighting function by the pitch loudness of the noise signal.

[0042] Among them, the preset autocorrelation function is a pre-defined mathematical function used to analyze the correlation of data time series; the pitch loudness of the noise signal is a comprehensive quantitative index characterizing the pitch and strength of the sound; the characteristic pitch of the noise signal is a key characteristic parameter that reflects the core pitch attribute of the noise; the pitch of the noise signal is a numerical value characterizing the stability of the pitch; the sound quality evaluation result of the electric drive assembly is a sensory quality assessment conclusion given for the sound generated by the electric drive assembly; and the weighting function of pitch loudness is a preset mathematical coefficient used to adjust the weight of each component of pitch loudness and highlight key information.

[0043] Specifically, to calculate the tone dispatch of the electric drive assembly noise signal, this embodiment of the application can estimate it using a preset autocorrelation function within a certain range. The preset autocorrelation function is then multiplied by the obtained pitch loudness of the noise signal. Furthermore, data within adjacent frequency bands are averaged, and the pitch loudness of the noise signal is calculated using an autocorrelation function window. Moreover, using the ratio of the pitch component to other components in the signal as a weighting function, multiplying it by the pitch loudness yields the characteristic pitch of the noise signal. Further, by calculating the average value in the time domain, the tone dispatch of the electric drive assembly noise signal based on a human hearing model can be obtained. The preset autocorrelation function is: ; in, This is a preset autocorrelation function; The bandwidth lengths corresponding to different critical band ratio scales; For delay The cross-correlation between the subsequent signal and the original signal; The signal is after rectification; For delay The following signal; For key frequency band scaling; This represents the number of delayed samples; This is the index for samples within the block.

[0044] Furthermore, to enable those skilled in the art to better understand the acoustic quality evaluation method for electric drive assemblies proposed in this application, the following is combined with... Figure 3 and Figure 4 Specific embodiments will be described below.

[0045] Specifically, Figure 3 This is a flowchart illustrating a method for calculating the pitch loudness of a noise signal, provided as an embodiment of this application.

[0046] like Figure 3 As shown, the method for calculating the pitch loudness of this noise signal mainly includes the following steps: 301, acquire the noise signal.

[0047] 302, Data Preprocessing.

[0048] 303, Human hearing filter.

[0049] 304, head-related transfer function and middle / inner ear transfer function.

[0050] 305, auditory filter, data partitioning.

[0051] 306, half-wave rectification.

[0052] 307, Root Mean Square Value Calculation.

[0053] 308, Auditory nonlinear processing.

[0054] 309, obtain the pitch loudness of the noise signal.

[0055] Furthermore, Figure 4 This is a flowchart of a noise signal tone scheduling calculation method provided in one embodiment of this application.

[0056] like Figure 4 As shown, the noise signal tone scheduling calculation method mainly includes the following steps: S401, Noise signal pitch loudness.

[0057] S402, Autocorrelation function calculation.

[0058] S403, average of autocorrelation functions of adjacent frequency bands.

[0059] S404, Autocorrelation function window.

[0060] S405, Noise signal pitch loudness calculation.

[0061] S406, Calculation of characteristic pitch of noise signal.

[0062] S407, acquire noise signal tone scheduling.

[0063] According to the acoustic quality evaluation method for electric drive assemblies proposed in this application, a preprocessed signal is obtained by preprocessing the noise signal. Then, the initial filtered noise signal is processed based on a preset human hearing model processing strategy to obtain the final noise signal. The final noise signal is then subjected to nonlinear processing to obtain its intensity. The pitch loudness is calculated using a preset autocorrelation function, thereby determining the characteristic pitch. The average value of the characteristic pitch in the time domain is calculated to obtain the pitch, ultimately yielding the acoustic quality evaluation result of the electric drive assembly. This solves the problem that there are few evaluation methods for electric drive assemblies of new energy vehicles in related technologies, which mainly rely on a single parameter, sound pressure level. This method ignores the subjective perception of noise from the electric drive assembly, resulting in evaluation results that cannot match the user's subjective experience, leading to poor overall vehicle acoustic quality. This negatively impacts the user's driving experience and the product brand's attractiveness, hindering the competitiveness of new energy vehicles in the market. This method simulates the subjective perception of the human ear, improves the NVH level of new energy vehicles, and enhances the user's driving experience.

[0064] Next, the acoustic quality evaluation device for the electric drive assembly proposed in this application is described with reference to the accompanying drawings.

[0065] Figure 5 This is a block diagram of the acoustic quality evaluation device for an electric drive assembly proposed in an embodiment of this application.

[0066] like Figure 5 As shown, the electric drive assembly sound quality evaluation device 10 includes: an acquisition module 100, a processing module 200, and an evaluation module 300.

[0067] The module includes: an acquisition module 100 for acquiring noise signals from the electric drive assembly; a processing module 200 for preprocessing the noise signals to obtain preprocessed signals, processing the initial filtered noise signals based on a preset human hearing model processing strategy to obtain final noise signals, performing nonlinear processing on the final noise signals, and obtaining the intensity of the noise signals based on the nonlinear processing results; and an evaluation module 300 for calculating the pitch loudness of the noise signals based on a preset autocorrelation function and the intensity of the noise signals, determining the characteristic pitch of the noise signals based on the pitch loudness of the noise signals, calculating the average value of the characteristic pitch of the noise signals in the time domain to obtain the tone pattern of the noise signals, and obtaining the sound quality evaluation result of the electric drive assembly based on the tone pattern of the noise signals.

[0068] Optionally, in some embodiments, the processing module 200 is specifically used to: perform human auditory filtering on the preprocessed signal based on a preset filter function to obtain an initial filtered noise signal; perform filtering processing on the initial filtered noise signal based on a preset auditory filter to obtain a new noise signal; perform data division and half-wave rectification processing on the noise signal to obtain a half-wave rectified noise signal, and perform mean calculation on the half-wave rectified noise signal to obtain a final noise signal.

[0069] Optionally, in some embodiments, the preset auditory filter is: ; in, This is a preset auditory filter; Extract the real part of the filter; It is a low-pass filter; The center frequency of the filter; For time; For low-pass filtering; For key frequency band scaling, It is the imaginary unit.

[0070] Optionally, in some embodiments, the final noise signal is: ; in, This is the final noise signal; The bandwidth lengths corresponding to different critical band ratio scales; This is the noise signal after half-wave rectification; The critical band ratio scale; For the sample index within the block; It is a half-wave rectifier function; For time blocks.

[0071] Optionally, in some embodiments, the preset filter function is: ; in, It is a second-order filter. For filter functions, The filter order; For frequency.

[0072] Optionally, in some embodiments, the evaluation module 300 is specifically used to: determine a weighting function for pitch loudness; and obtain the characteristic pitch of the noise signal based on the product of the weighting function and the pitch loudness of the noise signal.

[0073] Optionally, in some embodiments, the preset autocorrelation function is: ; in, This is a preset autocorrelation function; The bandwidth lengths corresponding to different critical band ratio scales; For delay The cross-correlation between the subsequent signal and the original signal; The signal is after rectification; For delay The following signal; For key frequency band scaling; This represents the number of delayed samples; This is the index for samples within the block.

[0074] It should be noted that the foregoing explanation of the embodiment of the electric drive assembly sound quality evaluation method also applies to the electric drive assembly sound quality evaluation device of this embodiment, and will not be repeated here.

[0075] According to the electric drive assembly sound quality evaluation device proposed in this application, a preprocessed signal is obtained by preprocessing a noise signal, and an initial filtered noise signal is processed based on a preset human hearing model processing strategy to obtain a final noise signal. The final noise signal is then subjected to nonlinear processing to obtain its intensity, and a preset autocorrelation function is used to calculate the pitch loudness, thereby determining the characteristic pitch. The average value of the characteristic pitch in the time domain is calculated to obtain the pitch, ultimately yielding the sound quality evaluation result of the electric drive assembly. This solves the problem that there are few evaluation methods for electric drive assemblies of new energy vehicles in related technologies, which mainly rely on a single parameter of sound pressure level. This method ignores the subjective perception of noise from the electric drive assembly by the human ear, resulting in evaluation results that cannot match the user's subjective experience, leading to poor overall vehicle sound quality, affecting the user's driving experience and product brand appeal, and hindering the competitiveness of new energy vehicles in the market. This device simulates the subjective perception of the human ear, improves the NVH level of new energy vehicles, and enhances the user's driving experience.

[0076] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0077] When the processor 602 executes the program, it implements the sound quality evaluation method for the electric drive assembly provided in the above embodiments.

[0078] Furthermore, the electronic device also includes: Communication interface 603 is used for communication between memory 601 and processor 602.

[0079] The memory 601 is used to store computer programs that can run on the processor 602.

[0080] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0081] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0082] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0083] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0084] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for evaluating the acoustic quality of an electric drive assembly as described in the first aspect embodiment.

[0085] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0086] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0087] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0089] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0090] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0091] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0092] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating the acoustic quality of an electric drive assembly, characterized in that, Includes the following steps: Acquire the noise signal of the electric drive assembly; The noise signal is preprocessed to obtain a preprocessed signal, and the initial filtered noise signal is processed based on a preset human hearing model processing strategy to obtain a final noise signal. The final noise signal is then subjected to nonlinear processing, and the intensity of the noise signal is obtained based on the nonlinear processing result. Based on a preset autocorrelation function, the pitch loudness of the noise signal is calculated according to the intensity of the noise signal, and the characteristic pitch of the noise signal is determined according to the pitch loudness of the noise signal. The average value of the characteristic pitch of the noise signal is calculated in the time domain to obtain the pitch mode of the noise signal, and the sound quality evaluation result of the electric drive assembly is obtained according to the pitch mode of the noise signal.

2. The method according to claim 1, characterized in that, The method of processing the initial filtered noise signal based on a preset processing strategy to obtain the final noise signal includes: Based on a preset filter function, the preprocessed signal is subjected to human auditory filtering to obtain an initial filtered noise signal; Based on a preset auditory filter, the initial filtered noise signal is filtered to obtain a new noise signal; The noise signal is divided into data and rectified by half-wave to obtain a half-wave rectified noise signal, and the mean of the half-wave rectified noise signal is calculated to obtain the final noise signal.

3. The method according to claim 2, characterized in that, The preset auditory filter is: ； in, This is a preset auditory filter; Extract the real part of the filter; It is a low-pass filter; The center frequency of the filter; For time; For low-pass filtering; For key frequency band scaling, It is the imaginary unit.

4. The method according to claim 2, characterized in that, The final noise signal is: ； in, This is the final noise signal; The bandwidth lengths corresponding to different critical band ratio scales; This is the noise signal after half-wave rectification; The critical band ratio scale; For the sample index within the block; It is a half-wave rectifier function; For time blocks.

5. The method according to claim 1, characterized in that, The preset filter function is: ； in, It is a second-order filter. For filter functions, The filter order; For frequency.

6. The method according to claim 1, characterized in that, Determining the characteristic pitch of the noise signal based on its loudness includes: Determine the weighting function for the pitch loudness; The characteristic pitch of the noise signal is obtained by multiplying the weighting function and the pitch loudness of the noise signal.

7. The method according to claim 1, characterized in that, The preset autocorrelation function is: ； in, This is a preset autocorrelation function; The bandwidth lengths corresponding to different critical band ratio scales; For delay The cross-correlation between the subsequent signal and the original signal; The signal is after rectification; For delay The following signal; For key frequency band scaling; This represents the number of delayed samples; This is the index for samples within the block.

8. A device for evaluating the acoustic quality of an electric drive assembly, characterized in that, include: The acquisition module is used to acquire the noise signal of the electric drive assembly; The processing module is used to preprocess the noise signal to obtain a preprocessed signal, and based on a preset human hearing model processing strategy, process the initial filtered noise signal to obtain a final noise signal, perform nonlinear processing on the final noise signal, and obtain the intensity of the noise signal based on the nonlinear processing result. The evaluation module is used to calculate the pitch loudness of the noise signal based on the intensity of the noise signal according to a preset autocorrelation function, determine the characteristic pitch of the noise signal based on the pitch loudness of the noise signal, calculate the average value of the characteristic pitch of the noise signal in the time domain to obtain the pitch mode of the noise signal, and obtain the sound quality evaluation result of the electric drive assembly based on the pitch mode of the noise signal.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for evaluating the acoustic quality of an electric drive assembly as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the method for evaluating the acoustic quality of the electric drive assembly as described in any one of claims 1-7.

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