Method, device, equipment and medium for evaluating NVH performance damping of electric drive systems

Abstract

Conventional NVH performance evaluation methods have certain limitations, as they rely on subjective evaluation and lack the ability to predict the NVH performance attenuation tendency. [Solution] This application discloses a method, device, equipment, and medium for evaluating NVH performance damping of an electric drive system, which relates to the field of NVH performance evaluation of an electric drive system, and includes the steps of obtaining a series of historical NVH performance values, determining model parameters based on the series values, constructing a prediction model based on the model parameters, where the prediction model is used to predict the NVH performance damping situation at a predetermined time point of the electric drive system, and extracting feature values ​​based on the prediction model and establishing an evaluation model based on the feature values, where the evaluation model is used to evaluate the NVH performance damping situation. This application realizes prediction of NVH performance damping tendency.

Description

[Technical Field]

[0001] The present application relates to the field of NVH performance evaluation, and in particular to a method, apparatus, device and medium for evaluating NVH performance damping of electric drive systems. [Background technology]

[0002] Electric drive system NVH refers to the noise, vibration, and harshness generated by the electric drive system (including components such as the motor, controller, and reducer) used in electric vehicles. With the rapid development of electric vehicle technology, the NVH performance of electric drive systems has become an important indicator for measuring the overall vehicle quality and ride comfort. Electric drive system NVH performance decay refers to the gradual deterioration of the noise, vibration, and harshness of an electric drive system over time and as the conditions of use change.

[0003] With increasing system integration, electric drive systems in new energy vehicles offer higher efficiency and performance, but may also bring more complex NVH performance issues.

[0004] However, conventional NVH performance evaluation methods have certain limitations, such as relying on subjective evaluation and lacking in predicting the NVH performance attenuation tendency. Summary of the Invention [Problem to be solved by the invention]

[0005] The present application aims to provide a method, device, apparatus and medium for evaluating NVH performance attenuation in an electric drive system, and to predict the NVH performance attenuation trend. [Means for solving the problem]

[0006] To achieve the above object, the present application provides the following solutions.

[0007] According to a first aspect, the present application provides a method for assessing electric drive system NVH performance damping, comprising: obtaining a series of predicted target historical NVH performance values; determining model parameters based on the sequence values; constructing a predictive model based on the model parameters, the predictive model being used to predict an NVH performance decay situation of the electric drive system at a preset time point; and extracting feature values ​​based on the prediction model and using them to establish an evaluation model based on the feature values, wherein the evaluation model is used to evaluate the NVH performance damping situation.

[0008] According to a second aspect, the present application provides an apparatus for assessing electric drive system NVH performance damping, comprising: an acquisition module for acquiring a series of historical NVH performance values; a determination module for determining model parameters based on the sequence values; a construction module for constructing a predictive model based on the model parameters, the predictive model being used to predict an NVH performance decay situation at a preset time point in the electric drive system; and an evaluation module for extracting feature values ​​based on the prediction model and establishing an evaluation model based on the feature values, wherein the evaluation model is used to evaluate the NVH performance damping situation.

[0009] According to a third aspect, the present application provides a computer device, comprising: a memory; a processor; and a computer program stored in the memory and operable on the processor, the processor executing the computer program to implement the steps of the method for assessing NVH performance damping of an electric drive system according to any one of the preceding claims.

[0010] According to a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program, the computer program being configured to implement the steps of the method for assessing NVH performance damping of an electric drive system as described in any one of the preceding claims when executed by a processor. [Effects of the Invention]

[0011] According to the specific embodiments provided in this application, the application discloses the following technical effects. This application provides a method, device, equipment, and medium for evaluating the NVH performance attenuation of an electric drive system, which obtains a series of historical NVH performance values, determines model parameters based on the series of values, and builds a prediction model based on the model parameters, which is used to predict the NVH performance attenuation state at a predetermined time point of the electric drive system, thereby realizing prediction of the NVH performance attenuation trend. Feature values ​​are extracted based on the prediction model, and an evaluation model is established based on the feature values, which is used to evaluate the NVH performance attenuation state, providing a scientific basis for the optimization and maintenance of the electric drive system. [Brief explanation of the drawings]

[0012] In order to more clearly describe the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings that need to be used in the embodiments. It is obvious that the drawings in the following description are only some embodiments of the present application, and those skilled in the art can further derive other drawings based on these drawings without any creative work. [Figure 1] 1 is an application environment diagram of an evaluation method for NVH performance damping of an electric drive system according to an embodiment of the present application; [Figure 2] 1 is a schematic flowchart of a method for evaluating NVH performance damping of an electric drive system provided in one embodiment of the present application. [Figure 3] 1 is a schematic flowchart illustrating acquisition of series values ​​of predicted target historical NVH performance provided in one embodiment of the present application. [Figure 4]1 is a functional module schematic diagram of an evaluation device for NVH performance damping of an electric drive system provided in one embodiment of the present application. FIG. [Figure 5] FIG. 1 is a structural schematic diagram of a computer device provided in an embodiment of the present application; DETAILED DESCRIPTION OF THE INVENTION

[0013] The following clearly and completely describes the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some of the embodiments of the present application, not all of the embodiments, and all other embodiments obtained by those skilled in the art based on the embodiments of the present application without any creative work are all within the scope of protection of the present application.

[0014] In addition, if there is no conflict, the embodiments and features in the embodiments in the present application can be combined with each other. In order to make the above-mentioned objects, features and advantages of the present application more clear and understandable, the present application will be described in more detail below with reference to the drawings and specific embodiments.

[0015] It can be understood that the electric drive system NVH performance prediction method provided in the embodiments of the present application is used in the electric drive system of new energy vehicles, whose NVH performance is a future development trend.

[0016] NVH, an abbreviation for noise, vibration, and harshness, is a concept in engineering and technology that is widely discussed, especially in the automotive manufacturing industry. NVH characteristics relate to a vehicle's ride comfort, quietness, and overall driving experience. Noise refers to various sounds generated during vehicle operation, including engine noise, tire-road noise, and wind noise, which can affect occupant comfort. Vibration refers to the vibration phenomenon of a vehicle's structure or components caused by various factors, such as engine vibration and bumps caused by road irregularities. Excessive vibration not only affects comfort but can also threaten vehicle durability and safety. Harshness is a more subjective evaluation index that describes the degree of discomfort or annoyance caused to occupants by the synergistic effect of noise and vibration. The impact of these factors on human perception is related to the degree to which a vehicle is perceived as "rough" or unacceptable. Manufacturers of automobiles and other transportation vehicles use NVH engineering to analyze, simulate and optimize these factors to improve the overall quality and market competitiveness of their products.

[0017] As shown in FIG. 1 , the method for evaluating NVH performance damping of an electric drive system provided in the embodiment of the present application can be applied to a hardware environment and / or software environment consisting of a terminal 102 and a server. Here, the terminal 102 communicates with the server 104 via a network. A data storage system can store data required for processing by the server 104. The data storage system can be installed independently, integrated into the server 104, or located in the cloud or another server. The terminal 102 can transmit the acquired series values ​​of the predicted target historical NVH performance to the server 104. The server 104 receives the series values ​​of the predicted target historical NVH performance. For the series values ​​to be processed, the server 104 determines model parameters based on the series values ​​and builds a prediction model based on the model parameters. The prediction model is used to predict the NVH performance damping status at a predetermined time point of the electric drive system. Feature values ​​are extracted based on the prediction model, and an evaluation model is established based on the feature values. The evaluation model is used to evaluate the NVH performance damping status. The server 104 can feed back the obtained prediction model and evaluation model to the terminal 102. In some embodiments, the method for evaluating NVH performance damping may be implemented solely by the server 104 or the terminal 102. For example, the terminal 102 may directly process the series of historical NVH performance values ​​of the target vehicle to be predicted to obtain a prediction model and an evaluation model. Alternatively, the server 104 may obtain the series of historical NVH performance values ​​of the target vehicle to be predicted from a data storage system and process the series of historical NVH performance values ​​to construct a prediction model and an evaluation model.

[0018] Here, the terminal 102 may be, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, Internet of Things-enabled devices, and portable wearable devices. The Internet of Things-enabled devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0019] For ease of understanding and explanation, the method, device, equipment and medium for evaluating NVH performance damping of an electric drive system provided in the embodiments of the present application will be described in detail below with reference to FIGS.

[0020] In one exemplary embodiment, as shown in FIG. 2, a method for evaluating NVH performance damping of an electric drive system is provided, the method being executed by a computer device, specifically, the method may be executed solely by a computer device such as a terminal or a server, or may be executed jointly by a terminal and a server. In the embodiment of the present application, the method is described as being applied to the server 104 in FIG. 1, and includes the following steps S201 to S204, where:

[0021] In step S201, the series values ​​of the predicted target historical NVH performance are obtained.

[0022] Specifically, NVH performance data refers to a series of numerical values ​​and analytical results obtained through comprehensive quantification evaluation of three key indicators in the automotive engineering field: vehicle noise, vibration, and harshness. These data are important parameters for measuring a vehicle's ride comfort, driving experience, and overall quality. To accurately monitor the operating status of an electric drive system during operation, a series of sensors are installed in key parts of the electric drive system, primarily including acceleration sensors and dedicated sensors for measuring sound pressure and sound power. Key parts include, but are not limited to, the motor, transmission belt, and bearings, as these are the main sources of vibration and noise. For example, multiple acceleration sensors and multiple sound pressure sensors are attached to the rotor, stator, and housing of the electric drive system, and these sensors can collect vibration and noise signals in real time while the electric drive system is operating at rated power.

[0023] In another exemplary embodiment of the present application, as shown in FIG. 3, the above step S201 is replaced by the following steps S2011 to S2012.

[0024] In step S2011, NVH performance data is obtained based on a durability test for the same electric drive system, and a sensor is provided in the electric drive system, where the time period corresponding to the NVH performance data includes an initial time period and a decay time period, and the preset number of cycles in the durability test corresponds to the number of mileages under actual road conditions.

[0025] Specifically, NVH (Noise, Vibration, and Harshness) performance data can be collected using pre-installed sensors in the electric drive system, which includes collecting vibration and noise signals. In the process of collecting NVH performance data, a time period can be pre-set, and an initial time period and a decay time period can be selected. The initial time period is the stage immediately after vibration and noise signals begin to be generated when the electric drive system starts operating, and the decay time period is the time period after NVH performance has decayed over time.

[0026] Optionally, various sensors are arranged in key parts of the electric drive system to accurately acquire vibration and noise signals, such as speed sensors, sound pressure sensors, and sound power sensors. These sensors can collect vibration and noise signals in real time during operation of the electric drive system. Among them, acceleration sensors are primarily used to measure the acceleration of an object and can be used to evaluate the magnitude and frequency of vibrations. Sound pressure and sound power sensors are used to measure sound intensity and energy and further characterize noise. The real-time collection of these sensors allows the inventors to comprehensively monitor and evaluate the NVH performance of the electric drive system, thereby providing valuable data support for optimizing the performance of the electric drive system. At the same time, these data can be further used to analyze and solve vibration and noise problems that may occur during operation of the electric drive system, thereby improving the reliability and comfort of the electric drive system.

[0027] Specifically, the purpose of durability testing is to simulate the wear and aging of electric drive systems in real-world use and predict their performance over their lifecycle. By repeatedly running a series of standardized test cycles in a laboratory environment, the aging of the electric drive system can be accelerated, and NVH performance data can be quickly obtained after long-term operation.

[0028] As will be appreciated, to ensure the accuracy and representativeness of the durability test, an equivalent relationship between the number of cycles and the actual mileage is established. This relationship is based on research into the performance decay rate of electric drive systems under different operating conditions. Specifically, each test cycle includes, but is not limited to, the following:

[0029] Urban Road Cycle: Simulates a daily commute, including frequent starting and parking maneuvers and the low-speed driving situations common in congested traffic.

[0030] High-speed cycle: The vehicle is traveling at a relatively high speed, simulating the conditions experienced by a driver during a long-distance journey.

[0031] Mountain Road Cycle: This cycle includes steep uphill and downhill runs, simulating mountain road driving. The vehicle must overcome gravity to provide sufficient power for uphill travel, while also properly managing braking force when descending to prevent the braking system from overheating.

[0032] Each test cycle is equivalent to a certain actual driving distance, ensuring that 1,000 cycles are equivalent to 300,000 kilometers of driving. Setting such an equivalent driving distance not only saves time and resources, but also provides a test environment that is similar to actual road conditions.

[0033] In step S2012, the NVH performance data is pre-processed to obtain the series values ​​of the historical NVH performance.

[0034] Specifically, the original NVH performance data always contains a wide range of frequency components, such as noise signals from the surrounding environment and other interference sources. To improve the noise quality, the data is preprocessed using a bandpass filter to remove irrelevant frequency components and retain important signal frequency bands. In this embodiment, the filter frequency range is set to 10 Hz to 1 kHz, which covers the main vibration and noise signals in the NVH characteristics of electric drive systems. After filtering, the signal is usually represented in the frequency domain. The inverse Fourier transform (IFT) is used to convert the signal from the frequency domain to the time domain, facilitating subsequent feature extraction and analysis. The signal is then normalized to adjust the signal amplitude to the same range and eliminate the influence of amplitude differences on subsequent analysis.

[0035] Time-domain features, such as the mean, standard deviation, maximum, minimum, and energy, are extracted from the signal. The mean reflects the average level of the signal, the standard deviation measures the fluctuation of the signal strength, the maximum represents the signal's maximum amplitude, the minimum represents the signal's minimum amplitude, and the energy represents the signal's total energy within a specified time period. The mean, standard deviation, maximum, minimum, and energy of each signal collectively constitute the signal's features. For the frequency bands of vibration and noise signals, the short-time Fourier transform (STFT) is used to analyze the signal's local spectral characteristics, such as its dominant frequency and frequency bandwidth. The short-time Fourier transform is a time-frequency analysis tool that can capture frequency components that change over time. Local features in the time and frequency dimensions of the signal are extracted using a wavelet transform method. All of the extracted features are combined into a feature vector. Each element of the feature vector corresponds to a specific feature value. For example, if 10 time-domain features and 10 frequency-domain features are extracted, the feature vector will have a length of 10.

[0036] In this embodiment, to balance time resolution and frequency resolution, the signal is divided into multiple 1-second intervals that overlap by 0.5 seconds. A Hanning window is used to reduce boundary effects, which helps smooth signal edges and avoid artifacts caused by sudden signal interruptions. For each time window, the feature extraction and feature vector generation steps are repeated to generate a feature vector. As the time window moves, a series of feature vectors is obtained. The feature vectors are then arranged in chronological order to form a feature vector sequence, which can be considered as a feature representation of the signal's time-varying behavior, i.e., a series of historical NVH performance values.

[0037] In step S202, model parameters are determined based on the sequence values.

[0038] Before step S202, it is necessary to perform a stationarity determination on the series values ​​of the historical NVH performance. If the series values ​​of the historical NVH performance are stationary, the original series composed of the series values ​​of the historical NVH performance is a stationary series. If the series values ​​are not stationary, a differential process is performed based on the series values ​​in the original series until a stationary series is obtained, where the number of differential processes is the differential order of the prediction model.

[0039] Specifically, stationarity primarily refers to the stationarity of the statistical characteristics of time-series data, meaning that the statistical characteristics of the time-series data, such as the mean value, variance, and autocovariance function, do not change over the entire observation period and do not change significantly over time. Before performing a series analysis on the series values ​​of historical NVH performance, it is first determined whether these series values ​​satisfy the stationarity condition. If the series data is non-stationary, its statistical characteristics change over time, making the series data unstable and thereby affecting the model's predictive ability.

[0040] As can be seen, if the series values ​​are determined to be non-stationary, the series data will be transformed by a differencing method until a stationary time data series is obtained, and the differencing operation is used to calculate the difference values ​​between adjacent terms in the series, which will remove the trend and seasonal effects of the data and make the time series data more stable.

[0041] Illustratively, the first order difference can be calculated as follows:

[0042] y t =x t -x t-1 is.

[0043] where y t is the series value after difference, and x t is the current value of the original series, and x t-1 is the value of the previous term in the element sequence.

[0044] It will be appreciated that if, after first-order differencing, the series still exhibits non-stationary characteristics, higher-order differencing is usually required, such as second-order differencing, which can be understood as again differencing the first-order differencing series. In this analogy, multiple differencing operations are performed on a series until the series exhibits stationarity. The number of times series values ​​are differencing affects the order of the forecasting model. In time series analysis, the order of differencing can be considered the differencing order in an autoregressive integrated moving average (ARIMA) model.

[0045] Illustratively, if two differencings are required for the series to reach a steady state, the differencing order of the forecast model is two.

[0046] In one embodiment, the number of autoregressive terms p and the number of moving average terms q of the model are determined based on the acquired stationary series using the AIC and BIC constant order methods. A prediction model is constructed based on the number of autoregressive terms p, the number of moving average terms q, and the difference order d.

[0047] Specifically, the accuracy of forecasting results can be further improved by determining the autoregressive term p and the moving average term q using two constant-order methods, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). First, a set of already stationary series data must be obtained. If the original data is not stationary, it must be stationaryized by differential processing and the differential order d must be recorded. Then, a reasonable range must be selected for the autoregressive term p and the moving average term q. For example, the range of possible values ​​for the autoregressive term p and the moving average term q is set to 0 to 4, respectively. ARIMA models are fitted using all possible (p, d, q) combinations, and the AIC and BIC values ​​for each model are calculated. The smaller the AIC and BIC values, the better the model. By comparing the AIC and BIC values ​​of different models, the optimal (p, d, q) combination is selected, i.e., the optimal ARIMA(p, d, q) model is established.

[0048] The formula for AIC is as follows:

[0049] AIC = 2k - 21n(L).

[0050] The BIC formula is expressed as follows:

[0051] BIC = kln(n)-21n(L).

[0052] where k represents the number of parameters in the model, n represents the number of samples, and L represents the maximum likelihood estimate of the model.

[0053] Optionally, when performing time series analysis, the autocorrelation coefficients and partial autocorrelation coefficients corresponding to different p and q can be generated based on the range of possible values ​​of the autoregressive term p and the moving average term q. The time series is analyzed based on the autocorrelation coefficients and partial autocorrelation coefficients. For the autocorrelation coefficients, it is first necessary to calculate the mean value of the time series, and then, for any two observed values ​​Y that are separated by k time units in the time series data, t and Y t-kThe covariance is calculated for the following:

[0054] JPEG0007820874000002.jpg1066

[0055] where Y t represents the observation at time t, and Y t-k is k time units before time t JPEG0007820874000003.jpg12151

[0056] The autocorrelation coefficient is obtained by the covariance formula.

[0057] JPEG0007820874000004.jpg1133

[0058] where Var(Y t ) is Y t represents the variance of , and construct one multiple regression model for each lag period k, where Y t is the dependent variable, and Y t-1 , Y t-2 , Y t-3 , ..., Y t-(k-1) is taken as an argument.

[0059] The partial autocorrelation coefficient can be expressed as follows:

[0060] π k =Corr(Y t ,Y t-k |Y t-1 ,Y t-k+1 ).

[0061] where Corr(Y t ,Y t-k |Y t-1 ,Y t-k+1 ) is Y t-1 and Y t-k+1 Y after removing the influence of t , Y t-k Represents the magnitude of correlation.

[0062] Specifically, the series values ​​in the sample set are substituted into the autocorrelation coefficients and partial autocorrelation coefficients to obtain a series of p and q values, where p and q represent the order parameters in the autoregressive model (AR) and the moving average model (MA), respectively. Each specific p and q value corresponds to a function value of the autocorrelation coefficient and partial autocorrelation coefficient. After obtaining the autocorrelation coefficients and partial autocorrelation coefficients corresponding to different p and q values, an information criterion can be used to determine the final p and q values, thereby constructing an autoregressive model (AR), a moving average model (MA), or an ARIMA model.

[0063] In step S203, a prediction model is constructed based on the model parameters, and the prediction model is used to predict the NVH performance attenuation status of the electric drive system at a preset time point.

[0064] Specifically, a prediction model is constructed based on an autoregressive term p, a moving average term q, and a difference order d, and feature values ​​characterizing the NVH performance attenuation are extracted from the residual of the prediction model. The feature values ​​include standard deviation, skewness, and kurtosis, and are used to predict the NVH performance attenuation state at a predetermined time point in the electric drive system.

[0065] For example, a time series data set is preset, and an appropriate ARIMA(p,d,q) model is determined. First, first-order difference is performed on the original time series to obtain a new series. The new series is then used to fit the ARIMA model. For each observation point, prediction is performed using the ARIMA model. The difference between the observed value and the predicted value is calculated as the residual. Feature values ​​characterizing the NVH performance damping are extracted from the residual, and the feature values ​​include standard deviation, skewness, and kurtosis.

[0066] The prediction model is expressed as follows:

[0067] JPEG0007820874000005.jpg1576

[0068] JPEG0007820874000006.jpg12151 represents the error term coefficient, B represents the backward operator, and ε t represents the error term for the data at time t.

[0069] Illustratively, the parameters of the ARIMA model are set to (1,1,2), that is, the values ​​of the order of the forecast model, the number of autoregressive terms, and the number of moving average terms are d=1, p=1, and q=2, respectively. JPEG0007820874000007.jpg25151

[0070] JPEG0007820874000008.jpg559

[0071] The above prediction model can predict the standard deviation, skewness, and kurtosis values ​​corresponding to different times, which are used to characterize important features of NVH performance damping. For example, the standard deviation (σ_res=0.02g), skewness (S_res=0.05), and kurtosis (K_res=3.0) were extracted at time 0, and the standard deviation, skewness, and kurtosis values ​​at other times are shown in Table 1. Analyzing these feature values ​​allows for more accurate prediction of the NVH performance damping status of the electric drive system at different times.

[0072] In step S204, a feature value is extracted based on the prediction model, and an evaluation model is established based on the feature value, and the evaluation model is used to evaluate the NVH performance damping situation.

[0073] The extracted feature values ​​are used to establish an evaluation model, and the support vector machine method is used to establish an evaluation model for the NVH damping level, which is used to evaluate the damping features, and the evaluation model is shown as follows:

[0074] JPEG0007820874000009.jpg1547

[0075] JPEG0007820874000010.jpg19151

[0076] As can be seen, based on the NVH damping level evaluation model, the root mean square of vibration acceleration and peak sound pressure value can be obtained every hour between time 0 and time 1000, where the root mean square of vibration acceleration value is used to represent the vibration value, the peak sound pressure value is used to represent the noise value, and the root mean square of vibration acceleration values ​​and peak sound pressure values ​​at the start and end are obtained by direct measurement using equipment, and the specific data are shown in Table 1.

[0077] [Table 1]

[0078] Observing the changes in the data in Table 1, we can see that the root mean square (RMS) of acceleration shows a gradual decrease over time. This trend clearly indicates a weakening of vibration force and intensity. Considering this change, we speculate that it is due to wear between mechanical components or other types of performance degradation. Furthermore, the decrease in the constancy of the sound pressure peaks may indicate a gradual improvement in the sound insulation capability of the electric drive system or a gradual weakening of the intensity of noise sources. The residual standard deviation also gradually decreases, indicating an improvement in the model's prediction accuracy. Furthermore, the skewness of the residuals changed from a slight right-skewed to a left-skewed, indicating that the system's operating conditions may be changing to some extent. The kurtosis of the residuals decreased from 3.00 to 1.80, indicating a gradual change in the distribution shape from strongly peaked to flat. This change may be a clear sign that the system performance is gradually becoming more stable and gentler.

[0079] Based on the same inventive concept, an embodiment of the present application further provides an evaluation device for realizing the above-mentioned related NVH performance damping of an electric drive system. The means for solving the problem provided by the device are similar to the means for realizing the means described in the above-mentioned method. Therefore, the specific limitations of the device embodiments of one or more methods for evaluating the NVH performance damping of an electric drive system provided below can refer to the limitations of the above-mentioned methods for evaluating the NVH performance damping of an electric drive system, and will not be described again here.

[0080] In one exemplary embodiment, as shown in FIG. 4, an electric drive system NVH performance damping estimator is provided, comprising: an acquisition module 410 for acquiring a series of predicted target historical NVH performance values; a determination module 420 for determining model parameters based on the sequence values; a construction module 430 for constructing a predictive model based on the model parameters, the predictive model being used to predict an NVH performance decay situation at a preset point in time of the electric drive system; an evaluation module 440 for extracting feature values ​​based on the prediction model and establishing an evaluation model based on the feature values, wherein the evaluation model is used to evaluate the NVH performance damping situation.

[0081] In an alternative embodiment, the acquisition module 410 specifically: Obtaining NVH performance data based on a durability test for the same electric drive system, a sensor being provided in the electric drive system, wherein the time periods corresponding to the NVH performance data include an initial time period and a decay time period, and the preset number of cycles in the durability test corresponds to a number of mileages under actual road conditions; It is used to preprocess NVH performance data and obtain a series of historical NVH performance values.

[0082] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure may be as shown in FIG. 5 . The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. An operating system, a computer program, and a database are stored in the non-volatile storage medium. The internal memory provides an environment for the operation of the operating system and the computer program stored in the non-volatile storage medium. The database of the computer device is used to store a series of predicted target historical NVH performance values. The I / O interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to connect to and communicate with an external terminal via a network. When executed by the processor, the computer program realizes a method for evaluating NVH performance damping of an electric drive system.

[0083] As will be understood by those skilled in the art, the structure shown in FIG. 5 is merely a block diagram of the structure of the portion relevant to the solution of the present application, and does not limit the computer device to which the solution of the present application is applied; a specific computer device may include more or fewer components than those shown in the figure, or may combine some components, or have a different component arrangement.

[0084] In one exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor, when executing the computer program, performs the steps in each of the method embodiments described above.

[0085] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program implementing the steps of each of the method embodiments described above when executed by a processor.

[0086] In addition, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) related to this application are all information and data authorized by the user or fully authorized by each party, and the collection, use and processing of related data must comply with relevant regulations.

[0087] Those skilled in the art will understand that implementing all or part of the steps in the above-described method embodiments can be accomplished by instructing relevant hardware using a computer program, which can be stored in a non-volatile computer-readable storage medium and, when executed, can include the steps of the above-described method embodiments. Any references to memory, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, flexible disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM), external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0088] The database according to each embodiment provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a distributed database based on a blockchain. The processor according to each embodiment provided herein may be, but is not limited to, a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc.

[0089] The technical features of the above embodiments can be combined in any desired manner, and for the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope of the present specification.

[0090] In this specification, specific examples are used to explain the principles and embodiments of the present application, and the explanation of the above examples is useful for understanding the method and core idea of ​​the present application, and at the same time, those skilled in the art can make any changes in the specific embodiments and application scope based on the idea of ​​the present application. As explained above, the contents of this specification should not be construed as limiting the present application.

Claims

1. 1. A method for evaluating electric drive system NVH performance damping, comprising: obtaining a series of historical NVH performance values ​​of the predicted target, the series representing the characteristics of the signal over time; determining model parameters based on the sequence values; constructing a prediction model based on the model parameters and extracting a feature value characterizing NVH performance decay from a residual of the prediction model, the prediction model being expressed as follows: establishing an evaluation model based on the characteristic values, the evaluation model being used to evaluate the NVH performance decay situation; The evaluation model is is expressed as where: represents the evaluation at time t of the attenuation level value at time t+h, represents the mean value of the time series data, represents the attenuation level estimate of the parameters of the prediction model, represents an error term of the data at time t+h-j, and based on the evaluation model, the root mean square of the vibration acceleration and the peak sound pressure value can be obtained for each hour between time 0 and time 1000, wherein the root mean square of the vibration acceleration is used to represent the vibration value, and the peak sound pressure value is used to represent the noise value; Specifically, the step of obtaining series values ​​of historical NVH performance of the predicted target includes: acquiring NVH performance data based on a durability test for the same electric drive system, wherein a sensor is provided in the electric drive system, wherein a time period corresponding to the NVH performance data includes an initial time period and a decay time period, and wherein a preset number of cycles for the durability test corresponds to a mileage under actual road conditions; and performing pre-processing on the NVH performance data to remove irrelevant frequency components and retain important signal frequency bands to obtain a series of historical NVH performance values; Before determining model parameters based on the series values, the method for evaluating electric drive system NVH performance decay further comprises: performing a stationarity determination on the series of historical NVH performance values; If the original series is stationary, the series of historical NVH performance values ​​is a stationary series, and if not stationary, performing difference processing based on the series values ​​in the original series until a stationary series is obtained, wherein the number of difference processing is a difference order of the prediction model; Specifically, the step of determining model parameters based on the sequence values ​​includes: A step of determining the number of autoregressive terms and the number of moving average terms of the prediction model by AIC and BIC constant order methods based on the acquired stationary series, 10. The method for evaluating NVH performance decay of an electric drive system, comprising the step of: determining whether the model parameters include the autoregressive term, the moving average term, and the differencing order.

2. 1. An electric drive system NVH performance damping estimator, comprising: an acquisition module for acquiring a series of historical NVH performance values ​​of the predicted target, the series representing the characteristics of the signal over time; a determination module for determining model parameters based on the sequence values; a construction module for constructing a predictive model based on the model parameters and extracting feature values ​​characterizing NVH performance decay from a residual of the predictive model, the predictive model being expressed as: an evaluation module for establishing an evaluation model based on the feature values, the evaluation model being used to evaluate the NVH performance decay situation; The evaluation model is is expressed as where: represents the evaluation at time t of the attenuation level value at time t+h, represents the mean value of the time series data, represents the attenuation level estimate of the parameters of the prediction model, represents an error term of the data at time t+h-j, and an evaluation module that can obtain the root mean square of the vibration acceleration and the peak sound pressure value for each hour between time 0 and time 1000 based on the evaluation model, wherein the root mean square of the vibration acceleration is used to represent the vibration value, and the peak sound pressure value is used to represent the noise value; The acquisition module specifically includes: NVH performance data is obtained based on a durability test for the same electric drive system, and a sensor is provided in the electric drive system, wherein the time periods corresponding to the NVH performance data include an initial time period and a decay time period, and the number of cycles preset in the durability test corresponds to a mileage under actual road conditions. preprocessing the NVH performance data to remove irrelevant frequency components and retain significant signal frequency bands, which is used to obtain a series of historical NVH performance values; Before determining model parameters based on the series values, the electric drive system NVH performance damping estimator further is used to perform a stationarity determination on the series values ​​of the historical NVH performance, If the original series is stationary, the series of historical NVH performance values ​​is a stationary series. If the original series is not stationary, a difference process is performed based on the series values ​​in the original series until a stationary series is obtained, where the number of difference processes is a difference order of the prediction model. The determination module specifically includes: Based on the acquired stationary series, the AIC and BIC constant order methods are used to determine the number of autoregressive terms and the number of moving average terms of the forecasting model; The apparatus for evaluating NVH performance damping of an electric drive system, wherein the model parameters include the autoregressive term, the moving average term, and the difference order.

3. A computer device including a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein the processor executes the computer program to implement the steps of the method for evaluating NVH performance damping of an electric drive system according to claim 1.

4. A computer-readable storage medium having a computer program stored thereon, the computer program implementing the steps of the method for evaluating NVH performance damping of an electric drive system according to claim 1 when executed by a processor.

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