NVH performance analysis methods, devices, equipment, media and products for electric drive systems

Abstract

The present application discloses a method, device, equipment, medium and product for analyzing the NVH performance of an electric drive system, and relates to the field of vehicle technology. The method comprises obtaining basic attribute information of each sample electric drive system; obtaining operating data of each sample electric drive system during the operation of each sample electric drive system, and the operating data comprises: operating conditions, sound data and operating condition categories; constructing an NVH database based on the basic attribute information and operating data; and analyzing the NVH performance based on the database information in the NVH database. The present application can analyze the NVH performance from multiple dimensions, so as to better reveal the complex relationship between various parameters and their comprehensive impact on the NVH performance.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, equipment, medium and product for analyzing NVH performance of an electric drive system. Background Art

[0002] With the rapid development of electric vehicles and electric drive systems, the performance and reliability of electric drive systems have become key indicators to measure their market competitiveness. Electric drive systems are composed of core components such as motors, controllers and transmission systems. Their performance is affected by many factors such as power level, voltage level, cooling method, winding type and IGBT type. These factors interact with each other and jointly determine the NVH characteristics of electric drive systems, namely the overall performance of noise, vibration and harshness.

[0003] However, traditional NVH data analysis methods mostly use single-dimensional analysis methods, which makes it difficult to reveal the complex relationship between various parameters and their comprehensive impact on system performance. Summary of the invention

[0004] The purpose of this application is to provide an NVH performance analysis method, device, equipment, medium and product for an electric drive system, which can analyze NVH performance from multiple dimensions, so as to better reveal the complex relationship between various parameters and their comprehensive impact on NVH performance.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for analyzing the NVH performance of an electric drive system, the method comprising:

[0007] Obtain basic property information of each sample electric drive system;

[0008] During the operation of each of the sample electric drive systems, obtaining operation data of each of the sample electric drive systems, the operation data including: operation conditions, sound data and operating condition categories;

[0009] Building an NVH database according to the basic attribute information and the operating data;

[0010] The NVH performance is analyzed based on the database information in the NVH database.

[0011] In a second aspect, the present application provides an NVH performance analysis device for an electric drive system, the NVH performance analysis device for an electric drive system comprising:

[0012] A first acquisition module is used to acquire basic attribute information of each sample electric drive system;

[0013] A second acquisition module is used to acquire operation data of each of the sample electric drive systems during operation of each of the sample electric drive systems, wherein the operation data includes: operation conditions, sound data and operating condition categories;

[0014] A construction module, used for constructing an NVH database according to the basic attribute information and the operating data;

[0015] An analysis module is used to analyze NVH performance according to database information in the NVH database.

[0016] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the NVH performance analysis method of an electric drive system described in any one of the above.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the NVH performance analysis method of an electric drive system described in any one of the above.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the NVH performance analysis method of an electric drive system described in any one of the above.

[0019] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0020] The present application provides a method, device, equipment, medium and product for analyzing the NVH performance of an electric drive system. In the present disclosure, when constructing an NVH database, data of a sample electric drive system will be collected in an all-round manner, including: basic attribute information, operating conditions, sound data and operating condition categories. Then, when performing NVH performance analysis, NVH performance can be analyzed from multiple dimensions, thereby better revealing the complex relationship between various parameters and their comprehensive impact on NVH performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a flow chart of a method for analyzing NVH performance of an electric drive system according to an exemplary embodiment;

[0023] Figure 2 is a functional module schematic diagram of an NVH performance analysis device for an electric drive system according to an exemplary embodiment;

[0024] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] With the rapid development of electric vehicles and electric drive systems, the performance and reliability of electric drive systems have become key indicators to measure their market competitiveness. Electric drive systems are composed of core components such as motors, controllers and transmission systems. Their performance is affected by many factors such as power level, voltage level, cooling method, winding type and IGBT type. These factors interact with each other and jointly determine the NVH characteristics of electric drive systems, namely the overall performance of noise, vibration and harshness.

[0028] Traditional NVH data analysis methods mostly use single-dimensional analysis methods, which are difficult to reveal the complex relationship between various parameters and their comprehensive impact on system performance. For example, the operating conditions such as motor speed, load and temperature, as well as the magnetic field distribution, current waveform and harmonic content inside the motor, will all have a significant impact on NVH characteristics. In addition, with the increase in the number of samples and the expansion of data dimensions, traditional analysis methods are difficult to adapt to the high-precision and high-efficiency requirements of modern electric vehicles for the performance evaluation of electric drive systems.

[0029] In order to solve the above technical problems, the present disclosure provides a NVH performance analysis method, device, equipment, medium and product of an electric drive system.

[0030] Figure 1 is a flow chart of a method for analyzing NVH performance of an electric drive system according to an exemplary embodiment. Figure 1As shown, the NVH performance analysis method of the electric drive system includes the following steps S101-S103:

[0031] In step S101, basic attribute information of each sample electric drive system is obtained.

[0032] For example, the basic attribute information here includes but is not limited to at least one of the following information: sample type, sample source information, voltage level, power parameter, speed parameter, torque parameter, switching frequency setting value and speed transmission ratio.

[0033] In step S102, during the operation of each sample electric drive system, operation data of each sample electric drive system is obtained, and the operation data includes: operation conditions, sound data and operating condition categories.

[0034] Among them, the operating conditions include but are not limited to at least one of the following conditions: voltage platform, cooling method, winding type and power device type; the sound data include but are not limited to at least one of the following data: A-weighted sound pressure level, electromagnetic order sound pressure level slice, reducer order sound pressure level slice and sound quality data; the operating condition category includes but is not limited to at least one of the following categories: WOT, POT and -POT.

[0035] In step S103, an NVH database is constructed according to basic attribute information and operation data;

[0036] In one possible implementation, building the NVH database according to the basic attribute information and the operating data in step S103 includes the following sub-steps:

[0037] S1031. Create a blank NVH database.

[0038] S1032. Obtain basic attribute information and parameter names in the operation data.

[0039] S1033. Write the parameter name into the blank NVH database.

[0040] S1034. Write the basic attribute information and operating data corresponding to the parameter name in each sample electric drive system into the corresponding table item to obtain an NVH database.

[0041] In order to ensure that the NVH database can efficiently store and manage a large amount of NVH data of electric drive systems, the database volume and structure design are key steps. The NVH database should take into account the diversity of samples and the testing requirements under different working conditions, and rationally plan the volume and structure of the database to ensure the comprehensiveness and operability of the data. In this disclosure, according to the diversity and testing requirements of electric drive systems, the database design should be able to accommodate different types of electric drive system samples and cover NVH data under different working conditions. Among them, when determining the overall capacity of the database, it can be determined based on basic parameters such as the number of samples, data storage format and capacity to ensure that the database can efficiently store and manage data.

[0042] The database can adopt a hierarchical structure to facilitate data storage and retrieval, specifically:

[0043] (1) Collect data from multiple sample electric drive systems, including basic property information and operating data.

[0044] The operating data includes: operating conditions, sound data and operating condition categories.

[0045] (2) The basic attribute information of the sample electric drive system includes: sample type, sample source information, voltage level, power parameters, speed parameters, torque parameters, switching frequency setting value, reduction ratio, etc.

[0046] (3) Operation data include: operating conditions, sound data and operating condition categories.

[0047] The operating conditions include but are not limited to at least one of the following conditions: voltage platform, cooling method, winding type and power device type, examples include: 400 / 800V, oil cooling / water cooling, flat wire / round wire winding, Si / SiC based IGBT.

[0048] The collection of basic sample information is the foundation of the NVH database construction. Comprehensive and accurate sample information can ensure the accuracy and traceability of subsequent data analysis. This information is crucial for understanding and analyzing the NVH characteristics of electric drive systems, so it is necessary to ensure the comprehensiveness and accuracy of the collected data.

[0049] The NVH database disclosed in the present invention can be presented in the form of a data table or a database list, recording the basic attribute information and operating data of each sample electric drive system. This can ensure that all data information in the database can be traced back to the specific test prototype and operating conditions. The operating data should be convenient for subsequent statistical screening as single-choice statistics.

[0050] Ensuring the consistency of data in the NVH database is the basis for building the NVH database, which can ensure the accuracy and reliability of the data in the NVH database. Through standardized test processes and data processing methods, the consistency and reliability of data collected on different machines and under different working conditions can be ensured, which can effectively reduce errors and deviations in the data collection and processing process. Therefore, the data can also be checked for consistency and exception processing, and abnormal data can be marked and processed to ensure data accuracy.

[0051] Specifically, the data consistency steps include:

[0052] (1) Develop a standardized testing process to ensure that the test conditions and steps for each prototype are consistent.

[0053] (2) Perform consistency checks on collected data and detect and mark abnormal data, such as sudden jumps in test data or deviations from the normal range.

[0054] (3) Process abnormal data, for example, by retesting or correcting the data to ensure data consistency.

[0055] In step S104, the NVH performance is analyzed according to the database information in the NVH database.

[0056] In the present disclosure, when constructing the NVH database, data of the sample electric drive system will be collected in an all-round manner, including: basic attribute information, operating conditions, sound data and operating condition categories. Then, when conducting NVH performance analysis, the NVH performance can be analyzed from multiple dimensions, so as to better reveal the complex relationship between various parameters and their comprehensive impact on NVH performance, wherein each parameter refers to the parameters in the basic attribute information, operating conditions, sound data and operating condition categories.

[0057] When analyzing NVH performance, it can be analyzed from different dimensions to help engineers fully understand NVH characteristics. For example, it can be analyzed in layers and grades based on data such as power level, operating conditions, sound pressure level, sound quality, sound quality characteristics, order information, suspension vibration, and comparison before and after optimization.

[0058] First, the database information in the NVH database can be presented in an overall data presentation, for example: divided by working conditions (WOT, POT, -POT), basic attribute information and other operating data are presented in the form of speed spectra (speed-sound pressure level spectrum, speed-order amplitude spectrum, etc.), but not limited to frequency domain spectra.

[0059] In one embodiment, analyzing the NVH performance according to the database information in the NVH database includes the following sub-steps A1-A2:

[0060] A1. Obtain the number of sample electric drive systems corresponding to different preset categories and basic attribute information and operation data of each sample electric drive system; the preset category is determined according to the parameter name of each basic attribute information or the parameter name of each operation data;

[0061] A2. Output the number of corresponding sample electric drive systems under different preset categories and basic attribute information and operation data of each sample electric drive system.

[0062] The analysis method of this embodiment can be called a first-level analysis. In this analysis method, each preset category can be used as a classification standard, and the basic attribute information and operating data corresponding to each preset category can be displayed in turn. Since it is a direct display, it can also be called a first-level intuitive statistical data analysis.

[0063] For example: classify according to the power parameters in the basic attribute information. The preset category at this time is the power level. Then, at this time, it is divided by power level, the number of samples of each power level is recorded, and statistical analysis is performed on the basic attribute information of each power level, such as average speed, average torque and average power, etc. Statistical analysis is performed on the operating data of each power level, such as statistical distribution of sound pressure levels of each power level under different working conditions.

[0064] In one embodiment, analyzing the NVH performance according to the database information in the NVH database includes the following sub-steps B1-B3:

[0065] B1. Acquire various sound data corresponding to the same basic attribute information in the NVH database to obtain a first mapping relationship between various basic attribute information and NVH performance.

[0066] For example: statistical analysis of sound quality data at different power levels, including parameters such as loudness, sharpness, TNR and PR; statistics on electromagnetic order sound pressure level slice information and reducer order sound pressure level slice information at each power level; record the A-weighted sound pressure level and sound quality data at different speeds.

[0067] B2. Acquire various sound data corresponding to the same operating conditions in the NVH database to obtain a second mapping relationship between various operating conditions and NVH performance.

[0068] For example: comparing A-weighted sound pressure level and sound quality data under different operating conditions.

[0069] B3. Output the first mapping relationship and the second mapping relationship.

[0070] The analysis method of this embodiment can be called secondary basic statistical data analysis, and the NVH performance at this time is reflected through sound data.

[0071] In one embodiment, analyzing the NVH performance according to the database information in the NVH database includes the following sub-steps C1-C4:

[0072] C1. Use the correlation model to perform correlation analysis on all database information in the NVH database, and calculate the first correlation coefficient between the parameters corresponding to each parameter name.

[0073] C2. Based on all the database information in the NVH database, a regression model is used to analyze the first influence level of the parameters corresponding to each parameter name on the NVH performance.

[0074] For example: establish a regression model to analyze the impact of various parameters in all database information on the A-weighted sound pressure level.

[0075] C3. According to the clustering algorithm model, the sample electric drive system is classified according to different sound data to obtain a first classification result.

[0076] For example, the sample electric drive systems are divided into several categories according to the A-weighted sound pressure level and sound quality data.

[0077] C4. Output the first correlation coefficient, the first impact level and the first classification result.

[0078] The analysis method of this embodiment can be called three-level statistical algorithm application data analysis.

[0079] In actual applications, the results of the above three-level analysis can be saved layer by layer in the form of a file tree in the database. When the user views it, he only needs to click on the folders corresponding to the parameters to be queried layer by layer.

[0080] Taking the example of a user wanting to view the sound quality data of different power levels, the first-level folder may be the basic attribute information. After the user clicks on the basic attribute information, folders corresponding to different basic attribute information will appear. The user then clicks on the folder corresponding to the power level. The power level folder includes folders corresponding to the A-weighted sound pressure level, electromagnetic order sound pressure level slices, reducer order sound pressure level slices and sound quality data. The user then clicks on the folder corresponding to the sound quality data to view the sound quality data of different power levels, which can also be displayed more vividly in the form of charts.

[0081] In one embodiment, the NVH performance analysis method of the electric drive system further includes the following sub-steps D1-D5:

[0082] D1. receiving data information to be evaluated of the electric drive system to be evaluated;

[0083] D2. Perform correlation analysis on the target data information using a correlation model to calculate the second correlation coefficient between the parameters corresponding to each parameter name; the target data information includes: the data information to be evaluated and the database information in the NVH database;

[0084] D3. Based on the target data information, a regression model is used to analyze the second impact level of the parameters corresponding to each parameter name on the NVH performance;

[0085] D4. Classifying the target electric drive system according to different sound data according to the clustering algorithm model to obtain a second classification result; the target electric drive system includes: each sample electric drive system and the electric drive system to be evaluated;

[0086] D5. Output the evaluation results, which include: a second correlation coefficient, a second impact level, and a second classification result. In the evaluation results, the data information to be evaluated corresponding to the electric drive system to be evaluated is displayed in a different way from the database information in the NVH database.

[0087] The analysis method of this embodiment can be called a four-level high-order statistical data analysis, which can evaluate the data information of other electric drive systems to be evaluated in combination with the database information in the previously established NVH database, so that the user can obtain the impact of the data information of the current electric drive system to be evaluated on the NVH performance.

[0088] For example, the data information to be evaluated of the electric drive system to be evaluated may be the information after optimizing and changing the basic attribute information. In this case, the changes in the A-weighted sound pressure level and sound quality data of each power level before and after the optimization may be analyzed. The data information to be evaluated of the electric drive system to be evaluated may also be a future sample electric drive system. In this case, the A-weighted sound pressure level and sound quality data of the future sample electric drive system may be predicted based on the database information in the existing NVH database.

[0089] The method of the present disclosure is described in detail below by way of examples.

[0090] Example 1: Construction of NVH database.

[0091] (1) Database size: 40 sample electric drive systems, including motor bodies and electric drive assemblies.

[0092] (2) Basic attribute information: sample type, voltage level, power parameters, speed parameters, torque parameters, switching frequency setting value and deceleration (speed change) transmission ratio;

[0093] (3) Operating conditions: voltage platform, cooling method, winding type and power device type;

[0094] (4) Sound data: A-weighted sound pressure level, electromagnetic order sound pressure level slices, reducer subsound pressure level slices and sound quality data;

[0095] Among them, the order characteristic data is divided into two types: reducer order and electromagnetic order. The order slices are all "speed-data" information, which can include:

[0096] a) Electromagnetic stator 1st order: the order that matches the number of stator slots, e.g. 48th order for a 48-slot motor;

[0097] b) Electromagnetic rotor 1st order: the order is the same as the rotor order, for example, the 10th order of a 10-pole motor;

[0098] c) Electromagnetic rotor 2nd order: the order is twice the rotor order, for example, the 20th order of a 10-pole motor;

[0099] d) Electromagnetic rotor 3rd order: the order is three times the number of rotor stages, for example, 30th order for a 10-pole motor;

[0100] e) Reducer 1st stage 1st order: high-speed main tooth number Z1, for example 22nd order;

[0101] f) Reducer 2nd stage 2nd order: low speed main gear divided by high speed ratio, for example 7.77 order;

[0102] (5) The data sample working condition is a transient working condition, which is divided into three modes: WOT, POT and -POT. The working condition statistical information corresponding to each sound data is as follows:

[0103] a) A-weighted sound pressure level: WOT, POT25%, 50%, 75%; -POT25%, 50%, 75%;

[0104] b) Electromagnetic stator 1st order: WOT, POT25%, 50%, 75%; -POT25%, 50%, 75%;

[0105] c) Electromagnetic stator 2nd order: WOT, POT25%, 50%, 75%; -POT25%, 50%, 75%;

[0106] d) Electromagnetic rotor 1st order: WOT, POT25%, 50%, 75%; -POT25%, 50%, 75%;

[0107] e) Electromagnetic rotor 2nd order: WOT, POT25%, 50%, 75%; -POT25%, 50%, 75%;

[0108] f) Reducer 1st level 1st stage (drive motor without): WOT, POT25%, 50%, 75%; -POT25%, 50%, 75%;

[0109] g) Reducer 2nd level 2nd stage (drive motor has none): WOT, POT25%, 50%, 75%; -POT25%, 50%, 75%;

[0110] h) Sound quality data: WOT, POT25%, 50%, 75%; -POT25%, 50%, 75%.

[0111] Specifically, a data table or database list is established to record the basic attribute information and operation data of each prototype. As shown in Table 1:

[0112] Table 1

[0113]

[0114] Worth noting:

[0115] (1) The test methods, test points and other standard requirements refer to QC / T 1132-2020 "Measurement Methods for Electric Powertrain Noise for Electric Vehicles".

[0116] (2) The test is carried out in a semi-anechoic chamber. The semi-anechoic chamber should comply with the requirements of Appendix A of GB / T 6882.

[0117] (3) Test equipment including microphones, cables, data acquisition, etc. should meet the requirements of Level 1 in GB / T 3785.1, and filters should meet the requirements of Level 1 in IEC 61260.

[0118] (4) Before and after each series of tests, the test equipment should be calibrated at one or more frequencies within the test frequency range using a Class 1 acoustic calibrator that complies with GB / T 15173. Without making any adjustments to the test system, the difference between the calibration values ​​before and after should be less than or equal to 0.5 dB, otherwise the test results are invalid.

[0119] (5) Semi-anechoic chambers, test equipment, etc. should be calibrated and pass the calibration. All calibration work should be carried out by laboratories with calibration qualifications or national authorized institutions, and ensure that they can be traced back to the corresponding measurement standards.

[0120] (6) Process abnormal data, for example, by retesting or correcting the data to ensure data consistency.

[0121] Example 2: Primary intuitive statistical data analysis.

[0122] The basic attribute information of 40 sample electric drive systems was collected and classified by power level. The number of samples and basic attribute information of each power level were counted, such as average speed, average torque, average power, etc., as well as the A-weighted sound pressure level distribution of each power level under WOT, POT, and -POT conditions. The specific data is shown in Table 2:

[0123] Table 2

[0124]

[0125] It is worth noting that, in actual presentation, the data in Table 2 can be presented in a graphical manner, such as a histogram.

[0126] Example 3: Secondary basic statistical data analysis.

[0127] The sound quality data of different power levels are statistically analyzed, including parameters such as loudness, sharpness, TNR and PR in the sound quality data. The specific data are shown in Table 3:

[0128] Table 3

[0129]

[0130] It is worth noting that, in actual presentation, the data in Table 3 can be presented in a graphical manner, such as a histogram.

[0131] Example 4: Three-level statistical algorithm application data analysis.

[0132] Conduct correlation analysis and calculate the first correlation coefficient between each parameter. Establish a regression model to analyze the impact of each parameter on the A-weighted sound pressure level. Conduct cluster analysis and classify the sample electric drive systems into several categories according to the A-weighted sound pressure level and sound quality data. The specific analysis is as follows:

[0133] The results of correlation analysis show that the first correlation coefficient between speed and sound pressure level is 0.85, indicating that the speed has a significant effect on the A-weighted sound pressure level.

[0134] Regression model analysis shows that speed, torque and cooling method are the main factors affecting the A-weighted sound pressure level.

[0135] The cluster analysis results show that the sample electric drive systems can be divided into a high A-weighted sound pressure level group and a low A-weighted sound pressure level group, and the sample electric drive systems in the high A-weighted sound pressure level group are mostly of high power level and water cooling.

[0136] Example 5: Four-level high-order statistical data analysis.

[0137] Conduct principal component analysis to extract the main influencing factors. Analyze the changes in A-weighted sound pressure level and sound quality at each power level before and after optimization. Predict the A-weighted sound pressure level and sound quality of future sample electric drive systems based on existing data. The specific analysis is as follows:

[0138] The results of principal component analysis show that speed, torque and cooling method are the main factors affecting the A-weighted sound pressure level, with contribution rates of 40%, 30% and 20% respectively.

[0139] Comparative analysis before and after optimization shows that after optimization, the A-weighted sound pressure level of each power level is reduced by an average of 5dBA, and the sound quality parameters are significantly improved.

[0140] The predictive analysis of future sample electric drive systems shows that based on the database information in the existing NVH database, the A-weighted sound pressure level of the new sample electric drive system is predicted to be between 50-70dBA, and the sound quality parameters are significantly improved.

[0141] In summary, the method disclosed herein can achieve the following beneficial effects:

[0142] 1. The method disclosed in the present invention can conduct a comprehensive and systematic analysis of the NVH data of the electric drive system, and can help R&D personnel better understand the NVH characteristics of the electric drive system, thereby formulating more effective calibration and optimization strategies.

[0143] 2. By adopting unified data collection standards and methods, we can ensure the accuracy and consistency of data, ensure the high quality of data, and significantly improve the efficiency of data analysis. Efficient data analysis methods can not only significantly shorten the R&D cycle, but also improve the work efficiency of R&D personnel.

[0144] 3. Comprehensive and in-depth analysis and optimization of the NVH characteristics of the electric drive system will help improve the market competitiveness of the product and significantly enhance the performance and reliability of the system. R&D personnel will be able to develop a more efficient, reliable and comfortable electric drive system, thereby enhancing the user's driving experience.

[0145] Based on the same inventive concept, the embodiment of the present application also provides an NVH performance analysis device for an electric drive system for implementing the NVH performance analysis method for an electric drive system involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the NVH performance analysis device for one or more electric drive systems provided below can refer to the limitations of the NVH performance analysis method for an electric drive system above, and will not be repeated here.

[0146] In an exemplary embodiment, Figure 2 As shown, a NVH performance analysis device for an electric drive system is provided, and the NVH performance analysis device for the electric drive system comprises:

[0147] A first acquisition module 21 is used to acquire basic attribute information of each sample electric drive system;

[0148] A second acquisition module 22 is used to acquire operation data of each of the sample electric drive systems during operation of each of the sample electric drive systems, wherein the operation data includes: operation conditions, sound data and operating condition categories;

[0149] A construction module 23, configured to construct an NVH database according to the basic attribute information and the operating data;

[0150] The analysis module 24 is used to analyze the NVH performance according to the database information in the NVH database.

[0151] As an optional implementation, the building block includes:

[0152] Establish a submodule for establishing a blank NVH database;

[0153] A first acquisition submodule is used to acquire the basic attribute information and parameter names in the operation data;

[0154] A writing submodule, used for writing the parameter name into the blank NVH database;

[0155] A determination submodule is provided, which is used to write the basic attribute information and the operating data corresponding to the parameter name in each of the sample electric drive systems into a corresponding table entry to obtain the NVH database.

[0156] As an optional implementation, the analysis module includes:

[0157] A second acquisition submodule is used to acquire the number of the sample electric drive systems corresponding to different preset categories and the basic attribute information and the operating data of each of the sample electric drive systems; the preset category is determined according to the parameter name of each type of the basic attribute information or the parameter name of each type of the operating data;

[0158] The first output submodule is used to output the number of the sample electric drive systems corresponding to the different preset categories and the basic attribute information and the operating data of each of the sample electric drive systems.

[0159] As an optional implementation, the analysis module includes:

[0160] A third acquisition submodule is used to acquire each of the sound data corresponding to the same basic attribute information in the NVH database, and obtain a first mapping relationship between each of the basic attribute information and the NVH performance;

[0161] A fourth acquisition submodule is used to acquire each of the sound data corresponding to the same operating condition in the NVH database, and obtain a second mapping relationship between each of the operating conditions and the NVH performance;

[0162] The second output submodule is used to output the first mapping relationship and the second mapping relationship.

[0163] As an optional implementation, the analysis module includes:

[0164] A first analysis submodule, configured to perform a correlation analysis on all database information in the NVH database using a correlation model, and calculate a first correlation coefficient between parameters corresponding to each of the parameter names;

[0165] A second analysis submodule is used to analyze the first influence level of the parameters corresponding to each of the parameter names on the NVH performance by using a regression model according to all database information in the NVH database;

[0166] A third analysis submodule is used to classify the sample electric drive system according to different sound data according to a clustering algorithm model to obtain a first classification result;

[0167] The third output submodule is used to output the first correlation coefficient, the first impact level and the first classification result.

[0168] As an optional implementation, the analysis module further includes:

[0169] A receiving submodule, used for receiving data information to be evaluated of the electric drive system to be evaluated;

[0170] A calculation submodule, configured to perform a correlation analysis on the target data information using the correlation model, and calculate a second correlation coefficient between the parameters corresponding to each of the parameter names; the target data information includes: the data information to be evaluated and the database information in the NVH database;

[0171] A fourth analysis submodule, configured to analyze, according to the target data information, a second influence level of the parameters corresponding to each of the parameter names on the NVH performance by using the regression model;

[0172] a fifth analysis submodule, configured to classify the target electric drive system according to the different sound data according to the clustering algorithm model to obtain a second classification result; the target electric drive system includes: each of the sample electric drive systems and the electric drive system to be evaluated;

[0173] The fourth output submodule is used to output the evaluation results, and the evaluation results include: the second correlation coefficient, the second impact level and the second classification result, and in the evaluation results, the data information to be evaluated corresponding to the electric drive system to be evaluated adopts a display effect different from the database information in the NVH database.

[0174] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data for NVH performance analysis. The input / output 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 communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for analyzing NVH performance of an electric drive system is implemented.

[0175] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0176] In an 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 implements the steps in the above-mentioned method embodiments when executing the computer program.

[0177] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0178] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0179] It should be noted that 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.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0180] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy 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 can include random access memory (RAM) or 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).

[0181] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be 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., but is not limited thereto.

[0182] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0183] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for analyzing NVH performance of an electric drive system, characterized in that: The NVH performance analysis method of the electric drive system includes: Obtain basic property information of each sample electric drive system; During the operation of each of the sample electric drive systems, obtaining operation data of each of the sample electric drive systems, the operation data including: operation conditions, sound data and operating condition categories; Building an NVH database according to the basic attribute information and the operating data; analyzing NVH performance according to database information in the NVH database; The analyzing the NVH performance according to the database information in the NVH database includes: Performing correlation analysis on all database information in the NVH database using a correlation model, and calculating a first correlation coefficient between parameters corresponding to each parameter name; According to all database information in the NVH database, a regression model is used to analyze the first influence level of the parameters corresponding to each parameter name on the NVH performance; Classifying the sample electric drive system according to the different sound data according to the clustering algorithm model to obtain a first classification result; outputting the first correlation coefficient, the first impact level, and the first classification result; The NVH performance analysis method of the electric drive system further includes: Receiving data information to be evaluated of the electric drive system to be evaluated; The target data information is subjected to correlation analysis using the correlation model to calculate the second correlation coefficients between the parameters corresponding to the parameter names; the target data information includes: the data information to be evaluated and the database information in the NVH database; According to the target data information, using the regression model, analyzing the second influence level of the parameters corresponding to the parameter names on the NVH performance; According to the clustering algorithm model, the target electric drive system is classified according to the different sound data to obtain a second classification result; the target electric drive system includes: each of the sample electric drive systems and the electric drive system to be evaluated; Output an evaluation result, the evaluation result including: the second correlation coefficient, the second impact level and the second classification result, and in the evaluation result, the data information to be evaluated corresponding to the electric drive system to be evaluated adopts a display effect different from the database information in the NVH database.

2. The NVH performance analysis method of the electric drive system according to claim 1, characterized in that: The constructing of the NVH database according to the basic attribute information and the operating data comprises: Establish a blank NVH database; Obtaining the basic attribute information and parameter names in the operation data; Writing the parameter name into the blank NVH database; The basic attribute information and the operating data corresponding to the parameter name in each of the sample electric drive systems are written into corresponding table entries to obtain the NVH database.

3. The NVH performance analysis method of the electric drive system according to claim 2, characterized in that: The analyzing the NVH performance according to the database information in the NVH database includes: Obtaining the number of the sample electric drive systems corresponding to different preset categories and the basic attribute information and the operating data of each of the sample electric drive systems; the preset category is determined according to the parameter name of each type of the basic attribute information or the parameter name of each type of the operating data; Output the number of the corresponding sample electric drive systems under the different preset categories and the basic attribute information and the operating data of each of the sample electric drive systems.

4. The NVH performance analysis method of the electric drive system according to claim 2, characterized in that: The analyzing the NVH performance according to the database information in the NVH database includes: Acquire each of the sound data corresponding to the same basic attribute information in the NVH database, and obtain a first mapping relationship between each of the basic attribute information and the NVH performance; Acquire each of the sound data corresponding to the same operating condition in the NVH database to obtain a second mapping relationship between each of the operating conditions and the NVH performance; The first mapping relationship and the second mapping relationship are output.

5. An NVH performance analysis device for an electric drive system, characterized in that: The NVH performance analysis device of the electric drive system comprises: A first acquisition module is used to acquire basic attribute information of each sample electric drive system; A second acquisition module is used to acquire operation data of each of the sample electric drive systems during operation of each of the sample electric drive systems, wherein the operation data includes: operation conditions, sound data and operating condition categories; A construction module, used for constructing an NVH database according to the basic attribute information and the operating data; An analysis module, used for analyzing NVH performance according to database information in the NVH database; The analysis module comprises: A first analysis submodule, configured to perform a correlation analysis on all database information in the NVH database using a correlation model, and calculate a first correlation coefficient between parameters corresponding to each parameter name; A second analysis submodule is used to analyze the first influence level of the parameters corresponding to each of the parameter names on the NVH performance by using a regression model according to all database information in the NVH database; A third analysis submodule is used to classify the sample electric drive system according to different sound data according to a clustering algorithm model to obtain a first classification result; A third output submodule, used to output the first correlation coefficient, the first impact level and the first classification result; The analysis module further includes: A receiving submodule, used for receiving data information to be evaluated of the electric drive system to be evaluated; A calculation submodule, configured to perform a correlation analysis on the target data information using the correlation model, and calculate a second correlation coefficient between the parameters corresponding to each of the parameter names; the target data information includes: the data information to be evaluated and the database information in the NVH database; A fourth analysis submodule, configured to analyze, according to the target data information, a second influence level of the parameters corresponding to each of the parameter names on the NVH performance by using the regression model; a fifth analysis submodule, configured to classify the target electric drive system according to the different sound data according to the clustering algorithm model to obtain a second classification result; the target electric drive system includes: each of the sample electric drive systems and the electric drive system to be evaluated; The fourth output submodule is used to output the evaluation results, and the evaluation results include: the second correlation coefficient, the second impact level and the second classification result, and in the evaluation results, the data information to be evaluated corresponding to the electric drive system to be evaluated adopts a display effect different from the database information in the NVH database.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the NVH performance analysis method of the electric drive system according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the NVH performance analysis method of the electric drive system described in any one of claims 1-4 are implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the NVH performance analysis method of the electric drive system described in any one of claims 1-4 are implemented.

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